An anoikis-based gene signature for predicting prognosis in malignant pleural mesothelioma and revealing immune infiltration

IntroductionMalignant pleural mesothelioma (MPM) is an aggressive, treatment-resistant tumor. Anoikis is a particular type of programmed apoptosis brought on by the separation of cell-cell or extracellular matrix (ECM). Anoikis has been recognized as a crucial element in the development of tumors. However, few studies have comprehensively examined the role of anoikis-related genes (ARGs) in malignant mesothelioma.MethodsARGs were gathered from the GeneCard database and the Harmonizome portals. We obtained differentially expressed genes (DEGs) using the GEO database. Univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm were utilized to select ARGs associated with the prognosis of MPM. We then developed a risk model, and time-dependent receiver operating characteristic (ROC) analysis and calibration curves were employed to confirm the ability of the model. The patients were divided into various subgroups using consensus clustering analysis. Based on the median risk score, patients were divided into low- and high-risk groups. Functional analysis and immune cell infiltration analysis were conducted to estimate molecular mechanisms and the immune infiltration landscape of patients. Finally, drug sensitivity analysis and tumor microenvironment landscape were further explored.ResultsA novel risk model was constructed based on the six ARGs. The patients were successfully divided into two subgroups by consensus clustering analysis, with a striking difference in the prognosis and landscape of immune infiltration. The Kaplan-Meier survival analysis indicated that the OS rate of the low-risk group was significantly higher than the high-risk group. Functional analysis, immune cell infiltration analysis, and drug sensitivity analysis showed that high- and low-risk groups had different immune statuses and drug sensitivity.ConclusionsIn summary, we developed a novel risk model to predict MPM prognosis based on six selected ARGs, which could broaden comprehension of personalized and precise therapy approaches for MPM.