Abstract
Background: Although gene expression profiling has been increasingly incorporated into clinical diagnostic criteria, there is no effective molecular-based prognostic biomarkers for patients with renal cell carcinoma (RCC). Methods: Gene expression data of patients with RCC were downloaded from TCGA database. Immune/stromal scores were calculated by using ESTIMATE package in R. The clinical data was analyzed to evaluate the relationship between ESTIMATE scores and clinical features. The differentially expressed genes (DEGs) based on ESTIMATE scores were identified by R language package. The prognostic values of DEGs were evaluated by Kaplan-Meier analysis. LASSO Cox regression model was applied to construct a multi-genes-based classifier. The prognostic or predictive accuracy was identified by time-dependent receiver operating characteristic (ROC). Findings: Gene expression data of RCC (885 cases) were downloaded from TCGA database. Immune/stromal scores are significantly correlated with TNM stage, clinical stage and overall survival (p<0.05). There were 419 DEGs based on immune scores and 738 DEGs based on stromal scores. Among these DEGs, a total of 406 DEGs based on stromal scores and 252 DEGs based on immune scores were significantly associated with overall survival (p<0.05). The biological functions of these DEGs were mainly enriched in immune response and regulation of cell migration and proliferation. These DEGs are mainly concentrated in a huge PPI network. LASSO Cox regression model was used to build a prognostic six-genes-based classifier (IL21R, ATP6V1C2, GBP1, P2RY10, GBP4 and TNNC2) (AUC = 0.776). The predictive model that combined this classifier and clinical prognostic factors has a better accuracy in predicting the patient's survival probability with RCC (the combined AUC = 0.899). Interpretation: There are meaningful correlation between immune/stromal scores and clinical/pathological staging. We obtained a set of tumor microenvironment-related genes that have powerful prognostic value in patients with RCC. Fundingl Statement: This study was supported by China Postdoctoral Science Foundation (No. 2018M640801). Declaration of Interests: The authors declare no potential conflicts of interest. Ethics Approval Statement: Missing.
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