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Isocitrate dehydrogenase ( IDH) 1 mutation is one of the most important genetic aberrations in glioma. Even several genetic events have refined its prognostic value, the genome-wide expression alteration has not been systematically profiled. In this work, RNA-seq expression data from 310 patients in the Chinese Glioma Genome Atlas database were included as training set, while another 297 patients with microarray data were used as internal validation set. An independent cohort of GSE16011 ( n = 205) constituted an external validation set. Approximately one fifth of the genes were differentially expressed in LGG according to IDH1 mutation status, yielding distinct gene expression profiles. A six-gene risk signature was established for IDH1-mutant LGG to distinguish low- from high-risk cases, which had distinct prognoses. The six-gene signature was an independent prognostic factor for IDH1-mutant LGG and had superior predictive value as compared to traditional clinicopathologic factors. Moreover, we depicted the differential expression pattern in GBM attributing to various IDH1 status, which was similar to that of LGG. It suggested that the effect of IDH1 mutation is conserved across histological classifications. The six-gene signature had equal prognostic value for IDH1-mutant GBM. By combining glioma grade, IDH1 status, and the six-gene signature, all glioma patients could be classified into six subgroups. These six subgroups could be further summarized into three sets with distinct prognosis. Taken together, a gene expression profile associated with IDH1 status was identified in LGG and GBM; a risk signature based on six genes was developed with equal prognostic value for IDH1-mutant LGG and GBM. When combined with clinicopathologic factors, the six-gene signature is a tool that enables precise risk stratification and can improve clinical management. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming is not only an essential hallmark in the progression of head and neck squamous cell carcinoma (HNSCC), but also an important regulator of cancer cell adaptation to tumor microenvironment (TME). However, the potential mechanism of metabolic reprogramming in TME of HNSCC is still unknown. The head and neck squamous cell carcinoma with survival information were obtained the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The metabolic‐related genes were identified by differential analysis and survival analysis. Univariate and multivariate Cox regression analyses were applied to determine an overall estimate of metabolic-related risk signature and related clinical parameters. The sensitivity and specificity of the risk signature were evaluated by time-dependent receiver operation characteristic (ROC) curves. TME immune cell infiltration mediated by metabolic‐related genes was explored by gene set enrichment analysis (GSEA) and correlation analysis. Seven metabolic-related genes (SMS, MTHFD2, HPRT1, DNMT1, PYGL, ADA, and P4HA1) were identified to develop a metabolic-related risk signature. The low-risk group had a better overall survival compared to that of the high-risk group in the TCGA and GSE65858 cohorts. The AUCs for 1-, 3-, and 5-year overall survival were 0.646 vs. 0.673, 0.694 vs. 0.639, and 0.673 vs. 0.573, respectively. The AUC vale of risk score was 0.727 vs. 0.673. The low-risk group was associated with immune cell infiltration in the TME. The metabolic-related risk signature were constructed and validated, which could involve in regulating the immune cell infiltration in the TME and act as an independent biomarker that predicted the prognosis of HNSCC. [ABSTRACT FROM AUTHOR]

Background: The existing therapeutic approaches for combating tumors are insufficient in completely eradicating malignancy, as cancer facilitates tumor relapse and develops resistance to treatment interventions. The potential mechanistic connection between SARS-CoV-2 and ESCC has received limited attention. Therefore, our objective was to investigate the characteristics of SARS-CoV-2-related-genes (SCRGs) in esophageal squamous cancer (ESCC)., Methods: Raw data were obtained from the TCGA and GEO databases. Clustering of SCRGs from the scRNA-seq data was conducted using the Seurat R package. A risk signature was then generated using Lasso regression, incorporating prognostic genes related to SCRGs. Subsequently, a nomogram model was developed based on the clinicopathological characteristics and the risk signature., Results: Eight clusters of SCRGs were identified in ESCC utilizing scRNA-seq data, of which three exhibited prognostic implications. A risk signature was then made up with bulk RNA-seq, which displayed substantial correlations with immune infiltration. The novel signature was verified to have excellent prognostic efficacy., Conclusion: The utilization of risk signatures based on SCRGs can efficiently forecast the prognosis of ESCC. A thorough characterization of the SCRGs signature in ESCC could facilitate the interpretation of ESCC's response to immunotherapy and offer innovative approaches to cancer therapy.

Background: Lung adenocarcinoma (LUAD) is an extraordinarily malignant tumor, with rapidly increasing morbidity and poor prognosis. Immunotherapy has emerged as a hopeful therapeutic modality for lung adenocarcinoma. Furthermore, a prognostic model (based on immune genes) can fulfill the purpose of early diagnosis and accurate prognostic prediction., Methods: Immune-related mRNAs (IRmRNAs) were utilized to construct a prognostic model that sorted patients into high- and low-risk groups. Then, the prediction efficacy of our model was evaluated using a nomogram. The differences in overall survival (OS), the tumor mutation landscape, and the tumor microenvironment were further explored between different risk groups. In addition, the immune genes comprising the prognostic model were subjected to single-cell RNA sequencing to investigate the expression of these immune genes in different cells. Finally, the functions of BIRC5 were validated through in vitro experiments., Results: Patients in different risk groups exhibited sharply significant variations in OS, pathway activity, immune cell infiltration, mutation patterns, and immune response. Single-cell RNA sequencing revealed that the expression level of BIRC5 was significantly high in T cells. Cell experiments further revealed that BIRC5 knockdown markedly reduced LUAD cell proliferation., Conclusion: This model can function as an instrumental variable in the prognostic, molecular, and therapeutic prediction of LUAD, shedding new light on the optimal clinical practice guidelines for LUAD patients., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Depression increases the risk of breast cancer recurrence and metastasis. However, there lacks potential biomarkers for predicting prognosis in breast cancer. 5-hydroxytryptamine (5-HT) plays a key role in the pathogenesis and treatment of depression. In this study, we developed a prognostic signature based on 5-HT receptors (5-HTRs) and elucidated its potential immune regulatory mechanisms for breast cancer prognosis., Methods: Oncomine, GEPIA, UALCAN, cBioPortal, Kaplan-Meier plotter, and TIMER were used to analyze differential expression, prognostic value, genetic alteration, and immune cell infiltration of HTRs in breast cancer patients. The model training and validation assays were based on the analyses of GSE1456 and GSE86166. A risk signature was established by univariate and multivariate Cox regression analyses. The transwell assay was utilized to verify the effect of the 5-HTRs expression on breast cancer invasion. Effects of HTR2A/2B inhibitor on CD8 + T cell proliferation and infiltration as well as apoptosis of 4T1 cells in the tumor microenvironment were detected by flow cytometry and TUNEL assay. Zebrafish and mouse breast cancer xenografts were used to determine the effect of HTR2A/2B inhibitor on breast cancer metastasis., Results: The expression levels of HTR1A, HTR1B, HTR2A, HTR2B, HTR2C, HTR4, and HTR7 were significantly downregulated in highly malignant breast cancer types. 5-HTRs were significantly associated with recurrence-free survival (RFS) in breast cancer patients. The genetic alteration of HTR1D, HTR3A, HTR3B, and HTR6 in breast cancer patients was significantly associated with shorter overall survival (OS). Finally, HTR2A and HTR2B were determined to construct the risk signature. The expression of HTR2A/2B was positively correlated with the infiltration of immune cells such as CD8 + T cells and macrophages. Furthermore, inhibition of HTR2A expression could suppress CD8 + T cell proliferation and enhance invasion and metastasis of breast cancer cells in both zebrafish and mice model., Conclusions: The HTR2A/2B risk signature not only highlights the significance of HTRs in breast cancer prognosis by modulating cancer immune microenvironment, but also provides a novel gene-testing tool for early prevention of depression in breast cancer patients and lead to an improved prognosis and quality of life., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhan, Wang, Zheng, Wang, Yang, Pan, Wang and Wang.)

