{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T06:33:19Z","timestamp":1780641199417,"version":"3.54.1"},"reference-count":69,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":46,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82473739"],"award-info":[{"award-number":["82473739"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82273742"],"award-info":[{"award-number":["82273742"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Applied Basic Research Project of Shanxi Province","award":["202303021211130"],"award-info":[{"award-number":["202303021211130"]}]},{"name":"Shanxi Province Research Funding Project for Returned Overseas Scholars","award":["2024\u2013081"],"award-info":[{"award-number":["2024\u2013081"]}]},{"name":"Shanxi Province Higher Education \u2018Billion Project\u2019 Science and Technology Guidance Project"},{"name":"Open Project Fund from Key Laboratory of Coal Environmental Pathogenicity and Prevention"},{"name":"Ministry of Education, China","award":["MEKLCEPP\/SXMU-202415"],"award-info":[{"award-number":["MEKLCEPP\/SXMU-202415"]}]},{"DOI":"10.13039\/100007709","name":"Michigan State University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007709","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Cancer is a highly heterogeneous disease characterized by complex molecular changes. Subtypes identified through multi-omics data hold significant promise for improving prognosis and facilitating personalized precision treatment. Recent multi-omics integration methods have mostly focused on capturing complementary information from different data types, often overlooking potential interactions between omics data. Here we develop a novel method named interactive multi-kernel learning (iMKL), which incorporates omics-omics interactions alongside heterogeneous data types under the unsupervised multi-kernel learning framework, to improve subtype identification. Using the sample-similarity kernel for each dataset, we propose a joint Hadamard product strategy to capture higher-order interactive effects from different omics data types. We applied iMKL to two renal cell carcinoma (RCC) datasets\u2014clear renal cell carcinoma (ccRCC) and type II papillary renal cell carcinoma (type II pRCC)\u2014both including miRNA expression, mRNA expression, and DNA methylation data. Stability analysis through random sampling of patients or features demonstrated that iMKL exhibits strong robustness and accuracy in identifying patient subtypes. The identified subtypes revealed dramatic differences in patient survival, with both ccRCC and type II pRCC classified into three distinct subtypes. The findings in the real application highlight potential biomarkers associated with adverse patient outcomes and demonstrate substantial advancement in cancer subtype identification. The iMKL method effectively identifies tumor molecular subtypes that are strongly associated with clinical features and survival rates, providing valuable insights for accurate cancer subtyping, clinical decision-making, and the realization of personalized treatment strategies.<\/jats:p>","DOI":"10.1093\/bib\/bbaf687","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T13:28:13Z","timestamp":1764941293000},"source":"Crossref","is-referenced-by-count":3,"title":["Multi-omics data integration for enhanced cancer subtyping via interactive multi-kernel learning"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0315-7156","authenticated-orcid":false,"given":"Hongyan","family":"Cao","sequence":"first","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]},{"name":"MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]},{"name":"Academy of Medical Sciences, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoyang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]},{"name":"Academy of Medical Sciences, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaiqin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]},{"name":"MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Thoracic Oncology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University , No. 99 Longcheng Street, Xiaodian District, Taiyuan, Shanxi 030032 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiling","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]},{"name":"MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhong","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]},{"name":"MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2710-3440","authenticated-orcid":false,"given":"Ping","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Xuzhou Medical University , No. 209 Tongshan Road, Yunlong District, Xuzhou, Jiangsu 221004 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongmei","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]},{"name":"MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4163","authenticated-orcid":false,"given":"Yanbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Health Statistics, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, School of Public Health, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]},{"name":"MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University , No. 56 South Xinjian Road, Yingze District, Taiyuan, Shanxi 030001 ,","place":["PR