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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