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Background: Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. Results: Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. Conclusions: Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach. [ABSTRACT FROM AUTHOR]

Objectives: Aim of this study was to explore the immune-related lncRNAs having prognostic role and establishing risk score model for better prognosis and immunotherapeutic coherence for esophageal cancer (EC) patients. Methods: To determine the role of immune-related lncRNAs in EC, we analyzed the RNA-seq expression data of 162 EC patients and 11 non-cancerous individuals and their clinically relevant information from the cancer genome atlas (TCGA) database. Bioinformatic and statistical analysis such as Differential expression analysis, co-expression analysis, Kaplan Meier survival analysis, Cox proportional hazards model, ROC analysis of risk model was employed. Results: Utilizing a cutoff criterion (log2FC > 1 + log2FC < −1 and FDR < 0.01), we identified 3737 RNAs were significantly differentially expressed in EC patients. Among these, 2222 genes were classified as significantly differentially expressed mRNAs (demRNAs), and 966 were significantly differentially expressed lncRNAs (delncRNA). Through Pearson correlation analysis between differentially expressed lncRNAs and immune related-mRNAs, we identified 12 immune-related lncRNAs as prognostic signatures for EC. Notably, through Kaplan-Meier analysis on these lncRNAs, we found the low-risk group patients showed significantly improved survival compared to the high-risk group. Moreover, this prognostic signature has consistent performance across training, testing and entire validation cohort sets. Using ESTIMATE and CIBERSORT algorithm we further observed significant enriched infiltration of naive B cells, regulatory T cells resting CD4+ memory T cells, and, plasma cells in the low-risk group compared to high-risk EC patients group. On the contrary, tumor-associated M2 macrophages were highly enriched in high-risk patients. Additionally, we confirmed immune-related biological functions and pathways such as inflammatory, cytokines, chemokines response and natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathways, JAK-STAT signaling pathways, chemokine signaling pathways significantly associated with identified IRlncRNA signature and their co-expressed immune genes. Furthermore, we assessed the predictive potential of the lncRNA signature in immune checkpoint inhibitors; we found that programed cell death ligand 1 (PD-L1; P-value =.048), programed cell death ligand 2 (PD-L2; P-value =.002), and T cell immunoglobulin and mucin-domain containing-3 (TIM-3; P-value =.045) expression levels were significantly higher in low-risk patients compared to high-risk patients. Conclusion: We believe this study will contribute to better prognosis prediction and targeted treatment of EC in the future. [ABSTRACT FROM AUTHOR]

Background: Transcriptomics can reveal much about cellular activity, and cancer transcriptomics have been useful in investigating tumor cell behaviors. Patterns in transcriptome-wide gene expression can be used to investigate biological mechanisms and pathways that can explain the variability in patient response to cancer therapies. Methods: We identified gene expression patterns related to patient drug response by clustering tumor gene expression data and selecting from the resulting gene clusters those where expression of cluster genes was related to patient survival on specific drugs. We then investigated these gene clusters for biological meaning using several approaches, including identifying common genomic locations and transcription factors whose targets were enriched in these clusters and performing survival analyses to support these candidate transcription factor-drug relationships. Results: We identified gene clusters related to drug-specific survival, and through these, we were able to associate observed variations in patient drug response to specific known biological phenomena. Specifically, our analysis implicated 2 stem cell-related transcription factors, HOXB4 and SALL4, in poor response to temozolomide in brain cancers. In addition, expression of SNRNP70 and its targets were implicated in cetuximab response by 3 different analyses, although the mechanism remains unclear. We also found evidence that 2 cancer-related chromosomal structural changes may impact drug efficacy. Conclusion: In this study, we present the gene clusters identified and the results of our systematic analysis linking drug efficacy to specific transcription factors, which are rich sources of potential mechanistic relationships impacting patient outcomes. We also highlight the most promising of these results, which were supported by multiple analyses and by previous research. We report these findings as promising avenues for independent validation and further research into cancer treatments and patient response. [ABSTRACT FROM AUTHOR]

Background: Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. Methods: In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell's C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. Results: Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. Conclusions: Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure. [ABSTRACT FROM AUTHOR]

Immunoglobulin superfamily member 8, or IGSF8, is a member of the recently identified immunoglobulin family of proteins. It is mostly produced on cell membranes and has a unique transmembrane structure. It has recently been demonstrated that there is a strong correlation between the expression variation of IGSF8 and the growth of gliomas. Therefore, we used data from the TCGA and CGGA databases to evaluate the function of IGSF8. The TCGA and GTEx data sets' RNA-seq data were utilized to examine IGSF8 expression. The Gene Cards database was utilized to get IGSF8 protein data. The Cluster Profiler data package was used to carry out the IGSF8 enrichment study. The GO and KEGG databases were used to examine the relationship between IGSF8 and cellular physiological and biochemical processes. The TCGA immune cell infiltration scores were obtained from online databases and published studies. Clinical survival data from TCGA and CGGA were used to investigate the predictive significance of IGSF8. TGGA revealed that the majority of cancers had differential expression of IGSF8. IGSF8 was discovered to be enriched in numerous significant pathways in tumor cells by GO and KEGG. Moreover, a strong correlation was seen between the expression of IGSF8 and the immunomodulatory interactions that occur between non-lymphocytes and lymphocytes. T-cell infiltration, immunological checkpoints, immune-activating and immune-suppressive genes, chemokines, and chemokine receptors were all strongly correlated with IGSF8 expression. Lastly, the TCGA and CGGA databases showed a strong correlation between IGSF8 and the grade and prognosis of gliomas. According to our findings, IGSF8 may be a glioma marker. In order to control the immunological microenvironment, IGSF8 may cooperate with a number of immune checkpoints. This information may be utilized to create novel targeted immunotherapy medications. [ABSTRACT FROM AUTHOR]

Background: Pathogenic BRCA1 or BRCA2 germline mutations contribute to hereditary breast, ovarian, prostate, and pancreatic cancer. Paradoxically, bi-allelic inactivation of BRCA1 or BRCA2 (bBRCA1/2) is embryonically lethal and decreases cellular proliferation. The compensatory mechanisms that facilitate oncogenesis in bBRCA1/2 tumors remain unclear. Methods: We identified recurrent genetic alterations enriched in human bBRCA1/2 tumors and experimentally validated if these improved proliferation in cellular models. We analyzed mutations and copy number alterations (CNAs) in bBRCA1/2 breast and ovarian cancer from the TCGA and ICGC. We used Fisher's exact test to identify CNAs enriched in bBRCA1/2 tumors compared to control tumors that lacked evidence of homologous recombination deficiency. Genes located in CNA regions enriched in bBRCA1/2 tumors were further screened by gene expression and their effects on proliferation in genome-wide CRISPR/Cas9 screens. A set of candidate genes was functionally validated with in vitro clonogenic survival and functional assays to validate their influence on proliferation in the setting of bBRCA1/2 mutations. Results: We found that bBRCA1/2 tumors harbor recurrent large-scale genomic deletions significantly more frequently than histologically matched controls (n = 238 cytobands in breast and ovarian cancers). Within the deleted regions, we identified 277 BRCA1-related genes and 218 BRCA2-related genes that had reduced expression and increased proliferation in bBRCA1/2 but not in wild-type cells in genome-wide CRISPR screens. In vitro validation of 20 candidate genes with clonogenic proliferation assays validated 9 genes, including RIC8A and ATMIN (ATM-Interacting protein). We identified loss of RIC8A, which occurs frequently in both bBRCA1/2 tumors and is synthetically viable with loss of both BRCA1 and BRCA2. Furthermore, we found that metastatic homologous recombination deficient cancers acquire loss-of-function mutations in RIC8A. Lastly, we identified that RIC8A does not rescue homologous recombination deficiency but may influence mitosis in bBRCA1/2 tumors, potentially leading to increased micronuclei formation. Conclusions: This study provides a means to solve the tumor suppressor paradox by identifying synthetic viability interactions and causal driver genes affected by large-scale CNAs in human cancers. [ABSTRACT FROM AUTHOR]

Introduction: The tumor microenvironment and IRGs are highly correlated with tumor occurrence, progression, and prognosis. However, their roles in grade II and III gliomas, termed LGGs in this study, remain to be fully elucidated. Our research aims to develop immune-related features for risk stratification and prognosis prediction in LGG. Methods: Using the ssGSEA method, we assessed the immune characteristics of the LGG population. We conducted differential analysis using LGG samples from the TCGA database and normal samples from GTEx, identifying 412 differentially expressed immune-related genes (DEIRGs). Subsequently, we utilized univariate Cox, LASSO, and multivariate Cox regression analyses to establish both a gene predictive model and a nomogram predictive model. Results: Here, we found that the ESTIMATE score, immune score and stromal score of high-immunity, high-grade and isocitrate dehydrogenase (IDH) wild-type glioma were higher than those of the corresponding group, and the tumor purity was lower. Higher ESTIMATE scores, stromal scores and immune scores indicated a poor prognosis in patients with LGG. Our four-gene prognostic model demonstrated superior accuracy compared to other molecular features. Validation using the CGGA as a testing set and the combined TCGA and CGGA cohort confirmed its robust prognostic value. Additionally, a nomogram integrating the prognostic model and clinical variables showed enhanced predictive capability. Discussion: Our study highlights the prognostic significance of the identified four DEIRGs (KLRC3, MR1, PDIA2, and RFXAP) in LGG patients. The predictive model and nomogram developed herein offer valuable tools for personalized treatment strategies in LGG. Future research should focus on further validating these findings and exploring the functional roles of these DEIRGs within the LGG tumor microenvironment. [ABSTRACT FROM AUTHOR]

