通过综合生物信息学分析鉴定 CXCL13 作为透明细胞肾细胞癌的潜在生物标志物。
Identification of CXCL13 as a potential biomarker in clear cell renal cell carcinoma via comprehensive bioinformatics analysis.
机构信息
Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Department of Pathogenic Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan 430030, China.
出版信息
Biomed Pharmacother. 2019 Oct;118:109264. doi: 10.1016/j.biopha.2019.109264. Epub 2019 Aug 4.
BACKGROUND
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.
METHODS
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.
RESULTS
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.
CONCLUSIONS
Our study illustrated that CXCL13 had good diagnostic and prognostic value, which may become a candidate biomarker and therapeutic target for ccRCC.
背景
透明细胞肾细胞癌（ccRCC）是泌尿系统最常见的恶性肿瘤之一。然而，目前仍没有可靠的生物标志物用于 ccRCC 的诊断和预后。本研究旨在筛选 ccRCC 的候选生物标志物和潜在治疗靶点。
方法
使用 NetworkAnalyst 筛选差异表达基因（DEGs）。利用蛋白质-蛋白质相互作用（PPI）网络和加权基因共表达网络分析（WGCNA）鉴定关键基因。然后，我们评估关键基因的预后和诊断价值，以筛选候选生物标志物。基因集富集分析（GSEA）用于揭示候选生物标志物在 ccRCC 中的潜在机制。Oncomine 数据库和 The Human Protein Atlas 在线用于验证候选生物标志物的表达。此外，qRT-PCR、酶联免疫吸附试验（ELISA）和免疫组织化学（IHC）检测用于验证候选生物标志物在 ccRCC 细胞和组织中的表达水平。
结果
共鉴定出 771 个 DEGs。GO 功能分析显示，DEGs 主要富集于排泄、细胞顶端和单价无机阳离子跨膜转运活性。KEGG 通路分析表明，DEGs 主要参与神经活性配体-受体相互作用。利用 PPI 网络和 WGCNA，鉴定出 9 个关键基因（IFNG、CXCR3、PMCH、CD2、FASLG、CXCL13、CD8A、CD3D 和 GZMA）。此外，生存分析显示，CXCL13 高表达与总生存期（OS）和无病生存期（DFS）均不良相关。ROC 曲线表明，CXCL13 可区分 ccRCC 样本与正常肾样本。GSEA 结果显示，CXCL13 高表达组与 RB 和 MEL18 通路高度相关。此外，qRT-PCR、ELISA 和 IHC 结果表明，CXCL13 在 ccRCC 中表达上调。
结论
本研究表明，CXCL13 具有良好的诊断和预后价值，可能成为 ccRCC 的候选生物标志物和治疗靶点。
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