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Disulfidptosis is a novel programmed cell death mode that has been reported to play a role in oncogenesis. Increasing evidences suggest that the long non-coding RNAs (lncRNAs) play crucial roles in the initiation and progression of bladder cancer (BLCA). However, the role and prognostic value of disulfidptosis-related lncRNAs in BLCA remain unknown.The aim of this study was to construct and validate a disulfidptosis-related lncRNA risk model for predicting the prognosis of BLCA patients. A risk model consisting of 5 disulfidptosis-related lncRNAs was developed to predict the prognosis of BLCA patients. The overall survival (OS) of BLCA patients in the high-risk group was significantly shorter than that in the low-risk group (P < 0.05). The effectiveness of this model was validated using the receiver operating characteristic (ROC) curve analysis, and this model proved superior in prognostic accuracy compared with other clinical features. Furthermore, the tumor immune dysfunction and exclusion (TIDE) score in the high-risk group was significantly higher than that in the low-risk group, suggesting that the high-risk group had a less favorable response to immunotherapy. Simultaneously, patients in the low-risk group exhibited significantly higher sensitivity to CTLA-4 monoclonal antibody therapy compared to those in the high-risk group, suggesting potential benefits of immunotherapy for patients in the low-risk group. The combination of high risk and low tumor mutational burden (TMB) could further shortened the OS of BLCA patients. Lastly, the drug sensitivity analysis revealed that the BLCA cells in the high-risk group showed an increased sensitivity to cisplatin, sunitinib, cetuximab, axitinib, docetaxel, saracatinib, vinblastine and pazopanib compared with those in the low-risk group. According to the Quantitative real time PCR results, we found that five lncRNAs of the risk model were more highly expressed in BCa cell lines than human immortalized uroepithelial cell line. The disulfidptosis-related lncRNA risk model has a valuable effect in assessing the prognosis of BLCA patients. [ABSTRACT FROM AUTHOR]

Background: Dysregulated splicing events are a common phenomenon in cancer with the Serine-arginine-rich splicing factor (SRSF) family emerging as pivotal regulators of gene expression, exerting influence over constitutive and alternative splicing processes. Although aberrations in a few SRSF family members have been implicated in various cancers, the comprehensive roles of other family constituents remain underexplored. Methods: This study delves into the expression profile of the entire SRSF family (SRSF1-SRSF12) in 23 cancerous cell lines originating from diverse tissues using quantitative Real-Time PCR. Further, the transcript levels of the SRSF family were examined in oral cancer patient samples stratified into Pre-cancer (n = 15), Early cancer (n = 11), Late cancer (n = 14), and adjacent non-tumor tissues (n = 26) as controls. The results were corroborated by a parallel investigation utilizing the transcriptomics data of oral squamous cell carcinoma (OSCC) patients (n = 319) and controls (n = 35) available in The Cancer Genome Atlas (TCGA) database. Results: Our investigation reveals a notable upregulation in the expression levels of key splicing factors, namely SRSF3, SRSF9, and SRSF10 in all oral cancer cell lines (SCC-4, UM-SCC-84, CAL33, SAS-H1). Conversely, no significant associations between SRSF family members and other cancer cell lines were discerned. Further, the expression profile of the SRSF family in oral cancer patient samples revealed significant upregulation of SRSF1, SRSF3, SRSF7, SRSF9, SRSF10, and SRSF11 in patients with late-stage oral cancer compared to controls. Transcriptomics data from TCGA database demonstrated remarkable upregulation of SRSF1, SRSF4, SRSF9, SRSF10, and SRSF11 in OSCC patients. Conclusion: Collectively our results underscore the critical involvement of SRSF family members in the context of oral cancer, highlighting their potential as key players in the altered splicing dynamics associated with cancer progression. [ABSTRACT FROM AUTHOR]

Abstract Disulfidptosis is a novel programmed cell death mode that has been reported to play a role in oncogenesis. Increasing evidences suggest that the long non-coding RNAs (lncRNAs) play crucial roles in the initiation and progression of bladder cancer (BLCA). However, the role and prognostic value of disulfidptosis-related lncRNAs in BLCA remain unknown.The aim of this study was to construct and validate a disulfidptosis-related lncRNA risk model for predicting the prognosis of BLCA patients. A risk model consisting of 5 disulfidptosis-related lncRNAs was developed to predict the prognosis of BLCA patients. The overall survival (OS) of BLCA patients in the high-risk group was significantly shorter than that in the low-risk group (P

Abstract Background Dysregulated splicing events are a common phenomenon in cancer with the Serine-arginine-rich splicing factor (SRSF) family emerging as pivotal regulators of gene expression, exerting influence over constitutive and alternative splicing processes. Although aberrations in a few SRSF family members have been implicated in various cancers, the comprehensive roles of other family constituents remain underexplored. Methods This study delves into the expression profile of the entire SRSF family (SRSF1-SRSF12) in 23 cancerous cell lines originating from diverse tissues using quantitative Real-Time PCR. Further, the transcript levels of the SRSF family were examined in oral cancer patient samples stratified into Pre-cancer (n = 15), Early cancer (n = 11), Late cancer (n = 14), and adjacent non-tumor tissues (n = 26) as controls. The results were corroborated by a parallel investigation utilizing the transcriptomics data of oral squamous cell carcinoma (OSCC) patients (n = 319) and controls (n = 35) available in The Cancer Genome Atlas (TCGA) database. Results Our investigation reveals a notable upregulation in the expression levels of key splicing factors, namely SRSF3, SRSF9, and SRSF10 in all oral cancer cell lines (SCC-4, UM-SCC-84, CAL33, SAS-H1). Conversely, no significant associations between SRSF family members and other cancer cell lines were discerned. Further, the expression profile of the SRSF family in oral cancer patient samples revealed significant upregulation of SRSF1, SRSF3, SRSF7, SRSF9, SRSF10, and SRSF11 in patients with late-stage oral cancer compared to controls. Transcriptomics data from TCGA database demonstrated remarkable upregulation of SRSF1, SRSF4, SRSF9, SRSF10, and SRSF11 in OSCC patients. Conclusion Collectively our results underscore the critical involvement of SRSF family members in the context of oral cancer, highlighting their potential as key players in the altered splicing dynamics associated with cancer progression. Graphical abstract

Abstract Background Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction. Methods The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes. Results Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer. Conclusion The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

Background: Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction. Methods: The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes. Results: Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer. Conclusion: The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future. [ABSTRACT FROM AUTHOR]

P53 overexpression plays a critical role in cancer pathogenesis by disrupting the intricate regulation of cellular proliferation. Despite its firmly established function as a tumor suppressor, elevated p53 levels can paradoxically contribute to tumorigenesis, influenced by factors such as exposure to carcinogens, genetic mutations, and viral infections. This phenomenon is observed across a spectrum of cancer types, including bladder (BLCA), ovarian (OV), cervical (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and uterine corpus endometrial carcinoma (UCEC). This broad spectrum of cancers is often associated with increased aggressiveness and recurrence risk. Effective therapeutic strategies targeting tumors with p53 overexpression require a comprehensive approach, integrating targeted interventions aimed at the p53 gene with conventional modalities such as chemotherapy, radiation therapy, and targeted drugs. In this extensive study, we present a detailed analysis shedding light on the multifaceted role of TP53 across various cancers, with a specific emphasis on its impact on disease-free survival (DFS). Leveraging data from the TCGA database and the GTEx dataset, along with GEPIA, UALCAN, and STRING, we identify TP53 overexpression as a significant prognostic indicator, notably pronounced in prostate adenocarcinoma (PRAD). Supported by compelling statistical significance (p < 0.05), our analysis reveals the distinct influence of TP53 overexpression on DFS outcomes in PRAD. Additionally, graphical representations of overall survival (OS) underscore the notable disparity in OS duration between tumors exhibiting elevated TP53 expression (depicted by the red line) and those with lower TP53 levels (indicated by the blue line). The hazard ratio (HR) further emphasizes the profound impact of TP53 on overall survival. Moreover, our investigation delves into the intricate TP53 protein network, unveiling genes exhibiting robust positive correlations with TP53 expression across 13 out of 27 cancers. Remarkably, negative correlations emerge with pivotal tumor suppressor genes. This network analysis elucidates critical proteins, including SIRT1, CBP, p300, ATM, DAXX, HSP 90-alpha, Mdm2, RPA70, 14-3-3 protein sigma, p53, and ASPP2, pivotal in regulating cell cycle dynamics, DNA damage response, and transcriptional regulation. Our study underscores the paramount importance of deciphering TP53 dynamics in cancer, providing invaluable insights into tumor behavior, disease-free survival, and potential therapeutic avenues. [ABSTRACT FROM AUTHOR]