Purpose: Prostate cancer (PCa) is a common malignancy in men, with an escalating mortality rate attributed to Recurrence and metastasis. Recent studies have illuminated collagen's critical regulatory role within the tumor microenvironment, significantly influencing tumor progression. Accordingly, this investigation is dedicated to examining the relationship between genes linked to collagen and the prognosis of PCa, with the objective of uncovering any possible associations between them. Methods: Gene expression data for individuals with prostate cancer were obtained from the TCGA repository. Collagen-related genes were identified, leading to the development of a risk score model associated with biochemical recurrence-free survival (BRFS). A prognostic nomogram integrating the risk score with essential clinical factors was crafted and evaluated for efficacy. The influence of key collagen-related genes on cellular behavior was confirmed through various assays, including CCK8, invasion, migration, cell cloning, and wound healing. Immunohistochemical detection was used to evaluate PLOD3 expression in prostate cancer tissue samples. Results: Our study identified four key collagen-associated genes (PLOD3, COL1A1, MMP11, FMOD) as significant. Survival analysis revealed that low-risk groups, based on the risk scoring model, had significantly improved prognoses. The risk score was strongly associated with prostate cancer prognosis. Researchers then created a nomogram, which demonstrated robust predictive efficacy and substantial clinical applicability.Remarkably, the suppression of PLOD3 expression notably impeded the proliferation, invasion, migration, and colony formation capabilities of PCa cells. Conclusion: The risk score, derived from four collagen-associated genes, could potentially act as a precise prognostic indicator for BRFS of patients. Simultaneously, our research has identified potential therapeutic targets related to collagen. Notably, PLOD3 was differentially expressed in cancer and para-cancer tissues in clinical specimens and it also was validated through in vitro studies and shown to suppress PCa tumorigenesis following its silencing. [ABSTRACT FROM AUTHOR]

Objective: Colon cancer is associated with multiple levels of molecular heterogeneity. RNA processing converts primary transcriptional RNA to mature RNA, which drives tumourigenesis and its maintenance. The characterisation of RNA processing genes in colon cancer urgently needs to be elucidated. Methods: In this study, we obtained 1033 relevant samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to explore the heterogeneity of RNA processing phenotypes in colon cancer. Firstly, Unsupervised hierarchical cluster analysis detected 4 subtypes with specific clinical outcomes and biological features via analysis of 485 RNA processing genes. Next, we adopted the least absolute shrinkage and selection operator (LASSO) as well as Cox regression model with penalty to characterise RNA processing-related prognostic features. Results: An RNA processing-related prognostic risk model based on 10 genes including FXR1, MFAP1, RBM17, SAGE1, SNRPA1, SRRM4, ADAD1, DDX52, ERI1, and EXOSC7 was identified finally. A composite prognostic nomogram was constructed by combining this feature with the remaining clinical variables including TNM, age, sex, and stage. Genetic variation, pathway activation, and immune heterogeneity with risk signatures were also analysed via bioinformatics methods. The outcomes indicated that the high-risk subgroup was associated with higher genomic instability, increased proliferative and cycle characteristics, decreased tumour killer CD8+ T cells and poorer clinical prognosis than the low-risk group. Conclusion: This prognostic classifier based on RNA-edited genes facilitates stratification of colon cancer into specific subgroups according to TNM and clinical outcomes, genetic variation, pathway activation, and immune heterogeneity. It can be used for diagnosis, classification and targeted treatment strategies comparable to current standards in precision medicine. It provides a rationale for elucidation of the role of RNA editing genes and their clinical significance in colon cancer as prognostic markers. [ABSTRACT FROM AUTHOR]

Background: Transient receptor potential (TRP) channels are involved in the development and progression of tumors. However, their role in colorectal cancer (CRC) remains unclear, and this study aims to investigate the role of TRP-related genes in CRC. Methods: Data was obtained from The Cancer Genome Atlas (TCGA) database, and analyses were conducted on the GSE14333 and GSE38832 datasets to assess the prognosis and mark TRP-related genes (TRGs). Subsequently, clustering analysis and immune infiltration analysis were performed to explore the relevant TRGs. In vitro validation of key TRGs' gene and protein expression was conducted using human colon cancer cells. Results: Compared to normal tissues, 8 TRGs were significantly upregulated in CRC, while 11 were downregulated. TRPA1 was identified as a protective prognostic factor, whereas TRPM5 (HR = 1.349), TRPV4 (HR = 1.289), and TRPV3 (HR = 1.442) were identified as prognostic risk factors. Receiver operating characteristic (ROC) curves and Kaplan-Meier (KM) analyses yielded similar results. Additionally, lower expression of TRPA1 and higher expression of TRPV4 and TRPM5 were negatively correlated with patient prognosis, and experimental validation confirmed the underexpression of TRPA1 and overexpression of TRPV4 and TRPM5 in CRC cell lines. Conclusion: This study identifies a TRP channel-related prognosis in CRC, providing a novel approach to stratifying CRC prognosis. [ABSTRACT FROM AUTHOR]

Background: Large-scale sequencing plays important roles in revealing the genomic map of ccRCC and predicting prognosis and therapeutic response to targeted drugs. However, the relevant clinical data is still sparse in Chinese population. Methods: Fresh tumor specimens were collected from 66 Chinese ccRCC patients, then the genomic RNAs were subjected to whole transcriptome sequencing (WTS). We comprehensively analyzed the frequently mutated genes from our hospital's cohort as well as TCGA-KIRC cohort. Results: VHL gene is the most frequently mutated gene in ccRCC. In our cohort, BAP1 and PTEN are significantly associated with a higher tumor grade and DNM2 is significantly associated with a lower tumor grade. The mutant type (MT) groups of BAP1 or PTEN, BAP1 or SETD2, BAP1 or TP53, BAP1 or MTOR, BAP1 or FAT1 and BAP1 or AR had a significantly correlation with higher tumor grade in our cohort. Moreover, we identified HMCN1 was a hub mutant gene which was closely related to worse prognosis and may enhance anti-tumor immune responses. Conclusions: In this preliminary research, we comprehensively analyzed the frequently mutated genes in the Chinese population and TCGA database, which may bring new insights to the diagnosis and medical treatment of ccRCC. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. Methods: Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. Results: We found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. Conclusions: These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management. Plain English Summary: Lung adenocarcinoma (LUAD) is a disease that affects males and females differently. Biological sex not only influences chances of developing the disease, but also how the disease progresses and how effective various therapies may be. We analyzed sex-specific gene regulatory networks consisting of transcription factors and the genes they regulate in both healthy lung tissue and in LUAD and identified sex-biased differences. We found that genes associated with cell proliferation, immune response, and drug metabolism are differentially targeted by transcription factors between males and females. We also found that several genes that are drug targets in LUAD, are also regulated differently between males and females. Importantly, these differences are also influenced by an individual's smoking history. Extending our analysis using a drug repurposing tool, we found candidate drugs with evidence that they might work better for one sex or the other. These results demonstrate that considering the differences in gene regulation between males and females will be essential if we are to develop precision medicine strategies for preventing and treating LUAD. Highlights: Genes associated with cell proliferation, immune response, and drug metabolism are differentially targeted by transcription factors between males and females in healthy lung tissue and lung adenocarcinoma samples. Gene regulatory differences between males and females are also affected by an individual's history of smoking. Significant sex bias exists in transcription factor targeting patterns of several clinically actionable oncogenes and tumor suppressor genes including AKT2 and KRAS. Analyzing sex-biased gene regulatory networks in lung adenocarcinoma identified small-molecule cancer therapeutics with potential sex-specific efficacy, underscoring the importance of considering sex-specific gene regulation in precision medicine. [ABSTRACT FROM AUTHOR]

Background: Muscle-invasive bladder cancer (MIBC) is a prevalent and aggressive malignancy. Ferroptosis and cuproptosis are recently discovered forms of programmed cell death (PCD) that have attracted much attention. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes remain unclear. Methods: Data from the TCGA-BLCA project (as the training set), cBioPortal database, and GEO datasets (GSE13507 and GSE32894, as the test sets) were utilized to identify hub ferroptosis/cuproptosis-related genes (FRGs and CRGs) and develop a prognostic signature. Differential expression analysis (DEA) was conducted, followed by univariate and multivariate Cox's regression analyses and multiple machine learning (ML) techniques to select genetic features. The performance of the ferroptosis/cuproptosis-related signature was evaluated using Kaplan–Meier (K–M) survival analysis and receiver-operating characteristics (ROC) curves. Mutational and tumour immune microenvironment landscapes were also explored. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) experiments confirmed the expression patterns of the hub genes, and functional assays assessed the effects of SCD knockdown on cell viability, proliferation, and migration. Results: DEA revealed dysregulated FRGs and CRGs in the TCGA MIBC cohort. SCD, DDR2, and MT1A were identified as hub genes. A prognostic signature based on the sum of the weighted expression of these genes demonstrated strong predictive efficacy in the training and test sets. Nomogram incorporating this signature accurately predicted 1-, 3-, and 5-year survival probabilities in the TCGA cohort and GSE13507 dataset. Copy number variation (CNV) and tumour immune microenvironment analysis revealed that high risk score level groups were associated with immunosuppression and lower tumour purity. The associations of risk scores with immunotherapy and chemical drugs were also explored, indicating their potential for guiding treatment for MIBC patients. The dysregulated expression patterns of three hub genes were validated by RT-qPCR experiments. Conclusions: Targeting hub FRGs and CRGs could be a promising therapeutic approach for MIBC. Our prognostic model offers a new framework for MIBC subtyping and can inform personalized therapeutic strategies. [ABSTRACT FROM AUTHOR]

Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, and the outcome of the patients remains dismal for the lack of effective biomarkers of early detection. Recent studies have elucidated the landscape of genomic alterations of gastric cancer and reveal some biomarkers of advanced-stage gastric cancer, however, information about early-stage biomarkers is limited. Here, we adopt Weighted Gene Co-expression Network Analysis (WGCNA) to screen potential biomarkers for early-stage STAD using RNA-Seq and clinical data from TCGA database. We find six gene clusters (or modules) are significantly correlated with the stage-I STADs. Among these, five hub genes, i.e., MS4A1, THBS2, VCAN, PDGFRB, and KCNA3 are identified and significantly deregulated in the stage-I STADs compared with the normal stomach gland tissues, which suggests they can serve as potential early diagnostic biomarkers. Moreover, we show that high expression of VCAN and PDGFRB is associated with poor prognosis of STAD. VCAN encodes a large chondroitin sulfate proteoglycan that is the main component of the extracellular matrix, and PDGFRB encodes a cell surface tyrosine kinase receptor for members of the platelet-derived growth factor (PDGF) family. Consistently, Gene Ontology (GO) analysis of differentially expressed genes in the STADs indicates terms associated with extracellular matrix and receptor ligand activity are significantly enriched. Protein-protein network interaction analysis (PPI) and Gene Set Enrichment Analysis (GSEA) further support the core role of VCAN and PDGFRB in the tumorigenesis. Collectively, our study identifies the potential biomarkers for early detection and prognosis of STAD. [ABSTRACT FROM AUTHOR]

Voltage-gated sodium channels (VGSCs) initiate action potentials in electrically excitable cells and tissues. Surprisingly, some VGSC genes are aberrantly expressed in a variety of cancers, derived from "non-excitable" tissues that do not generate classic action potentials, showing potential as a promising pharmacological target for cancer. Most of the previous review articles on this topic are limited in scope, and largely unable to provide researchers with a comprehensive understanding of the role of VGSC in cancers. Here, we review the expression patterns of all nine VGSC α-subunit genes (SCN1A-11A) and their four regulatory β-subunit genes (SCN1B-4B). We reviewed data from the Cancer Genome Atlas (TCGA) database, complemented by an extensive search of the published papers. We summarized and reviewed previous independent studies and analyzed the VGSC genes in the TCGA database regarding the potential impact of VGSC on cancers. A comparison between evidence gathered from independent studies and data review was performed to scrutinize potential biases in prior research and provide insights into future research directions. The review supports the view that VGSCs play an important role in diagnostics as well as therapeutics of some cancer types, such as breast, colon, prostate, and lung cancer. This paper provides an overview of the current knowledge on voltage-gated sodium channels in cancer, as well as potential avenues for further research. While further research is required to fully understand the role of VGSCs in cancer, the potential of VGSCs for clinical diagnosis and treatment is promising. [ABSTRACT FROM AUTHOR]

The complexity of cancer development involves intricate interactions among multiple biomarkers, such as gene-environment interactions. Utilizing microarray gene expression profile data for cancer classification is anticipated to be effective, thus drawing considerable interest in the fields of bioinformatics and computational biology. Due to the characteristics of genomic data, problems of high-dimensional interactions and noise interference do exist during the analysis process. When building cancer diagnosis models, we often face the dilemma of model adaptation errors due to an imbalance of data types. To mitigate the issues, we apply the SMOTE-Tomek procedure to rectify the imbalance problem. Following this, we utilize the overlapping group screening method alongside a binary logistic regression model to integrate gene pathway information, facilitating the identification of significant biomarkers associated with clinically imbalanced cancer or normal outcomes. Simulation studies across different imbalanced rates and gene structures validate our proposed method's effectiveness, surpassing common machine learning techniques in terms of classification prediction accuracy. We also demonstrate that prediction performance improves with SMOTE-Tomek treatment compared to no imbalance treatment and SMOTE treatment across various imbalance rates. In the real-world application, we integrate clinical and gene expression data with prior pathway information. We employ SMOTE-Tomek and our proposed methods to identify critical biomarkers and gene-environment interactions linked to the imbalanced binary outcomes (cancer or normal) in patients from the Cancer Genome Atlas datasets of lung adenocarcinoma and breast invasive carcinoma. Our proposed method consistently achieves satisfactory classification accuracy. Additionally, we have identified biomarkers indicative of gene-environment interactions relevant to cancer and have provided corresponding estimates of odds ratios. Moreover, in high-dimensional imbalanced data, for achieving good prediction results, we recommend considering the order of balancing processing and feature screening. [ABSTRACT FROM AUTHOR]

Dysregulation of histone deacetylases (HDACs) is closely associated with cancer development and progression. Here, we comprehensively analyzed the association between all HDAC family members and several clinicopathological and molecular traits of solid tumors across 22 distinct tumor types, focusing primarily on cancer stemness and immunity. To this end, we used publicly available TCGA data and several bioinformatic tools (i.e., GEPIA2, TISIDB, GSCA, Enrichr, GSEA). Our analyses revealed that class I and class II HDAC proteins are associated with distinct cancer phenotypes. The transcriptomic profiling indicated that class I HDAC members, including HDAC2, are positively associated with cancer stemness, while class IIA HDAC proteins, represented by HDAC7, show a negative correlation to cancer stem cell-like phenotypes in solid tumors. In contrast to tumors with high amounts of HDAC7 proteins, the transcriptome signatures of HDAC2-overexpressing cancers are significantly enriched with biological terms previously determined as stemness-associated genes. Moreover, high HDAC2-expressing tumors are depleted with immune-related processes, and HDAC2 expression correlates with tumor immunosuppressive microenvironments. On the contrary, HDAC7 upregulation is significantly associated with enhanced immune responses, followed by enriched infiltration of CD4+ and CD8+ T cells. This is the first comprehensive report demonstrating robust and versatile associations between specific HDAC family members, cancer dedifferentiation, and anti-tumor immune statuses in solid tumors. [ABSTRACT FROM AUTHOR]

The objective of this research was to create a prognostic model focused on genes related to ubiquitination (UbRGs) for evaluating their clinical significance in head and neck squamous cell carcinoma (HNSCC) patients. The transcriptome expression data of UbRGs were obtained from The Cancer Genome Atlas (TCGA) database, and weighted gene co-expression network analysis (WGCNA) was used to identify specific UbRGs within survival-related hub modules. A multi-gene signature was formulated using LASSO Cox regression analysis. Furthermore, various analyses, including time-related receiver operating characteristics (ROCs), Kaplan–Meier, Cox regression, nomogram prediction, gene set enrichment, co-expression, immune, tumor mutation burden (TMB), and drug sensitivity, were conducted. Ultimately, a prognostic signature consisting of 11 gene pairs for HNSCC was established. The Kaplan–Meier curves indicated significantly improved overall survival (OS) in the low-risk group compared to the high-risk group (p < 0.001), suggesting its potential as an independent and dependable prognostic factor. Additionally, a nomogram with AUC values of 0.744, 0.852, and 0.861 at 1-, 3-, and 5-year intervals was developed. Infiltration of M2 macrophages was higher in the high-risk group, and the TMB was notably elevated compared to the low-risk group. Several chemotherapy drugs targeting UbRGs were recommended for low-risk and high-risk patients, respectively. The prognostic signature derived from UbRGs can effectively predict prognosis and provide new personalized therapeutic targets for HNSCC. [ABSTRACT FROM AUTHOR]

Background: Disruptions in calcium homeostasis are associated with a wide range of diseases, and play a pivotal role in the development of cancer. However, the construction of prognostic models using calcium extrusion-related genes in colon adenocarcinoma (COAD) has not been well studied. We aimed to identify whether calcium extrusion-related genes serve as a potential prognostic biomarker in the COAD progression. Methods: We constructed a prognostic model based on the expression of calcium extrusion-related genes (SLC8A1, SLC8A2, SLC8A3, SLC8B1, SLC24A2, SLC24A3 and SLC24A4) in COAD. Subsequently, we evaluated the associations between the risk score calculated by calcium extrusion-related genes and mutation signature, immune cell infiltration, and immune checkpoint molecules. Then we calculated the immune score, stromal score, tumor purity and estimate score using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm. The response to immunotherapy was assessed using tumor immune dysfunction and exclusion (TIDE). Finally, colorectal cancer cells migration, growth and colony formation assays were performed in RKO cells with the overexpression or knockdown SLC8A3, SLC24A2, SLC24A3, or SLC24A4. Results: We found that patients with high risk score of calcium extrusion-related genes tend to have a poorer prognosis than those in the low-risk group. Additionally, patients in high-risk group had higher rates of KRAS mutations and lower MUC16 mutations, implying a strong correlation between KRAS and MUC16 mutations and calcium homeostasis in COAD. Moreover, the high-risk group showed a higher infiltration of regulatory T cells (Tregs) in the tumor microenvironment. Finally, our study identified two previously unreported model genes (SLC8A3 and SLC24A4) that contribute to the growth and migration of colorectal cancer RKO cells. Conclusions: Altogether, we developed a prognostic risk model for predicting the prognosis of COAD patients based on the expression profiles of calcium extrusion-related genes, Furthermore, we validated two previously unreported tumor suppressor genes (SLC8A3 and SLC24A4) involved in colorectal cancer progression. [ABSTRACT FROM AUTHOR]