Background: Multiple myeloma (MM) is a fatal malignant tumor in hematology. Mitophagy plays vital roles in the pathogenesis and drug sensitivity of MM., Methods: We acquired transcriptomic expression data and clinical index of MM patients from NCI public database, and 36 genes involved in mitophagy from the gene set enrichment analysis (GSEA) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to construct a risk score prognostic model. Kaplan-Meier survival analysis and receiver operation characteristic curves (ROC) were conducted to identify the efficiency of prognosis and diagnosis. ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) was performed to uncover the level of immune infiltration. QRT-PCR was performed to verify gene expression in clinical samples of MM patients. The sensitivity to chemotherapy drugs was evaluated upon the database of the genomics of drug sensitivity in cancer (GDSC)., Results: Fifty mitophagy-related genes were differently expressed in two independent cohorts. Ten out of these genes were identified to be related to MM overall survival (OS) rate. A prognostic risk signature model was built upon on these genes: VDAC1, PINK1, VPS13C, ATG13, and HUWE1, which predicted the survival of MM accurately and stably both in training and validation cohorts. MM patients suffered more adverse prognosis showed more higher risk core. In addition, the risk score was considered as an independent prognostic element for OS of MM patients by multivariate cox regression analysis. Functional pathway enrichment analysis of differentially expressed genes (DEGs) based on risk score showed terms of cell cycle, immune response, mTOR pathway, and MYC targets were obviously enriched. Furthermore, MM patients with higher risk score were observed lower immune scores and lower immune infiltration levels. The results of qRT-PCR verified VDAC1, PINK1, and HUWE1 were dysregulated in new diagnosed MM patients. Finally, further analysis indicated MM patients showed more susceptive to bortezomib, lenalidomide and rapamycin in high-risk group., Conclusion: Our research provided a neoteric prognostic model of MM based on mitophagy genes. The immune infiltration level based on risk score paved a better understanding of the participation of mitophagy in MM., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Glioma is the most frequent primary tumor of the central nervous system. The high heterogeneity of glioma tumors enables them to adapt to challenging environments, leading to resistance to treatment. Therefore, to detect the driving factors and improve the prognosis of glioma, it is essential to have a comprehensive understanding of the genomic heterogeneity, stemness, and immune microenvironment of glioma., Methods: We classified gliomas into various subtypes based on stemness, genomic heterogeneity, and immune microenvironment consensus clustering analysis. We identified risk hub genes linked to heterogeneous characteristics using WGCNA, LASSO, and multivariate Cox regression analysis and utilized them to create an effective risk model., Results: We thoroughly investigated the genomic heterogeneity, stemness, and immune microenvironment of glioma and identified the risk hub genes RAB42, SH2D4A, and GDF15 based on the TCGA dataset. We developed a risk model utilizing these genes that can reliably predict the prognosis of glioma patients. The risk signature showed a positive correlation with T cell exhaustion and increased infiltration of immunosuppressive cells, and a negative correlation with the response to immunotherapy. Moreover, we discovered that SH2D4A, one of the risk hub genes, could stimulate the migration and proliferation of glioma cells., Conclusions: This study identified risk hub genes and established a risk model by analyzing the genomic heterogeneity, stemness, and immune microenvironment of glioma. Our findings will facilitate the diagnosis and prediction of glioma prognosis and may lead to potential treatment strategies for glioma.

Background: Breast cancer (BRCA) represents a significant threat with high mortality rates due to relapse, metastasis, and chemotherapy resistance. As a regulated cell death process characterized by the induction of immunogenic signals, immunogenic cell death (ICD) has been identified as an effective anti-tumorigenesis approach. However, the comprehensive study and its clinical value of ICD-related lncRNAs in BRCA is still missing., Methods: The transcriptome matrix and corresponding clinical information of BRCA patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was performed to identify ICD-related lncRNAs (ICDRLs). To determine the prognostic value of the identified ICDRLs, univariate Cox regression analysis, LASSO algorithm, and multivariate Cox regression analysis were employed to construct a risk model. The prognostic risk model was subsequently evaluated using univariate and multivariate Cox regression analysis, as well as Nomogram analysis. In vitro experiments were also conducted to validate the bioinformatics findings using quantitative real-time PCR (qRT-PCR)., Results: We established a prognostic risk signature consisting of five ICDRLs. The prognostic value of this model was subsequently confirmed in guiding BRCA prognostic stratification. Furthermore, we explored the correlation of the risk score with various clinical characteristics and chemotherapy response. qRT-PCR result confirmed the abnormal expression of ICDRLs, which was consistent with the bioinformatics data., Conclusions: Our findings provide evidence of the critical role of ICDRLs in BRCA and offer a novel perspective for exploring precise treatment options for BRCA patients.

Despite the utilization of anti-PD-1 therapy in gastric cancer (GC), the absence of a reliable predictive biomarker continues to pose a challenge. In this study, we utilized bioinformatic analysis and immunohistochemistry to develop a prediction model for activated CD4+ memory T cells, considering both mRNA and protein levels. An elevation of activated CD4+ memory T cells in GC was noted, which exhibited a strong association with the patients' overall survival. By utilizing WGCNA and DEG analysis, we discovered that BATF2, MYB, and CD36 are genes that exhibit differential expression and are linked to activated CD4+ memory T cells. Afterwards, a forecast model was built utilizing Stepwise regression and immunohistochemistry relying on the three genes. The model's high-risk score showed significant associations with a suppressive immune microenvironment. Moreover, our model exhibited encouraging prognostic value and superior performance in predicting response to immune checkpoint blockade therapy compared with the conventional CD8+PD-L1 model. In terms of mechanism, CD36 could function as a receptor upstream that identifies Helicobacter pylori and fatty acids. This recognition then results in the reduction of the BATF2-MYB protein complex and subsequent alterations in the transcription of genes associated with classical T cell activation. As a result, the activation state of CD4+ memory T cells is ultimately suppressed. The CD36-BATF2/MYB signature serves as a robust predictor of anti-PD-1 immunotherapy response in GC., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Background: Accumulating research substantiated that tumor-associated macrophages (TAMs) have a significant impact on the tumorigenesis, progression, and distant metastasis, representing a novel target for various cancers. However, the underlying dynamic changes and interactions between TAMs and tumor cells remain largely elusive in colorectal cancer (CRC)., Methods: We depicted the dynamic changes of macrophages using sing-cell RNA-seq data and extracted TAM differentiation-related genes. Next, we utilized the weighted gene co-expression network analysis (WGCNA) to acquire CMS-related modular genes using bulk RNA-seq data. Finally, we utilized univariate Cox and Lasso Cox regression analyses to identify TAM differentiation-related biomarkers and established a novel risk signature model. We employed quantitative real-time polymerase chain reaction (qRT-PCR) on CRC tissue samples and used immunohistochemistry (IHC) data frome the HPA database to validate the mRNA and protein expression of prognostic genes. The interaction of TAMs and each consensus molecular subtype (CMS) subpopulation was analyzed at the cellular level., Results: A total of 47,285 cells from single-cell dataset and 1197 CRC patients from bulk dataset were obtained. Among those, 6400 myeloid cells were re-clustered and annotated. RNASE1, F13A1, DAPK1, CLEC10A, RPN2, REG4 and RGS19 were identified as prognostic genes and the risk signature model was established based on the above genes. The qRT-PCR analysis indicated that the expression of RNASE1 and DAPK1 were significantly up-regulated in CRC tumor tissues. The cell-cell communication analysis demonstrated complex interactions between TAMs and CMS malignant cell subpopulations., Conclusion: This study presents an in-depth dissection of the dynamic features of TAMs in the tumor microenvironment and provides promising therapeutic targets for CRC., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Introduction: Bladder cancer (BLCA) is the ninth most common malignancy worldwide and the fourth most common cancer in men. Copper levels are significantly altered in patients with thyroid, breast, lung, cervical, ovarian, pancreatic, oral, gastric, bladder, and prostate cancers. Outcomes can be predicted by constructing signatures using lncRNA-related genes associated with outcomes., Methods: We identified lncRNAs related to outcomes, those differentially expressed in bladder cancer, and cuproptosis-related lncRNAs from TCGA. We identified the intersection to obtain 12 genes and established a prognostic risk signature consisting of eight genes using LASSO-penalized multivariate Cox analysis. We constructed a training set, performed survival analysis on the high-and low-risk groups, and performed validation in the test and full sets. There existed a substantial contrast in the likelihood of survival among the cohorts of high and low risk. An in-depth analysis of the gene mutations associated with tumors was conducted to evaluate the risk of developing cancer. We also performed gene analysis on neoadjuvant chemotherapy. We conducted experimental validation on the key gene UBE2Q1-AS1 in our prognostic signature., Results: The risk signature we constructed shows significant differences between the high-risk group and the low-risk group. Univariate survival analysis of the eight genes in our signature showed that each gene distinguished between high- and low-risk groups. Sub-group analysis revealed that our risk score differed significantly in tumor stage, age, and gender. The analysis results of the tumor mutation burden (TMB) showed a significant difference in the TMB between the low- and high-risk groups, which had a direct impact on the outcomes. These findings highlight the importance of TMB as a potential prognostic marker in cancer detection and prevention. We analyzed the immune microenvironment and found significant differences in immune function, validation responses, immunotherapy-related positive markers, and critical steps in the tumor immunity cycle between the high- and low-risk groups. We found that the effect of anti-CTLA4 and PD-1 was higher in the high-risk group than in the low-risk group.Gene analysis of neoadjuvant chemotherapy revealed that the treatment effect in the high-risk group was better than in the low-risk group. The key gene UBE2Q1-AS1 in our prognostic signature can significantly influence the cell viability, migration, and proliferation of cancer cells., Discussion: We established a signature consisting of eight genes constructed from cuproptosis-related lncRNAs that have potential clinical applications for outcomes prediction, diagnosis, and treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Shen, Du, Liang, Wang and Bi.)

