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Network diffusion methods such as Random Walk with Restart (RWR) can propagate mutation signals across protein\u2013protein interaction (PPI) networks to address sparsity, yet their integration within a deep learning Cox survival framework has not been comprehensively benchmarked across multiple cancer cohorts. We present RCoxNet, a deep learning framework that maps somatic mutation profiles onto a ConsensusPathDB-derived PPI network via RWR, selects prognostic genes by log-rank filtering, and processes network-informed mutation scores through three fully connected hidden layers feeding into a Cox proportional hazards output. RCoxNet was evaluated on The Cancer Genome Atlas (TCGA) cohorts for four cancer types (breast invasive carcinoma [BRCA], lung adenocarcinoma [LUNG], glioblastoma multiforme [GBM], and ovarian serous cystadenocarcinoma [OV]) using 20 independent random splits. The model achieved mean C-index values of 0.807 \u00b1 0.044 (BRCA), 0.750 \u00b1 0.039 (LUNG), 0.704 \u00b1 0.041 (GBM), and 0.668 \u00b1 0.036 (OV), consistently outperforming DeepSurv, Cox-nnet, SurvivalNet, Cox Elastic-Net (Cox-EN), and DeepHit, with statistically significant gains over Cox-EN, Cox-nnet, SurvivalNet, and DeepHit across the majority of cohorts. RCoxNet demonstrates that embedding sparse mutation profiles into a PPI network context substantially improves cancer survival prediction and yields biologically interpretable prognostic features relevant to precision oncology.<\/jats:p>","DOI":"10.1177\/15578666261486475","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T09:53:05Z","timestamp":1789465985000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["RCoxNet: A Deep Learning Framework Integrating Random Walk with Restart, Mutation, and Clinical Data for Cancer Survival Prediction"],"prefix":"10.1177","author":[{"given":"Stuti","family":"Kumari","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, IIIT-Delhi, New Delhi, India."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sakshi","family":"Gujral","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, IIIT-Delhi, New Delhi, India."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhishek","family":"Halder","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, IIIT-Delhi, New Delhi, India."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Smruti","family":"Panda","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, IIIT-Delhi, New Delhi, India."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernadette","family":"Mathew","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, IIIT-Delhi, New Delhi, India."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prashant","family":"Gupta","sequence":"additional","affiliation":[{"name":"Wellcome Sanger Institute, Hinxton, UK."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ralf","family":"Herwig","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Molecular Genetics, Berlin, Germany."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaurav","family":"Ahuja","sequence":"additional","affiliation":[{"name":"Department of Computational Biology, IIIT-Delhi, New Delhi, India."},{"name":"Center for Artificial Intelligence, IIIT-Delhi, New Delhi, India."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6353-5411","authenticated-orcid":false,"given":"Debarka","family":"Sengupta","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, IIIT-Delhi, New Delhi, India."},{"name":"Department of Computational Biology, IIIT-Delhi, New Delhi, India."},{"name":"Center for Artificial Intelligence, IIIT-Delhi, New Delhi, India."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1172\/JCI133081"},{"key":"e_1_3_4_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2018.00621"},{"key":"e_1_3_4_4_1","doi-asserted-by":"publisher","DOI":"10.1158\/2159-8290.CD-12-0095"},{"key":"e_1_3_4_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2021-004485"},{"key":"e_1_3_4_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006076"},{"key":"e_1_3_4_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg.2017.38"},{"key":"e_1_3_4_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1972.tb00899.x"},{"key":"e_1_3_4_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-10138-8"},{"key":"e_1_3_4_10_1","doi-asserted-by":"publisher","DOI":"10.1126\/scisignal.2004088"},{"key":"e_1_3_4_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.903461"},{"key":"e_1_3_4_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac636"},{"key":"e_1_3_4_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-009-0674-9"},{"key":"e_1_3_4_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2020.05.008"},{"key":"e_1_3_4_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12920-020-0686-1"},{"key":"e_1_3_4_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.breast.2023.06.001"},{"key":"e_1_3_4_17_1","doi-asserted-by":"publisher","DOI":"10.3389\/fcell.2021.721505"},{"key":"e_1_3_4_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkab1128"},{"key":"e_1_3_4_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-018-0482-1"},{"key":"e_1_3_4_20_1","unstructured":"Kingma DP Ba J. 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