Introduction: An increasing number of immune-related adverse events (irAEs) induced by immune checkpoint inhibitors (ICIs) have been reported during clinical treatment. We aimed to explore the clinical characteristics of patients with ICIsinduced ITP under different therapeutic strategies based on the FAERS database and explore the potential biological mechanisms in combination with TCGA pancancer data. Methods: Data from FAERS were collected for ICIs adverse reactions between January 2012 and December 2022. Disproportionality analysis identified ICIsinduced ITP in the FAERS database using the reporting odds ratio (ROR), proportional reporting ratio (PRP), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker algorithms (MGPS). The potential biological mechanisms underlying ITP induced by ICIs were examined using TCGA transcriptome data on cancers. Results: In the FAERS, 345 ICIs-induced ITP reports were retrieved, wherein 290 (84.06%) and 55 (15.94%) were reported as monotherapy and combination therapy, respectively. The median age of the reported patients with ICIsinduced ITP was 69 years (IQR 60-76), of which 62 (18%) died and 47 (13.6%) had a life-threatening outcome. The majority of reported indications were lung, skin, and bladder cancers, and the median time to ITP after dosing was 42 days (IQR 17-135), with 64 patients (43.5%) experiencing ITP within 30 days of dosing and 88 patients experiencing ITP in less than 2 months (59.9%). The occurrence of ICIs-induced ITP may be associated with ICIs-induced dysregulation of the mTORC1 signaling pathway and megakaryocyte dysfunction. Conclusion: There were significant reporting signals for ITP with nivolumab, pembrolizumab, cemiplimab, atezolizumab, avelumab, durvalumab, ipilimumab, nivolumab/ipilimumab, and pembrolizumab/ipilimumab. Patients treated with anti-PD-1 in combination with anti-CTLA-4 are more likely to have an increased risk of ICIs-induced ITP. Patients with melanoma are at a higher risk of developing ITP when treated with ICI and should be closely monitored for this risk within 60 days of treatment. The potential biological mechanism of ICIs-induced ITP may be related to the dysfunction of megakaryocyte autophagy through the overactivation of the mTORrelated signaling pathway. This study provides a comprehensive understanding of ICIsinduced ITP. Clinicians should pay attention to this potentially fatal adverse reaction. [ABSTRACT FROM AUTHOR]

Background As a newly identified subtype of HER2-negative tumors associated with a less favorable prognosis, it remains crucial to evaluate potential prognostic and predictive factors, particularly non-invasive biomarkers, for individuals with human epidermal growth factor 2 (HER2) low early-stage breast cancer (EBC). Multiple investigations have highlighted that HER2-negative patients with EBC exhibiting high homologous recombination deficiency (HRD) scores display lower rates of pathological complete response (PCR) to neoadjuvant chemotherapy (NAC). Nevertheless, no study to date has explored the correlation between HRD and the long-term prognosis in HER2-low patients with EBC. Patients and methods This retrospective observational study focuses on primary EBC sourced from The Cancer Genome Atlas dataset (TCGA). It reveals the gene mutation landscape in EBC with low HER2 expression and elucidates the tumor immune landscape across different HRD states. Utilizing bioinformatics analysis and Cox proportional models, along with the Kaplan-Meier method, the study assesses the correlation between HRD status and disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Subgroup analyses were conducted to identify potential variations in the association between HRD and prognosis. Results In the patients with HER2-low breast cancer, patients with homologous recombination related genes (HRRGs) defects had an HRD score about twice that of those without related genes mutations, and were at higher risk of acquiring ARID1A , ATM , and BRCA2 mutations. We also found that most immune cell abundances were significantly higher in EBC tumors with high HRD than in EBC tumors with low HRD or HRD-medium, particularly plasma B-cell abundance, CD8 T-cell abundance, and M1 macrophages. In addition, these tumors with HRD-high also appear to have significantly higher tumor immune scores and lower interstitial scores. Then, we analyzed the relationship between different HRD status and prognosis. There was statistical significance (P  = .036 and P  = .046, respectively) in DSS and PFI between the HRD-low and HRD-high groups, and patients with HRD-high EBC showed relatively poor survival outcomes. A medium HRD score (hazard ratio, HR = 2.15, 95% CI: 1.04-4.41, P  = .038) was a significant risk factor for PFI. Hormone receptor positivity is an important factor in obtaining medium-high HRD score and poor prognosis. Conclusion Higher HRD scores were associated with poorer PFI outcomes, particularly in people with HR+/HER2-low. Varied HRD states exhibited distinctions in HRRGs and the tumor immune landscape. These insights have the potential to assist clinicians in promptly identifying high-risk groups and tailoring personalized treatments for patients with HER2-low EBC, aiming to enhance long-term outcomes. [ABSTRACT FROM AUTHOR]

Chordin (CHRD) is a secreted protein important in early development, yet a role for CHRD in human disease has not been identified. In this study we investigated CHRD in cancer and normal adult tissues using the wealth of genome-wide data available in public databases. We found that Chordin is amplified in the DNA of specific cancers such as lung squamous cell and others, although copy number variation did not strictly correlate with higher mRNA expression. In some cancers, such as renal and stomach carcinomas, increased CHRD expression significantly correlated with poor survival. In normal adult human tissues, CHRD mRNA was highest in hepatocytes. Crossveinless-2/BMPER, a component of the Chordin morphogenetic pathway expressed at the opposite side in embryos, was expressed in liver stellate cells. This raises the intriguing possibility that a BMP gradient might be established in the extracellular matrix of the space of Disse that surrounds portal sinusoid capillaries.

Abstract Background Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. Results Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. Conclusions Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach.

YuXuan Zhang,1,2,&ast; Yuhong Shen,3,&ast; Jiayuan Wu,2 Jun Zhang,4 Chenxi Cao,5 Juanfen Mo,2 Yi Bao2,5 1The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, 310000, People’s Republic of China; 2The Key Laboratory, the Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, People’s Republic of China; 3Transfusion Center, the Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, People’s Republic of China; 4The Department of Thoracic Surgery, the Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, People’s Republic of China; 5The Department of Oncology, the Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yi Bao, The Key Laboratory, The Department of Oncology, The Second Hospital of Jiaxing, 1518 Huanchen North Road, Zhejiang, People’s Republic of China, 314000, Email ybao2011@zjxu.edu.cn Juanfen Mo, The Key Laboratory, The Second Affiliated Hospital of Jiaxing University, 1518 Huancheng North Road, Jiaxing, Zhejiang, People’s Republic of China, 314000, Email mojuanfen@163.comObjective: PIK3CA-mutant non-small-cell lung cancer (NSCLC) is associated with other genetic mutations and may influence treatment strategies and clinical outcomes. We aimed to characterize PIK3CA mutations co-occurring with several major driver mutations using data from published cohorts and our medical center.Materials and Methods: We analyzed NSCLC patients harboring PIK3CA mutations from The Cancer Genome Atlas (TCGA) and Memorial Sloan Kettering (MSK) databases and retrospectively identified NSCLC patients with PIK3CA-mutants at a single medical center from our electronic records. The Log rank test was used to determine the association between PIK3CA mutations and overall survival (OS) in NSCLC patients.Results: Common hotspot mutations in PIK3CA were found in exon 9 (c.1633G > A, E545K, and c.1624G > A, E542K) and exon 20 (c.3140A > G, H1047R) in all cohorts. Co-occurring mutations of PIK3CA with EGFR, KRAS, and TP53 have been frequently observed in patients with NSCLC, with different percentages in these datasets generated by different background. PIK3CA mutations were observed to be significantly associated with poor OS in lung adenocarcinomas patients in the MSKCC cohort (hazard ratio [HR] = 0.519, 95% confidence interval [CI] = 0.301– 0.896; P < 0.05).Conclusion: PIK3CA co-occurring mutations in other genes may represent distinct subsets of NSCLC. Further elucidation of the roles of PIK3CA hotspot mutations combined with other driver mutations, including EGFR and KRAS, is needed to guide effective treatment in patients with advanced NSCLC.Keywords: co-occurring mutation, non-small cell lung cancer, PIK3CA, The Cancer Genome Atlas, Memorial Sloan Kettering Cancer Center

Abstract Background Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. Methods In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell’s C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. Results Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. Conclusions Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure.