HCC is a globally high‐incidence malignant tumour, and its pathogenesis is still unclear. Recently, STRN3 has been found to be elevated in various tumours, but its expression and biological functions in HCC have not been studied. In the study, clinical correlation analysis was performed on 371 liver cancer patients from TCGA database and liver cancer tissues and normal tissues from the GEO database. qRT‐PCR and western blotting were used to detect relevant proteins in cells, and CCK8 and colony formation experiments were performed to analyse cell proliferation ability. Transwell and wound healing experiments were performed to detect cell invasion ability, and flow cytometry was used to detect cell apoptosis. Single‐cell sequencing data and multiple immunofluorescence were analysed for the expression abundance and distribution of certain proteins. Immunohistochemistry was used to assess the expression of STRN3 in patients' tumour and adjacent non‐cancerous tissues. The results indicated STRN3 was highly expressed in liver tumour tissues and was closely associated with poor prognosis. Knockdown of STRN3 could significantly inhibit cell proliferation and migration ability. At the same time, we found that STRN3 could inhibit the Hippo pathway and promote the entry of YAP protein into the nucleus. Our study first found that STRN3 could promote tumour growth by inhibiting the Hippo pathway. The study of STRN3 can promote the understanding and treatment of the occurrence and development of HCC. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the second leading cause of cancer-related death. Immunotherapy is one of the new options for cancer treatment. This study aimed to develop an immune-related signature associated with CRC. We performed differential analysis to screen out the differentially expressed genes (DEGs) of The Cancer Genome Atlas–Colorectal Cancer (TCGA-CRC) datasets. Weighted gene co-expression network analysis (WGCNA) was performed to obtain the key module genes associated with differential immune cells. The candidate genes were obtained through overlapping key DEGs and key module genes. The univariate and multivariate Cox regression analyses were adopted to build a CRC prognostic signature. We further conducted immune feature estimation and chemotherapy analysis between two risk subgroups. Finally, we verified the expression of immune-related prognostic genes at the transcriptional level. A total of 61 candidate genes were obtained by overlapping key DEGs and key module genes associated with differential immune cells. Then, an immune-related prognostic signature was built based on the three prognostic genes (HAMP, ADAM8, and CD1B). The independent prognostic analysis suggested that age, stage, and RiskScore could be used as independent prognostic factors. Further, we found significantly higher expression of three prognostic genes in the CRC group compared with the normal group. Finally, real-time polymerase chain reaction verified the expression of three genes in patients with CRC. The prognostic signature comprising HAMP, ADAM8, and CD1B based on immune cells was established, providing a theoretical basis and reference value for the research of CRC. [ABSTRACT FROM AUTHOR]

Simple Summary: Poor survival rates of squamous cell carcinomas (SCCs) are due in part to a limited number of reliable biomarkers and molecular targets. NAD+ metabolism plays a relevant role in SCC chemoprevention and therapy and is found dysregulated in several tumors. Therefore, the aim of our study was to identify new biomarkers based on NAD+ metabolism-related gene (NMRG) expression. Our findings indicate that NAD+ metabolism-related gene profiles can be an important source of SCC biomarkers and potential therapeutic targets. These findings agree with SCC preclinical and clinical studies using NAD+ precursors. Further in-depth studies on identified NMRGs may provide additional insights into SCC pathogenesis and improve therapeutic choice. Poor survival rates of squamous cell carcinomas (SCCs) are associated with high recurrence, metastasis, and late diagnosis, due in part to a limited number of reliable biomarkers. Thus, the identification of signatures improving the diagnosis of different SCC types is mandatory. Considering the relevant role of NAD+ metabolism in SCC chemoprevention and therapy, the study aimed at identifying new biomarkers based on NAD+ metabolism-related gene (NMRG) expression. Gene expression of 18 NMRGs and clinical-pathological information for patients with head and neck SCC (HNSCC), lung SCC (LuSCC), and cervix SCC (CeSCC) from The Cancer Genome Atlas (TCGA) were analyzed by several bioinformatic tools. We identified a 16-NMRG profile discriminating 3 SCCs from 3 non-correlated tumors. We found several genes for HNSCC, LuSCC, and CeSCC with high diagnostic power. Notably, three NMRGs were SCC-type specific biomarkers. Furthermore, specific signatures displayed high diagnostic power for several clinical-pathological characteristics. Analyzing tumor-infiltrating immune cell profiles and PD-1/PD-L1 levels, we found that NMRG expression was associated with suppressive immune microenvironment mainly in HNSCC. Finally, the evaluation of patient survival identified specific genes for HNSCC, LuSCC, and CeSCC with potential prognostic power. Therefore, our analyses indicate NAD+ metabolism as an important source of SCC biomarkers and potential therapeutic targets. [ABSTRACT FROM AUTHOR]

This study was based on the use of whole-genome DNA methylation sequencing technology to identify DNA methylation biomarkers in tumor tissue that can predict the prognosis of patients with pancreatic cancer (PCa). TCGA database was used to download PCa-related DNA methylation and transcriptome atlas data. Methylation driver genes (MDGs) were obtained using the MethylMix package. Candidate genes in the MDGs were screened for prognostic relevance to PCa patients by univariate Cox analysis, and a prognostic risk score model was constructed based on the key MDGs. ROC curve analysis was performed to assess the accuracy of the prognostic risk score model. The effects of PIK3C2B knockdown on malignant phenotypes of PCa cells were investigated in vitro. A total of 2737 differentially expressed genes were identified, with 649 upregulated and 2088 downregulated, using 178 PCa samples and 171 normal samples. MethylMix was employed to identify 71 methylation-driven genes (47 hypermethylated and 24 hypomethylated) from 185 TCGA PCa samples. Cox regression analyses identified eight key MDGs (LEF1, ZIC3, VAV3, TBC1D4, FABP4, MAP3K5, PIK3C2B, IGF1R) associated with prognosis in PCa. Seven of them were hypermethylated, while PIK3C2B was hypomethylated. A prognostic risk prediction model was constructed based on the eight key MDGs, which was found to accurately predict the prognosis of PCa patients. In addition, the malignant phenotypes of PANC-1 cells were decreased after the knockdown of PIK3C2B. Therefore, the prognostic risk prediction model based on the eight key MDGs could accurately predict the prognosis of PCa patients.

Clear cell renal cell carcinoma (ccRCC) is known as the most common type of renal cancer. Recently, a series of advances have been made in targeted therapy for ccRCC. To combat this highly metastatic tumor, novel therapeutic targets still need to be developed. C-type lectins (CLECs) contain a characteristic C-type lectin-like domain and affect several physiological functions. The effects of C-type lectin 2D (CLEC2D) on cancer progression have been revealed in several types of cancers; however, its expression in ccRCC tissues, and the possible effects on the progression and metastasis of ccRCC, are still unclear. Herein, we found the high mRNA and protein levels of CLEC2D in ccRCC tissues. We further found that CLEC2D expression was correlated with the prognosis of ccRCC patients and correlated with the tumor size (p = 0.019*) of patients. In addition, CLEC2D affected tumor immune infiltration, confirmed by the further analysis. CLEC2D knockdown suppressed the proliferation of ccRCC cells in vitro and restrained ccRCC tumor growth and immune infiltration in mice. Therefore, we believe that CLEC2D has the potential to serve as a promising ccRCC therapeutic target.

Abstract Background Researches have manifested that the disorder of iron metabolism is participated in Gastric cancer (GC), but whether iron metabolism-relevant genes (IMRGs) is related to the survival outcome of GC remain unknown. Methods Eleven tumor as well as nine adjacent normal tissues from GC patients were underwent mRNA sequencing, and the The Cancer Genome Atlas Stomach Cancer (TCGA-STAD) datasets were acquired from the TCGA database. Cox analyses and least absolute shrinkage and selection operator (LASSO) regression were applied to build a IMRGs signature. The relationship between signature genes and the infiltration profiling of 24 immune cells were investigated using single-sample GSEA (ssGSEA). Meanwhile, the potential biological significance, genes that act synergistically with signature genes, and the upstream regulatory targets were predicted. Finally, the abundance of the signature genes were measured via the quantitative real-time PCR (qRT-PCR). Results A IMRGs signature was constructed according to the expression and corresponding coefficient of DOHH, P4HA3 and MMP1 (The Schoenfeld individual test showed risk score was not significant with P values = 0.83). The prognostic outcome of patients in the high-risk group was terrible (p 0.6). The nomogram was performed well for clinical utilize (C-index = 0.60), and the MMP1 expression significantly increased in the cohorts at age > 60 and Stage II-IV (p

The pathogenesis of head and neck squamous cell carcinoma (HNSCC) is associated with human papillomavirus (HPV) infection. However, the molecular mechanisms underlying the interactions between HNSCC and HPV remain unclear. Bioinformatics was used to analyze the gene expression dataset of HPV-associated HNSCC based on the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) in HPV-positive and HPV-negative HNSCC were screened. Gene function enrichment, protein–protein interactions (PPI), survival analysis, and immune cell infiltration of DEGs were performed. Furthermore, the clinical data of HNSCC tissue samples were analyzed using immunohistochemistry. In total, 194 DEGs were identified. A PPI network was constructed and 10 hub genes (EREG, PLCG1, ERBB4, HBEGF, ZFP42, CBX6, NFKBIA, SOCS1, ATP2B2, and CEND1) were identified. Survival analysis indicated that low expression of SOCS1 was associated with worse overall survival. Immunohistochemistry demonstrated that SOCS1 expression was higher in HPV-negative HNSCC than in HPV-positive HNSCC, and there was a positive correlation between SOCS1 expression and patient survival. This study provides new information on biological targets that may be relevant to the molecular mechanisms underpinning the occurrence and development of HNSCC. SOCS1 may play an important role in the interaction between HPV and HNSCC and serve as a potential biomarker for future therapeutic targets.

