Abstract TM9SF1, a transmembrane protein implicated in various cancers, has yet to receive the attention it deserves in oncological research. Leveraging machine learning and publicly available datasets—including TCGA, GTEx, and UALCAN—this study examined TM9SF1 expression patterns across multiple cancer types. We evaluated its prognostic significance through Cox regression and Kaplan-Meier survival analyses, while also delving into genetic mutations, methylation profiles, immune infiltration, and therapeutic drug responses. Our findings revealed that TM9SF1 is markedly overexpressed in numerous cancers and correlates with unfavorable patient outcomes. The protein’s presence was tied to heightened mutation rates, stronger immune and stromal activity, and interactions with diverse immune cell populations and checkpoint molecules. Additionally, TM9SF1 showed associations with tumor heterogeneity, stem-like properties, and DNA methylation regulators. In hepatocellular carcinoma (HCC), it emerged as an independent risk factor, influenced drug sensitivity, and appeared to mediate its effects through Tex cells, as indicated by single-cell sequencing. This multifaceted investigation highlights TM9SF1’s promise as both a prognostic biomarker and a candidate for immunotherapy, paving the way for broader exploration in pan-cancer studies.
Luan et al. (Thu,) studied this question.