China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8099-1753","authenticated-orcid":false,"given":"Yuehua","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Statistics and Probability, Michigan State University , 619 Red Cedar Road, East Lansing, MI 48824 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"2025121710545017000_ref1","doi-asserted-by":"publisher","first-page":"5237","DOI":"10.1093\/bioinformatics\/btaa655","article-title":"PAMOGK: A pathway graph kernel-based multiomics approach for patient clustering","volume":"36","author":"Tepeli","year":"2021","journal-title":"Bioinformatics"},{"key":"2025121710545017000_ref2","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1093\/bib\/bby120","article-title":"Graph- and rule-based learning algorithms: A comprehensive review of their applications for cancer type classification and prognosis using genomic data","volume":"21","author":"Mallik","year":"2020","journal-title":"Brief Bioinform"},{"key":"2025121710545017000_ref3","doi-asserted-by":"crossref","first-page":"e1009224","DOI":"10.1371\/journal.pcbi.1009224","article-title":"Evaluation and comparison of multi-omics data integration methods for cancer subtyping","volume":"17","author":"Duan","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"2025121710545017000_ref4","doi-asserted-by":"crossref","first-page":"10546","DOI":"10.1093\/nar\/gky889","article-title":"Multi-omic and multi-view clustering algorithms: Review and cancer benchmark","volume":"46","author":"Rappoport","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2025121710545017000_ref5","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1093\/bib\/bbx167","article-title":"Multi-omics integration\u2014A comparison of unsupervised clustering methodologies","volume":"20","author":"Tini","year":"2019","journal-title":"Brief Bioinform"},{"key":"2025121710545017000_ref6","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1038\/s41467-020-20430-7","article-title":"Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer","volume":"12","author":"Cantini","year":"2021","journal-title":"Nat Commun"},{"key":"2025121710545017000_ref7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12864-015-2223-8","article-title":"Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: Application to cancer molecular classification","volume":"16","author":"Wu","year":"2015","journal-title":"BMC Genomics"},{"key":"2025121710545017000_ref8","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1093\/bib\/bby115","article-title":"Multilevel heterogeneous omics data integration with kernel fusion","volume":"21","author":"Yang","year":"2020","journal-title":"Brief Bioinform"},{"key":"2025121710545017000_ref9","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1101\/gr.215129.116","article-title":"A novel approach for data integration and disease subtyping","volume":"27","author":"Nguyen","year":"2017","journal-title":"Genome Res"},{"key":"2025121710545017000_ref10","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1093\/bioinformatics\/btp543","article-title":"Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis","volume":"25","author":"Shen","year":"2009","journal-title":"Bioinformatics"},{"key":"2025121710545017000_ref11","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nmeth.2810","article-title":"Similarity network fusion for aggregating data types on a genomic scale","volume":"11","author":"Wang","year":"2014","journal-title":"Nat Methods"},{"key":"2025121710545017000_ref12","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1038\/nrg3868","article-title":"Methods of integrating data to uncover genotype\u2013phenotype interactions","volume":"16","author":"Ritchie","year":"2015","journal-title":"Nat Rev Genet"},{"key":"2025121710545017000_ref13","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1093\/bioinformatics\/btx682","article-title":"Unsupervised multiple kernel learning for heterogeneous data integration","volume":"34","author":"Mariette","year":"2018","journal-title":"Bioinformatics"},{"key":"2025121710545017000_ref14","doi-asserted-by":"crossref","first-page":"bbac488","DOI":"10.1093\/bib\/bbac488","article-title":"Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning","volume":"24","author":"Wei","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025121710545017000_ref15","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.jneumeth.2018.08.027","article-title":"A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia","volume":"309","author":"Alam","year":"2018","journal-title":"J Neurosci Methods"},{"key":"2025121710545017000_ref16","first-page":"230","article-title":"Multimodal fusion of brain imaging data: A key to finding the missing link (s) in complex mental illness","volume":"1","author":"Calhoun","year":"2016","journal-title":"Biol Psychiatry: Cognit Neurosci Neuroimaging"},{"key":"2025121710545017000_ref17","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.1109\/TMI.2022.3162870","article-title":"A deep generative\u2013discriminative learning for multimodal representation in imaging genetics","volume":"41","author":"Ko","year":"2022","journal-title":"IEEE Trans Med