Background: Glycolytic metabolic reprogramming is a phenomenon in which cells undergo altered metabolic patterns during malignant transformation, mainly involving various aspects of glycolysis, electron transport chain, oxidative phosphorylation, and pentose phosphate pathway. This reprogramming phenomenon can be used as one of the markers of tumorigenesis and development. Pyruvate kinase is the third rate-limiting enzyme in the sugar metabolism process by specifically catalyzing the irreversible conversion of PEP to pyruvate. Purpose: This study aimed to reveal the critical mediator(s) that regulate glycolytic metabolism reprogramming in gastric cancer and their underlying molecular mechanism and then explore the molecular mechanisms by which LHX9 may be involved in regulating gastric cancer (GC) progression. Methods: Firstly, we downloaded the GC and glycolysis-related microarray datasets from TCGA and MSigDB databases and took the intersection to screen out the transcription factor LHX9 that regulates GC glycolytic metabolic reprogramming. Software packages were used for differential analysis, single gene predictive analysis, and Venn diagram. In addition, an enrichment analysis of the glycolytic pathway was performed. Immunohistochemical staining was performed for LHX9 and PKM2 protein expression in 90 GC patients, and the association between their expressions was evaluated by Spearman's correlation coefficient method. Three human GC cell lines (AGS, NCI-N87, HGC-27) were selected for in vitro experimental validation. Flow cytometry was utilized to determine the stem cell marker CD44 expression status in GCSCs. A sphere formation assay was performed to evaluate the sphere-forming capabilities of GCSCs. In addition, RT-qPCR and Western blot experiments were employed to investigate the tumor stem cell markers OCT4 and SOX2 expression levels in GCSCs. Furthermore, a lentiviral expression vector was constructed to assess the impact of downregulating LHX9 or PKM2 on the glycolytic metabolic reprogramming of GCSCs. The proliferation, migration, and invasion of GCSCs were then detected by CCK-8, EdU, and Transwell assays. Subsequently, the mutual binding of LHX9 and PKM2 was verified using chromatin immunoprecipitation and dual luciferase reporter genes. In vivo experiments were verified by establishing a subcutaneous transplantation tumor model in nude mice, observing the size and volume of tumors in vivo in nude mice, and obtaining fresh tissues for subsequent experiments. Results: Bioinformatics analysis revealed that LHX9 might be involved in the occurrence and development of GC through regulating glycolytic metabolism. High LHX9 expression could be used as a reference marker for prognosis prediction of GC patients. Clinical tissue assays revealed that LHX9 and PKM2 were highly expressed in GC tissues. Meanwhile, GC tissues also highly expressed glycolysis-associated protein GLUT1 and tumor cell stemness marker CD44. In vitro cellular assays showed that LHX9 could enhance its activity and induce glycolytic metabolic reprogramming in GCSCs through direct binding to PKM2. In addition, the knockdown of LHX9 inhibited PKM2 activity and glycolytic metabolic reprogramming and suppressed the proliferation, migration, and invasive ability of GCSCs. In vivo animal experiments further confirmed that the knockdown of LHX9 could reduce the tumorigenic ability of GCSCs in nude mice by inhibiting PKM2 activity and glycolytic metabolic reprogramming. Conclusion: The findings suggest that both LHX9 and PKM2 are highly expressed in GCs, and LHX9 may induce the reprogramming of glycolytic metabolism through transcriptional activation of PKM2, enhancing the malignant biological properties of GCSCs and ultimately promoting GC progression. [ABSTRACT FROM AUTHOR]

Background: Researches have manifested that the disorder of iron metabolism is participated in Gastric cancer (GC), but whether iron metabolism-relevant genes (IMRGs) is related to the survival outcome of GC remain unknown. Methods: Eleven tumor as well as nine adjacent normal tissues from GC patients were underwent mRNA sequencing, and the The Cancer Genome Atlas Stomach Cancer (TCGA-STAD) datasets were acquired from the TCGA database. Cox analyses and least absolute shrinkage and selection operator (LASSO) regression were applied to build a IMRGs signature. The relationship between signature genes and the infiltration profiling of 24 immune cells were investigated using single-sample GSEA (ssGSEA). Meanwhile, the potential biological significance, genes that act synergistically with signature genes, and the upstream regulatory targets were predicted. Finally, the abundance of the signature genes were measured via the quantitative real-time PCR (qRT-PCR). Results: A IMRGs signature was constructed according to the expression and corresponding coefficient of DOHH, P4HA3 and MMP1 (The Schoenfeld individual test showed risk score was not significant with P values = 0.83). The prognostic outcome of patients in the high-risk group was terrible (p < 0.05). Receiver operating characteristic (ROC) curves confirmed that the IMRGs signature presented good efficiency for predicting GC prognosis (AUC > 0.6). The nomogram was performed well for clinical utilize (C-index = 0.60), and the MMP1 expression significantly increased in the cohorts at age > 60 and Stage II-IV (p < 0.05). The positive correlation of P4HA3 and MMP1 expression as well as the negative correlation of DOHH expression with risk score (p < 0.0001) and worse prognosis (p < 0.05) were detected as well. Furthermore, 11 differential immune cells were associated with these signature genes (most p < 0.01). Finally, qRT-PCR revealed that the abundance of DOHH, P4HA3 and MMP1 were high in tumor cases, indicating the complex mechanism between the high expression of DOHH as a protective factor and the high expression of P4HA3 and MMP1 as the risk factors in the development of GC. Conclusion: An iron metabolism-related signature was constructed and has significant values for foretelling the OS of GC. [ABSTRACT FROM AUTHOR]

Stomach Adenocarcinoma (STAD) is a leading cause of death worldwide. Somatic Copy Number Alterations (SCNAs), which result in Homologous recombination (HR) deficiency in double-strand break repair, are associated with the progression of STAD. However, the landscape of frequent breakpoints of SCNAs (hotspots) and their functional impacts remain poorly understood. In this study, we aimed to explore the frequency and impact of these hotspots in 332 STAD patients and 1,043 cancer cells using data from the Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE). We studied the rates of DSB (Double-Strand Breaks) loci in STAD patients by employing the Non-Homogeneous Poisson Distribution (λ), based on which we identified 145 DSB-hotspots with genes affected. We further verified DNA cytosine deamination as a critical process underlying the burden of DSB in STAD. Finally, we illustrated the clinical impact of the significant biological processes. Our findings highlighted the relationship between DNA cytosine deamination and SCNA in cancer was associated with recurrent Somatic Copy Number Alterations in STAD. [ABSTRACT FROM AUTHOR]

Background: The overall survival (OS) rate of adult patients suffering from acute myeloid leukaemia (AML) remains unsatisfactory at less than 40%. Current risk stratification systems fail to provide accurate guidelines for precise treatment. Novel biomarkers for predicting prognosis are urgently needed. Plexin B2 (PLXNB2), a functional receptor of angiogenin (ANG), has been found to be aberrantly expressed in multitudinous tumours. We detected overexpression of PLXNB2 mRNA in AML via transcriptome microarray analysis. This study aims to explore the potential role of PLXNB2 as a biomarker of prognosis and a prospective target of AML. Methods: qRT‒PCR was conducted to verify the expression of PLXNB2 mRNA in bone marrow mononuclear cells from AML patients. Immunohistochemical and immunofluorescence staining were performed and confirmed increased expression of PLXNB2 protein in AML bone marrow tissues. Data on PLXNB2 expression, prognosis and clinical features were accessed from multiple bioinformatic databases, including The Cancer Genome Atlas (TCGA). Genes coexpressed and correlated with PLXNB2 were identified and analysed in the TCGA AML cohort. Metascape was applied for functional and pathway enrichment analysis of genes related to PLXNB2. Small molecular agents and traditional Chinese medicines potentially targeting genes related to PLXNB2 were screened via the Connectivity Map, TCMSP and HIT databases. Results: PLXNB2 mRNA and protein levels are higher in AML samples than in normal controls. Overexpression of PLXNB2 is associated with worse OS in AML. Patients with high PLXNB2 expression might benefit more from haematopoietic stem cell transplantation (HSCT) (indicated by prolonged OS) than those with only chemotherapy treatment. Differentially expressed genes between the high and low PLXNB2 expression groups were overlapped with PLXNB2-coexpressed genes, and genes that overlapped were enriched in immune functions, endothelial cell regulation and cell interaction gene sets, indicating the potential function of PLXNB2 in AML. A total of 36 hub genes were identified from the differentially expressed genes, and MRC1, IL10, CD163 and CCL22 had significant prognostic value for AML. Analysis of the connectivity map and traditional agents revealed that honokiol, morphines, triptolide and paeoniflorin could be potential treatment regimens. Conclusions: The overexpression of PLXNB2 is an adverse prognostic factor in adult AML patients and could be used as a potential biomarker. PLXNB2 might exert an oncogenic role by modulating immune functions, endothelial cell functions and cell interactions. AML patients with high PLXNB2 expression could benefit more from HSCT. Highlights: PLXNB2 expression could be a potential biomarker of poor prognosis in AML. HSCT could improve the prognosis of patients with high PLXNB2 expression. PLXNB2 might regulate immunity, endothelial cells and cell interactions. [ABSTRACT FROM AUTHOR]

