{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T12:14:41Z","timestamp":1771416881120,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006545","name":"the National Institutes on Minority Health and Health Disparities","doi-asserted-by":"publisher","award":["5U54MD007592"],"award-info":[{"award-number":["5U54MD007592"]}],"id":[{"id":"10.13039\/100006545","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Prostate cancer (PrCa), the second most common cancer diagnosed in men globally, remains a critical challenge in precision oncology. While PrCa can be deadly, it is highly treatable if detected early. Identifying associative factors influencing disease progression risks can help inform preliminary steps that will further the expedition of clinical therapeutic intervention decisions, which will improve treatment outcomes. While conventional PrCa progression assessment tools rely heavily on a few clinical parameters, the importance of genomic information is increasingly recognized. In this study, we evaluate the prognostic value of patients\u2019 clinicogenomic profiles in modeling progression-free survival (PFS) of PrCa. Three survival models, namely the penalized Cox model, random survival forest, and a deep learning survival neural network, were deployed with extensive tuning applied to a dataset for a cohort of 494 patients with PrCa. This dataset, compiled from public data in The Cancer Genome Atlas (TCGA) accessed via cBioPortal, consists of relevant clinical features and single-nucleotide variant information on likely PrCa-related genes. The survival models demonstrated satisfactory discriminatory performance, with Harrell\u2019s concordance index ranging from approximately 0.80 to 0.87 on held-out test data, indicating their ability to rank patients according to their relative progression risk among patients, while exhibiting distinct dynamics, all three models consistently identified clinical variables that indicated neoadjuvant treatment history, neoplasm cancer status, and tumor recurrence as well as the gene MYH6 as important predictor variables for PrCa PFS. Our findings suggest the incorporation of genomic data into the survival modeling workflow, thereby allowing the use of integrated clinicogenomics information to gain insights into progression risks for patients with PrCa.<\/jats:p>","DOI":"10.3390\/ijerph23020256","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T11:45:58Z","timestamp":1771415158000},"page":"256","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinicogenomic Insights for Progression-Free Survival in Prostate Cancer"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4169-2459","authenticated-orcid":false,"given":"Kelvin","family":"Ofori-Minta","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3651-6802","authenticated-orcid":false,"given":"Bofei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Leukemia, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4440-4608","authenticated-orcid":false,"given":"Jonathon E.","family":"Mohl","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA"},{"name":"Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhijit","family":"Mandal","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA"},{"name":"Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2035-1214","authenticated-orcid":false,"given":"Ming-Ying","family":"Leung","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA"},{"name":"Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"ref_1","unstructured":"Mayo Clinic (2025, June 23). Metastatic Prostate Cancer\u2014Symptoms and Causes. Available online: https:\/\/www.mayoclinic.org\/diseases-conditions\/metastatic-prostate-cancer\/symptoms-causes\/syc-20377966."},{"key":"ref_2","unstructured":"Healthline (2025, June 23). Aggressive Prostate Cancer: What It Is and How It\u2019s Treated. Available online: https:\/\/www.healthline.com\/health\/prostate-cancer\/aggressive-prostate-cancer."},{"key":"ref_3","unstructured":"American Cancer Society (2025). Cancer Facts & Figures 2025, American Cancer Society. Available online: https:\/\/www.cancer.org\/research\/cancer-facts-statistics\/all-cancer-facts-figures\/2025-cancer-facts-figures.html."},{"key":"ref_4","first-page":"10","article-title":"Cancer statistics, 2025","volume":"75","author":"Siegel","year":"2025","journal-title":"CA A Cancer J. Clin."},{"key":"ref_5","first-page":"686","article-title":"Serum and urine biomarkers for detecting clinically significant prostate cancer","volume":"39","author":"Becerra","year":"2020","journal-title":"Urol. Oncol. Semin. Orig. Investig."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.pathol.2020.10.003","article-title":"Molecular pathology of prostate cancer: A practical approach","volume":"53","author":"Vlajnic","year":"2020","journal-title":"Pathology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.trecan.2024.06.005","article-title":"Metastatic hormone-na\u00efve prostate cancer: A distinct biological entity","volume":"10","author":"Zacchi","year":"2024","journal-title":"Trends Cancer"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1158\/2159-8290.CD-23-0882","article-title":"The Germline and Somatic Origins of Prostate Cancer Heterogeneity","volume":"15","author":"Yamaguchi","year":"2025","journal-title":"Cancer Discov."