Evidence map›Paper›PMID 42488123›Full record

ArticleFrontiers in oncology2026

Identification and validation of prognostic genes associated with clear cell renal cell carcinoma: based on public whole transcriptome sequencing datasets.

Pengcheng Chang, Zitong Qin, Runzhang Liu, Binxian Wang, Huaiquan Lu, Suoshi Jing, Chenhao Guo, Weiping Li

Abstract read
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Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Pengcheng Chang *Department of Urology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
Zitong Qin *The First Clinical Medical College of Lanzhou University, Lanzhou, Gansu, China.
Runzhang LiuThe First Clinical Medical College of Lanzhou University, Lanzhou, Gansu, China.
Binxian WangDepartment of Urology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
Huaiquan LuDepartment of Urology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
Suoshi JingDepartment of Urology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
Chenhao GuoDepartment of Urology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
Weiping LiDepartment of Urology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is a prevalent malignancy, representing 80-90% of kidney cancer cases. This study aimed to identify potential prognostic genes to improve patient survival prediction and provide new insights into the pathogenesis and treatment of ccRCC through comprehensive whole transcriptome sequencing analysis. Methods: The analysis utilized whole transcriptomic data from publicly available datasets, including TCGA-KIRC and GSE96574. Methods involved identifying differentially expressed mRNAs and long non-coding RNAs, prognostic gene screening via MCODE plugin and risk model construction, followed by functional enrichment, molecular network construction, drug prediction, and molecular docking. The expression levels of key genes were subsequently validated using reverse transcription quantitative PCR (RT-qPCR) on clinical samples. Results: Eight prognostic genes (IFNG, CXCL13, KLRK1, LAG3, ITGAX, TNFRSF9, CD2, CD8B) were identified and formed a risk stratification model. These genes were primarily enriched in immune-related pathways such as antigen processing and presentation. A regulatory network involving transcription factors, miRNAs, lncRNA PVT1, and circRNAs was constructed. Drug prediction and molecular docking suggested potential targeted drugs, including amitriptyline hydrochloride for CD8B and IFNG, and rituximab for CXCL13 and IFNG, with strong binding affinities noted for ITGAX and KLRK1. RT-qPCR validation confirmed significantly elevated expression of IFNG, LAG3, TNFRSF9, and CD8B in ccRCC patients compared to controls (p < 0.05). Conclusions: This study identifies an eight-gene signature as a promising prognostic biomarker for ccRCC, deeply involved in the tumor immune microenvironment. The findings offer novel insights into ccRCC pathogenesis and highlight potential therapeutic targets and agents, paving the way for improved prognostic strategies and immunotherapeutic approaches.

Indexed as

clear cell renal cell carcinomaprognostic genesregulatory networkrisk modelwhole transcriptome sequencing

Identifiers

PMID42488123
PMCPMC13388226

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