Objective We aimed to identify for macrophage-2(M2)characteristic genes with hub prognostic value through machine learning combined with bioinformatics techniques,and to explore their relationship with the immune microenvironment and tumor immunotherapy.Methods This study collected the TCGA-COAD dataset and the dataset(GSE39582)from the GEO database.The CIBERSORT method was used to calculate the levels of M2-type macrophages in tumor samples,and characteristic genes were screened through correlation analysis,univariate and multivariate Cox regression analysis,and the random survival forest algorithm.The ESTIMATE algorithm was employed to calculate the immune microenvironment scores(stromal score and immune score)of tumor samples,and to study the characteristic genes and their relationships,finally validating in an immunotherapy cohort.Results This study identified PPM1M and MRAS as core prognostic genes determined by machine learning.In the TCGA data,populations with high expression levels of MRAS had shorter progression-free survival(P=0.0013).In the GEO data,high expression of PPM1M gene(P=0.031)and MRAS gene(P=0.002)were both associated with recurrence.Both PPM1M and MRAS genes were positively correlated with tumor immune score and stromal score,and positively correlated with the levels of suppressive regulatory T cells(Treg).Finally,in the evaluation of immunotherapy,patients with high expression of PPM1M and MRAS had better prognosis after receiving immunotherapy.Conclusion Characteristic genes of M2-type macrophages determined by machine learning are related to survival,recurrence,and progression.In the immune microenvironment,PPM1M and MRAS are both positively correlated with suppressive tumor immune components and stromal components.Furthermore,PPM1M and MRAS may serve as novel biomarkers for the efficacy of immunotherapy.
Colon neoplasmsM2 type macrophageRandom survival forestImmune microenvironmentSurvival analysis
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