Yaonan Hong,1,2,&ast; Qi Liu,1,2,&ast; Chuanao Xin,1,2,&ast; Huijin Hu,1,2 Zhenchao Zhuang,1– 3 Hangping Ge,1,2,4 Yingying Shen,1,2,4 Yuechao Zhao,1,2,4 Yuhong Zhou,1,2,4 Baodong Ye,1,2,4 Dijiong Wu1,2,4,5 1Department of Hematology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, People’s Republic of China; 2The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China; 3Department of Clinical Laboratory, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China; 4National Traditional Chinese Medicine Clinical Research Base (Hematology), Hangzhou, Zhejiang, People’s Republic of China; 5Department of Oncology and Hematology, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine Affiliated to Zhejiang Chinese Medicine University, Wenzhou, Zhejiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Dijiong Wu, Department of Hematology, the First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Chinese Medicine, Hangzhou, Zhejiang, 310006, People’s Republic of China, Tel +86-571-86620325, Email wudijiong@zcmu.edu.cnBackground: Limited data were available to understand the significance of ferroptosis in leukemia prognosis, regardless of the genomic background.Methods: RNA-seq data from 151 AML patients were analyzed from The Cancer Genome Atlas (TCGA) database, along with 70 healthy samples from the Genotype-Tissue Expression (GTEx) database. Ferroptosis-related genes (FRGs) features were constructed by multivariate COX regression analysis and risk scores were calculated for each sample and a novel prediction model was identified. The validation was carried out using data from 35 AML patients and 13 healthy controls in our cohort. Drug sensitivity analysis was conducted on various chemotherapeutic drugs.Results: A signature of 10 FRGs was identified, as prognostic predictors for AML, and the risk scores were calculated to constructed the prognostic features of FRGs. Significantly lower overall survival was observed in the high-risk group. The predictive ability of these features for AML prognosis was confirmed using Cox regression analysis, ROC curves, and DCA. The prediction model performed well in our clinical practices, and had its potential superiority when comparing to classical NCCN risk stratification. Multiple chemotherapy drugs, including paclitaxel, dactinomycin, cisplatin, etc. had a lower IC50 in FRGs high-risk group than low-risk group.Conclusion: The AML prognosis model based on FRGs accurately predicts AML prognosis and drug sensitivity, and the drugs identified worthy further investigation.Keywords: ferroptosis, prediction model, TCGA, acute myeloid leukemia, drug sensitivity

Abstract Purpose Prostate cancer (PCa) is a common malignancy in men, with an escalating mortality rate attributed to Recurrence and metastasis. Recent studies have illuminated collagen’s critical regulatory role within the tumor microenvironment, significantly influencing tumor progression. Accordingly, this investigation is dedicated to examining the relationship between genes linked to collagen and the prognosis of PCa, with the objective of uncovering any possible associations between them. Methods Gene expression data for individuals with prostate cancer were obtained from the TCGA repository. Collagen-related genes were identified, leading to the development of a risk score model associated with biochemical recurrence-free survival (BRFS). A prognostic nomogram integrating the risk score with essential clinical factors was crafted and evaluated for efficacy. The influence of key collagen-related genes on cellular behavior was confirmed through various assays, including CCK8, invasion, migration, cell cloning, and wound healing. Immunohistochemical detection was used to evaluate PLOD3 expression in prostate cancer tissue samples. Results Our study identified four key collagen-associated genes (PLOD3, COL1A1, MMP11, FMOD) as significant. Survival analysis revealed that low-risk groups, based on the risk scoring model, had significantly improved prognoses. The risk score was strongly associated with prostate cancer prognosis. Researchers then created a nomogram, which demonstrated robust predictive efficacy and substantial clinical applicability.Remarkably, the suppression of PLOD3 expression notably impeded the proliferation, invasion, migration, and colony formation capabilities of PCa cells. Conclusion The risk score, derived from four collagen-associated genes, could potentially act as a precise prognostic indicator for BRFS of patients. Simultaneously, our research has identified potential therapeutic targets related to collagen. Notably, PLOD3 was differentially expressed in cancer and para-cancer tissues in clinical specimens and it also was validated through in vitro studies and shown to suppress PCa tumorigenesis following its silencing.

Abstract Background Transient receptor potential (TRP) channels are involved in the development and progression of tumors. However, their role in colorectal cancer (CRC) remains unclear, and this study aims to investigate the role of TRP-related genes in CRC. Methods Data was obtained from The Cancer Genome Atlas (TCGA) database, and analyses were conducted on the GSE14333 and GSE38832 datasets to assess the prognosis and mark TRP-related genes (TRGs). Subsequently, clustering analysis and immune infiltration analysis were performed to explore the relevant TRGs. In vitro validation of key TRGs’ gene and protein expression was conducted using human colon cancer cells. Results Compared to normal tissues, 8 TRGs were significantly upregulated in CRC, while 11 were downregulated. TRPA1 was identified as a protective prognostic factor, whereas TRPM5 (HR = 1.349), TRPV4 (HR = 1.289), and TRPV3 (HR = 1.442) were identified as prognostic risk factors. Receiver operating characteristic (ROC) curves and Kaplan-Meier (KM) analyses yielded similar results. Additionally, lower expression of TRPA1 and higher expression of TRPV4 and TRPM5 were negatively correlated with patient prognosis, and experimental validation confirmed the underexpression of TRPA1 and overexpression of TRPV4 and TRPM5 in CRC cell lines. Conclusion This study identifies a TRP channel-related prognosis in CRC, providing a novel approach to stratifying CRC prognosis.

Abstract Background Muscle-invasive bladder cancer (MIBC) is a prevalent and aggressive malignancy. Ferroptosis and cuproptosis are recently discovered forms of programmed cell death (PCD) that have attracted much attention. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes remain unclear. Methods Data from the TCGA-BLCA project (as the training set), cBioPortal database, and GEO datasets (GSE13507 and GSE32894, as the test sets) were utilized to identify hub ferroptosis/cuproptosis-related genes (FRGs and CRGs) and develop a prognostic signature. Differential expression analysis (DEA) was conducted, followed by univariate and multivariate Cox’s regression analyses and multiple machine learning (ML) techniques to select genetic features. The performance of the ferroptosis/cuproptosis-related signature was evaluated using Kaplan–Meier (K–M) survival analysis and receiver-operating characteristics (ROC) curves. Mutational and tumour immune microenvironment landscapes were also explored. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) experiments confirmed the expression patterns of the hub genes, and functional assays assessed the effects of SCD knockdown on cell viability, proliferation, and migration. Results DEA revealed dysregulated FRGs and CRGs in the TCGA MIBC cohort. SCD, DDR2, and MT1A were identified as hub genes. A prognostic signature based on the sum of the weighted expression of these genes demonstrated strong predictive efficacy in the training and test sets. Nomogram incorporating this signature accurately predicted 1-, 3-, and 5-year survival probabilities in the TCGA cohort and GSE13507 dataset. Copy number variation (CNV) and tumour immune microenvironment analysis revealed that high risk score level groups were associated with immunosuppression and lower tumour purity. The associations of risk scores with immunotherapy and chemical drugs were also explored, indicating their potential for guiding treatment for MIBC patients. The dysregulated expression patterns of three hub genes were validated by RT-qPCR experiments. Conclusions Targeting hub FRGs and CRGs could be a promising therapeutic approach for MIBC. Our prognostic model offers a new framework for MIBC subtyping and can inform personalized therapeutic strategies.

Abstract Background Large-scale sequencing plays important roles in revealing the genomic map of ccRCC and predicting prognosis and therapeutic response to targeted drugs. However, the relevant clinical data is still sparse in Chinese population. Methods Fresh tumor specimens were collected from 66 Chinese ccRCC patients, then the genomic RNAs were subjected to whole transcriptome sequencing (WTS). We comprehensively analyzed the frequently mutated genes from our hospital’s cohort as well as TCGA-KIRC cohort. Results VHL gene is the most frequently mutated gene in ccRCC. In our cohort, BAP1 and PTEN are significantly associated with a higher tumor grade and DNM2 is significantly associated with a lower tumor grade. The mutant type (MT) groups of BAP1 or PTEN, BAP1 or SETD2, BAP1 or TP53, BAP1 or MTOR, BAP1 or FAT1 and BAP1 or AR had a significantly correlation with higher tumor grade in our cohort. Moreover, we identified HMCN1 was a hub mutant gene which was closely related to worse prognosis and may enhance anti-tumor immune responses. Conclusions In this preliminary research, we comprehensively analyzed the frequently mutated genes in the Chinese population and TCGA database, which may bring new insights to the diagnosis and medical treatment of ccRCC.

Prostate cancer (PCa) is a widespread global health concern affecting males. Numerous investigations have shown the substantial implications of inflammation to PCa progression. To evaluate the predictive capacity of inflammatory-related genes (IRGs) in PCa, we conducted univariate Cox regression analysis and the Least Absolute Shrinkage and Selector Operation (LASSO) regression to formulate a prognostic model based on IRG expression. The training dataset comprised The Cancer Genome Atlas (TCGA) cohort, with validation performed using the cBioPortal cohort. Subsequently, an overall survival (OS) prediction was performed using a nomogram within the TCGA-PCa cohort, and we explored the association between the risk model and various factors such as immune cell infiltration, immune-related pathway functionality, tumor microenvironment characteristics, cancer stem cell scores and drug sensitivity. A comprehensive selection of 17 IRGs was identified to establish this prognostic risk model, where individuals with elevated risk scores exhibited unfavorable prognosis. The genomic nomogram, constructed based on these central IRGs, demonstrated remarkable accuracy in predicting PCa prognosis. Evaluation of tumor-infiltrating immune cells and immune-related pathways suggested that high-risk groups may manifest an immune-active phenotype. Moreover, there was a significant correlation between the expression levels of IRGs and tumor cell sensitivity to chemotherapeutic drugs. In conclusion, these findings indicate that an IRG-based risk model associated with inflammation can effectively predict PCa prognosis, presenting a promising strategy for further investigation.

