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Multimolecular characteristics of cell-death related hub genes in human cancers: a comprehensive pan-cancer analysis.

ABSTRACT: Failure of the normal process of cell death pathways contributes to the defection of immune systems and the occurrence of cancers. The key genes, the multimolecular mechanisms, and the immune functions of these genes in pan-cancers remain unclear. Using online databases of The Cancer Genome Atlas, GEPIA2, TISIDB, HPA, Kaplan-Meier Plotter, PrognoScan, cBioPortal, GSCALite, TIMER, and Sangerbox, we identified the key genes from the six primary cell death-related pathways and performed a comprehensive analysis to investigate the multimolecular characteristics and immunological functions of the hub genes in 33 human cancers. We identified five hub genes in the six primary cell death-related pathways (JUN, NFKB1, CASP3, PARP1, and TP53). We found that CASP3, PARP1, and TP53 were overexpressed in 28, 23, and 27 cancers. The expression of the five genes was associated with the development and prognosis of many cancers. Particularly, JUN, NFKB1, CASP3, and TP53 have prognostic values in Brain Lower Grade Glioma (LGG), while PARP1 and CASP3 could predict the survival outcomes in Adrenocortical carcinoma (ACC). In addition, an extensive association between five genes' expression, DNA methylation, and tumor-immune system interactions was noticed. The five cell death-related hub genes could function as potential biomarkers for various cancers, particularly LGG and ACC. The immunological function analysis of the five genes also proposes new targets for developing immunosuppressants and improving the immunotherapy efficacy of cancers. However, further extensive clinical and experimental research are required to validate their clinical values.

SUBMITTER: Hu D

PROVIDER: S-EPMC9645254 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Multimolecular characteristics of cell-death related hub genes in human cancers: a comprehensive pan-cancer analysis.

Hu Dingtao D Zhang Tingyu T Yan Ziye Z Wang Linlin L Wang Yuhua Y Meng Nana N Tu Bizhi B Teng Ying Y Li Zhen Z Lou Xiaoqi X Lei Yu Y Ren Xiaoshuang X Zou Yanfeng Y Wang Fang F

Cell cycle (Georgetown, Tex.) 20220718 22

Failure of the normal process of cell death pathways contributes to the defection of immune systems and the occurrence of cancers. The key genes, the multimolecular mechanisms, and the immune functions of these genes in pan-cancers remain unclear. Using online databases of The Cancer Genome Atlas, GEPIA2, TISIDB, HPA, Kaplan-Meier Plotter, PrognoScan, cBioPortal, GSCALite, TIMER, and Sangerbox, we identified the key genes from the six primary cell death-related pathways and performed a comprehen ...[more]

PMID: 35848940

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Project description:Introduction Periodontitis is an inflammatory disease and its molecular mechanisms is not clear. A recently discovered cell death pathway called cuproptosis, may related to the disease. Methods The datasets GSE10334 of human periodontitis and control were retrieved from the Gene Expression Omnibus database (GEO) for analysis.Following the use of two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature removal (SVM-RFE) were used to find CRG-based signature. Then the Receiver operating characteristic (ROC) curves was used to evaluate the gene signature's discriminatory ability. The CIBERSORT deconvolution algorithm was used to study the link between hub genes and distinct types of immune cells. Next, the association of the CRGs with immune cells in periodontitis and relevant clusters of cuproptosis were found. The link between various clusters was ascertained by the GSVA and CIBERSORT deconvolution algorithm. Finally, An external dataset (GSE16134) was used to confirm the diagnosis capacity of the identified biomarkers. In addition, clinical samples were examined using qRT-PCR and immunohistochemistry to verifiy the expression of genes related to cuprotosis in periodontitis and the signature may better predict the periodontitis. Results 15 periodontitis-related DE-CRGs were found,then 11-CRG-based signature was found by using of LASSO and SVM-RFE. ROC curves also supported the value of signature. CIBERSORT results of immune cell signature in periodontitis showed that signature genes is a crucial component of the immune response.The relevant clusters of cuproptosis found that the NFE2L2, SLC31A1, FDX1,LIAS, DLD, DLAT, and DBT showed a highest expression levels in Cluster2 ,while the NLRP3, MTF1, and DLST displayed the lowest level in Cluster 2 but the highest level in Cluster1. The GSVA results also showed that the 11 cuproptosis diagnostic gene may regulate the periodontitis by affecting immune cells. The external dataset (GSE16134) confirm the diagnosis capacity of the identified biomarkers, and clinical samples examined by qRT-PCR and immunohistochemistry also verified that these cuprotosis related signiture genes in periodontitis may better predict the periodontitis. Conclusion These findings have important implications for the cuproptosis and periodontitis, and highlight further research is needed to better understand the mechanisms underlying this relationship between the cuproptosis and periodontitis.

Project description:Glycolysis plays significant roles in tumor progression and immune response. However, the exact role of glycolysis in prognosis and immune regulation has not been explored in all cancer types. This study first calculated a novel glycolysis score and screened out 12 glycolytic hub genes, and comprehensively analyzed molecular expression, clinical implications, and immune features of glycolysis among pan-cancer. The glycolysis score was derived by the single sample gene set enrichment analysis (ssGSEA) algorithm. The correlations of glycolysis with clinical parameters were analyzed using "limma" package. Downstream pathways of glycolysis were identified by Gene Set Enrichment Analysis (GSEA). The immune cell infiltration was explored and validated by three databases. The association between glycolysis and some immunotherapy biomarkers was explored by Pearson correlation analysis. Single-nucleotide variation (SNV), copy number variation (CNV), DNA methylation, and drug sensitivity analyses of 12 glycolytic hub genes were investigated. IMvigor210 and GSE91061 immunotherapeutic cohorts were retrieved to assess the ability of glycolysis score to predict immunotherapy efficacy. The expression of glycolysis key genes was detected in normal and endometrial cancer cell lines. We found that glycolysis score was generally higher in tumor tissues compared to normal tissues and a high glycolysis score predominated as a risk prognostic factor. A high glycolysis score was associated with decreased immunostimulatory natural killer (NK) cells and CD8+ T cells infiltration, well increased immunosuppressive M2-tumor-associated macrophages (M2-TAM) cells infiltration. Tumor mutational burden (TMB), microsatellite instability (MSI), and immune checkpoints (ICPs) all closely interacted with glycolysis score and the frequency of gene mutation was confirmed to be higher in colon adenocarcinoma (COAD) patients with higher glycolysis score. The SNV, CNV, and DNA methylation of 12 glycolysis key genes occurred at different frequencies and showed different impacts on survival outcomes. The predictive and prognostic value of glycolysis score for immunotherapy outcomes was validated in two immunotherapy cohorts. The expression levels of key genes differ in normal endometrial and three endometrial cancer cell lines. This work indicated that glycolysis score and 12 glycolytic hub genes were correlated with an immunosuppressive microenvironment. They could be served as promising biomarkers aiding diagnosis, predicting prognosis and immunotherapy response for some tumor patients.

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Multimolecular characteristics of cell-death related hub genes in human cancers: a comprehensive pan-cancer analysis.

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Multimolecular characteristics of cell-death related hub genes in human cancers: a comprehensive pan-cancer analysis.

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