Evidence mapPaperPMID 36969076Full record

ArticleFrontiers in oncology2023

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

Peizhi Zhang, Jiayi Li, Zicheng Wang, Leizuo Zhao, Jiechuan Qiu, Yingkun Xu, Guangzhen Wu, Qinghua Xia

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Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

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8 authors.

Peizhi ZhangDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Jiayi LiSchool of Business, Hanyang University, Seoul, Republic of Korea.
Zicheng WangDepartment of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Leizuo ZhaoDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Jiechuan QiuDepartment of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Yingkun XuDepartment of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Guangzhen WuDepartment of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Qinghua XiaDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The 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. Methods: In 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). Results: We 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. Conclusions: This study constructs a KIRC prognosis prediction model involving 14 MAPK signaling pathway-related genes, which is essential for exploring potential biomarkers for KIRC diagnosis.

Indexed as

KIRCMAPK pathwayprognostic modelTCGAtumor biomarkers

Identifiers

PMID36969076
PMCPMC10036835

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