Background and purpose: The seventh most common type of cancer with increasing diagnosis rates around the world is head and neck squamous cell carcinoma (HNSCC). Specificity proteins (SPs) have been known for their role in the regulation of cellular division, growth, and apoptotic pathways in various cancers. In this work, we analyzed the expression levels of SPs in HNSCC to assess their diagnostic and prognostic biomarker potential. Experimental approach: Differential gene expression and correlation analysis methods were used to determine the top dysregulated genes in HNSCC. Functional enrichment and protein-protein interaction analyses were done with the DAVID database and Cytoscape software to understand their function and biological processes. Receiver operating test, logistic regression, and Cox regression analyses were performed to check SP genes’ diagnostic and prognostic potential. Findings/Results: SP1 (LogFC = -0.27, P = 0.0013) and SP2 (LogFC = -0.20, P = 0.0019) genes were upregulated in HNSCC samples, while SP8 (LogFC = 2.57, P < 0.001) and SP9 (LogFC = 2.57, P < 0.001) genes were downregulated in cancer samples. A moderate positive correlation was observed among the expression levels of SP1, SP2, and SP3 genes. The SP8 and SP9 genes with AUC values of 0.79 and 0.75 demonstrated diagnostic potential which increased to 0.84 when both genes were assessed by logistic regression test. Also, the SP1 gene held a marginally significant prognostic potential. Conclusion and implications: Our findings clarify the potential of SP transcription factors as candidate diagnostic and prognostic biomarkers for early screening and treatment of HNSCC.

Abstract Voltage-gated sodium channels (VGSCs) initiate action potentials in electrically excitable cells and tissues. Surprisingly, some VGSC genes are aberrantly expressed in a variety of cancers, derived from “non-excitable” tissues that do not generate classic action potentials, showing potential as a promising pharmacological target for cancer. Most of the previous review articles on this topic are limited in scope, and largely unable to provide researchers with a comprehensive understanding of the role of VGSC in cancers. Here, we review the expression patterns of all nine VGSC α-subunit genes (SCN1A-11A) and their four regulatory β-subunit genes (SCN1B-4B). We reviewed data from the Cancer Genome Atlas (TCGA) database, complemented by an extensive search of the published papers. We summarized and reviewed previous independent studies and analyzed the VGSC genes in the TCGA database regarding the potential impact of VGSC on cancers. A comparison between evidence gathered from independent studies and data review was performed to scrutinize potential biases in prior research and provide insights into future research directions. The review supports the view that VGSCs play an important role in diagnostics as well as therapeutics of some cancer types, such as breast, colon, prostate, and lung cancer. This paper provides an overview of the current knowledge on voltage-gated sodium channels in cancer, as well as potential avenues for further research. While further research is required to fully understand the role of VGSCs in cancer, the potential of VGSCs for clinical diagnosis and treatment is promising.

Abstract Cancer‐associated fibroblasts (CAFs) are the center of cross‐communication between various cells in the tumor stroma. However, how CAFs‐associated genes play an important role in Head and neck squamous cell carcinoma (HNSCC) prognosis has not been reported. Transcriptome data were downloaded from TCGA and GEO databases. Devtools, DPIC, xCell, MCPcounter, and Estimate packages were used to calculate CAFs scores and immune infiltration. Prognosis and weighted gene coexpression network analysis (WGCNA) analysis were performed between high or low risk populations based on CAF scores. Hub genes were identified, intersected, and enriched between TCGA and GEO databases. CAFs related genes were used to construct a prognostic model and the tumor immune dysfunction and exclusion database was used to evaluate the immune infiltration. Drug sensitivity, difference analysis and the HPA database were used to identify sensitive drugs and verify their expression. TCGA and GEO data suggested that CAFs scores had a role in HNSCC prognosis prediction. Based on CAFs scores, WGCNA and core gene enrichment analysis were performed to construct a CAFs‐related prognostic model. The prognostic model composed of a total of 12 CAFs genes could predict the prognosis well and was validated in the validation dataset, demonstrating its applicability to external data. According to the model, although there was no statistical difference in immune escape between the high and low risk groups, the proportion of patients who responded to immunotherapy was different. Drug sensitivity also differed between the two groups. This study suggests that CAFs associated genetic signatures may help to optimize risk stratification and provide new insights into individualized cancer treatment.

Abstract Immunogenic cell death (ICD) is a newly discovered form of cellular demise that triggers adaptive immune responses mediated by T cells. However, the immunogenic cell death-related lncRNAs (ICDRLs) involved in bladder cancer (BC) development and progression remain to be further elucidated. Molecular profiling data and clinicopathological information for BC patients were obtained from TCGA, and the ICDRGs list was obtained from published literature. For the identification of ICDRLs, Pearson co-expression analysis was performed, and a prognostic signature based on 13 ICDRLs was constructed by univariate assays and LASSO assays. Herein, an ICDRLSig consisting of 13 ICDRLs was constructed. KM curves and ROC curves demonstrated that the constructed signature in the TCGA training, testing, entire and external sets have good predictive performance. Multivariate assays illuminated that the signature is an independent predictor for BC patients’ OS, exhibiting greater predictive power for the survival than traditional clinicopathological features. Additionally, patients in the high-ICDRLSig risk subgroup had more abundant immune infiltration, higher immune checkpoint gene expression, lower TMB and poorer response to immunotherapy. We have developed a novel ICDRLSig that can be exploited for survival prediction and provide a reference for further individualized treatment.

Abstract Background Our previous studies have indicated that mRNA and protein levels of PPIH are significantly upregulated in Hepatocellular Carcinoma (LIHC) and could act as predictive biomarkers for patients with LIHC. Nonetheless, the expression and implications of PPIH in the etiology and progression of common solid tumors have yet to be explored, including its potential as a serum tumor marker. Methods We employed bioinformatics analyses, augmented with clinical sample evaluations, to investigate the mRNA and protein expression and gene regulation networks of PPIH in various solid tumors. We also assessed the association between PPIH expression and overall survival (OS) in cancer patients using Kaplan-Meier analysis with TCGA database information. Furthermore, we evaluated the feasibility and diagnostic efficacy of PPIH as a serum marker by integrating serological studies with established clinical tumor markers. Results Through pan-cancer analysis, we found that the expression levels of PPIH mRNA in multiple tumors were significantly different from those in normal tissues. This study is the first to report that PPIH mRNA and protein levels are markedly elevated in LIHC, Colon adenocarcinoma (COAD), and Breast cancer (BC), and are associated with a worse prognosis in these cancer patients. Conversely, serum PPIH levels are decreased in patients with these tumors (LIHC, COAD, BC, gastric cancer), and when combined with traditional tumor markers, offer enhanced sensitivity and specificity for diagnosis. Conclusion Our findings propose that PPIH may serve as a valuable predictive biomarker in tumor patients, and its secreted protein could be a potential serum marker, providing insights into the role of PPIH in cancer development and progression.

Abstract Background Bladder urothelial cancer is the most common malignancy of the urinary system and the 10th most common cancer worldwide with incidence appearing to show a geographical and exposure pattern. Advances in genomic technologies provide abundant data and insight into tumors at the single-cell resolution and are usually stored in repositories like The Cancer Genome Atlas (TCGA). However, data sources for the TCGA appear to be focused on European and American populations. The extent to which genomic and survival data can be applied to populations not included in the study remains somewhat uncertain. Methods We explored the genomic and survival characteristics of the TCGA pan-cancer atlas of bladder urothelial cancer. We decluttered these characteristics based on racial groups and compared between and among the races and the overall dataset. Results Significant variations were seen in age groups especially Asians (51–60) years and Blacks (61–70) years compared to Whites and the BLCA dataset with a statistically significant difference in mean diagnosis age (p = 0.0048) between Asians and the whole dataset. Overall survival characteristics were similar but genetic features were vastly different. Significant inter-racial alterations could be seen among genes involved in different pathways, oncogenes, tumor suppressors, cytoband amplification and/or deletion, mutation count, and aneuploidy scores. Conclusion The TCGA pan-cancer atlas for bladder urothelial cancer adequately represents White populations only. The genomic features do not apply to Blacks and Asians. We recommend better coverage for other populations to ensure adequate data for clinicians and researchers.