Renal cell carcinoma (RCC) with poor prognosis and high incidence rate is a common malignant disease. Current therapies could bring little benefit for the patients with advanced-stage RCC. PDIA2 is an isomerase responsible for protein folding and its role in cancer including RCC is under investigation. In this study, we found that PDIA2 was expressed much higher in RCC tissues than the control but the methylation level of PDIA2 promoter was lower based on the TCGA data. Patients with higher PDIA2 expression exerted worse survival. In clinical specimen, PDIA2 expression was correlated to patients' clinical factors such as TNM stage (I/II vs III/IV, p = 0.025) and tumor size (≤ 7 cm vs > 7 cm, p = 0.004). Moreover, K-M analysis showed that PDIA2 was associated with patients' survival in RCC. PDIA2 was expressed much higher in cancer cells A498 than 786-O than that in 293 T cells. After PDIA2 was knocked down, cell proliferation, migration and invasion was potently inhibited. But cell apoptotic rate increased reversely. Furthermore, the efficacy of Sunitinib on RCC cells was strengthened after PDIA2 knockdown. In addition, knockdown of PDIA2 gene leaded to downregulation of levels of JNK1/2, phosphorylated JNK1/2, c-JUN, and Stat3. But this inhibition was partially released when JNK1/2 was overexpressed. In consistent, cell proliferation was also partially recovered. In summary, PDIA2 plays important role in progression of RCC and JNK signaling pathway might be regulated by PDIA2. This study suggests PDIA2 as a candidate target for therapy of RCC. [ABSTRACT FROM AUTHOR]

N6-methyladenosine modification and lncRNAs are closely related to the prognosis and immunotherapy response of breast cancer patients. LncRNAs related to m6A-associated genes were predicted based on co-expression analysis of the TCGA database. We established a novel 7-m6A-associated lncRNA signature for predicting patient prognosis and validated it. The model was significantly correlated with survival time and survival status and was an independent predictor of overall survival (OS). Except for the M1 disease group, the model had good predictive value for OS in different subgroups. We constructed a prognostic model based on 7 m6A-associated lncRNAs in breast cancer. This model could serve as an independent prognostic factor with tremendous predictive ability for breast cancer patients. [ABSTRACT FROM AUTHOR]

Objective Use TCGA data to screen the significantly expressed genes in gastric cancer, then analyze the biological functions of characteristic genes. To identify potential targets for the diagnosis, prognosis and therapeutic monitoring of gastric cancer. Methods Download the high-throughput gastric cancer patient tissue mRNA expression data from the TCGA database, and used the edgeR software package to perform gene differences using the Rstudio software according to the |logFC|>1 and p<0.05 standards analysis, then performed enrichment analysis and constructed protein interaction network of the differently expressed genes to find the significant genes. Finally, analyzed the relationship between significant genes and gastric cancer patient prognosis. Results The RNA-seq expression data of 375 cases of gastric cancer and 32 adjacent tissues were obtained from the TCGA database. The differential analysis yielded 4320 differential genes, of which 2718 were highly expressed and 1602 were low expressed. Through enrichment analysis, it is found that the differential genes are mainly related to the neural active ligand-receptor interaction, calcium signaling pathway, Protein digestion and absorption, chemical carcinogenesis, and cytochrome P450 metabolism of foreign compounds. Further survival analysis screened out the five significant genes of ALB, AFP, IGFBP1, APOH, and TF are related to the prognosis of gastric cancer. Conclusion The five significant genes screened by TCGA data may be related to the prognosis of gastric cancer and are expected to become prognostic biomarkers. [ABSTRACT FROM AUTHOR]

Simple Summary: Guanine nucleotide-binding protein-like 3 (GNL3) and proliferation-associated protein 2G4 (PA2G4) are molecules involved during metaphase-to-anaphase transition and growth regulation. GNL3 and PA2G4 have been found to be overexpressed in several human cancers, including prostate cancer. Clinical data suggest that GNL3 and PA2G4 could be developed as prognostic biomarkers of clinical significance in prostate cancer. This review article mainly highlights the function of GNL3 and PA2G4 and focuses on the opportunities for their development as prognostic biomarkers in prostate cancer. Prostate cancer is a multifocal and heterogeneous disease common in males and remains the fifth leading cause of cancer-related deaths worldwide. The prognosis of prostate cancer is variable and based on the degree of cancer and its stage at the time of diagnosis. Existing biomarkers for the prognosis of prostate cancer are unreliable and lacks specificity and sensitivity in guiding clinical decision. There is need to search for novel biomarkers having prognostic and predictive capabilities in guiding clinical outcomes. Using a bioinformatics approach, we predicted GNL3 and PA2G4 as biomarkers of prognostic significance in prostate cancer. A progressive increase in the expression of GNL3 and PA2G4 was observed during cancer progression having significant association with poor survival in prostate cancer patients. The Receiver Operating Characteristics of both genes showed improved area under the curve against sensitivity versus specificity in the pooled samples from three different GSE datasets. Overall, our analysis predicted GNL3 and PA2G4 as prognostic biomarkers of clinical significance in prostate cancer. [ABSTRACT FROM AUTHOR]

Background: Cutaneous melanoma (CM) is an aggressive type of skin cancer. Even after standard treatment, the recurrence and malignant progression of CM were almost inevitable. The overall survival (OS) of patients with CM varied widely, making it critical for prognostic prediction. Based on the correlation between CCR6 and melanoma incidence, we aimed to investigate the prognostic role of CCR6 and its relationship with immune infiltration in CM. Methods: We obtained RNA sequencing data from The Cancer Genome Atlas (TCGA) to analyze the CM expression. Functional enrichment analyses, immune infiltration analyses, immune checkpoint analyses, and clinicopathology analyses were performed. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. A nomogram model had been developed. Kaplan-Meier survival analysis and log-rank test were used to estimate the relationship between OS and CCR6 expression. Results: CCR6 was significantly upregulated in CM. Functional enrichment analyses revealed that CCR6 was correlated with immune response. Most immune cells and immune checkpoints were positively correlated with CCR6 expression. Kaplan-Meier analyses showed that high CCR6 expression was associated with a good outcome in CM and its subtypes. Cox regression showed that CCR6 was an independent prognostic factor in patients with CM (HR = 0.550, 95% CI = 0.332-0.912, p<0.05). Conclusions: CCR6 is considered to be a new prognostic biomarker for patients with CM, and our study provides a potential therapeutic target for CM treatment. [ABSTRACT FROM AUTHOR]

Introduction: GEO- and TCGA-based data analysis suggested the differential expression of miR-29c in pancreatic cancer. However, limited data are available on the downstream mechanistic actions of miR-29c, which may fuel the in vitro and in vivo studies of pancreatic cancer. Methods: The downstream target gene of miR-29c and the downstream ERK/MAPK pathway involved in pancreatic cancer were predicted by bioinformatics tools. Next, the expression of miR-29c and MAPK1 was determined in pancreatic cancer tissues and cells. After ectopic expression and depletion experiments in pancreatic cancer cells, oncogenic phenotypes of pancreatic cancer cells were tested by MTS assay, Transwell assay, and flow cytometry. Effects of miR-29c/MAPK1 on tumorigenic ability in vivo were evaluated in pancreatic cancer xenografts in nude mice. Results: Through differential analysis, five pancreatic cancer-related miRNAs (hsa-miR-29c, hsa-miR-107, hsa-miR-324-3p, hsa-miR-375, and hsa-miR-210) were screened out, among which miR-29c was selected as the key miRNA related to prognosis of pancreatic cancer patients. miR-29c could target and inhibit MAPK1 to suppress the activation of ERK/MAPK pathway. miR-29c was downregulated in pancreatic cancer, and its high expression was related to the good prognosis of pancreatic cancer patients. Both in vitro and in vivo experiments demonstrated that restoration of miR-29c inhibited oncogenic phenotypes of pancreatic cancer cells, as well as repressed tumorigenic ability of pancreatic cancer cells in nude mice. Conclusions: Taken together, we unveil a novel miR-29c/MAPK1/ERK/MAPK axis that suppresses pancreatic cancer both in vitro and in vivo. [ABSTRACT FROM AUTHOR]

BackgroundCutaneous melanoma (CM) is an aggressive type of skin cancer. Even after standard treatment, the recurrence and malignant progression of CM were almost inevitable. The overall survival (OS) of patients with CM varied widely, making it critical for prognostic prediction. Based on the correlation between CCR6 and melanoma incidence, we aimed to investigate the prognostic role of CCR6 and its relationship with immune infiltration in CM.MethodsWe obtained RNA sequencing data from The Cancer Genome Atlas (TCGA) to analyze the CM expression. Functional enrichment analyses, immune infiltration analyses, immune checkpoint analyses, and clinicopathology analyses were performed. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. A nomogram model had been developed. Kaplan–Meier survival analysis and log-rank test were used to estimate the relationship between OS and CCR6 expression.ResultsCCR6 was significantly upregulated in CM. Functional enrichment analyses revealed that CCR6 was correlated with immune response. Most immune cells and immune checkpoints were positively correlated with CCR6 expression. Kaplan–Meier analyses showed that high CCR6 expression was associated with a good outcome in CM and its subtypes. Cox regression showed that CCR6 was an independent prognostic factor in patients with CM (HR = 0.550, 95% CI = 0.332–0.912, p