Pancreatic cancer (PC) is a highly malignant digestive tract tumor, with a dismal 5-year survival rate. Recently, cuproptosis was found to be copper-dependent cell death. This work aims to establish a cuproptosis-related lncRNA signature which could predict the prognosis of PC patients and help clinical decision-making. Firstly, cuproptosis-related lncRNAs were identified in the TCGA-PAAD database. Next, a cuproptosis-related lncRNA signature based on five lncRNAs was established. Besides, the ICGC cohort and our samples from 30 PC patients served as external validation groups to verify the predictive power of the risk signature. Then, the expression of CASC8 was verified in PC samples, scRNA-seq dataset CRA001160, and PC cell lines. The correlation between CASC8 and cuproptosis-related genes was validated by Real-Time PCR. Additionally, the roles of CASC8 in PC progression and immune microenvironment characterization were explored by loss-of-function assay. As showed in the results, the prognosis of patients with higher risk scores was prominently worse than that with lower risk scores. Real-Time PCR and single cell analysis suggested that CASC8 was highly expressed in pancreatic cancer and related to cuproptosis. Additionally, gene inhibition of CASC8 impacted the proliferation, apoptosis and migration of PC cells. Furthermore, CASC8 was demonstrated to impact the expression of CD274 and several chemokines, and serve as a key indicator in tumor immune microenvironment characterization. In conclusion, the cuproptosis-related lncRNA signature could provide valuable indications for the prognosis of PC patients, and CASC8 was a candidate biomarker for not only predicting the progression of PC patients but also their antitumor immune responses., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

• A relevant study analysis in clinical trials was conducted, all of which showed that the model had good stability and potential application value for predicting outcomes. • The correlation between immune landscape and risk score and CPT1B was identified. We identified the drugs which patients are more likely to benefit from. • We performed the relevant basic assay validation for CPT1B, and the proliferation ability of RCC cells was inhibited after the knockdown of protein expression of CPT1B. • In conclusion, we established a four-gene model that can predict prognosis of RCC with potential applications in diagnosis and clinical treatment. Three subtypes of samples were generated based on genes involved in fatty acid metabolism in The Cancer Genome Atlas (TCGA)-RCC patients using a non-negative matrix factorization (NMF) algorithm. 32 co-expressed modules were identified using WCGNA. We constructed a four-gene signature in our training set using least absolute shrinkage selection operator regression analysis and verified it in our testing and overall sets. A relevant study analysis in clinical trials was conducted, which showed the model had good stability and potential application value for predicting outcomes. We analyzed the immune microenvironment using MCPcounter , CIBERSORT, quanTIseq, TIMER and ESTIMATE algorithms, and the result indicated risk was positively related to T cells, B-lineage, and fibroblasts and negatively correlated with monocytic lineage, myeloid dendritic cells, neutrophils, and endothelial cells, and CPT1B was positively related to T cells, CD8 + T cells, Cytotoxic lymphocytes and NK cells, and negatively correlated with myeloid dendritic cells, fibroblasts, endothelial cells. Tumor mutation burden was positively related to risk score and the expression of CPT1B using the R packages corrplot , circlize. Through the R package pRRophetic, drug sensitivity tests showed that the low-risk score group would benefit more from sunitinib and less from pazopanib, sorafenib, temsirolimus, gemcitabine and doxorubicin than the high-risk score group. We performed the relevant basic assay validation for CPT1B, and the proliferation ability of RCC cells was inhibited after the knockdown of protein expression of CPT1B. In conclusion, we established a four-gene model that can predict outcomes of RCC with potential applications in diagnosis and treatment. [ABSTRACT FROM AUTHOR]

• Using the ESTIMATE algorithm, CRC was divided into three subtypes based on 1640 samples with 19,956 genes. • The expression of C5orf46, CYP1B1, MIR100HG, SFRP2 and CXCL13 in CRC tissues was related to clinical prognostic indicators, such as differentiation degree and Ki67. • The 3 subtypes which were constructed basing on the expression of C5orf46, CYP1B1, MIR100HG, SFRP2 and CXCL13 have different prognoses in CRC. • A prognostic signature was established based on 5 immune-related differentially expressed genes among the three subtypes. The mechanism of immunity in the development of colorectal cancer (CRC) has been studied in-depth, but knowledge of its role in the treatment of CRC is limited. This study aimed to classify CRC based on immunology and construct an immune-related prognostic model. Nine expression profile datasets of CRC, comprising 1640 samples, were downloaded from the NCBI GEO database. Immune infiltration of CRC was estimated using 5 algorithms. Based on the relative infiltration level of immune cells, immune score, and stromal score, immunosubtype analysis of tumors was conducted. Differentially expressed genes (DEGs) between the two subtypes were screened, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Hematoxylin eosin (HE) staining, immunohistochemical (IHC) staining and qPCR were used to verify the correlation between DEGs and differentiation degree of cancer and the expression of Ki67. Subsequently, a risk signature was constructed based on the least absolute shrinkage and selection operator (LASSO) model. Based on the infiltration level, immune score, and stromal score of each immune cell, CRC was divided into three immune cell subtypes. Most immune checkpoint genes showed highly significant differences among the three cell subtypes, and most of the co-stimulatory and co-inhibitory molecules were lower in cluster 1 and the highest in cluster 3. Next, 50 common DEGs were determined from the intersections of the different subtypes. Among these common DEGs, 25 were identified to be relevant to the prognosis of CRC patients. The mRNA expressions of C5orf46, CYP1B1, MIR100HG, SFRP2 and CXCL13 was related to clinical prognostic indicators. Finally, these 5 DEGs were included in a prognostic risk signature model, which effectively identified high-risk groups among CRC patients in both the training and validation sets. In this study, CRCs were divided into three subtypes based on immunology, and the different subtypes led to different prognosis. Additionally, a prognostic model was constructed based on five immune-related DEGs to distinguish the three subtypes. [ABSTRACT FROM AUTHOR]

