Establishment of a new prognostic risk model of MAPK pathway-related molecules in kidney renal clear cell carcinoma based on genomes and transcriptomes analysis

作者全名："Zhang, Peizhi; Li, Jiayi; Wang, Zicheng; Zhao, Leizuo; Qiu, Jiechuan; Xu, Yingkun; Wu, Guangzhen; Xia, Qinghua"

作者地址："[Zhang, Peizhi; Zhao, Leizuo; Xia, Qinghua] Shandong Univ, Shandong Prov Hosp, Cheeloo Coll Med, Dept Urol, Jinan, Peoples R China; [Li, Jiayi] Hanyang Univ, Sch Business, Seoul, South Korea; [Wang, Zicheng; Qiu, Jiechuan] Shandong First Med Univ, Dept Urol, Shandong Prov Hosp, Jinan, Peoples R China; [Zhao, Leizuo] Dongying Peoples Hosp, Dept Urol, Dongying, Peoples R China; [Xu, Yingkun] Chongqing Med Univ, Dept Breast & Thyroid Surg, Affiliated Hosp 1, Chongqing, Peoples R China; [Wu, Guangzhen] Dalian Med Univ, Dept Urol, Affiliated Hosp 1, Dalian, Peoples R China"

通信作者："Xia, QH (通讯作者)，Shandong Univ, Shandong Prov Hosp, Cheeloo Coll Med, Dept Urol, Jinan, Peoples R China."

来源：FRONTIERS IN ONCOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000951897000001

JCR分区：Q2

影响因子：3.5

年份：2023

卷号：13

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：KIRC; tumor biomarkers; MAPK pathway; TCGA; prognostic model

摘要："PurposeThe mitogen-activated protein kinase (MAPK) signaling pathway is often studied in oncology as the most easily mentioned signaling pathway. This study aims to establish a new prognostic risk model of MAPK pathway related molecules in kidney renal clear cell carcinoma (KIRC) based on genome and transcriptome analysis. MethodsIn our study, RNA-seq data were acquired from the KIRC dataset of The Cancer Genome Atlas (TCGA) database. MAPK signaling pathway-related genes were obtained from the gene enrichment analysis (GSEA) database. We used ""glmnet"" and the ""survival"" extension package for LASSO (Least absolute shrinkage and selection operator) regression curve analysis and constructed a prognosis-related risk model. The survival curve and the COX regression analysis were used the ""survival"" expansion packages. The ROC curve was plotted using the ""survival ROC"" extension package. We then used the ""rms"" expansion package to construct a nomogram plot. We performed a pan-cancer analysis of CNV (copy number variation), SNV (single nucleotide variant), drug sensitivity, immune infiltration, and overall survival (OS) of 14 MAPK signaling pathway-related genes using several analysis websites, such as GEPIA website and TIMER database. Besides, the immunohistochemistry and pathway enrichment analysis used The Human Protein Atlas (THPA) database and the GSEA method. Finally, the mRNA expression of risk model genes in clinical renal cancer tissues versus adjacent normal tissues was further verified by real-time quantitative reverse transcription (qRT-PCR). ResultsWe performed Lasso regression analysis using 14 genes and created a new KIRC prognosis-related risk model. High-risk scores suggested that KIRC patients with lower-risk scores had a significantly worse prognosis. Based on the multivariate Cox analysis, we found that the risk score of this model could serve as an independent risk factor for KIRC patients. In addition, we used the THPA database to verify the differential expression of proteins between normal kidney tissues and KIRC tumor tissues. Finally, the results of qRT-PCR experiments suggested large differences in the mRNA expression of risk model genes. ConclusionsThis study constructs a KIRC prognosis prediction model involving 14 MAPK signaling pathway-related genes, which is essential for exploring potential biomarkers for KIRC diagnosis."

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