Macrophage M2, Tumor Purity, and Tumor Mutation Burden Evaluation

We downloaded The Cancer Genome Atlas TCGA—KIRC FPKM data (http://cancergenome.nih.gov/) containing 539 renal clear cell cancer tissue samples and 72 normal tissues. {"type":"entrez-geo","attrs":{"text":"GSE8050","term_id":"8050"}}GSE8050 (Weinzierl et al., 2008), {"type":"entrez-geo","attrs":{"text":"GSE12606","term_id":"12606"}}GSE12606 (Stickel et al., 2009), {"type":"entrez-geo","attrs":{"text":"GSE14762","term_id":"14762"}}GSE14762 (Wang et al., 2009), and {"type":"entrez-geo","attrs":{"text":"GSE36895","term_id":"36895"}}GSE36895 (Peña-Llopis et al., 2012) were also downloaded from the GEO (http://www.ncbi.nlm.nih.gov/geo/) database. The Robust Multi-Array Average (RMA) algorithm of the “sva” (Leek et al., 2012) package was used to remove batch effects among the four GEO cohorts. The TCGA cohort was used to select M2-related genes. Four GEO cohorts were combined using “sva” packages and to verify the results. The Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) is a deconvolution algorithm based on a gene expression profile that characterizes the cell composition of complex tissues, quantifies immune cells, and accurately estimates the immune components of tumor samples. It expands the potential of the genomic database, showing the pattern of Renal Clear Cell Carcinoma with comprehensive immune cells. We calculated macrophage M2 cell proportions based on the LM22 matrix using the CIBERSORT (Chen et al., 2018) algorithm, Cibersort was used as an obvious method to evaluate the significance of infiltration of immune cells in the samples. The assessment results of some samples were not statistically significant, and we used P < 0.05 to screen the samples. The Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) is a method that infers the fraction of stromal and immune cells using gene expression signatures (Yoshihara et al., 2013). Using the ESTIMATE package, we calculated tumor purity in each renal clear cell cancer sample. TMB (tumor mutation burden) per megabyte is calculated by dividing the total number of mutations by the size of the target coding region (Li et al., 2020; Yang et al., 2020).