Background. Stomach adenocarcinoma (STAD) is a kind of cancer that begins in the stomach cells and has a poor overall survival rate. Following resection surgery, chemotherapy has been suggested as a curative method for stomach cancer. However, it is ineffective. Pyroptosis, a kind of inflammatory programmed cell death, has been shown to play a significant role in the development and progression of STAD. However, whether pyroptosis-related genes (PRGs) can be utilized to predict the diagnosis and prognosis of gastric cancer remains unknown. Method. The research measured at predictive PRGs in STAD samples from TCGA and GEO. Lasso regression was used to build the prediction model. Coexpression analysis revealed that gene expression was linked to pyroptosis. PRGs were found to be overexpressed in high-risk individuals, implying that they could be used in a model to predict STAD prognosis. Result. Immunological and tumor-related pathways were discovered using GSEA. In STAD patients, the genes GPX3, PDGFRL, RGS2, and SERPINE1 may be connected to the cancer process. The levels of expression also differed between the two risk groups. Conclusion. The purpose of this study is to identify and verify STAD-associated PRGs that can effectively guide prognosis and the immunological milieu in STAD patients as well as offer evidence for the development of pyroptosis-related molecularly targeted therapeutics. Therefore, PRGs and the link between immunological and PRGs in STAD may be therapeutic targets.
Background: Cellular senescence is a stable state of cell cycle arrest that plays a crucial role in the tumor microenvironment (TME) and cancer progression. Nevertheless, the accurate prognosis of gastric cancer (GC) is complicated to predict due to tumor heterogeneity. The work aimed to build a novel prognostic model in GC.Methods: LASSO and Cox regression analysis were constructed to develop a prognostic senescence-related signature. The Gene Expression Omnibus dataset was used for external validation of signature. Afterward, we performed correlation analysis for the risk score and the infiltrating abundance of immune cells, TME scores, drug response, tumor mutational burden (TMB), and immunotherapy efficacy.Results: Five senescence-related genes (AKR1B1, CTNNAL1, DUSP16, PLA2R1, and ZFP36) were screened to build a signature. The high-risk group had a shorter overall survival, cancer-specific survival, and progression-free survival when compared to the low-risk group. We further constructed a nomogram based on risk score and clinical traits, which can predict the prognosis of GC patients more accurately. Moreover, the risk score was evidently correlated with infiltration of immune cells, TME score, TMB, TIDE score, and chemotherapy sensitivity. Meanwhile, the Kyoto Encyclopedia of Genes and Genomes pathway showed that the PI3K-Akt and Wnt signaling pathway were differentially enriched in the high-risk group.Conclusions: The senescence-related signature was an accurate tool to guide the prognosis and might promote the progress of personalized treatment.Abbreviations: DEGs = differentially expressed genes, GC = gastric cancer, IC50 = half-maximal inhibitory concentration, OS = overall survival, PCA = principal component analyses, TIDE = tumor immune dysfunction and exclusion, TIICs = tumor-infiltrating immune cells, TMB = tumor mutational burden, TME = tumor microenvironment.
Background: Senescence, as an effective barrier against tumorigenesis, plays a critical role in cancer therapy. However, the role of senescence in colorectal cancer (CRC) has not yet been reported. This study aimed to build a prognostic signature for the prognosis of patients with CRC based on senescence-related genes.Methods: A prognostic signature was built from TCGA based on differentially expressed senescence-related genes by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses, which were further validated using two Gene Expression Omnibus (GEO) cohorts. The CIBERSORT and ssGSEA algorithms were utilized to analyze the infiltrating abundance of immune cells. The relationship of signature with the immune therapy and the sensitivity of different therapies was explored.Results: We found 93 genes associated with senescence that were differentially expressed. Based on expression and clinical parameters, we developed a senescence-related prognostic signature and its effectiveness was verified using two external validation cohorts. Overall survival was predicted using a prognostic nomogram that incorporated the predictive values of the risk score and clinical traits. Additionally, the risk score was significantly correlated with immune cells infiltration, tumor immune microenvironment (TME) score, immune checkpoints, immunotherapeutic efficacy, and chemotherapy sensitivity.Conclusion: The senescence-related prognostic model can well predict the prognosis, immunotherapeutic response, and identify potential drug targets, which can help guide individualized treatment.
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