Identification of CXCL13 as a potential biomarker in clear cell renal cell carcinoma via comprehensive bioinformatics analysis

肾透明细胞癌 图谱 生物 候选基因 生物标志物 癌症研究 基因表达谱 小桶 基因 计算生物学 基因表达 肾细胞癌 转录组 肿瘤科 医学 遗传学 蛋白质表达
作者
Tianbo Xu,Hailong Ruan,Zhengshuai Song,Qi Cao,Keshan Wang,Lin Bao,Di Liu,Junwei Tong,Hongmei Yang,Ke Chen,Xiaoping Zhang
出处
期刊:Biomedicine & Pharmacotherapy [Elsevier]
卷期号:118: 109264-109264 被引量:30
标识
DOI:10.1016/j.biopha.2019.109264
摘要

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system. However, there are still no reliable biomarkers for the diagnosis and prognosis of ccRCC. In this study, we aimed to screen candidate biomarkers and potential therapeutic targets for ccRCC. Differentially expressed genes (DEGs) were screened using NetworkAnalyst. Protein-protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA) were utilized to identify hub genes. Then, we assessed the prognostic and diagnostic values of hub genes to screen candidate biomarkers. Gene Set Enrichment Analysis (GSEA) was applied to reveal potential mechanisms of candidate biomarkers in ccRCC. Oncomine database and The Human Protein Atlas were used to verify the expression of candidate biomarkers online. In addition, qRT-PCR, Enzyme linked immunosorbent assay (ELISA) and Immunohistochemistry (IHC) assays were performed to validate the expression level of candidate biomarkers in ccRCC cells and tissues. A total of 771 genes were identified as DEGs. GO function analysis showed that DEGs were mostly enriched in excretion, apical part of cell and monovalent inorganic cation transmembrane transporter activity. KEGG pathway analysis demonstrated that DEGs were mostly involved in Neuroactive ligand-receptor interaction. After utilizing PPI network and WGCNA, nine genes (IFNG, CXCR3, PMCH, CD2, FASLG, CXCL13, CD8A, CD3D and GZMA) were identified as the hub genes. Moreover, survival analysis exhibited that high expression of CXCL13 predicted poor survival in both overall survival (OS) and disease free survival (DFS). The ROC curves indicated that CXCL13 could distinguish ccRCC samples from normal kidney samples. High expression of CXCL13 group was mostly associated with RB and MEL18 pathways by GSEA. Furthermore, qRT-PCR, ELISA and IHC results showed that the expression of CXCL13 was elevated in ccRCC. Our study illustrated that CXCL13 had good diagnostic and prognostic value, which may become a candidate biomarker and therapeutic target for ccRCC.

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