Introduction: Hepatocellular carcinoma (HCC) patients may benefit from chemotherapy, but drug resistance is an important obstacle to favorable prognoses. Overcoming drug resistance is an urgent problem to be solved.Methods: Differential expression analysis was used to identify long non-coding RNAs (LncRNAs) that differed in chemotherapy-sensitive and chemotherapy-resistant patients. Machine learning algorithms including random forest (RF), lasso regression (LR), and support vector machines (SVMs) were used to identify important chemotherapy-related LncRNAs. A back propagation (BP) network was then used to validate the predictive capacity of important LncRNAs. The molecular functions of hub LncRNAs were investigated via qRT-PCR and cell proliferation assay. Molecular-docking technique was used to explore candidate drug of targets of hub LncRNA in the model.Results: A total of 125 differentially expressed LncRNAs between sensitive and resistant patients. Seventeen important LncRNAs were identified via RF, and seven factors were identified via LR. With respect to SVM, the top 15 LncRNAs of AvgRank were selected. Five merge chemotherapy-related LncRNAs were used to predict chemotherapy resistance with high accuracy. CAHM was a hub LncRNA of model and expression high in sorafenib resistance cell lines. In addition, the results of CCK8 showed that the sensitivity of HepG2-sorafenib cells to sorafenib was significantly lower than that of HepG2; and the sensitivity of HepG2-sorafenib cells transfected with sh-CAHM was significantly higher than that of Sorafenib. In the non-transfection group, the results of clone formation experiments showed that the number of clones formed by HepG2-sorafenib cells treated with sorafenib was significantly more than that of HepG2; after HepG2-sorafenib cells were transfected with sh-CAHM, the number of clones formed by Sorafenib treatment was significantly higher than that of HepG2 cells. The number was significantly less than that of HepG2-s + sh-NC group. Molecular Docking results indicate that Moschus was candidate drug for target protein of CAHM.Conclusion: Five chemotherapy-related LncRNAs could predict drug resistance in HCC with high accuracy, and the hub LncRNA CAHM has potential as a new biomarker for HCC chemotherapy resistance.

Background and aimsHepatocellular carcinoma (HCC) is a common cause of cancer-related death in humans. Increasing evidence indicates that an imbalance in N6-methyladenosine (m6A) methylation is linked to the occurrence and development of cancer. We then developed a prognostic model as an independent risk factor with which predict the prognosis of HCC.MethodsWe obtained the gene expression and clinical data of HCC patients from the TCGA databases. The prognostic value of m6A methylation-related genes in patients who had HCC were subjected to comprehensive bioinformatics analysis. We use RiskScore=∑i=1nCoefi×Xi to construct the risk scoring formula. We collected pathological specimens from 68 patients who had HCC, and conducted immunohistochemical staining experiments on the specimens.ResultsThere was a significant correlation between candidate m6A methylation-related genes (YTHDF2, METTL14 and ZC3H13) overall survival of HCC patients. Among the 68 HCC patient specimens that underwent immunohistochemical staining, all cancer tissues were positive for METTL14, YTHDF2, and ZC3H13 staining in contrast to the adjacent tissues. We conducted a Kaplan-Meier survival analysis. The results showed that patients who had low METTL14 expression had a longer survival time than those of patients who had high METTL14 expression. Also, patients with low YTHDF2 expression had a longer survival time than patients with high YTHDF2 expression. Finally, patients with high ZC3H13 expression lived longer than those with low ZC3H13 expression. This result is consistent with the bioinformatics analysis conclusion above.ConclusionsGenerally, the prognostic model that was based on m6A methylation-related genes in this study can effectively predict the prognosis of HCC patients.

EBV and Helicobacter pylori (H. pylori) cause highly prevalent persistent infections as early as in childhood. Both pathogens are associated with gastric carcinogenesis. H. pylori interferes with iron metabolism, enhancing the synthesis of acute-phase proteins hepcidin, C-reactive protein (CRP), and α-1 glycoprotein (AGP), but we do not know whether EBV does the same. In this study, we correlated the EBV antibody levels and the serum levels of hepcidin, CRP, and AGP in 145 children from boarding schools in Mexico City. We found that children IgG positive to EBV antigens (VCA, EBNA1, and EA) presented hepcidin, AGP, and CRP levels higher than uninfected children. Hepcidin and AGP remained high in children solely infected with EBV, while CRP was only significantly high in coinfected children. We observed positive correlations between hepcidin and EBV IgG antibodies (p < 0.5). Using the TCGA gastric cancer database, we also observed an association between EBV and hepcidin upregulation. The TCGA database also allowed us to analyze the two important pathways controlling hepcidin expression, BMP–SMAD and IL-1β/IL-6. We observed only the IL-1β/IL-6-dependent inflammatory pathway being significantly associated with EBV infection. We showed here for the first time an association between EBV and enhanced levels of hepcidin. Further studies should consider EBV when evaluating iron metabolism and anemia, and whether in the long run this is an important mechanism of undernourishment and EBV gastric carcinogenesis. [ABSTRACT FROM AUTHOR]

Tumor cells have significant heterogeneity in metabolism and are closely related to prognosis, gene mutation, and subtype. However, this association has not been demonstrated in reports of gastrointestinal tumors. In this study, we constructed four metabolic subtypes and identified four gene signatures using the expression data and clinical information of 252 metabolism-related genes from TCGA and NCBI databases for gastric adenocarcinoma (STAD) and colorectal cancer (COAD and READ). MC1 had the worst prognosis compared to other classifications. GSig1 was mainly related to drug metabolism and was the highest in MC1 with the worst prognosis, while the other subtypes were mainly related to glucose metabolism pathways. This difference also existed in other different malignant tumors. In addition, metabolic typing was associated with chemotherapeutic drug response and tumor heterogeneity, which indicated that monitoring metabolic typing could contribute to drug efficacy and gene-targeted therapy. In conclusion, we identified differences among subtypes in clinical characteristics such as prognosis and revealed the potential function of metabolic subtype in response to chemotherapeutic agents and oncogene mutations. This work highlighted the potential clinical meaning of metabolic subtype and characteristics in drug therapy and prognosis assessment of malignant tumors. [ABSTRACT FROM AUTHOR]

In this study, we intend to identify key immune-related genes (IRGs) in gliomas using the TCGA and GTEx databases. Following collection of the RNA-seq data of lower-grade glioma (LGG) and glioblastoma (GBM) patients from the TCGA and GTEx databases, the differentially expressed IRGs (DE-IRGs) were screened. The ESTIMATE algorithm was utilized to evaluate StromalScore and ImmuneScore of LGG and GBM samples and a multifactorial Cox risk model was constructed to identify the related risk genes. The core IRGs of LGG and GBM were screened through a PPI network, followed by exploration of their correlation with glioma prognosis. The relationship between IRGs and immune cells in LGG and GBM was detected. In vitro assays were performed to detect the effect of CXCL9 on glioma cell development. We screened 403 and 492 DE-IRGs in LGG and GBM. StromalScore and ImmuneScore were related to overall survival in LGG, but not in GBM. CXCL9 was identified as a core gene in LGG and GBM and shared association with the prognosis of gliomas. Furthermore, a correlation was found between CXCL9 and immune infiltration of LGG and GBM. Glioma cell proliferation and invasion could be inhibited by silencing of CXCL9. Overall, CXCL9 is correlated to the prognosis of glioma patients and may accelerate glioma development via immune regulation. [ABSTRACT FROM AUTHOR]

Abstract Background Lung adenocarcinoma accounts for approximately 40% of all primary lung cancers; however, the mortality rates remain high. Successfully predicting progression and overall (OS) time will provide clinicians with more options to manage this disease. Methods We analyzed RNA sequencing data from 510 cases of lung adenocarcinoma from The Cancer Genome Atlas database using CIBERSORT, ImmuCellAI, and ESTIMATE algorithms. Through these data we constructed 6 immune subtypes and then compared the difference of OS, immune infiltration level and gene expression between these immune subtypes. Also, all the subtypes and immune cells infiltration level were used to evaluate the relationship with prognosis and we introduced lasso-cox method to constructe an immune-related prognosis model. Finally we validated this model in another independent cohort. Results The C3 immune subtype of lung adenocarcinoma exhibited longer survival, whereas the C1 subtype was associated with a higher mutation rate of MUC17 and FLG genes compared with other subtypes. A multifactorial correlation analysis revealed that immune cell infiltration was closely associated with overall survival. Using data from 510 cases, we constructed a nomogram prediction model composed of clinicopathologic factors and immune signatures. This model produced a C-index of 0.73 and achieved a C-index of 0.844 using a validation set. Conclusions Through this study we constructed an immune related prognosis model to instruct lung adenocarcinoma’s OS and validated its value in another independent cohost. These results will be useful in guiding treatment for lung adenocarcinoma based on tumor immune profiles.