Background: As a component of membrane lipids and the precursor of oxysterols and steroid hormones, reprogrammed cholesterol metabolism contributes to the initiation and progression of multiple cancers. Thus, we aim to further investigate the significances of cholesterol metabolism in lower-grade gliomas (LGGs)., Methods: The present study included 413 LGG samples from TCGA RNA-seq dataset (training cohort) and 172 LGG samples from CGGA RNA-seq dataset (validation cohort). The cholesterol metabolism-related signature was identified by the LASSO regression model. Bioinformatics analyses were performed to explore the functional roles of this signature in LGGs. Kaplan-Meier and Cox regression analyses were enrolled to estimate prognostic value of the risk signature., Results: Our findings suggested that cholesterol metabolism was tightly associated clinicopathologic features and genomic alterations of LGGs. Bioinformatics analyses revealed that cholesterol metabolism played a key role in immunosuppression of LGGs, mainly by promoting macrophages polarization and T cell exhaustion. Kaplan-Meier curve and Cox regression analysis showed that cholesterol metabolism was an independent prognostic indicator for LGG patients. To improve the clinical application value of the risk signature, we also constructed a nomogram model to predict the 1-, 3- and 5-year survival of LGG patients., Conclusion: The cholesterol metabolism was powerful prognostic indicator and could serve as a promising target to enhance personalized treatment of LGGs., (© 2023. The Author(s).)

Background: Colon cancer (CC) is a highly heterogeneous malignancy associated with high morbidity and mortality. Pyroptosis is a type of programmed cell death characterized by an inflammatory response that can affect the tumor immune microenvironment and has potential prognostic and therapeutic value. The aim of this study was to evaluate the association between pyroptosis-related gene (PRG) expression and CC., Methods: Based on the expression profiles of PRGs, we classified CC samples from The Cancer Gene Atlas and Gene Expression Omnibus databases into different clusters by unsupervised clustering analysis. The best prognostic signature was screened and established using least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses. Subsequently, a nomogram was established based on multivariate COX regression analysis. Next, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential molecular mechanisms between the high- and low-risk groups and to explore the differences in clinicopathological characteristics, gene mutation characteristics, abundance of infiltrating immune cells, and immune microenvironment between the two groups. We also evaluated the association between common immune checkpoints and drug sensitivity using risk scores. The immunohistochemistry staining was utilized to confirm the expression of the selected genes in the prognostic model in CC., Results: The 1163 CC samples were divided into two clusters (clusters A and B) based on the expression profiles of the 33 PRGs. Genes with prognostic value were screened from the DEGs between the two clusters, and an eight PRGs prognostic model was constructed. GSEA and GSVA of the high- and low-risk groups revealed that they were mainly enriched in inflammatory response-related pathways. Compared to those in the low-risk group, patients in the high-risk group had worse overall survival, an immunosuppressive microenvironment, and worse sensitivity to immunotherapy and drug treatment., Conclusion: Our findings provide a foundation for future research targeting pyroptosis and new insights into prognosis and immunotherapy from the perspective of pyroptosis in CC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wu, Hao, Wang, Su, Du, Zhou, Yang and Du.)

Objectives: To evaluate the role and biological function of nucleic acid binding protein 2 (NABP2) in hepatocellular carcinoma (HCC)., Methods: Our study was based on comprehensive bioinformatics methods and functional analysis experiments using HCC cells to reveal the expression of NABP2, the prognostic role of NABP2, the relationship between NABP2 and the infiltration of immune cells and the expression of immune-related cytokines, potential effective drugs against HCC, and the biological function of NABP2 in HCC., Results: Our results indicated that NABP2 expression was markedly elevated in HCC, which suggested a worse prognosis and shorter survival time in HCC patients. Moreover, NABP2 was an independent prognostic factor and was associated with cancer-related signal pathways in HCC. Further functional analysis showed that knockdown of NABP2 dramatically inhibited proliferation and migration, and promoted apoptosis of HCC cells. Subsequently, we identified NABP2-related genes and NABP2-related clusters. Next, we constructed a NABP2-related risk signature based on differentially expressed genes that were responsible for NABP2-related clusters. We found that the risk signature was an independent prognostic factor for patients with HCC that was associated with dysregulated immune infiltration. Finally, drug sensitivity analysis revealed eight potentially effective drugs for beneficial treatment options for HCC patients with high-risk scores., Conclusions: These findings indicated that NABP2 is a prognostic biomarker and therapeutic target for HCC, and a NABP2-related risk signature could guide clinicians to judge the prognosis and suggest drug treatments for HCC patients., Competing Interests: None., (AJTR Copyright © 2023.)

The heterogeneity of lung adenocarcinoma (LUAD) indicated that target therapies and immunotherapies may not be effective in all patients. The exploration of the feature of the immune landscape of different gene mutations may provide novel perspectives. In this study, we obtained LUAD samples from The Cancer Genome Atlas. By applying ESTIMATE and ssGSEA, KRAS-mutated group was discovered to be associated with lower immune infiltration, lower expression of immune checkpoints, especially, a lower abundance of B cell, CD8+ T cell, dendritic cell, natural killer cell, and macrophage, higher abundance of neutrophil and endothelial cell. Through ssGSEA, we found that the process of antigen-presenting cell co-inhibition and co-stimulation were inhibited, cytolytic activity and human leukocyte antigen molecules were downregulated in the KRAS-mutated group. And KRAS mutation is negatively related to antigen presentation and procession, cytotoxic lymphocyte activity, cytolytic activities, and cytokine interaction signaling pathway via gene function enrichment analysis. Finally, 24 immune-related genes were identified to establish an immune-related gene signature with excellent prognostic prediction capacity, whose 1-, 3- and 5-year AUCs were 0.893, 0.986, and 0.999. Our findings elucidate the features of the immune landscape of KRAS-mutated groups and successfully established a prognostic signature on the basis of immune-related genes in LUAD.

Osteosarcoma is a primary malignant tumor found mainly in teenagers and young adults. Patients have very little long-term survival. MYC controls tumor initiation and progression by regulating the expression of its target genes; thus, constructing a risk signature of osteosarcoma MYC target gene set will benefit the evaluation of both treatment and prognosis. In this paper, we used GEO data to download the ChIP-seq data of MYC to obtain the MYC target gene. Then, a risk signature consisting of 10 MYC target genes was developed using Cox regression analysis. The signature indicates that patients in the high-risk group performed poorly. After that, we verified it in the GSE21257 dataset. In addition, the difference in tumor immune function among the low- and high-risk populations was compared by single sample gene enrichment analysis. Immunotherapy and prediction of response to the anticancer drug have shown that the risk signature of the MYC target gene set was positively correlated with immune checkpoint response and drug sensitivity. Functional analysis has demonstrated that these genes are enriched in malignant tumors. Finally, STX10 was selected for functional experimentation. STX10 silence has limited osteosarcoma cell migration, invasion, and proliferation. Therefore, these findings indicated that the MYC target gene set risk signature could be used as a potential therapeutic target and prognostic indicator in patients with osteosarcoma., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gong, Zhao, Liu, Zhao, Yi, Lv, Yu, Bian and Tian.)