Imaging"},{"key":"2025121710545017000_ref18","doi-asserted-by":"publisher","first-page":"2706","DOI":"10.1093\/bioinformatics\/btx176","article-title":"Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data","volume":"33","author":"Shi","year":"2017","journal-title":"Bioinformatics"},{"key":"2025121710545017000_ref19","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1214\/12-AOAS545","article-title":"Gene-centric gene\u2013gene interaction: A model-based kernel machine method","volume":"6","author":"Li","year":"2012","journal-title":"Ann Appl Stat"},{"key":"2025121710545017000_ref20","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.neuroimage.2015.01.029","article-title":"A kernel machine method for detecting effects of interaction between multidimensional variable sets: An imaging genetics application","volume":"109","author":"Ge","year":"2015","journal-title":"NeuroImage"},{"key":"2025121710545017000_ref21","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.jmva.2012.08.016","article-title":"ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis","volume":"115","author":"Durrande","year":"2013","journal-title":"J Multivar Anal"},{"key":"2025121710545017000_ref22","doi-asserted-by":"publisher","first-page":"i268","DOI":"10.1093\/bioinformatics\/btv244","article-title":"Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery","volume":"31","author":"Speicher","year":"2015","journal-title":"Bioinformatics"},{"key":"2025121710545017000_ref23","first-page":"90","article-title":"Review on determining number of cluster in K-means clustering","volume":"1","author":"Kodinariya","year":"2013","journal-title":"Int J Adv Res Comput Sci Manag Stud"},{"key":"2025121710545017000_ref24","first-page":"1601","article-title":"Self-tuning spectral clustering","volume":"17","author":"Zelnik-Manor","year":"2004","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2025121710545017000_ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-06921-8","article-title":"Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival","volume":"9","author":"Ramazzotti","year":"2018","journal-title":"Nat Commun"},{"key":"2025121710545017000_ref26","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/978-1-4939-1062-5_25","article-title":"miRWalk database for miRNA-target interactions","volume":"1182","author":"Dweep","year":"2014","journal-title":"Methods Mol Biol"},{"key":"2025121710545017000_ref27","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.biosystems.2014.11.005","article-title":"CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks","volume":"127","author":"Tang","year":"2015","journal-title":"Biosystems"},{"key":"2025121710545017000_ref28","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: Tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Gene Ontol Consort Nat Genet"},{"key":"2025121710545017000_ref29","doi-asserted-by":"publisher","first-page":"D109","DOI":"10.1093\/nar\/gkr988","article-title":"KEGG for integration and interpretation of large-scale molecular data sets","volume":"40","author":"Kanehisa","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2025121710545017000_ref30","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1089\/omi.2011.0118","article-title":"clusterProfiler: An R package for comparing biological themes among gene clusters","volume":"16","author":"Yu","year":"2012","journal-title":"OMICS"},{"key":"2025121710545017000_ref31","doi-asserted-by":"publisher","first-page":"W509","DOI":"10.1093\/nar\/gkaa407","article-title":"TIMER2.0 for analysis of tumor-infiltrating immune cells","volume":"48","author":"Li","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2025121710545017000_ref32","first-page":"1","article-title":"Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression","volume":"17","author":"Becht","year":"2016","journal-title":"Genome Biol"},{"key":"2025121710545017000_ref33","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J Comput Appl Math"},{"key":"2025121710545017000_ref34","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.18421\/TEM103-13","article-title":"Davies bouldin index algorithm for optimizing clustering case studies mapping school facilities","volume":"10","author":"Wijaya","year":"2021","journal-title":"TEM J"},{"key":"2025121710545017000_ref35","doi-asserted-by":"crossref","first-page":"bbae541","DOI":"10.1093\/bib\/bbae541","article-title":"IPFMC: An iterative pathway fusion approach for enhanced multi-omics clustering in cancer research","volume":"25","author":"Zhang","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025121710545017000_ref36","doi-asserted-by":"publisher","first-page":"e8124","DOI":"10.15252\/msb.20178124","article-title":"Multi-omics factor analysis\u2014A framework for unsupervised integration of multi-omics data sets","volume":"14","author":"Argelaguet","year":"2018","journal-title":"Mol Syst Biol"},{"key":"2025121710545017000_ref37","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1093\/biostatistics\/kxx017","article-title":"A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data","volume":"19","author":"Mo","year":"2018","journal-title":"Biostatistics"},{"key":"2025121710545017000_ref38","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","article-title":"Tutorial in biostatistics multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors","volume":"15","author":"Harrell","year":"1996","journal-title":"Stat