},{"key":"ref_9","unstructured":"Leslie, S.W., Soon-Sutton, T.L., and Skelton, W.P. (2025, June 23). Prostate Cancer, StatPearls, Available online: http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK470550\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1200\/JCO.23.01535","article-title":"Radiographic Progression-Free Survival and Clinical Progression-Free Survival as Potential Surrogates for Overall Survival in Men with Metastatic Hormone-Sensitive Prostate Cancer","volume":"42","author":"Halabi","year":"2024","journal-title":"J. Clin. Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1038\/s41416-020-0805-y","article-title":"Progression-free survival as a surrogate for overall survival in oncology trials: A methodological systematic review","volume":"122","author":"Belin","year":"2020","journal-title":"Br. J. Cancer"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"368","DOI":"10.5534\/wjmh.210072","article-title":"Genomic Profiling of Prostate Cancer: An Updated Review","volume":"40","author":"Hatano","year":"2022","journal-title":"World J. Men\u2019s Health"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1158\/2767-9764.CRC-24-0210","article-title":"Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States","volume":"4","author":"Chen","year":"2024","journal-title":"Cancer Res. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4601","DOI":"10.1038\/s41467-021-24919-7","article-title":"An integrated functional and clinical genomics approach reveals genes driving aggressive metastatic prostate cancer","volume":"12","author":"Das","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e259128","DOI":"10.1001\/jamanetworkopen.2025.9128","article-title":"Race, Ethnicity, and Tumor Genomic Testing in Prostate Cancer","volume":"8","author":"Ozay","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, J., Chen, Z., Sun, K., Wu, X., Wu, J., and Sheng, L. (2022). A seven-gene prognosis model to predict biochemical recurrence for prostate cancer based on the TCGA database. Front. Surg., 9.","DOI":"10.3389\/fsurg.2022.923473"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7875","DOI":"10.1038\/s41598-023-35023-9","article-title":"Accurate prognosis for localized prostate cancer through coherent voting networks with multi-omic and clinical data","volume":"13","author":"Pellegrini","year":"2023","journal-title":"Sci Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1158\/2159-8290.CD-12-0095","article-title":"The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data","volume":"2","author":"Cerami","year":"2012","journal-title":"Cancer Discov."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"pl1","DOI":"10.1126\/scisignal.2004088","article-title":"Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal","volume":"6","author":"Gao","year":"2013","journal-title":"Sci. Signal."},{"key":"ref_20","first-page":"1","article-title":"mice: Multivariate Imputation by Chained Equations in R","volume":"45","year":"2011","journal-title":"J. Stat. Softw."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, B., Mohl, J., and Leung, M.-Y. (2020, January 16\u201319). Computational Prediction of Functional Effects for Cancer Related Genetic Sequence Variants. Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Online.","DOI":"10.1109\/BIBM49941.2020.9313326"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1093\/bioinformatics\/btx536","article-title":"FATHMM-XF: Accurate prediction of pathogenic point mutations via extended features","volume":"34","author":"Rogers","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2745","DOI":"10.1093\/bioinformatics\/btv195","article-title":"PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels","volume":"31","author":"Choi","year":"2015","journal-title":"Bioinformatics"},{"key":"ref_24","unstructured":"Wang, B. (2021). Identification Of Prostate Cancer-Associated Genomic Alterations By Analyzing Variant Frequencies, Functional Effects, And Protein Interactions. [Doctoral Dissertation, University of Texas at El Paso]. Available online: https:\/\/scholarworks.utep.edu\/open_etd\/3459."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kotz, S., and Johnson, N.L. (1992). Nonparametric Estimation from Incomplete Observations. Breakthroughs in Statistics: Methodology and Distribution, Springer.","DOI":"10.1007\/978-1-4612-4380-9"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"185","DOI":"10.2307\/2344317","article-title":"Asymptotically Efficient Rank Invariant Test Procedures","volume":"135","author":"Peto","year":"1972","journal-title":"J. R. Stat. Society. Ser. A (Gen.)"