Background: Current paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing to cancer stroma's functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with tumor progression and therapy resistance. Thus, we aimed to probe into the CAFs characteristics in esophageal squamous cancer (ESCC) and construct a risk signature based on CAFs to predict the prognosis of ESCC patients., Methods: The GEO database provided the single-cell RNA sequencing (scRNA-seq) data. The GEO and TCGA databases were used to obtain bulk RNA-seq data and microarray data of ESCC, respectively. CAF clusters were identified from the scRNA-seq data using the Seurat R package. CAF-related prognostic genes were subsequently identified using univariate Cox regression analysis. A risk signature based on CAF-related prognostic genes was constructed using Lasso regression. Then, a nomogram model based on clinicopathological characteristics and the risk signature was developed. Consensus clustering was conducted to explore the heterogeneity of ESCC. Finally, PCR was utilized to validate the functions that hub genes play on ESCC., Results: Six CAF clusters were identified in ESCC based on scRNA-seq data, three of which had prognostic associations. A total of 642 genes were found to be significantly correlated with CAF clusters from a pool of 17080 DEGs, and 9 genes were selected to generate a risk signature, which were mainly involved in 10 pathways such as NRF1, MYC, and TGF-Beta. The risk signature was significantly correlated with stromal and immune scores, as well as some immune cells. Multivariate analysis demonstrated that the risk signature was an independent prognostic factor for ESCC, and its potential in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the CAF-based risk signature and clinical stage was developed, which exhibited favorable predictability and reliability for ESCC prognosis prediction. The consensus clustering analysis further confirmed the heterogeneity of ESCC., Conclusion: The prognosis of ESCC can be effectively predicted by CAF-based risk signatures, and a comprehensive characterization of the CAF signature of ESCC may aid in interpreting the response of ESCC to immunotherapy and offer new strategies for cancer treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ren, Zhang, Zhang, Feng, Li, Lin and Yu.)

Tumor mutation burden (TMB) level is identified as a useful predictor in multiple tumors including colon adenocarcinoma (COAD). However, the function of TMB related genes has not been explored previously. In this study, we obtained patients' expression and clinical data from The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI). TMB genes were screened and subjected to differential expression analysis. Univariate Cox and LASSO analyses were utilized to construct the prognostic signature. The efficiency of the signature was tested by using a receiver operating characteristic (ROC) curve. A nomogram was further plotted to assess the overall survival (OS) time of patients with COAD. In addition, we compared the predictive performance of our signature with other four published signatures. Functional analyses indicated that patients in the low-risk group have obviously different enrichment of tumor related pathways and tumor infiltrating immune cells from that of high-risk patients. Our findings suggested that the ten genes' prognostic signature could exert undeniable prognostic functions in patients with COAD, which might provide significant clues for the development of personalized management of these patients., (© 2023. The Author(s).)

As a novel cell death modality, oxeiptosis is mainly caused by oxidative stress. However, the associations of uterine corpus endometrial carcinoma (UCEC) with oxeiptosis-associated long non-coding RNAs (lncRNAs) are unknown. Here, to identify hub oxeiptosis-associated lncRNAs in UCEC, we collected the data for lncRNAs and gene expression in UCEC from The Cancer Genome Atlas (TCGA) database. Then, a lncRNA risk signature was constructed, and its prognostic value was further evaluated. Finally, the expression levels of hub lncRNA HOXB-AS3 were validated by quantitative RT-PCR analysis. MTT and wounding analyses were also applied to confirm the role of HOXB-AS3 knockdown on UCEC cells. Five lncRNAs associated with oxeiptosis and connected to the prognosis of UCEC were identified, and a risk signature was constructed based on these identified lncRNAs. Our clinical value analyses suggested that the risk signature was closely connected to the overall survival, TNM stage, and grade of UCEC patients. Meanwhile, compared to the conventional clinicopathological characteristics, this risk signature exhibited significantly higher diagnostic accuracy. Moreover, the potential mechanism analysis indicated a close connection of this risk signature to tumor stemness, m6A-related genes, immune cell infiltration, and immune subtypes. Based on the risk scores, we constructed a nomogram. In vitro experiments found that HOXB-AS3 was significantly higher expressed in UCEC cells, and the silence of HOXB-AS3 inhibited the proliferation and migration of UCEC cells. In conclusion, using five hub lncRNAs associated with oxeiptosis, we generated a risk signature, which could be applied in the novel therapeutic strategies of UCEC development.

Background: Bladder cancer (BLCA) is the ninth most common cancer globally, as well as the fourth most common cancer in men, with an incidence of 7%. However, few effective prognostic biomarkers or models of BLCA are available at present.Methods: The prognostic genes of BLCA were screened from one cohort of The Cancer Genome Atlas (TCGA) database through univariate Cox regression analysis and functionally annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The intersecting genes of the BLCA gene set and focal adhesion-related gene were obtained and subjected to the least absolute shrinkage and selection operator regression (LASSO) to construct a prognostic model. Gene set enrichment analysis (GSEA) of high- and low-risk patients was performed to explore further the biological process related to focal adhesion genes. Univariate and multivariate Cox analysis, receiver operating characteristic (ROC) curve analysis, and Kaplan-Meier survival analysis (KM) were used to evaluate the prognostic model. DNA methylation analysis was presented to explore the relationship between prognosis and gene methylation. Furthermore, immune cell infiltration was assessed by CIBERSORT, ESTIMATE, and TIMER. The model was verified in an external GSE32894 cohort of the Gene Expression Omnibus (GEO) database, and the Prognoscan database presented further validation of genes. The HPA database validated the related protein level, and functional experiments verified significant risk factors in the model.Results: VCL, COL6A1, RAC3, PDGFD, JUN, LAMA2, and ITGB6 were used to construct a prognostic model in the TCGA-BLCA cohort and validated in the GSE32894 cohort. The 7-gene model successfully stratified the patients into both cohorts' high- and low-risk groups. The higher risk score was associated with a worse prognosis.Conclusions: The 7-gene prognostic model can classify BLCA patients into high- and low-risk groups based on the risk score and predict the overall survival, which may aid clinical decision-making. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD) is an aggressive disease of heterogeneous characteristics with poor prognosis and high mortality. Pyroptosis, a newly uncovered type of programmed cell death with an inflammatory nature, has been determined to hold substantial importance in the progression of tumors. Despite this, the knowledge about pyroptosis-related genes (PRGs) in LUAD is limited. This study aimed to develop and validate a prognostic signature for LUAD based on PRGs. Methods: In this research, gene expression information from The Cancer Genome Atlas (TCGA) served as the training cohort and data from Gene Expression Omnibus (GEO) was utilized as the validation cohort. PRGs list was taken from the Molecular Signatures Database (MSigDB) and previous studies. Univariate Cox regression and Lasso analysis were then conducted to identify prognostic PRGs and develop a LUAD prognostic signature. The Kaplan-Meier method, univariate and multivariate Cox regression models were employed to assess the independent prognostic value and forecasting accuracy of the pyroptosis-related prognostic signature. The correlation between prognostic signature and immune infiltrating was analyzed to examine the role in tumor diagnosis and immunotherapy. Further, RNA-seq as well as quantitative real-time polymerase chain reaction (qRT-PCR) analysis in separate data sets was applied in order to validate the potential biomarkers for LUAD. Results: A novel prognostic signature based on 8 PRGs (BAK1, CHMP2A, CYCS, IL1A, CASP9, NLRC4, NLRP1, and NOD1) was established to predict the survival of LUAD. The prognostic signature proved to be an independent prognostic factor of LUAD with satisfactory sensitivity and specificity in the training and validation sets. High-risk scores subgroups in the prognostic signature were significantly associated with advanced tumor stage, poor prognosis, less immune cell infiltration, and immune function deficiency. RNA sequencing and qRT-PCR analysis confirmed that the expression of CHMP2A and NLRC4 could be used as biomarkers for LUAD. Conclusion: We have successfully developed a prognostic signature consisting of eight PRGs that providing a novel perspective on predicting prognosis, assessing infiltration levels of tumor immune cells, and determining the outcomes of immunotherapy for LUAD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Zhou, Li, Chen, Wei, Chen and Jiang.)