Med"},{"key":"2025121710545017000_ref39","doi-asserted-by":"crossref","first-page":"3927","DOI":"10.1002\/sim.2427","article-title":"A time-dependent discrimination index for survival data","volume":"24","author":"Antolini","year":"2005","journal-title":"Stat Med"},{"key":"2025121710545017000_ref40","doi-asserted-by":"crossref","first-page":"e0176278","DOI":"10.1371\/journal.pone.0176278","article-title":"Integrative clustering of multi-level \u2018omic data based on non-negative matrix factorization algorithm","volume":"12","author":"Peddada","year":"2017","journal-title":"PLoS One"},{"key":"2025121710545017000_ref41","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1111\/1467-9868.00293","article-title":"Estimating the number of data clusters via the gap statistic","volume":"63","author":"Hastie","year":"2001","journal-title":"J R Stat Soc B"},{"key":"2025121710545017000_ref42","doi-asserted-by":"crossref","first-page":"e89","DOI":"10.1093\/nar\/gky423","article-title":"CancerDetector: Ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data","volume":"46","author":"Li","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2025121710545017000_ref43","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3892\/mco.2024.2719","article-title":"Exploring the anticancer potential of hydrogen sulfide and BAY-876 on clear cell renal cell carcinoma cells: Uncovering novel mutations in VHL and KDR genes among ccRCC patients","volume":"20","author":"Hamadamin","year":"2024","journal-title":"Mol Clin Oncol"},{"key":"2025121710545017000_ref44","doi-asserted-by":"publisher","first-page":"6591","DOI":"10.7150\/jca.49175","article-title":"Biological functions and prognostic value of RNA binding proteins in clear cell renal cell carcinoma","volume":"11","author":"Zhu","year":"2020","journal-title":"J Cancer"},{"key":"2025121710545017000_ref45","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.3390\/jcm11174973","article-title":"Interleukin 17 and its involvement in renal cell carcinoma","volume":"11","author":"Jarocki","year":"2022","journal-title":"J Clin Med"},{"key":"2025121710545017000_ref46","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1158\/1541-7786.MCR-12-0117","article-title":"State of the science: An update on renal cell carcinoma","volume":"10","author":"Jonasch","year":"2012","journal-title":"Mol Cancer Res"},{"key":"2025121710545017000_ref47","doi-asserted-by":"crossref","first-page":"3207","DOI":"10.1038\/s41598-019-39646-9","article-title":"The uremic toxin p-cresyl sulfate induces proliferation and migration of clear cell renal cell carcinoma via microRNA-21\/ HIF-1alpha axis signals","volume":"9","author":"Wu","year":"2019","journal-title":"Sci Rep"},{"key":"2025121710545017000_ref48","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.jgg.2015.03.003","article-title":"The PI3K\/AKT pathway and renal cell carcinoma","volume":"42","author":"Guo","year":"2015","journal-title":"J Genet Genomics"},{"key":"2025121710545017000_ref49","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s12291-016-0611-8","article-title":"Expression of telomere binding proteins (RAP1 and POT1) in renal cell carcinoma and their correlation with Clinicopathological parameters","volume":"32","author":"Pal","year":"2017","journal-title":"Indian J Clin Biochem"},{"key":"2025121710545017000_ref50","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1186\/s12885-021-08818-0","article-title":"Identification of C3 and FN1 as potential biomarkers associated with progression and prognosis for clear cell renal cell carcinoma","volume":"21","author":"Dong","year":"2021","journal-title":"BMC Cancer"},{"key":"2025121710545017000_ref51","doi-asserted-by":"crossref","first-page":"912155","DOI":"10.3389\/fonc.2022.912155","article-title":"A novel prognostic signature associated with the tumor microenvironment in kidney renal clear cell carcinoma","volume":"12","author":"Pei","year":"2022","journal-title":"Front Oncol"},{"key":"2025121710545017000_ref52","doi-asserted-by":"crossref","first-page":"873923","DOI":"10.3389\/fmed.2022.873923","article-title":"Spatially resolved transcriptomes of mammalian kidneys illustrate the molecular complexity and interactions of functional nephron segments","volume":"9","author":"Raghubar","year":"2022","journal-title":"Front Med (Lausanne)"},{"key":"2025121710545017000_ref53","doi-asserted-by":"crossref","first-page":"e004206","DOI":"10.1136\/jitc-2021-004206","article-title":"Single-cell transcriptomics reveals a low CD8(+) T cell infiltrating state mediated by fibroblasts in recurrent renal cell carcinoma","volume":"10","author":"Peng","year":"2022","journal-title":"J Immunother Cancer"},{"key":"2025121710545017000_ref54","doi-asserted-by":"publisher","first-page":"5676","DOI":"10.18632\/aging.205671","article-title":"Identification and validation of a novel signature based on macrophage marker genes for predicting prognosis and drug response in kidney renal clear cell carcinoma by integrated analysis of single cell and bulk RNA sequencing","volume":"16","author":"Chen","year":"2024","journal-title":"Aging (Albany NY)"},{"key":"2025121710545017000_ref55","doi-asserted-by":"crossref","first-page":"e2103240118","DOI":"10.1073\/pnas.2103240118","article-title":"Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response","volume":"118","author":"Zhang","year":"2021","journal-title":"Proc