Machine Learning Developed a T Cell Exhaustion Transcription Factor Signature for Predicting Clinical Outcomes and Immunotherapy Response in Skin Cutaneous Melanoma

Background: The persistence of CD8 + Tex in the tumor microenvironment and their interactions with tumor cells and other immune cells are the keys to tumor immune escape and treatment failure. Transcription factors play a key role in the regulation of gene expression in cells. Few studies have fully clarified the characteristics of T cell exhaustion transcription factors in SKCM. Methods: In the TCGA, GSE54467, GSE59455, and GSE65904 cohorts, an integrative machine learning analysis procedure comprising 10 algorithms was performed to develop a T cell exhaustion transcription factor signature (TTS). The relevance of TTS in predicting the advantages of immunotherapy was assessed using a number of measures, including tumor immune dysfunction and exclusion (TIDE) score, tumor mutation burden (TMB) score, immunophenoscore, intratumor heterogeneity score, and three immunotherapy datasets (IMvigor210, GSE91061, and GSE78220). Results: The optimal prognostic TTS developed by the LASSO algorithm acted as an independent risk factor and had a stable and powerful performance in predicting the overall survival rate of SKCM patients, with the AUC of 1‐, 3‐, and 5‐year ROC curve being 0.785, 0.812, and 0.839 in the TCGA cohort. SKCM patients with low TTS scores exhibited a lower TIDE score, lower immune escape score, lower intratumor heterogeneity score and higher TMB score, and higher PD1 and CTLA4 immunophenoscore. Gene sets implicated in angiogenesis, NOTCH signaling, hypoxia, and glycolysis exhibited lower scores in SKCM patients with low TTS scores. In vitro experiments showed that FOXM1 was upregulated in SKCM and knockout of FOXM1 inhibited tumor cell proliferation. Conclusion: The current study constructed a novel TTS in SKCM, which acted as an indicator for predicting the clinical outcome and immunotherapy response of SKCM patients.