Identification of Key Genes of Prognostic Value in Clear Cell Renal Cell Carcinoma Microenvironment and a Risk Score Prognostic Model

Abstract

Objective. We aimed at identifying the key genes of prognostic value in clear cell renal cell carcinoma (ccRCC) microenvironment and construct a risk score prognostic model. Materials and Methods. Immune and stromal scores were calculated using the ESTIMATE algorithm. A total of 539 ccRCC cases were divided into high- and low-score groups. The differentially expressed genes in immune and stromal cells for the prognosis of ccRCC were screened. The relationship between survival outcome and gene expression was evaluated using univariate and multivariate Cox proportional hazard regression analyses. A risk score prognostic model was constructed based on the immune/stromal scores. Results. The median survival time of the low immune score group was longer than that of the high immune score group (p=0.044). Ten tumor microenvironment-related genes were selected by screening, and a predictive model was established, based on which patients were divided into high- and low-risk groups with markedly different overall survival (p<0.0001). Multivariate Cox analyses showed that the risk score prognostic model was independently associated with overall survival, with a hazard ratio of 1.0437 (confidence interval: 1.0237–1.0641, p<0.0001). Conclusions. Low immune scores were associated with extended survival time compared to high immune scores. The novel risk predictive model based on tumor microenvironment-related genes may be an independent prognostic biomarker in ccRCC.

An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

“…Previous studies have demonstrated that infiltrating immune cells are strongly related to the prognosis and treatment of malignant cancers [ 35 – 37 ]. Using CIBERSORT, we compared the differences in the immune infiltration of 22 immune cell types between low- and high-risk groups.…”

Factors associated with survival in paediatric and adolescent renal cell carcinoma: a population‐based study

“…Third, there is selective bias in retrospective studies. Finally, there is no information in the database about tumour biomarkers or microenvironment‐related genes that may predict RCC prognosis 23,24 . The lack of this information makes it difficult to determine whether biological factors or access to treatment are to blame for racial survival disparities.…”