Natl Acad Sci"},{"key":"2025121710545017000_ref56","first-page":"6480865","article-title":"Identification of EGFR as a novel key gene in clear cell renal cell carcinoma (ccRCC) through bioinformatics analysis and meta-analysis","volume":"2019","author":"Wang","year":"2019","journal-title":"Biomed Res Int"},{"key":"2025121710545017000_ref57","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1111\/j.1349-7006.2008.00756.x","article-title":"Tumor-derived tumor necrosis factor-alpha promotes progression and epithelial-mesenchymal transition in renal cell carcinoma cells","volume":"99","author":"Chuang","year":"2008","journal-title":"Cancer Sci"},{"key":"2025121710545017000_ref58","doi-asserted-by":"crossref","first-page":"117","DOI":"10.18632\/oncoscience.13","article-title":"Altered metabolic pathways in clear cell renal cell carcinoma: A meta-analysis and validation study focused on the deregulated genes and their associated networks","volume":"1","author":"Zaravinos","year":"2014","journal-title":"Oncoscience"},{"key":"2025121710545017000_ref59","doi-asserted-by":"crossref","first-page":"6153215","DOI":"10.1155\/2016\/6153215","article-title":"MAPK pathways are involved in neuropathic pain in rats with chronic compression of the dorsal root ganglion","volume":"2016","author":"Qu","year":"2016","journal-title":"Evid Based Complement Alternat Med"},{"key":"2025121710545017000_ref60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/mc.21917","article-title":"MicroRNA target site polymorphisms in the VHL-HIF1alpha pathway predict renal cell carcinoma risk","volume":"53","author":"Wei","year":"2014","journal-title":"Mol Carcinog"},{"key":"2025121710545017000_ref61","doi-asserted-by":"publisher","first-page":"9597","DOI":"10.1111\/jcmm.16903","article-title":"NS398 as a potential drug for autosomal-dominant polycystic kidney disease: Analysis using bioinformatics, and zebrafish and mouse models","volume":"25","author":"Chen","year":"2021","journal-title":"J Cell Mol Med"},{"key":"2025121710545017000_ref62","doi-asserted-by":"crossref","first-page":"101835","DOI":"10.1016\/j.tranon.2023.101835","article-title":"miR-1182-mediated ALDH3A2 inhibition affects lipid metabolism and progression in ccRCC by activating the PI3K-AKT pathway","volume":"40","author":"Lv","year":"2024","journal-title":"Transl Oncol"},{"key":"2025121710545017000_ref63","doi-asserted-by":"crossref","first-page":"815","DOI":"10.7150\/ijbs.20052","article-title":"FOXO signaling pathways as therapeutic targets in cancer","volume":"13","author":"Farhan","year":"2017","journal-title":"Int J Biol Sci"},{"key":"2025121710545017000_ref64","doi-asserted-by":"publisher","first-page":"90","DOI":"10.20892\/j.issn.2095-3941.2016.0086","article-title":"Roles of Rap1 signaling in tumor cell migration and invasion","volume":"14","author":"Zhang","year":"2017","journal-title":"Cancer Biol Med"},{"key":"2025121710545017000_ref65","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1002\/cncr.24841","article-title":"p53 and MDM2 in renal cell carcinoma: Biomarkers for disease progression and future therapeutic targets?","volume":"116","author":"Noon","year":"2010","journal-title":"Cancer"},{"key":"2025121710545017000_ref66","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1186\/s13046-021-02026-1","article-title":"Kidney cancer biomarkers and targets for therapeutics: Survivin (BIRC5), XIAP, MCL-1, HIF1\u03b1, HIF2\u03b1, NRF2, MDM2, MDM4, p53, KRAS and AKT in renal cell carcinoma","volume":"40","author":"Li","year":"2021","journal-title":"J Exp Clin Cancer Res"},{"key":"2025121710545017000_ref67","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1186\/s12935-019-0986-8","article-title":"Overexpression of CENPF correlates with poor prognosis and tumor bone metastasis in breast cancer","volume":"19","author":"Sun","year":"2019","journal-title":"Cancer Cell Int"},{"key":"2025121710545017000_ref68","first-page":"1033","article-title":"Identified differently expressed genes in renal cell carcinoma by using multiple microarray datasets running head: Differently expressed genes in renal cell carcinoma","volume":"18","author":"Cheng","year":"2014","journal-title":"Eur Rev Med Pharmacol Sci"},{"key":"2025121710545017000_ref69","doi-asserted-by":"crossref","first-page":"e982","DOI":"10.1002\/mgg3.982","article-title":"Weighted gene coexpression network analysis identifies a new biomarker of CENPF for prediction disease prognosis and progression in nonmuscle invasive bladder cancer","volume":"7","author":"Shi","year":"2019","journal-title":"Mol Genet Genomic Med"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/6\/bbaf687\/65943721\/bbaf687.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/6\/bbaf687\/65943721\/bbaf687.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T15:55:05Z","timestamp":1765986905000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf687\/8382581"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":69,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf687","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,11]]},"published":{"date-parts":[[2025,11,1]]},"article-number":"bbaf687"}}