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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.

SUBMITTER: Zhao E

PROVIDER: S-EPMC7487089 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Zhao Enfa E Bai Xiaofang X

Disease markers 20200903

<h4>Objective</h4>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.<h4>Materials and methods</h4>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 outcom ...[more]

PMID: 32952743

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Project description:Numerous studies have shown that the Krüppel-like factors (KLFs) family of transcription factors regulate various eukaryotic physiological processes including the proliferation, differentiation, senescence, death, and carcinogenesis of animal cells. In addition, they are involved in the regulation of key biological processes such as cell cycle, DNA repair, and immune response. Current studies focus on investigating the role of KLFs in normal physiological conditions and the incidence and development of diseases. However002C the significance of KLFs family genes in clear cell renal cell carcinoma (ccRCC) remains partly understood; therefore, an in-depth investigation of their role and clinical value in this cancer is desired. The study aimed to investigate the role of KLF family genes in the incidence, development, and prognosis of ccRCC, and to identify the related potential biomarkers and therapeutic targets. The expression of KLFs in the RNA sequencing data of 613 ccRCCs from the TCGA database was analyzed using R software, and UALCAN and GEPIA assessed the expression of KLF genes in ccRCC. Real-time fluorescence quantitative PCR analysis was performed using 10 pairs of paired ccRCC sample tissues and renal cancer cell lines from the First Affiliated Hospital of Nanchang University. Overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) of Kidney Clear Cell Carcinoma (KIRC) samples at differential expressions of KLFs in the TCGA database were analyzed using the R software, followed by generating a nomogram prediction model. GSCALite assessed the interactions of KLF genes with miRNAs and generated network maps. Protein interaction network maps of 50 neighboring genes associated with KLF mutations were analyzed using STRING with GO and KEGG functional enrichment analyses. The cBioPortal determined the probability of KLF gene mutations and their impact on OS and disease-free survival (DFS) in patients with ccRCC. Immune cell infiltration of KLFs was analyzed using TIMER. Finally, GSCALite was used to analyze the drug sensitivity and associated pathways of action of KLFs. Correlation validation using cellular experiments. KLF3/5/9/15 were significantly downregulated in ccRCC tissues, whereas KLF16/17 were upregulated compared with the adjacent tissues. Patients with high mRNA levels of KLF16/17 showed significantly lower OS, PFI, and DSS, whereas KLF3/5/9 showed a reverse trend. In patients with ccRCC, a significant correlation was observed between KLF mutations and OS and DSS. Furthermore, the correlation of KLF3/5/9 with immune cell infiltration was stronger than that of KLF15/16, while KLF17 was significantly associated with the Epithelial-Mesenchymal Transition (EMT) pathway. Overexpression of KLF5 inhibits the proliferative and migratory capacity of renal cancer cells (786-O and OS-RC-2), as well as their sensitivity to relevant small molecule drugs. Our research revealed the expression levels and biological significance of KLF genes in ccRCC, particularly highlighting the potential of KLF5 as a promising biomarker and therapeutic target for effective prognosis and diagnosis of ccRCC.

Dataset Information

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

Publications

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

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