{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T03:39:29Z","timestamp":1792381169542,"version":"4.4.0"},"reference-count":36,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T00:00:00Z","timestamp":1788652800000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Reports"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Clear cell renal cell carcinoma (ccRCC) is hallmarked by profound metabolic reprogramming; however, its intricate crosstalk with the tumor immune microenvironment (TIME) and its clinical ramifications remain inadequately elucidated. This study aims to systematically decipher the metabolic\u2010immune interplay in ccRCC through multi\u2010omics integration, with the goal of identifying robust prognostic biomarkers and actionable therapeutic vulnerabilities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>This study aims to systematically decipher the metabolic\u2013immune interplay in clear cell renal cell carcinoma (ccRCC) through multi\u2011omics integration, and to identify robust prognostic biomarkers and actionable therapeutic vulnerabilities that can inform precision risk stratification and individualized treatment strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We integrated bulk transcriptomic, genomic, and clinical data from multiple ccRCC cohorts. Differential expression and functional enrichment analyses were performed to characterize metabolic pathway alterations. Mendelian randomization (MR) was employed to infer causal relationships between metabolic disorders and ccRCC risk. A machine learning\u2010based prognostic framework, incorporating SHAP (SHapley Additive exPlanations) for feature interpretability, was constructed and rigorously validated. TIME heterogeneity was dissected using deconvolution algorithms, while drug sensitivity, tumor mutation burden (TMB), and TIDE scores were utilized to assess therapeutic responses and immune evasion. Candidate gene function was evaluated through in\u00a0vitro gain\u2010 and loss\u2010of\u2010function assays, with expression validated via TCGA, HPA, western blot, and qRT\u2010PCR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Enrichment analysis identified coordinated dysregulation in lipid metabolism, energy homeostasis, and hypoxia response pathways. MR analysis confirmed lipid metabolism disorders as a causal risk factor for ccRCC. Our machine\u2010learning model, centered on five core SHAP\u2010identified features (SUCLA2, ACAT1, PC, SUCLG1, and HMGCS2), demonstrated superior predictive accuracy over conventional clinical staging. Immune profiling unveiled dichotomous TIME states: the low\u2010risk group retained active immune surveillance, whereas the high\u2010risk group was enriched with immunosuppressive subsets. Drug sensitivity screening pinpointed LY2109761 and carmustine as high\u2010risk\u2010specific candidate agents. Furthermore, TMB and TIDE analyses stratified high\u2010risk patients displaying genomic instability and immune evasion phenotypes. Functionally, SUCLA2 knockdown significantly enhanced ccRCC cell proliferation and invasion, while its overexpression suppressed these malignant phenotypes, corroborating its tumor\u2010suppressive role. Expression patterns of the hub genes were consistently validated across multi\u2010level datasets and experimental assays.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study establishes a precision oncology framework for ccRCC by functionally linking metabolic biomarkers, immunophenotypes, and stratified therapeutic strategies. Importantly, we identify SUCLA2 as a potential functional tumor suppressor and a promising target for further mechanistic and translational investigation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cnr2.70673","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T04:37:22Z","timestamp":1788755842000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010Omics Integration Identifies a Five\u2010Gene Metabolic Signature With Experimental Validation in Clear Cell Renal Cell Carcinoma"],"prefix":"10.1002","volume":"9","author":[{"given":"Yi","family":"Peng","sequence":"first","affiliation":[{"name":"Department of Urology The People's Hospital of Danyang; Affiliated Danyang Hospital of Nantong University  Danyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suobing","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of 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