000168382 001__ 168382
000168382 005__ 20260204153543.0
000168382 0247_ $$2doi$$a10.1016/j.isci.2025.113061
000168382 0248_ $$2sideral$$a147831
000168382 037__ $$aART-2025-147831
000168382 041__ $$aeng
000168382 100__ $$aYin, Liangwei
000168382 245__ $$aSample-specific network analysis identifies gene co-expression patterns of immunotherapy response in clear cell renal cell carcinoma
000168382 260__ $$c2025
000168382 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168382 5203_ $$aImmunotherapies have recently emerged as a standard of care for advanced cancers, offering remarkable improvements in patient prognosis. However, only a small subset of patients benefit, and robust molecular predictors remain elusive. We present a computational framework leveraging sample-specific gene coexpression networks to identify features predictive of immunotherapy response in kidney cancer. Our results reveal that patients with similar clinical outcomes exhibit comparable gene co-expression patterns. Notably, increased gene connectivity and stronger negative gene-gene associations are hallmarks of poor responders. We further developed sample-specific pathway-level network scores to detect dysregulated biological pathways linked to treatment outcomes. Finally, incorporating these sample-level network features improves the predictive performance of gene expression-based machine learning models. This work highlights the value of personalized gene network features for stratifying patients with cancer and optimizing immunotherapy strategies.
000168382 536__ $$9info:eu-repo/grantAgreement/EC/H2020/101017453/EU/Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity/KATY$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101017453-KATY
000168382 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000168382 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168382 700__ $$aTraversa, Pietro$$uUniversidad de Zaragoza
000168382 700__ $$aElati, Mohamed
000168382 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000168382 700__ $$aMarek-Trzonkowska, Natalia
000168382 700__ $$aBattail, Christophe
000168382 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000168382 773__ $$g28, 8 (2025), 113061$$piScience$$tISCIENCE$$x2589-0042
000168382 787__ $$tSample-specific network analysis identifies gene co-expression patterns of immunotherapy response in clear cell renal cell carcinoma$$whttps://zenodo.org/records/15723818
000168382 8564_ $$s10527372$$uhttps://zaguan.unizar.es/record/168382/files/texto_completo.pdf$$yVersión publicada
000168382 8564_ $$s1286888$$uhttps://zaguan.unizar.es/record/168382/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168382 909CO $$ooai:zaguan.unizar.es:168382$$particulos$$pdriver
000168382 951__ $$a2026-02-04-13:14:55
000168382 980__ $$aARTICLE