Abstract Background Ovarian cancer (OC) is a gynecological malignancy tumor with high recurrence and mortality rates. Programmed cell death (PCD) is an essential regulator in cancer metabolism, whose functions are still unknown in OC. Therefore, it is vital to determine the prognostic value and therapy response of PCD-related genes in OC. Methods By mining The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Genecards databases, we constructed a prognostic PCD-related genes model and performed Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic (ROC) curve for its predictive ability. A nomogram was created via Cox regression. We validated our model in train and test sets. Quantitative real-time PCR (qRT-PCR) was applied to identify the expression of our model genes. Finally, we analyzed functional analysis, immune infiltration, genomic mutation, tumor mutational burden (TMB) and drug sensitivity of patients in low- and high-risk group based on median scores. Results A ten-PCD-related gene signature including protein phosphatase 1 regulatory subunit 15 A (PPP1R15A), 8-oxoguanine-DNA glycosylase (OGG1), HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 (HERC1), Caspase-2.(CASP2), Caspase activity and apoptosis inhibitor 1(CAAP1), RB transcriptional corepressor 1(RB1), Z-DNA binding protein 1 (ZBP1), CD3-epsilon (CD3E), Clathrin heavy chain like 1(CLTCL1), and CCAAT/enhancer-binding protein beta (CEBPB) was constructed. Risk score performed well with good area under curve (AUC) (AUC3 − year =0.728, AUC5 − year = 0.730). The nomogram based on risk score has good performance in predicting the prognosis of OC patients (AUC1 − year =0.781, AUC3 − year =0.759, AUC5 − year = 0.670). Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the erythroblastic leukemia viral oncogene homolog (ERBB) signaling pathway and focal adhesion were enriched in the high-risk group. Meanwhile, patients with high-risk scores had worse OS. In addition, patients with low-risk scores had higher immune-infiltrating cells and enhanced expression of checkpoints, programmed cell death 1 ligand 1 (PD-L1), indoleamine 2,3-dioxygenase 1 (IDO-1) and lymphocyte activation gene-3 (LAG3), and were more sensitive to A.443,654, GDC.0449, paclitaxel, gefitinib and cisplatin. Finally, qRT-PCR confirmed RB1, CAAP1, ZBP1, CEBPB and CLTCL1 over-expressed, while PPP1R15A, OGG1, CASP2, CD3E and HERC1 under-expressed in OC cell lines. Conclusion Our model could precisely predict the prognosis, immune status and drug sensitivity of OC patients.

Immunogenic cell death (ICD) is a newly discovered form of cellular demise that triggers adaptive immune responses mediated by T cells. However, the immunogenic cell death-related lncRNAs (ICDRLs) involved in bladder cancer (BC) development and progression remain to be further elucidated. Molecular profiling data and clinicopathological information for BC patients were obtained from TCGA, and the ICDRGs list was obtained from published literature. For the identification of ICDRLs, Pearson co-expression analysis was performed, and a prognostic signature based on 13 ICDRLs was constructed by univariate assays and LASSO assays. Herein, an ICDRLSig consisting of 13 ICDRLs was constructed. KM curves and ROC curves demonstrated that the constructed signature in the TCGA training, testing, entire and external sets have good predictive performance. Multivariate assays illuminated that the signature is an independent predictor for BC patients' OS, exhibiting greater predictive power for the survival than traditional clinicopathological features. Additionally, patients in the high-ICDRLSig risk subgroup had more abundant immune infiltration, higher immune checkpoint gene expression, lower TMB and poorer response to immunotherapy. We have developed a novel ICDRLSig that can be exploited for survival prediction and provide a reference for further individualized treatment. [ABSTRACT FROM AUTHOR]

Insights from public DNA methylation data derived from cancer or normal tissues from cancer patients or healthy people can be obtained by machine learning. The goal is to determine methylation patterns that could be useful for predicting the prognosis for cancer patients and correcting lifestyles for healthy people. DNA methylation data were obtained from the DNA of 446 healthy participants from the Korean Genome Epidemiology Study (KoGES) and from the DNA of normal tissues or from cancer tissues of 11 types of carcinomas from The Cancer Genome Atlas (TCGA) database. To correct for the batch effect, R's ComBat function was used. Using the K-mean clustering (k = 3), the survival rates of the cancer patients and the incidence of chronic diseases were compared between the three clusters for TCGA and KoGES, respectively. Based on the public DNA methylation and clinical data of healthy participants and cancer patients, I present an analysis pipeline that integrates and clusters the methylation data from the two groups. As a result of clustering, CpG sites from gene or genomic regions, such as AFAP1, NINJ2, and HOOK2 genes, that correlated with survival rate and chronic disease are presented. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to analyze the prognostic and immunological value of CDC45 in kidney renal clear cell carcinoma (KIRC) using single-cell and bulk RNA-sequencing approaches. The expression of CDC45 in KIRC was evaluated by the HPA database, the TCGA-KIRC dataset and verified by PCR analysis and single-cell RNA-sequencing. The ability of CDC45 to independently predict prognosis in KIRC was confirmed by univariate/multivariate regression analysis. Gene set enrichment analysis (GSEA) was employed to explore CDC45-related pathways in KIRC. In addition, Relationships between CDC45 and immunity were also examined. Elevated CDC45 expression in KIRC was demonstrated at mRNA and protein levels. The results of the correlation analysis showed that as CDC45 expression increased, so did the histological grade, clinical stage, and TNM stage of the patients (p < 0.05). Univariate/multivariate regression analysis suggested CDC45 as an independent prognostic factor for KIRC. Seven pathways related to CDC45 were screened through GSEA. Meanwhile, we found that CDC45 was correlated with tumor mutational burden (TMB) and microsatellite instability (MSI) but not tumor neoantigen burden (TNB). Regarding immunity, CDC45 exhibited correlations with the tumor microenvironment, immune cell infiltration, and immune checkpoints. Besides, low CDC45 expression was shown to be associated with a better response to immunotherapy. Single-cell RNA-sequencing revealed that CDC45 was differently expressed in T cells (p < 0.05). CDC45 showed potential as a prognostic biomarker and therapeutic target for KIRC. Meanwhile, the CDC45 low expression group was more sensitive to immunotherapy. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV) infection has been causally linked to hepatocellular carcinoma (HCC) in more than 50% cases. MicroRNAs (miRNAs) play cross-cutting mechanistic roles in the complex interplay between viral pathogenesis, host survival, and clinical outcomes. The present study set out to identify etiologically significant human miRNAs associated with HBV infection in liver-related pathologies leading to HCC. In diverse tissue types, we assembled 573 miRNAs differentially expressed in HBV-associated liver pathologies, HBV infection, fibrosis, cirrhosis, acute on chronic liver failure, and HCC. Importantly, 43 human differentially expressed miRNAs (hDEmiRs) were regulated in serum/plasma and liver tissue of patients with HBV-positive conditions. However, only two hDEmiRs, hsa-miR-21-5p and hsa-miR-143-3p, were regulated across all disease conditions. To shortlist the functional miRNAs in HBV-induced HCC pathogenesis, a reverse bioinformatics analysis was performed using eight GEO datasets and the TCGA database containing the list of differentially regulated mRNAs in HCC. A comparative study using these data with the identified targets of hDEmiRs, a set of unidirectionally regulated hDEmiRs with the potential to modulate mRNAs in HCC, were found. Moreover, our study identified five miRNAs; hsa-miR-98-5p, hsa-miR-193b-3p, hsa-miR-142-5p, hsa-miR-522-5p, and hsa-miR-370-3p targeting PIGC, KNTC1, CSTF2, SLC41A2, and RAB17, respectively, in HCC. These hDEmiRs and their targets could be pivotal in HBV infection and subsequent liver pathologies modulating HCC clinical progression. HBV infection is the largest contributor to HCC, and the present study comprises the first of its kind compendium of hDEmiRs related to HBV-related pathologies. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD) is one of the most invasive malignant tumor of the respiratory system. It is also the common pathological type leading to the death of LUAD. Maintaining the homeostasis of immune cells is an important way for anti‐tumor immunotherapy. However, the biological significance of maintaining immune homeostasis and immune therapeutic effect has not been well studied. Methods: We constructed a diagnostic and prognostic model for LUAD based on B and T cells homeostasis‐related genes. Minimum absolute contraction and selection operator (LASSO) analysis and multivariate Cox regression are used to identify the prognostic gene signatures. Based on the overall survival time and survival status of LUAD patients, a 10‐gene prognostic model composed of ABL1, BAK1, IKBKB, PPP2R3C, CCNB2, CORO1A, FADD, P2RX7, TNFSF14, and ZC3H8 was subsequently identified as prognostic markers from The Cancer Genome Atlas (TCGA)‐LUAD to develop a prognostic signature. This study constructed a gene prognosis model based on gene expression profiles and corresponding survival information through survival analysis, as well as 1‐year, 3‐year, and 5‐year ROC curve analysis. Enrichment analysis attempted to reveal the potential mechanism of action and molecular pathway of prognostic genes. The CIBERSORT algorithm calculated the infiltration degree of 22 immune cells in each sample and compared the difference of immune cell infiltration between high‐risk group and low‐risk group. At the cellular level, PCR and CKK8 experiments were used to verify the differences in the expression of the constructed 10‐gene model and its effects on cell viability, respectively. The experimental results supported the significant biological significance and potential application value of the molecular model in the prognosis of lung cancer. Enrichment analyses showed that these genes were mainly related to lymphocyte homeostasis. Conclusion: We identified a novel immune cell homeostasis prognostic signature. Targeting these immune cell homeostasis prognostic genes may be an alternative for LUAD treatment. The reliability of the prediction model was confirmed at bioinformatics level, cellular level, and gene level. [ABSTRACT FROM AUTHOR]