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.1001\/jama.1982.03320430047030","article-title":"Evaluating the yield of medical tests","volume":"247","author":"Harrell","year":"1982","journal-title":"JAMA"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.eswa.2016.07.018","article-title":"On the use of Harrell\u2019s C for clinical risk prediction via random survival forests","volume":"63","author":"Schmid","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.3348\/kjr.2021.0223","article-title":"Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches)","volume":"22","author":"Park","year":"2021","journal-title":"Korean J. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v106.i01","article-title":"Elastic Net Regularization Paths for All Generalized Linear Models","volume":"106","author":"Tay","year":"2023","journal-title":"J. Stat. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"btad723","DOI":"10.1093\/bioinformatics\/btad723","article-title":"survex: An R package for explaining machine learning survival models","volume":"39","author":"Spytek","year":"2023","journal-title":"Bioinformatics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1214\/08-AOAS169","article-title":"Random survival forests","volume":"2","author":"Ishwaran","year":"2008","journal-title":"Ann. Appl. Stat."},{"key":"ref_33","unstructured":"Ishwaran, H., and Kogalur, U.B. (2024, November 30). Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). Manual. Available online: https:\/\/cran.r-project.org\/package=randomForestSRC."},{"key":"ref_34","unstructured":"Ishwaran, H., Lu, M., and Kogalur, U.B. (2021). RandomForestSRC: Variable Importance (VIMP) with Subsampling Inference Vignette. RFSRC."},{"key":"ref_35","unstructured":"Ishwaran, H., Chen, X., Minn, A.J., Lu, M., Lauer, M.S., and Kogalur, U.B. (2021). RandomForestSRC: Minimal Depth Vignette. RFSRC."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., and Kluger, Y. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol., 18.","DOI":"10.1186\/s12874-018-0482-1"},{"key":"ref_37","unstructured":"Li, J. (2024, November 30). Jinli-Stat\/DeepSurv-R-Keras. Available online: https:\/\/github.com\/jinli-stat\/DeepSurv-R-Keras."},{"key":"ref_38","unstructured":"Kingma, D.P., and Ba, L.J. (2024, November 30). Adam: A Method for Stochastic Optimization. Available online: https:\/\/dare.uva.nl\/search?identifier=a20791d3-1aff-464a-8544-268383c33a75."},{"key":"ref_39","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017). Self-Normalizing Neural Networks. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s10107-012-0629-5","article-title":"Gradient methods for minimizing composite functions","volume":"140","author":"Nesterov","year":"2013","journal-title":"Math. Program."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Senior, A., Heigold, G., Ranzato, M., and Yang, K. (2013). An empirical study of learning rates in deep neural networks for speech recognition. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.","DOI":"10.1109\/ICASSP.2013.6638963"},{"key":"ref_42","unstructured":"Simonyan, K., Vedaldi, A., and Zisserman, A. (2013). Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arXiv."},{"key":"ref_43","unstructured":"Sundararajan, M., Taly, A., and Yan, Q. (2017, January 6\u201311). Axiomatic Attribution for Deep Networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia. Available online: https:\/\/api.semanticscholar.org\/CorpusID:16747630."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, O.A.M., L\u00f3pez, A.M., and Crossa, D.J. (2022). Overfitting, Model Tuning, and Evaluation of Prediction Performance. Multivariate Statistical Machine Learning Methods for Genomic Prediction, Springer.","DOI":"10.1007\/978-3-030-89010-0_4"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"19685","DOI":"10.1038\/s41598-024-70665-3","article-title":"MYH6 suppresses tumor progression by downregulating KIT expression in human prostate cancer","volume":"14","author":"Wang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_46","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: https:\/\/books.google.com\/books?id=Np9SDQAAQBAJ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. Available online: https:\/\/books.google.com\/books?id=eBSgoAEACAAJ.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.1001\/jama.2016.16435","article-title":"Confounding by Indication in Clinical Research","volume":"316","author":"Kyriacou","year":"2016","journal-title":"JAMA"}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/23\/2\/256\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T11:48:47Z","timestamp":1771415327000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/23\/2\/256"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,18]]},"references-count":48,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["ijerph23020256"],"URL":"https:\/\/doi.org\/10.3390\/ijerph23020256","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,18]]}}}