As a type of cell apoptosis, anoikis is caused by cells detachment from the extracellular matrix and anoikis resistance is central to cancer metastasis. Here, SNCG was identified as hub anoikis-associated gene in GC and associated with prognosis of patients with GC. To screen the hub anoikis-associated genes connected to GC, the database of Cancer Genome Atlas (TCGA) was employed. For further validating these identified genes, the Gene Expression Omnibus (GEO) dataset was applied, and Western blotting and quantitative Real-Time PCR were carried out. To Identify hub genes, we conducted the analyses of univariate Cox regression, differential expression, and weighted gene co-expression network analysis (WGCNA). According to the identified hub genes, we constructed a model of prognosis. Following complex analysis, SNCG was finally identified as hub anoikis-associated gene in GC. Indeed, K-M and receiver operating characteristic analyses suggested that the expression patterns of SNCG can be used as prognostic factors for GC survival. The expression and survival trends of SNCG were verified in the validation cohort and in vitro experimental analyses. The analysis of immune cell infiltration showed that the infiltrated immune cells varied among patients with GC and gene SNCG. Furthermore, due to the significant association of the constructed risk signature with patient age and survival, this risk signature can be used to predict the prognosis of GC. We suggest that SNCG was served as hub anoikis-associated gene in GC. Meanwhile, SNCG may have prognostic potential for overall patient survival.

Glioblastoma (GBM) is the most malignant and aggressive primary brain tumor and is highly resistant to current therapeutic strategies. Previous studies have demonstrated that reactive oxygen species (ROS) play an important role in the regulation of signal transduction and immunosuppressive environment in GBM. To further study the role of ROS in prognosis, tumor micro-environment (TME) and immunotherapeutic response in GBM, an ROS-related nine-gene signature was constructed using the Lasso-Cox regression method and validated using three other datasets in our research, based on the hallmark ROS-pathway-related gene sets and the Cancer Genome Atlas GBM dataset. Differences in prognosis, TME scores, immune cell infiltration, immune checkpoint expression levels, and drug sensitivity between high-risk and low-risk subgroups were analyzed using R software. Collectively, our research uncovered a novel ROS-related prognostic model for primary GBM, which could prove to be a potential tool for clinical diagnosis of GBM, and help assess the immune and molecular characteristics of ROS in the tumorigenesis and immunosuppression of GBM. Our research also revealed that the expressions of ROS-related genes-HSPB1, LSP1, and PTX3-were closely related to the cell markers of tumor-associated macrophages (TAMs) and M2 macrophages validated by quantitative RT-PCR, suggesting them could be potential targets of immunotherapy for GBM., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Breast cancer (BRCA) is a life-threatening malignancy in women with an unsatisfactory prognosis. The purpose of this study was to explore the prognostic biomarkers and a risk signature based on ferroptosis-related RNA-binding proteins (FR-RBPs). Methods: FR-RBPs were identified using Spearman correlation analysis. Differentially expressed genes (DEGs) were identified by the "limma" R package. The univariate Cox and multivariate Cox analyses were executed to determine the prognostic genes. The risk signature was constructed and verified with the training set, testing set, and validation set. Mutation analysis, immune checkpoint expression analysis in high- and low-risk groups, and correlation between risk signature and chemotherapeutic agents were conducted using the "maftools" package, "ggplot2" package, and the CellMiner database respectively. The Human Protein Atlas (HPA) database was employed to confirm protein expression trends of prognostic genes in BRCA and normal tissues. The expression of prognostic genes in cell lines was verified by Real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-meier (KM) plotter database analysis was applied to predict the correlation between the expression levels of signature genes and survival statuses. Results: Five prognostic genes (GSPT2, RNASE1, TIPARP, TSEN54, and SAMD4A) to construct an FR-RBPs-related risk signature were identified and the risk signature was validated by the International Cancer Genome Consortium (ICGC) cohort. Univariate and multivariate Cox regression analysis demonstrated the risk score was a robust independent prognostic factor in overall survival prediction. The Tumor Mutational Burden (TMB) analysis implied that the high- and low-risk groups responded differently to immunotherapy. Drug sensitivity analysis suggested that the risk signature may serve as a chemosensitivity predictor. The results of GSEA suggested that five prognostic genes might be related to DNA replication and the immune-related pathways. RT-qPCR results demonstrated that the expression trends of prognostic genes in cell lines were consistent with the results from public databases. KM plotter database analysis suggested that high expression levels of GSPT2, RNASE1, and SAMD4A contributed to poor prognoses. Conclusion: In conclusion, this study identified the FR-RBPs-related prognostic genes and developed an FR-RBPs-related risk signature for the prognosis of BRCA, which will be of great significance in developing new therapeutic targets and prognostic molecular biomarkers for BRCA., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chen, Yang, Wang, He, Sun, Lyu, Zou, Fang, Dai and Dong.)

Background: Clear cell renal cell carcinoma (ccRCC) is common in urinary system tumors. Cuproptosis is a non-apoptotic cell death pathway. Copper binds to fatty acylated mitochondrial proteins and activates various forms of cell death. LncRNA LINC02154 is significantly highly expressed in cells and tissues of many types of tumors, and the risk signature of LINC02154 in some tumors has been validated for effectiveness., Methods: We constructed a risk prognostic signature by obtaining differentially expressed long noncoding RNAs (lncRNAs) associated with ccRCC outcomes and cuproptosis from The Cancer Genome Atlas (TCGA). We used TCGA to construct training and testing sets to analyze the risk signature and the impact of LINC02154, and we performed relevant survival analyses. Tumor mutational burdens were analyzed in different LINC02154 expression groups and risk score groups. We next analyzed the immune microenvironment of LINC20154. We performed LINC20154-related drug sensitivity analyses. We also investigated the cellular function of LINC02154 in the ACHN cell line and performed CCK-8 assay, EdU, wound-healing assay, and Transwell assay. The essential genes FDX1 and DLST of cuproptosis were detected by western blot., Results: We demonstrated that LINC02154's impact on outcomes was statistically significant. We also demonstrated the association of different ages, genders, stages, and grades with LINC02154 and risk models. The results showed a significant difference in tumor mutation burden between the groups, which was closely related to clinical prognosis. We found differences in immune cells among groups with different levels of LINC02154 expression and significant differences in immune function, immunotherapeutic positive markers, and critical steps of the immune cycle. The sensitivity analysis showed that differential expression of LINC02154 discriminated between sensitivity to axitinib, doxorubicin, gemcitabine, pazopanib, sorafenib, sunitinib, and temsirolimus. This difference was also present in the high-risk group and low-risk group. We demonstrated that the proliferation and migration of t ACHN cells in the LINC02154 knockdown group were inhibited. The western blot results showed that the knockdown of LINC02154 significantly affected the expression of FDX1 and DLST, critical genes of cuproptosis., Conclusion: Finally, we demonstrated that LINC02154 and our constructed risk signature could predict outcomes and have potential clinical value., (© 2023. The Author(s).)

