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We employ advanced feature selection techniques, including sure independence screening (SIS) combined with the least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD), information gain (IG), and permutation variable importance (VIMP) methods, to effectively navigate the challenges posed by ultra-high-dimensional data. Through these methods, we identify critical genes like MED8 as significant markers for LIHC. These markers are further analyzed using advanced survival analysis models, including the Cox proportional hazards model, survival tree, and random survival forests. Our findings reveal that SIS-Lasso demonstrates strong predictive accuracy, particularly in combination with the Cox proportional hazards model. However, when coupled with the random survival forests method, the SIS-VIMP approach achieves the highest overall performance. This comprehensive approach not only enhances the prediction of LIHC outcomes but also provides valuable insights into the genetic mechanisms underlying the disease, thereby paving the way for personalized treatment strategies and advancing the field of cancer genomics.<\/jats:p>","DOI":"10.3390\/e26090767","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T05:06:06Z","timestamp":1725858366000},"page":"767","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Optimizing Prognostic Predictions in Liver Cancer with Machine Learning and Survival Analysis"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2262-3869","authenticated-orcid":false,"given":"Kaida","family":"Cai","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China"},{"name":"Department of Statistics and Actuarial Science, School of Mathematics, Southeast University, Nanjing 211189, China"},{"name":"Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzhi","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, School of Mathematics, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, School of Mathematics, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, School of Mathematics, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanwen","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, School of Mathematics, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyang","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, School of Mathematics, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1056\/NEJMra1713263","article-title":"Hepatocellular carcinoma","volume":"380","author":"Villanueva","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wu, X.N., Xue, F., Zhang, N., Zhang, W., Hou, J.J., Lv, Y., Xiang, J.X., and Zhang, X. (2024). Global burden of liver cirrhosis and other chronic liver diseases caused by specific etiologies from 1990 to 2019. BMC Public Health, 24.","DOI":"10.1186\/s12889-024-17948-6"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.jhep.2018.09.014","article-title":"Burden of liver diseases in the world","volume":"70","author":"Asrani","year":"2019","journal-title":"J. Hepatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1056\/NEJMoa0708857","article-title":"Sorafenib in advanced hepatocellular carcinoma","volume":"359","author":"Llovet","year":"2008","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1056\/NEJMoa1606774","article-title":"Pembrolizumab versus chemotherapy for PD-L1-Positive Non-Small-Cell lung cancer","volume":"375","author":"Reck","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/j.1467-9868.2008.00674.x","article-title":"Sure independence screening for ultrahigh dimensional feature space","volume":"70","author":"Fan","year":"2008","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_7","first-page":"70","article-title":"High-dimensional variable selection for Cox\u2019s proportional hazards model","volume":"Volume 6","author":"Fan","year":"2010","journal-title":"Borrowing Strength: Theory Powering Applications\u2014A Festschrift for Lawrence D. Brown"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.jmva.2011.08.002","article-title":"Principled sure independence screening for Cox models with ultra-high-dimensional covariates","volume":"105","author":"Zhao","year":"2012","journal-title":"J. Multivar. Anal."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1198\/jasa.2011.tm10563","article-title":"Model-Free feature screening for ultrahigh-dimensional data","volume":"106","author":"Zhu","year":"2011","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1198\/016214501753382273","article-title":"Variable selection via nonconcave penalized likelihood and its oracle properties","volume":"96","author":"Fan","year":"2001","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_12","first-page":"18","article-title":"Feature selection based on information gain","volume":"2","author":"Azhagusundari","year":"2013","journal-title":"Int. J. Innov. Technol. Explor. Eng. (IJITEE)"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e41","DOI":"10.5808\/GI.2019.17.4.e41","article-title":"Review of statistical methods for survival analysis using genomic data","volume":"17","author":"Lee","year":"2019","journal-title":"Genom. Inform."},{"key":"ref_14","unstructured":"Lawless, J.F. (2011). Statistical Models and Methods for Lifetime Data, John Wiley & Sons."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3214306","article-title":"Machine learning for survival analysis: A survey","volume":"51","author":"Wang","year":"2019","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_16","first-page":"1065","article-title":"Tree-structured survival analysis","volume":"69","author":"Gordon","year":"1985","journal-title":"Cancer Treat. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Khan, F.M., and Zubek, V.B. (2008, January 15\u201319). Support vector regression for censored data (SVRc): A novel tool for survival analysis. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.","DOI":"10.1109\/ICDM.2008.50"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1002\/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3","article-title":"The lasso method for variable selection in the Cox model","volume":"16","author":"Tibshirani","year":"1997","journal-title":"Stat. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1214\/aos\/1015362185","article-title":"Variable selection for Cox\u2019s proportional hazards model and frailty model","volume":"30","author":"Fan","year":"2002","journal-title":"Ann. Stat."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Robinson, M., and Oshlack, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol., 11.","DOI":"10.1186\/gb-2010-11-3-r25"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s11222-016-9646-1","article-title":"Correlation and variable importance in random forests","volume":"27","author":"Gregorutti","year":"2017","journal-title":"Stat. Comput."},{"key":"ref_23","first-page":"25","article-title":"Random survival forests for R","volume":"7","author":"Ishwaran","year":"2007","journal-title":"R News"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5381","DOI":"10.1002\/sim.5958","article-title":"Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks","volume":"32","author":"Blanche","year":"2013","journal-title":"Stat. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"STRING v11: Protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jin, X., Song, Y., An, Z., Wu, S., Cai, D., Fu, Y., Zhang, C., Chen, L., Tang, W., and Zheng, Z. (2022). A predictive model for prognosis and therapeutic response in hepatocellular carcinoma based on a panel of three MED8-related immunomodulators. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.868411"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8556888","DOI":"10.1155\/2021\/8556888","article-title":"SLC41A3 exhibits as a carcinoma biomarker and promoter in liver hepatocellular carcinoma","volume":"2021","author":"Chang","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.2147\/OTT.S296187","article-title":"High expression of SLC41A3 correlates with poor prognosis in hepatocellular carcinoma","volume":"14","author":"Li","year":"2021","journal-title":"OncoTargets Ther."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/9\/767\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:50:13Z","timestamp":1760111413000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/9\/767"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,7]]},"references-count":28,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["e26090767"],"URL":"https:\/\/doi.org\/10.3390\/e26090767","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,7]]}}}