Breast cancer (BC) is a prevalent cancer of the female reproductive system and a major contributor to cancer-related mortality. The activation of NLRP3, a key inflammasome, has been extensively associated with tumor-related molecular and cellular processes; however, the regulatory mechanisms and specific role of NLRP3 in breast cancer remain incompletely elucidated. This study aimed to evaluate the molecular mechanisms of NLRP3-related genes in BC. Utilizing bioinformatics methods, the present research analyzed the TCGA-BRCA dataset, which included four groups of transcriptome sequencing data as follows, normal (WT), NLRP3 knockout (KO), non-knockout-BRCA (BC-WT), and NLRP3-knockout-BRCA (BC-KO). Results indicated that NLRP3 was significantly down-regulated in TCGA-BRCA. Key module genes were mainly enriched in leukocyte cell-cell adhesion and cytokine-cytokine receptor interaction. Moreover, correlation analysis showed that NLRP3 was positively associated with cancer-associated fibroblasts and negatively associated with CD4+ Th1 T-cells. In addition, the DEGs1 and DEGs2 overlapping indicated 505 feature genes, with Chac1 (negative) and Ugt8a (positive) had the strongest correlation with differential immune cells (class-switched memory B cells). Pathway intersection revealed 13 co-KEGG pathways. The BC-KO group indicated markedly reduced levels of four genes (Ccl19, Ccl20, Ccl21a, and H2-Oa) and increased levels of two genes (Il2ra and H2-Ob). This study delved into the role of NLRP3 in BC, exploring its regulatory mechanisms and the impact gene knockout. Bioinformatics approaches identified NLRP3-associated genes, their enriched pathways, and interactions within the tumor microenvironment (TME), providing novel insights into NLRP3 function, TME dynamics, and potential targets for BC prevention and treatment. • NLRP3 plays a significant role in breast cancer (BC). • TCGA-BRCA and four transcriptome sequencing data revealed the role of NLRP3 in BC. • NLRP3 may affect the development or function of Class-switched B-memory cells. • Six NLRP3-related genes were identified participating in the progression of BC. [ABSTRACT FROM AUTHOR]

Abstract Background Microvascular invasion (MVI) adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma (HCC). There is no study addressing genetic changes in HCC patients with MVI. We first screened differentially expressed genes (DEGs) in patients with and without MVI based on TCGA data, established a prediction model and explored the prognostic value of DEGs for HCC patients with MVI. Methods In this paper, gene expression and clinical data of liver cancer patients were downloaded from the TCGA database. The DEG analysis was conducted using DESeq2. Using the least absolute shrinkage and selection operator, MVI-status-related genes were identified. A Kaplan-Meier survival analysis was performed using these genes. Finally, we validated two genes, HOXD9 and HOXD10, using two sets of HCC tissue microarrays from 260 patients. Results Twenty-three MVI-status-related key genes were identified. Based on the key genes, we built a classification model using random forest and time-dependent receiver operating characteristic (ROC), which reached 0.814. Then, we performed a survival analysis and found ten genes had a significant difference in survival time. Simultaneously, using two sets of 260 patients’ HCC tissue microarrays, we validated two key genes, HOXD9 and HOXD10. Our study indicated that HOXD9 and HOXD10 were overexpressed in HCC patients with MVI compared with patients without MVI, and patients with MVI with HOXD9 and 10 overexpression had a poorer prognosis than patients with MVI with low expression of HOXD9 and 10. Conclusion We established an accurate TCGA database-based genomics prediction model for preoperative MVI risk and studied the prognostic value of DEGs for HCC patients with MVI. These DEGs that are related to MVI warrant further study regarding the occurrence and development of MVI.

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies and exhibits a high rate of relapse and unfavorable outcomes. Ferroptosis, a relatively recently described type of cell death, has been reported to be involved in cancer development. However, the prognostic value of ferroptosis-related genes (FRGs) in AML remains unclear. In this study, we found 54 differentially expressed ferroptosis-related genes (DEFRGs) between AML and normal marrow tissues. 18 of 54 DEFRGs were correlated with overall survival (OS) (P<0.05). Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we selected 10 DEFRGs that were associated with OS to build a prognostic signature. Data from AML patients from the International Cancer Genome Consortium (ICGC) cohort as well as the First Affiliated Hospital of Wenzhou Medical University (FAHWMU) cohort were used for validation. Notably, the prognostic survival analyses of this signature passed with a significant margin, and the riskscore was identified as an independent prognostic marker using Cox regression analyses. Then we used a machine learning method (SHAP) to judge the importance of each feature in this 10- gene signature. Riskscore was shown to have the highest correlation with this 10-gene signature compared with each gene in this signature. Further studies showed that AML was significantly associated with immune cell infiltration. In addition, drug-sensitive analysis showed that 8 drugs may be beneficial for treatment of AML. Finally, the expressions of 10 genes in this signature were verified by real-time quantitative polymerase chain reaction. In conclusion, our study establishes a novel 10-gene prognostic risk signature based on ferroptosis-related genes for AML patients and FRGs may be novel therapeutic targets for AML. [ABSTRACT FROM AUTHOR]

Recent studies have increasingly focused on PIK3CA mutations in esophageal squamous cell carcinoma (ESCC); however, the clinicopathological significance of these mutations within the tumor microenvironment remains underexplored. This study aimed to evaluate and compare the clinicopathological significance of mutated PIK3CA in ESCC using in silico analyses of the ESCC dataset from the TCGA database. We assessed prognosis, differential expression, correlation with immune cell infiltration and immune checkpoint expression, heterogeneity, and drug sensitivity in comparison with wild-type PIK3CA. Our findings revealed that PIK3CA mutation is associated with increased tumor mutation burden and significantly correlated with the infiltration of CD4 naive and effector memory CD4 T cells. Additionally, ESCC cells harboring PIK3CA mutations exhibited reduced sensitivity to p38/JNK MAPK inhibitors compared to those with wild-type PIK3CA. Collectively, our in silico analysis suggests that mutational PIK3CA plays a role in resistance to p38 and JNK MAPK inhibitors in ESCC., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shutao Zheng reports was provided by State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, PR China. Shutao Zheng reports a relationship with State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, PR China that includes: funding grants., (Copyright © 2024 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.)

Abstract Background Esophageal adenocarcinoma (EAC) is an aggressive malignancy with a poor prognosis. The immune-related genes (IRGs) are crucial to immunocytes tumor infiltration. This study aimed to construct a IRG-related prediction signature in EAC. Methods The related data of EAC patients and IRGs were obtained from the TCGA and ImmPort database, respectively. The cox regression analysis constructed the prediction signature and explored the transcription factors regulatory network through the Cistrome database. TIMER database and CIBERSORT analytical tool were utilized to explore the immunocytes infiltration analysis. Results The prediction signature with 12 IRGs (ADRM1, CXCL1, SEMG1, CCL26, CCL24, AREG, IL23A, UCN2, FGFR4, IL17RB, TNFRSF11A, and TNFRSF21) was constructed. Overall survival (OS) curves indicate that the survival rate of the high-risk group is significantly shorter than the low-risk group (P = 7.26e−07), and the AUC of 1-, 3- and 5- year survival prediction rates is 0.871, 0.924, and 0.961, respectively. Compared with traditional features, the ROC curve of the risk score in the EAC patients (0.967) is significant than T (0.57), N (0.738), M (0.568), and Stage (0.768). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are accurate by the combined analysis of the risk score, Sex, M stage, and Stage (The AUC of 1- and 3-years are 0.911, and 0.853). Conclusion The 12 prognosis-related IRGs might be promising therapeutic targets for EAC.

Sijia Ma,1 Mingming Zhao,1 Jiao Fan,1 Meiying Chang,1 Zhiyu Pan,1 Ziyan Zhang,1 Shunxuan Xue,1 Qi Li,2 Yu Zhang1 1Department of Nephrology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 2Department of Clinical Laboratory, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of ChinaCorrespondence: Yu Zhang Email zhangyu8225@126.comIntroduction: Renal clear cell carcinoma (ccRCC) is a common tumor of the urinary system, most of which are primary malignant tumors with high metastatic rate and remaining incurable. Ferroptosis is a newly discovered form of iron-dependent programmed cell necrosis in recent years, which is inextricably linked to the occurrence and development of tumors progression. Due to the complexity of the interaction between genes in ccRCC, the research on the pathogenesis of ccRCC is still not remarkably accurate. Therefore, whether ferroptosis-related genes (FRGs) can play a role in predicting prognosis in ccRCC needs to be discussed.Methods: We entered the Cancer Genome Mapping Project (TCGA) database and downloaded the relevant genes and clinical research data of ccRCC patients. Lasso Cox regression was used to construct a multi-gene prognostic model in the TCGA cohort. R language software was used for drawing pictures related to our study.Results: Most of the genes involved in ferroptosis (86.2%) existing differences between the tumor and normal tissues in the TCGA public gene database. In terms of univariate Cox regression analysis, 20 differentially expressed genes (DEGs) were associated with prognosis and survival (P< 0.05). A prognostic model of 12 FRGs was constructed, and patients were segmented into two different groups depending on how risky they are. Considering overall survival, the high-risk group is dramatically lower than the low-risk group (P< 0.001). In multivariate Cox regression analysis, risk scores and stage turned out be an independent prognostic factor (P< 0.001). GO and KEGG analysis and ssGSEA analysis of DEGs revealed that these genes were related to immune-related pathways (P< 0.05).Conclusion: This study established and identified the changes in FRGs expression and prognostic factors of ccRCC, which can be helpful for prognosis evaluation and clinical treatment of this disease.Keywords: renal clear cell carcinoma, ferroptosis, TCGA database, the prognosis

