{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:17:36Z","timestamp":1783487856923,"version":"3.55.0"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Clear cell renal cell carcinoma (ccRCC) is a prevalent malignancy, representing 80\u221290% 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2212qPCR) on clinical samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2212qPCR validation confirmed significantly elevated expression of IFNG, LAG3, TNFRSF9, and CD8B in ccRCC patients compared to controls (p &amp;lt; 0.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1857894","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T04:53:47Z","timestamp":1783486427000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification and validation of prognostic genes associated with clear cell renal cell carcinoma: based on public whole transcriptome sequencing datasets"],"prefix":"10.3389","volume":"16","author":[{"given":"Pengcheng","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Urology, The First Hospital of Lanzhou University","place":["Lanzhou, Gansu, 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