Prognosis analysis of necroptosis-related genes in colorectal cancer based on bioinformatic analysis - Liang, Xiaojie; Cheng, Zhaoxiang; Chen, Xinhao; Li, Jun (Frontiers in Genetics, 2022)
Background: Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involving in CRC predicting the prognosis. Methods: CRC’s data was acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and Least Absolute Shrinkage and Selectionator Operator (LASSO) analysis were applied to selected DENRGs concerning with patients’s overall survival and to construct a prognostic biomarker. And the effectiveness of this biomarker was assessed by Kaplan-Meier curve and Receiver Operating Characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through Gene Set Enrichment Analysis (GSEA). The single sample Gene Set Enrichment Analysis (ssGSEA) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes’ expression were examined in the GSE41258 dataset. Results: 27 DENRGs were filtered and a necroptosis-related prognostic signature based on 6 DENRGs was constructed which may better understand overall survival (OS) of CRC. Kaplan-Meier curve manifested the effectiveness of the prognostic signature and ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that the age, pathology T and RiskScore were independent prognostic factors and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with apoptosis pathway. Meanwhile, 24 immune cells represented significantly differences between two groups, like Activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores and T-cell exclusion scores were significantly different between the two groups. Finally, 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset. Conclusion: Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess overall survival of CRC.