Genes associated with cellular senescence favor melanoma prognosis by stimulating immune responses in tumor microenvironment. (May 2023)

• Record Type: Journal Article
• Authors: Liang, Xiaofeng; Lin, Xiaobing; Lin, Zien; Lin, Weiyi; Peng, Zhishen; Wei, Shanshan
• Is Part Of: Computers in biology and medicine. Volume 158(2023)
• Publication Date: 2023-05
• Subjects: Skin cutaneous melanoma -- Cellular senescence -- Tumor microenvironment -- Prognostic genes -- Machine learning -- Risk model; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106850
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Purpose: Skin cutaneous melanoma (SKCM), a malignant tumor from melanocytes, is the fifth most prevalent tumor. Immune checkpoint inhibitor (ICI) immunotherapy improves prognosis of SKCM, but immune response varies for different populations. Cellular senescence in the tumor microenvironment (TME) promotes antitumor immunity, mediated by dendritic cells (DC) and CD8 + T cells. Therefore, we sought to explore the role of cellular senescence in the TME of SKCM through bioinformatics and machine learning. Methods: First, we obtained 93 cellular senescence-prognosis genes (CSPGs) by univariate survival analysis. Thereafter, 23 optimal CSPGs were obtained by least absolute shrinkage and selection operator (lasso) analysis. Based on the riskscore obtained by lasso analysis and clinical information from multivariate cox, we obtained the nomogram of SKCM, which was validated in the validation cohort. Based on the riskscore, the patients were split into low- and high-risk groups. Functional differences between the two groups were analyzed using Metascape and GSEA, and immune infiltration differences were achieved by multiple algorithms. We obtained a risk prediction nomogram for the validated SKCM based on the lasso model by univariate and multivariate cox regression analysis. Results: In the low-risk group, immune responses were in an active state. NK, CD8 + T, DC, macrophages, and neutrophils were significantly upregulated, and ICI‐relevant genes were notably upregulated. With the differentially expressed genes (DEGs) and optimal CSPGs, we obtained the hub genes: NOX4, NTN4, PROX1, and TRPM8. The hub genes were mainly expressed by cancer-associated fibroblasts (CAFs) and endothelial cells by single cell analysis, which were mainly associated with angiogenesis. Conclusion: Genes associated with cellular senescence favor SKCM prognosis by stimulating immune responses in TME. Patients with high expression of cellular senescence associated genes in the TME might have better benefit from ICI immunotherapy. Cellular senescence functions as a pro-tumor agent in mesenchymal cells and needs further study. Highlights: By comprehensive analysis and machine learning, we found that cellular senescence genes promote immune responses in SKCM.