Background: Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. There is still a lack of effective biomarkers to predict its prognosis. Exosomes participate in intercellular communication and play an important role in the development and progression of cancers.Methods: In this study, two machine learning methods (univariate feature selection and random forest (RF) algorithm) were used to select 13 exosome-related genes (ERGs) and construct an ERG signature. Based on the ERG signature score and ERG signature-related pathway score, a novel RF signature was generated. The expression of BSG and SFN, members of 13 ERGs, was examined using real-time quantitative polymerase chain reaction and immunohistochemistry. Finally, the effects of the inhibition of BSG and SFN on cell proliferation were examined using the cell counting kit-8 (CCK-8) assays.Results: The ERG signature had a good predictive performance, and the ERG score was determined as an independent predictor of HCC overall survival. Our RF signature showed an excellent prognostic ability with the area under the curve (AUC) of 0.845 at 1 year, 0.811 at 2 years, and 0.801 at 3 years in TCGA, which was better than the ERG signature. Notably, the RF signature had a good performance in the prediction of HCC prognosis in patients with the high exosome score and high NK score. Enhanced BSG and SFN levels were found in HCC tissues compared with adjacent normal tissues. The inhibition of BSG and SFN suppressed cell proliferation in Huh7 cells.Conclusion: The RF signature can accurately predict prognosis of HCC patients and has potential clinical value.

Abstract Background Muscular invasive bladder cancer (MIBC) is a common malignant tumor in the world. Because of their heterogeneity in prognosis and response to treatment, biomarkers that can predict survival or help make treatment decisions in patients with MIBC are essential for individualized treatment. Aim We aimed to integrate bioinformatics research methods to identify a set of effective biomarkers capable of predicting, diagnosing, and treating MIBC. To provide a new theoretical basis for the diagnosis and treatment of bladder cancer. Methods and results Gene expression profiles and clinical data of MIBC were obtained by downloading from the Cancer Genome Atlas database. A dataset of 129 MIBC cases and controls was included. 2084 up‐regulated genes and 2961 down‐regulated genes were identified by differentially expressed gene (DEG) analysis. Then, gene ontology analysis was performed to explore the biological functions of DEGs, respectively. The up‐regulated DEGs are mainly enriched in epidermal cell differentiation, mitotic nuclear division, and so forth. They are also involved in the cell cycle, p53 signaling pathway, PPAR signaling pathway, and so forth. The weighted gene co‐expression network analysis yielded five modules related to pathological stages and grading, of which blue and turquoise were the most relevant modules for MIBC. Next, Using Kaplan–Meier survival analysis to identify further hub genes, the screening criteria at p ≤ .05, we found CNKSR1, HIP1R, CFL2, TPM1, CSRP1, SYNM, POPDC2, PJA2, and RBBP8NL genes associated with the progression and prognosis of MIBC patients. Finally, immunohistochemistry experiments further confirmed that CNKSR1 plays a vital role in the tumorigenic context of MIBC. Conclusion The research suggests that CNKSR1, POPDC2, and PJA2 may be novel biomarkers as therapeutic targets for MIBC, especially we used immunohistochemical further to validate CNKSR1 as a therapeutic target for MIBC which may help to improve the prognosis for MIBC.

Background: As the crosstalk between metabolism and antitumor immunity continues to be unraveled, we aim to develop a prognostic gene signature that integrates lipid metabolism and immune features for patients with lung adenocarcinoma (LUAD). Methods: First, differentially expressed genes (DEGs) related to lipid metabolism in LUAD were detected, and subgroups of LUAD patients were identified via the unsupervised clustering method. Based on lipid metabolism and immune-related DEGs, variables were determined by the univariate Cox and LASSO regression, and a prognostic signature was established. The prognostic value of the signature was evaluated by the Kaplan-Meier method, time-dependent ROC, and univariate and multivariate analyses. Five independent GEO datasets were employed for external validation. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to investigate the underlying mechanisms. The sensitivity to common chemotherapeutic drugs was estimated based on the GDSC database. Finally, we selected PSMC1 involved in the signature, which has not been reported in LUAD, for further experimental validation. Results: LUAD patients with different lipid metabolism patterns exhibited significant differences in overall survival and immune infiltration levels. The prognostic signature incorporated 10 genes and stratified patients into highand low-risk groups by median value splitting. The areas under the ROC curves were 0.69 (1-year), 0.72 (3-year), 0.74 (5-year), and 0.74 (10-year). The Kaplan-Meier survival analysis revealed a significantly poorer overall survival in the high-risk group in the TCGA cohort (p < 0.001). In addition, both univariate and multivariate Cox regression analyses indicated that the prognostic model was the individual factor affecting the overall survival of LUAD patients. Through GSEA and GSVA, we found that tumor progression and inflammatory and immune-related pathways were enriched in the high-risk group. Additionally, patients with high-risk scores showed higher sensitivity to chemotherapeutic drugs. The in vitro experiments further confirmed that PSMC1 could promote the proliferation and migration of LUAD cells. Conclusions: We developed and validated a novel signature incorporating both lipid metabolism and immune-related genes for all-stage LUAD patients. This signature can be applied not only for survival prediction but also for guiding personalized chemotherapy and immunotherapy regimens. [ABSTRACT FROM AUTHOR]

Background: The role of cancer stem cells in esophageal squamous cell carcinoma (ESCC) remains unclear. Methods: The mRNA stemness index (mRNAsi) of 179 ESCC patients (GSE53625) was calculated using a machine learning algorithm based on their mRNA expression. Stemness-related genes were identified by weighted correlation network analysis (WGCNA) and LASSO regression, whose associations with mutation status, immune cell infiltrations, and potential compounds were also analyzed. The role of these genes in proliferation and their expressions was assessed in ESCC cell lines and 112 samples from our center. Results: The ESCC samples had significantly higher mRNAsi than the normal tissues. Patients with high mRNAsi exhibited higher worse OS. Seven stemness-related genes were identified by WGCNA and LASSO regression, based on which a risk-predicted score model was constructed. Among them, CST1, CILP, PITX2, F2RL2, and RIOX1 were favorable for OS, which were adverse for DPP4 and ZFHX4 in the GSE53625 dataset. However, RIOX1 was unfavorable for OS in patients from our center. In vitro assays showed that CST1, CILP, PITX2, F2RL2, and RIOX1 were pro-proliferated, which were opposite for DDP4 and ZFHX4. In addition, SMARCA4, NOTCH3, DNAH5, and KALRN were more mutated in the low-score group. The low-score group had significantly more memory B cells, monocytes, activated NK cells, and Tregs and less macrophages M2, resting mast cells, and resting dendritic cells. Conclusions: Seven stemness-related genes are significantly related to the prognosis, gene mutations, and immune cell infiltration of ESCC. Some potential anticancer compounds may be favorable for OS. [ABSTRACT FROM AUTHOR]

Objective: Calcium-activated chloride channel 2 (CLCA2) is closely related to the invasion, metastasis, and prognosis of some common malignant tumors. The present study aimed to evaluate the role of CLCA2 in cervical squamous cell carcinoma (CESC) using bioinformatics analysis. Materials and methods: The mRNA sequencing data and the corresponding clinical data were obtained from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database respectively. Then univariate analysis of variance was used to analyze the differential mRNA expression of CLCA2 between normal, cervical Intraepithelial neoplasia (CIN), and CESC tissues and clinicopathological characteristics. The Gene Expression Profiling Interactive Analysis (GEPIA) was used to assess the association between CLCA2 and Disease-Free Survival (DFS), overall survival (OS). The Gene Set Enrichment Analysis (GSEA) was used to explore the associated signaling pathways. The Tumor Immune Estimation Resource (TIMER) was used to predict the potential biological roles of CLCA2 in tumor-immune of CESC. Results: CLCA2 expression was significantly decreased in CESC tissues compared with normal and CIN tissues (P 0.05). The survival analysis indicated that for DFS, CESC with high CLCA2 expression was associated with better prognoses compared with those with low expression levels (P

Regulation of intracellular concentration of calcium levels is crucial for cell signaling, homeostasis, and in the pathology of diseases including cancer. Agonist-induced entry of calcium ions into the non-excitable cells is mediated by store-operated calcium channels (SOCs). This pathway is activated by the release of calcium ions from the endoplasmic reticulum and further regulated by the calcium uptake through mitochondria leading to calcium-dependent inactivation of calcium-release activated calcium channels (CARC). SOCs including stromal interaction molecules (STIM) and ORAI proteins have been implicated in tumor growth, progression, and metastasis. In the present study, we analyzed the mRNA and protein expression of genes mediating SOCs—STIM1, STIM2, ORAI1, ORAI2, ORAI3, TRPC1, TRPC3, TRPC4, TRPC5, TRPC6, TRPC7, TRPV1, TRPV2, TRPM1, and TRPM7 in head and neck squamous cell cancer (HNSC) patients using TCGA and CPTAC analysis. Further, our in silico analysis showed a significant correlation between the expression of SOCs and genes involved in the mitochondrial dynamics (MDGs) both at mRNA and protein levels. Protein-protein docking results showed lower binding energy for SOCs with MDGs. Subsequently, we validated these results using gene expression and single-cell RNA sequencing datasets retrieved from Gene Expression Omnibus (GEO). Single-cell gene expression analysis of HNSC tumor tissues revealed that SOCs expression is remarkably associated with the MDGs expression in both cancer and fibroblast cells. [ABSTRACT FROM AUTHOR]