Background: Ovarian cancer (OC) is a gynecological malignancy tumor with high recurrence and mortality rates. Programmed cell death (PCD) is an essential regulator in cancer metabolism, whose functions are still unknown in OC. Therefore, it is vital to determine the prognostic value and therapy response of PCD-related genes in OC. Methods: By mining The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Genecards databases, we constructed a prognostic PCD-related genes model and performed Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic (ROC) curve for its predictive ability. A nomogram was created via Cox regression. We validated our model in train and test sets. Quantitative real-time PCR (qRT-PCR) was applied to identify the expression of our model genes. Finally, we analyzed functional analysis, immune infiltration, genomic mutation, tumor mutational burden (TMB) and drug sensitivity of patients in low- and high-risk group based on median scores. Results: A ten-PCD-related gene signature including protein phosphatase 1 regulatory subunit 15 A (PPP1R15A), 8-oxoguanine-DNA glycosylase (OGG1), HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 (HERC1), Caspase-2.(CASP2), Caspase activity and apoptosis inhibitor 1(CAAP1), RB transcriptional corepressor 1(RB1), Z-DNA binding protein 1 (ZBP1), CD3-epsilon (CD3E), Clathrin heavy chain like 1(CLTCL1), and CCAAT/enhancer-binding protein beta (CEBPB) was constructed. Risk score performed well with good area under curve (AUC) (AUC3 − year =0.728, AUC5 − year = 0.730). The nomogram based on risk score has good performance in predicting the prognosis of OC patients (AUC1 − year =0.781, AUC3 − year =0.759, AUC5 − year = 0.670). Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the erythroblastic leukemia viral oncogene homolog (ERBB) signaling pathway and focal adhesion were enriched in the high-risk group. Meanwhile, patients with high-risk scores had worse OS. In addition, patients with low-risk scores had higher immune-infiltrating cells and enhanced expression of checkpoints, programmed cell death 1 ligand 1 (PD-L1), indoleamine 2,3-dioxygenase 1 (IDO-1) and lymphocyte activation gene-3 (LAG3), and were more sensitive to A.443,654, GDC.0449, paclitaxel, gefitinib and cisplatin. Finally, qRT-PCR confirmed RB1, CAAP1, ZBP1, CEBPB and CLTCL1 over-expressed, while PPP1R15A, OGG1, CASP2, CD3E and HERC1 under-expressed in OC cell lines. Conclusion: Our model could precisely predict the prognosis, immune status and drug sensitivity of OC patients. [ABSTRACT FROM AUTHOR]

Mitochondria and their related genes (MTRGs) are pivotal in the tumour microenvironment (TME) of cervical cancer, influencing prognosis and treatment response. This study developed a prognostic model using MTRGs to predict overall survival (OS) in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), aiming for personalized therapy. Analysing 14 MTRGs like ISCU and NDUFA11 through techniques such as univariate Cox regression, we found that a low mitochondrial (MT) score is associated with better survival, while a high MT score predicts poorer outcomes. The TME score, particularly influenced by CD8 T cells, also correlates with prognosis, with a high score indicating favourable outcomes. The interplay between MT and TME subtypes revealed that the best prognosis is seen in patients with a low MT and high TME score. Our findings highlight the role of MTRGs as potential biomarkers and therapeutic targets in cervical cancer, offering a novel approach to improving patient outcomes through a more nuanced understanding of mitochondrial function and immune interactions within the TME. This model presents a promising avenue for enhancing the precision of prognostic assessments in CESC. [ABSTRACT FROM AUTHOR]

Glypicans are linked to various aspects of neoplastic behavior, and their therapeutic value has been proposed in different cancers. Here, we have systematically assessed the impact of GPC4 on cancer progression through functional genomics and transcriptomic analyses across a broad range of cancers. Survival analysis using TCGA cancer patient data reveals divergent effects of GPC4 expression across various cancer types, revealing elevated GPC4 expression levels to be associated with both poor and favorable prognoses in a cancer-dependent manner. Detailed investigation of the role of GPC4 in glioblastoma and non-small cell lung adenocarcinoma by genetic perturbation studies displays opposing effects on these cancers, where the knockout of GPC4 with CRISPR/Cas9 attenuated proliferation of glioblastoma and augmented proliferation of lung adenocarcinoma cells and the overexpression of GPC4 exhibited a significant and opposite effect. Further, the overexpression of GPC4 in GPC4-knocked-down glioblastoma cells restored the proliferation, indicating its mitogenic effect in this cancer type. Additionally, a survival analysis of TCGA patient data substantiated these findings, revealing an association between elevated levels of GPC4 and a poor prognosis in glioblastoma, while indicating a favorable outcome in lung carcinoma patients. Finally, through transcriptomic analysis, we attempted to assign mechanisms of action to GPC4, as we find it implicated in cell cycle control and survival core pathways. The analysis revealed upregulation of oncogenes, including FGF5, TGF-β superfamily members, and ITGA-5 in glioblastoma, which were downregulated in lung adenocarcinoma patients. Our findings illuminate the pleiotropic effect of GPC4 in cancer, underscoring its potential as a putative prognostic biomarker and indicating its therapeutic implications in a cancer type dependent manner. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate gene expression alterations associated with overall survival (OS) in glioblastoma (GBM). Using the Nanostring nCounter platform, we identified four genes (COL1A2, IGFBP3, NGFR, and WIF1) that achieved statistical significance when comparing GBM with non-neoplastic brain tissue. The four genes were included in a multivariate Cox Proportional Hazard model, along with age, extent of resection, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation, to create a unique glioblastoma prognostic index (GPI). The GPI score inversely correlated with survival: patient with a high GPI had a median OS of 7.5 months (18-month OS = 9.7%) whereas patients with a low GPI had a median OS of 20.1 months (18-month OS = 54.5%; log rank p-value = 0.004). The GPI score was then validated in 188 GBM patients from The Cancer Genome Atlas (TCGA) from a national data base; similarly, patients with a high GPI had a median OS of 10.5 months (18-month OS = 12.4%) versus 16.9 months (18-month OS = 41.5%) for low GPI (log rank p-value = 0.0003). We conclude that this novel mRNA-based prognostic index could be useful in classifying GBM patients into risk groups and refine prognosis estimates to better inform treatment decisions or stratification into clinical trials. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) imposes a significant healthcare burden globally, prompting the quest for innovative biomarkers to enhance diagnostic and therapeutic strategies. This study investigates the G-protein signaling modulator (GPSM) family across several cancers and presents a comprehensive pan-cancer analysis of the GPSM2 gene across several gastrointestinal (GI) cancers. Leveraging bioinformatics methodologies, we investigated GPSM2 expression patterns, protein interactions, functional enrichments, prognostic implications, genetic alterations, and immune infiltration associations. Furthermore, the expression of the GPSM2 gene was analyzed using real-time analysis. Our findings reveal a consistent upregulation of GPSM2 expression in all GI cancer datasets analyzed, suggesting its potential as a universal biomarker in GI cancers. Functional enrichment analysis underscores the involvement of GPSM2 in vital pathways, indicating its role in tumor progression. The prognostic assessment indicates that elevated GPSM2 expression correlates with adverse overall and disease-free survival outcomes across multiple GI cancer types. Genetic alteration analysis highlights the prevalence of mutations, particularly missense mutations, in GPSM2. Furthermore, significant correlations between GPSM2 expression and immune cell infiltration are observed, suggesting its involvement in tumor immune evasion mechanisms. Collectively, our study underscores the multifaceted role of GPSM2 in GI cancers, particularly in CRC, emphasizing its potential as a promising biomarker for prognosis and therapeutic targeting. Further functional investigations are warranted to elucidate its clinical utility and therapeutic implications in CRC management. [ABSTRACT FROM AUTHOR]

Background: The Wnt/β-catenin pathway plays a critical role in the tumorigenesis and maintenance of glioma stem cells. This study aimed to evaluate significant genes associated with the Wnt/β-catenin pathway involved in mortality and disease progression in patients with grade II and III glioma, using the Cancer Genome Atlas (TCGA) database. Methods: We obtained clinicopathological information and mRNA expression data from 515 patients with grade II and III gliomas from the TCGA database. We performed a multivariate Cox regression analysis to identify genes independently associated with glioma prognosis. Results: The analysis of 34 genes involved in Wnt/β-catenin signaling demonstrated that four genes (CER1, FRAT1, FSTL1, and RPSA) related to the Wnt/β-catenin pathway were significantly associated with mortality and disease progression in patients with grade II and III glioma. We also identified additional genes related to the four significant genes of the Wnt/β-catenin pathway mentioned above. The higher expression of BMP2, RPL18A, RPL19, and RPS12 is associated with better outcomes in patients with glioma. Conclusions: Using a large-scale open database, we identified significant genes related to the Wnt/β-catenin signaling pathway associated with mortality and disease progression in patients with grade II and III gliomas. [ABSTRACT FROM AUTHOR]

IntroductionThe ability to accurately predict whether a woman will develop breast cancer later in her life, should reduce the number of breast cancer deaths. Different predictive models exist for breast cancer based on family history, BRCA status, and SNP analysis. The best of these models has an accuracy (area under the receiver operating characteristic curve, AUC) of about 0.65. We have developed computational methods to characterize a genome by a small set of numbers that represent the length of segments of the chromosomes, called chromosomal-scale length variation (CSLV).MethodsWe built machine learning models to differentiate between women who had breast cancer and women who did not based on their CSLV characterization. We applied this procedure to two different datasets: the UK Biobank (1534 women with breast cancer and 4391 women who did not) and the Cancer Genome Atlas (TCGA) 874 with breast cancer and 3381 without.ResultsWe found a machine learning model that could predict breast cancer with an AUC of 0.836 95% CI (0.830.0.843) in the UK Biobank data. Using a similar approach with the TCGA data, we obtained a model with an AUC of 0.704 95% CI (0.702, 0.706). Variable importance analysis indicated that no single chromosomal region was responsible for significant fraction of the model results.ConclusionIn this retrospective study, chromosomal-scale length variation could effectively predict whether or not a woman enrolled in the UK Biobank study developed breast cancer.