Although N7-methylguanosine (m7G) is one of the most frequent RNA modifications, it has received little attention. Adrenocortical carcinoma (ACC) is a highly malignant and easily metastatic tumor, eagerly needing for novel therapeutic strategy. Herein, a novel m7G risk signature (METTL1, NCBP1, NUDT1 and NUDT5) was constructed using the Lasso regression analysis. It possessed highly prognostic value and could improve the predictive accuracy and clinical making-decision benefit of traditional prognostic model. Its prognostic value was also successfully validated in GSE19750 cohort. Through CIBERSORT, ESTIMATE, ssGSEA and GSEA analyzes, high-m7G risk score was found to be closely associated with increased enrichment of glycolysis and suppression of anti-cancer immune response. Therapeutic correlation of m7G risk signature was also investigated using tumor mutation burden, the expressions of immune checkpoints, TIDE score, IMvigor 210 cohort and TCGA cohort. m7G risk score was a potential biomarker for predicting the efficacy of ICBs and mitotane. Furthermore, we explored the biofunctions of METTL1 in ACC cells through a series of experimentations. Overexpression of METTL1 stimulated the proliferation, migration and invasion of H295R and SW13 cells. Immunofluorescence assays revealed that the infiltrating levels of CD8+ T cells was lower and that of macrophages was higher in clinical ACC samples with high METTL1 expression compared to that in low expression ones. Silencing METTL1 could significantly inhibited tumor growth in mouse xenograft model. Western blot assays showed that METTL1 positively regulated the expression of glycolysis rate-limiting enzyme HK1. Finally, miR-885-5p and CEBPB were predicted as the upstream regulators of METTL1 through data mining of the public databases. In conclusions, m7G regulatory genes well represented by METTL1 profoundly affected the prognosis, tumor immune, therapeutic outcomes, and malignant progression of ACC., Competing Interests: None., (AJCR Copyright © 2023.)

Background: Multiple myeloma (MM) is a highly malignant hematological tumor with a poor overall survival (OS). Due to the high heterogeneity of MM, it is necessary to explore novel markers for the prognosis prediction for MM patients. Ferroptosis is a form of regulated cell death, playing a critical role in tumorigenesis and cancer progression. However, the predictive role of ferroptosis-related genes (FRGs) in MM prognosis remains unknown., Methods: This study collected 107 FRGs previously reported and utilized the least absolute shrinkage and selection operator (LASSO) cox regression model to construct a multi-genes risk signature model upon FRGs. The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were carried out to evaluate immune infiltration level. Drug sensitivity was assessed based on the Genomics of Drug Sensitivity in Cancer database (GDSC). Then the synergy effect was determined with Cell counting kit-8 (CCK-8) assay and SynergyFinder software., Results: A 6-gene prognostic risk signature model was constructed, and MM patients were divided into high and low risk groups. Kaplan-Meier survival curves showed that patients in the high risk group had significantly reduced OS compared with patients in the low risk group. Besides, the risk score was an independent predictor for OS. Receiver operating characteristic (ROC) curve analysis confirmed the predictive capacity of the risk signature. Combination of risk score and ISS stage had better prediction performance. Enrichment analysis revealed immune response, MYC, mTOR, proteasome and oxidative phosphorylation were enriched in high risk MM patients. We found high risk MM patients had lower immune scores and immune infiltration levels. Moreover, further analysis found that MM patients in high risk group were sensitive to bortezomib and lenalidomide. At last, the results of the in vitro experiment showed that ferroptosis inducers (RSL3 and ML162) may synergistically enhance the cytotoxicity of bortezomib and lenalidomide against MM cell line RPMI-8226., Conclusion: This study provides novel insights into roles of ferroptosis in MM prognosis prediction, immune levels and drug sensitivity, which complements and improves current grading systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gao, Liu, Lv, Feng, Hong, Xu, Hu, He and Yang.)

The relationship between metabolism and immune microenvironment remains to be studied in bladder cancer (BCa). We aimed to construct a metabolic-associated signature for prognostic prediction and investigate its relationship with the immune microenvironment in BCa. The RNA expression of metabolism associated genes was obtained from a combined data set including The Cancer Genome Atlas, GSE48075, and GSE13507 to divide BCa patients into different clusters. A metabolic-associated signature was constructed using the differentially expressed genes between clusters in the combined data set and validated in the IMvigor210 trial and our center. The composition of tumor-infiltrating immune cells (TIICs) was evaluated using the single-sample Gene Set Variation Analysis. BCa patients in Cluster A or high-risk level were associated with advanced clinicopathological features and poor survival outcomes. The percentage of high-risk patients was significantly lower in patients responding to anti-PD-L1 treatment. Compared with low-risk patients, the IC50 values of cisplatin and gemcitabine were significantly lower in high-risk patients. Thiosulfate transferase (TST) and S100A16 were significantly associated with clinicopathological features and prognosis. Downregulation of TST promoted BCa cell invasion, migration, and epithelial-to-mesenchymal transition, which are inhibited by downregulation of S100A16. CD8 + T cells, neutrophils, and dendritic cells had higher infiltration in the TST low-level and the S100A16 high-level. Furthermore, loss of function TST and S100A16 significantly affected the expression of PD-L1 and CD47. The metabolic-associated signature can stratify BCa patients into distinct risk levels with different immunotherapeutic susceptibility and survival outcomes. Metabolism disorder promoted the dysregulation of immune microenvironment, thus contributing to immunosuppression., (© 2022 Wiley Periodicals LLC.)

• Chromatin organization was highly associated with tumorigenesis, however, information on its relationship to hepatocellular carcinoma is currently limited. • A total of four genes were screened by learning vector quantization, random forest, and lasso regression to construct the novel prognostic signature. • The signature could be used as a predictive biomarker for prognosis as well as an index of response to immune checkpoint inhibitor immunotherapy. • The signature was verified in 64 samples from Fujian Provincial Hospital via quantitative PCR. Chromatin organization is associated with tumorigenesis; however, information on its role in hepatocellular carcinoma (HCC) is limited. Moreover, although immune checkpoint inhibitors (ICIs) have proven effective against HCC, the optimal index remains unknown. In this study, we aimed to construct a chromatin organization-related gene signature (CORGS) for prognosis and predicting response to ICIs in HCC. HCC-related data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Construction (ICGC). Chromatin organization-related genes (CORGs) were retrieved from Gene Set Enrichment Analysis. Differentially expressed genes (DEGs) and prognostic genes were then applied to select candidate genes using advanced statistical methods, including learning vector quantization, random forest, and lasso regression. Subsequently, the CORGS was established based on chromatin organization-related hub genes using multivariate Cox regression analysis, evaluated with Kaplan-Meier survival curves, and verified in 64 samples of HCC patients from Fujian Provincial Hospital (FPH) via quantitative PCR. Subsequently, functional enrichment analysis, tumor somatic mutation analysis, and tumor immune analysis were performed to evaluate the potential value of the CORGS. Three hundred and thirty-nine CORGs were identified as DEGs, and 186 were associated with HCC prognosis (all P < 0.05). Four intersection genes were selected to establish the CORGS using TCGA cohort, which was found to serve as an independent risk factor for HCC patients. CORGS was then validated in an ICGC cohort. In addition, CORGS reliability was verified in 64 samples from HCC patients and 26 adjacent non-tumorous tissues, collected from the FPH. The CORGS was also associated with tumor immune microenvironment characteristics and ICI response. Moreover, data from "IMvigor 210" revealed that more patients in the low CORGS group responded to atezolizumab compared to high CORGS patients (P < 0.05). Finally, a nomogram of tumor characteristics and the CORGS was established, exhibiting superior discrimination and calibration compared to the current staging system and published models. CORGS may serve as an effective predictive biomarker for HCC as well as a potential index of the tumor immune microenvironment and ICI response. [ABSTRACT FROM AUTHOR]