The objective was to construct a prognostic risk model of stomach adenocarcinoma (STAD) based on The Cancer Proteome Atlas (TCPA) to search for prognostic biomarkers. Protein data and clinical data on STAD were downloaded from the TCGA database, and differential expressions of proteins between carcinoma and para-carcinoma tissues were screened using the R package. The STAD data were randomly divided into a training set and a testing set in a 1:1 ratio. Subsequently, a linear prognostic risk model of proteins was constructed using Cox regression analysis based on training set data. Based on the scores of the prognostic model, sampled patients were categorized into two groups: a high-risk group and a low-risk group. Using the testing set and the full sample, ROC curves and K-M survival analysis were conducted to measure the predictive power of the prognostic model. The target genes of proteins in the prognostic model were predicted and their biological functions were analyzed. A total of 34 differentially expressed proteins were screened (19 up-regulated, 15 down-regulated). Based on 176 cases in the training set, a prognostic model consisting of three proteins (COLLAGEN VI, CD20, TIGAR) was constructed, with moderate prediction accuracy (AUC=0.719). As shown by the Kaplan-Meier and survival status charts, the overall survival rate of the low-risk group was better than that of the high-risk group. Moreover, a total of 48 target proteins were identified to have predictive power, and the level of proteins in hsa05200 (Pathways in cancer) was the highest. According to the results of the Univariate and multivariate COX analysis, tri-protein was identified as an independent prognostic factor. Therefore, the tri-protein prognostic risk model can be used to predict the likelihood of STAD and guide clinical treatment. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma accounts for approximately 40% of all primary lung cancers; however, the mortality rates remain high. Successfully predicting progression and overall (OS) time will provide clinicians with more options to manage this disease.Methods: We analyzed RNA sequencing data from 510 cases of lung adenocarcinoma from The Cancer Genome Atlas database using CIBERSORT, ImmuCellAI, and ESTIMATE algorithms. Through these data we constructed 6 immune subtypes and then compared the difference of OS, immune infiltration level and gene expression between these immune subtypes. Also, all the subtypes and immune cells infiltration level were used to evaluate the relationship with prognosis and we introduced lasso-cox method to constructe an immune-related prognosis model. Finally we validated this model in another independent cohort.Results: The C3 immune subtype of lung adenocarcinoma exhibited longer survival, whereas the C1 subtype was associated with a higher mutation rate of MUC17 and FLG genes compared with other subtypes. A multifactorial correlation analysis revealed that immune cell infiltration was closely associated with overall survival. Using data from 510 cases, we constructed a nomogram prediction model composed of clinicopathologic factors and immune signatures. This model produced a C-index of 0.73 and achieved a C-index of 0.844 using a validation set.Conclusions: Through this study we constructed an immune related prognosis model to instruct lung adenocarcinoma's OS and validated its value in another independent cohost. These results will be useful in guiding treatment for lung adenocarcinoma based on tumor immune profiles. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a common malignant tumor of the digestive system. Recent studies revealed that high gamma-glutamyl-transferase 5 (GGT5) expression was associated with a poor prognosis of gastric cancer patients. In the present study, we aimed to confirm the expression and prognostic value of GGT5 and its correlation with immune cell infiltration in gastric cancer. First, we compared the differential expression of GGT5 between gastric cancer tissues and normal gastric mucosa in the cancer genome atlas (TCGA) and GEO NCBI databases using the most widely available data. Then, the Kaplan-Meier method, Cox regression, and univariate logistic regression were applied to explore the relationships between GGT5 and clinical characteristics. We also investigated the correlation of GGT5 with immune cell infiltration, immune-related genes, and immune checkpoint genes. Finally, we estimated enrichment of gene ontologies categories and relevant signaling pathways using GO annotations, KEGG, and GSEA pathway data. The results showed that GGT5 was upregulated in gastric cancer tissues compared to normal tissues. High GGT5 expression was significantly associated with T stage, histological type, and histologic grade (p < 0.05). Moreover, gastric cancer patients with high GGT5 expression showed worse 10-years overall survival (p = 0.008) and progression-free intervals (p = 0.006) than those with low GGT5 expression. Multivariate analysis suggested that high expression of GGT5 was an independent risk factor related to the worse overall survival of gastric cancer patients. A nomogram model for predicting the overall survival of GC was constructed and computationally validated. GGT5 expression was positively correlated with the infiltration of natural killer cells, macrophages, and dendritic cells but negatively correlated with Th17 infiltration. Additionally, we found that GGT5 was positively co-expressed with immune-related genes and immune checkpoint genes. Functional analysis revealed that differentially expressed genes relative to GGT5 were mainly involved in the biological processes of immune and inflammatory responses. In conclusion, GGT5 may serve as a promising prognostic biomarker and a potential immunological therapeutic target for GC, since it is associated with immune cell infiltration in the tumor microenvironment. [ABSTRACT FROM AUTHOR]

Abstract Background Hepatocellular carcinoma (HCC) is a common malignant primary cancer with high mortality. Previous studies have demonstrated that RNA binding proteins (RBPs) are involved in the biological processes of cancers, including hepatocellular cancer. Methods In this study, we aimed to identify the clinical value of RNA-binding proteins for hepatocellular carcinoma. We obtained gene expression and clinical data of hepatocellular carcinoma patients from the TCGA and ICGC databases. The prognostic value of RBP-related genes in patients with hepatocellular carcinoma and their function were studied by comprehensive bioinformatics analyses. The gene signature of SMG5, EZH2, FBLL1, ZNF239, and IGF2BP3 was generated by univariate and multivariate Cox regression and LASSO regression analyses. We built and verified a prognostic nomogram based on RBP-related genes. The gene signature was validated by the ICGC database. The expression of RBP-related genes was validated by the Oncomine database, the Human Protein Atlas and Kaplan–Meier plotter. Result Most RBP-related genes were significantly different in cancer and normal tissues. The survival of patients in the different groups was significantly different. The gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters. Conclusion Gene signatures based on RNA-binding proteins can be independent risk factors for hepatocellular carcinoma patients.

Regulation of intracellular concentration of calcium levels is crucial for cell signaling, homeostasis, and in the pathology of diseases including cancer. Agonist-induced entry of calcium ions into the non-excitable cells is mediated by store-operated calcium channels (SOCs). This pathway is activated by the release of calcium ions from the endoplasmic reticulum and further regulated by the calcium uptake through mitochondria leading to calcium-dependent inactivation of calcium-release activated calcium channels (CARC). SOCs including stromal interaction molecules (STIM) and ORAI proteins have been implicated in tumor growth, progression, and metastasis. In the present study, we analyzed the mRNA and protein expression of genes mediating SOCs—STIM1, STIM2, ORAI1, ORAI2, ORAI3, TRPC1, TRPC3, TRPC4, TRPC5, TRPC6, TRPC7, TRPV1, TRPV2, TRPM1, and TRPM7 in head and neck squamous cell cancer (HNSC) patients using TCGA and CPTAC analysis. Further, our in silico analysis showed a significant correlation between the expression of SOCs and genes involved in the mitochondrial dynamics (MDGs) both at mRNA and protein levels. Protein-protein docking results showed lower binding energy for SOCs with MDGs. Subsequently, we validated these results using gene expression and single-cell RNA sequencing datasets retrieved from Gene Expression Omnibus (GEO). Single-cell gene expression analysis of HNSC tumor tissues revealed that SOCs expression is remarkably associated with the MDGs expression in both cancer and fibroblast cells.

The objective was to construct a prognostic risk model of stomach adenocarcinoma (STAD) based on The Cancer Proteome Atlas (TCPA) to search for prognostic biomarkers. Protein data and clinical data on STAD were downloaded from the TCGA database, and differential expressions of proteins between carcinoma and para-carcinoma tissues were screened using the R package. The STAD data were randomly divided into a training set and a testing set in a 1:1 ratio. Subsequently, a linear prognostic risk model of proteins was constructed using Cox regression analysis based on training set data. Based on the scores of the prognostic model, sampled patients were categorized into two groups: a high-risk group and a low-risk group. Using the testing set and the full sample, ROC curves and K-M survival analysis were conducted to measure the predictive power of the prognostic model. The target genes of proteins in the prognostic model were predicted and their biological functions were analyzed. A total of 34 differentially expressed proteins were screened (19 up-regulated, 15 down-regulated). Based on 176 cases in the training set, a prognostic model consisting of three proteins (COLLAGEN VI, CD20, TIGAR) was constructed, with moderate prediction accuracy (AUC=0.719). As shown by the Kaplan-Meier and survival status charts, the overall survival rate of the low-risk group was better than that of the high-risk group. Moreover, a total of 48 target proteins were identified to have predictive power, and the level of proteins in hsa05200 (Pathways in cancer) was the highest. According to the results of the Univariate and multivariate COX analysis, tri-protein was identified as an independent prognostic factor. Therefore, the tri-protein prognostic risk model can be used to predict the likelihood of STAD and guide clinical treatment.