Characterization of the Survival Influential Genes in Carcinogenesis

Abstract

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.

Ensemble learning model for identifying the hallmark genes of NFκB/TNF signaling pathway in cancers

“…Subsequently, to further understand the roles of voted genes in cancer progression, we explore their impact on patient survival using a pan-cancer survival influential gene set compiled by our previous study [ 59 ]. We observed that genes linked to poor survival outcomes are significantly overrepresented among the genes identified by the pan-cancer model (24.99%, p -value = 2.56 × 10 –43 , Fisher's exact test).…”