Background: Pancreatic cancer is a malignancy with a high mortality rate and worse prognosis. Recently, public databases and bioinformatics tools make it easy to develop the prognostic risk model of pancreatic cancer, but the aging-related risk signature has not been reported. The present study aimed to identify an aging-related risk signature with potential prognostic value for pancreatic cancer patients. Method: Gene expression profiling and human clinical information of pancreatic cancer were derived from The Cancer Genome Atlas database (TCGA). Aging-related gene sets were downloaded from The Molecular Signatures Database and aging-related genes were obtained from the Human Ageing Genomic Resources database. Firstly, Gene set enrichment analysis was carried out to investigate the role of aging process in pancreatic cancer. Secondly, differentially expressed genes and aging-related prognostic genes were screened on the basis of the overall survival information. Then, univariate COX and LASSO analysis were performed to establish an aging-related risk signature of pancreatic cancer patients. To facilitate clinical application, a nomogram was established to predict the survival rates of PCa patients. The correlations of risk score with clinical features and immune status were evaluated. Finally, potential therapeutic drugs were screened based on the connectivity map (Cmap) database and verified by molecular docking. For further validation, the protein levels of aging-related genes in normal and tumor tissues were detected in the Human Protein Atlas (HPA) database. Result: The genes of pancreatic cancer were markedly enriched in several aging-associated signaling pathways. We identified 14 key aging-related genes related to prognosis from 9,020 differentially expressed genes and establish an aging-related risk signature. This risk model indicated a strong prognostic capability both in the training set of TCGA cohort and the validation set of PACA-CA cohort and GSE62452 cohort. A nomogram combining risk score and clinical variables was built, and calibration curve and Decision curve analysis (DCA) have proved that it has a good predictive value. Additionally, the risk score was tightly linked with tumor immune microenvironment, immune checkpoints and proinflammatory factors. Moreover, a candidate drug, BRD-A47144777, was screened and verified by molecular docking, indicating this drug has the potential to treat PCa. The protein expression levels of GSK3B, SERPINE1, TOP2A, FEN1 and HIC1 were consistent with our predicted results. Conclusion: In conclusion, we identified an aging-related signature and nomogram with high prediction performance of survival and immune cell infiltration for pancreatic cancer. This signature might potentially help in providing personalized immunotherapy for patients with pancreatic cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Zhang, Wang, Zhang and Xu.)

Endoplasmic reticulum (ER) stress, as an emerging hallmark feature of cancer, has a considerable impact on cell proliferation, metastasis, invasion, and chemotherapy resistance. Ovarian cancer (OvCa) is one of the leading causes of cancer-related mortality across the world due to the late stage of disease at diagnosis. Studies have explored the influence of ER stress on OvCa in recent years, while the predictive role of ER stress-related genes in OvCa prognosis remains unexplored. Here, we enrolled 552 cases of ER stress-related genes involved in OvCa from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts for the screening of prognosis-related genes. The least absolute shrinkage and selection operator (LASSO) regression was applied to establish an ER stress-related risk signature based on the TCGA cohort. A seven-gene signature revealed a favorable predictive efficacy for the TCGA, International Cancer Genome Consortium (ICGC), and another GEO cohort ( P <0.001, P <0.001, and P =0.04, respectively). Moreover, functional annotation indicated that this signature was enriched in cellular response and senescence, cytokines interaction, as well as multiple immune-associated terms. The immune infiltration profiles further delineated an immunologic unresponsive status in the high-risk group. In conclusion, ER stress-related genes are vital factors predicting the prognosis of OvCa, and possess great application potential in the clinic.

Background: Hepatocellular carcinoma (HCC) is a common abdominal cancer with dissatisfactory therapeutic effects. The discovery of cuproptosis lights on new approach for cancer treatment and assessment. So far, there is extremely limited research investigating the roles of cuproptosis-related (CR) genes in cancers., Methods: A novel CR risk signature was constructed using the Lasso regression analysis. Its prognostic value was assessed via a series of survival analyses and validated in three GEO cohorts. The effects of CR risk signature on tumor immune microenvironment (TIM) were explored through CIBERSORT, ESTIMATE, and ssGSEA algorithms. Using GESA, we investigated its impacts on various metabolism process. The somatic mutation features of CR signature genes were also explored via cBioPortal database. Using tumor mutation burden, expressions of immune checkpoints, TIDE score, IMvigor 210 cohort, and GSE109211 dataset, we explored the potential associations of CR risk score with the efficacy of immune checkpoint inhibitors (ICIs) and sorafenib. Finally, the biofunctions of DLAT in HCC cells were ascertained through qPCR, immunohistochemistry, colony formation, and Transwell assays., Results: FDX1, DLAT, CDKN2A and GLS constituted the CR risk signature. CR risk signature possessed high prognostic value and was also applicable to three validation cohorts. Meanwhile, it could improve the accuracy and clinical making-decision benefit of traditional prognostic model. Moreover, high CR risk was indicative of unfavorable anti-tumor immune response and active metabolisms of glycolysis and nucleotide. As for therapeutic correlation, CR risk score was a potential biomarker for predicting the efficacy of ICIs and sorafenib. Through qPCR and immunohistochemistry detection in clinical samples, we reconfirmed DLAT was significantly upregulated in HCC samples. Overexpression of DLAT could promote the proliferation, migration, and invasion of HepG2 and HuH-7 cells., Conclusions: The novel CR risk signature greatly contributed to the clinical assessment of HCC. Cuproptosis regulatory gene DLAT possessed cancer-promoting capacities and was expected to be a promising therapeutic target for HCC., (© 2023. The Author(s).)

Aim: Osteosarcoma is the most common primary malignant tumor of bone. However, our understanding of the prognostic indicators and the genetic mechanisms of the disease progression are still incomplete. The aim of this study was to identify a long noncoding RNA (lncRNA) risk signature for osteosarcoma survival prediction. Methods: RNA sequencing data and relevant clinical information of osteosarcoma patients were downloaded from the database of Therapeutically Applicable Research to Generate Effective Treatments (TARGET). We analyzed the differentially expressed lncRNAs between deceased and living patients by univariate and multivariate Cox regression analysis to identify a risk signature. We calculated a prognostic risk score for each sample according to this prognosis signature, and divided patients into high-risk and low-risk groups according to the median value of the risk score (0.975). Kaplan-Meier analysis and receiver operating characteristic (ROC) curve statistics were used to evaluate the performance of the signature. Next, we analyzed the signature's potential function through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene-set enrichment analysis (GSEA). Lastly, qRT-PCR was used to validate the expression levels of the four lncRNAs in clinical samples. Results: Twenty-six differentially expressed lncRNAs were identified between deceased and living patients. Four of these lncRNAs (CTB-4E7.1, RP11-553A10.1, RP11-24N18.1, and PVRL3-AS1) were identified as independent prognostic factors, and a risk signature of these four lncRNAs for osteosarcoma survival prediction was constructed. Kaplan-Meier analysis showed that the five-year survival time in high-risk and low-risk groups was 33.1% and 82.5%, and the area under the curve (AUC) of the ROC was 0.784, which demonstrated that the prognostic signature was reliable and had the potential to predict the survival of patients with osteosarcoma. The expression level of the four lncRNAs in osteosarcoma tissues and cells was determined by qRT-PCR. Functional enrichment analysis suggested that the signature might be related to osteosarcoma through regulation of the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the extracellular matrix and also provided new insights into the study of osteosarcoma, including the role of papillomavirus infection, olfactory receptor activity, and olfactory transduction in osteosarcoma. Conclusion: We constructed a novel lncRNA risk signature that served as an independent biomarker for predicting the prognosis of osteosarcoma patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Liu, Chen, Liu and Wang.)