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The study leverages TCGA transcriptomic profiles with clinical metadata to assign Gleason grade, apply grade-wise linear and network modeling (WGCNA), and extract consensus biomarkers. Two computational strategies converge on 35 grade-discriminative biomarkers, used to train ML classifiers for benign/indolent/aggressive prediction. A RandomForest model reaches ~86% balanced accuracy in external validation, and PRADclass enables trialable risk differentiation of prostate cancers.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/pradclass-hybrid-gleason-grade-informed-computational-strategy-identifies-consensus-biomarker-features-predictive-of-aggressive-prostate-adenocarcinoma/329991/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/pradclass-hybrid-gleason-grade-informed-computational-strategy-identifies-consensus-biomarker-features-predictive-of-aggressive-prostate-adenocarcinoma/329991.png","ImageObject",300,407,{"name":92,"@type":93},"Jiven","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-09","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does PRADclass address in prostate cancer research?","Question",{"text":112,"@type":113},"PRADclass targets the gap in molecular understanding of why prostate adenocarcinomas progress and become aggressive, enabling early recognition to optimize treatment and reduce mortality.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were candidate biomarkers identified?",{"text":117,"@type":113},"Using TCGA transcriptomic data, the approach assigns Gleason grade and runs grade-wise linear modeling with multiple contrasts and grade-wise WGCNA network modeling. Consensus biomarkers come from the overlap of both strategies.",{"name":119,"@type":110,"acceptedAnswer":120},"How well does PRADclass perform in classifying patients?",{"text":121,"@type":113},"A RandomForest model trained on 35 consensus biomarkers for benign/indolent/aggressive ternary classification achieves about 86% balanced accuracy on external validation.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},329991,1790730600,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":31,"preview_page_count":4},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399","PRADclass: Hybrid Gleason Grade-Informed Computational Strategy Identifies Consensus Biomarker Features Predictive of Aggressive Prostate Adenocarcinoma  \nAlex Stanley Balraj, MTech 1, Sangeetha Muthamilselvan, MTech 1  , Rachanaa Raja, BTech2 , and Ashok Palaniappan, PhD 1   \nTechnology in Cancer Research & Treatment  \nVolume 23: 1-20  \n© The Author(s) 2024  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/15330338231222389](DOI: 10.1177/15330338231222389)[ ](DOI: 10.1177/15330338231222389)[journals.sagepub.com/home/tct](journals.sagepub.com/home/tct)  \nAbstract  \nBackground: Prostate adenocarcinoma (PRAD) is a common cancer diagnosis among men globally, yet large gaps in our knowledge persist with respect to the molecular bases of its progression and aggression. It is mostly indolent and slow-growing, but aggressive prostate cancers need to be recognized early for optimising treatment, with a view to reducing mortality. Methods: Based on TCGA transcriptomic data pertaining to PRAD and the associated clinical metadata, we determined the sample Gleason grade, and used it to execute:(i) Gleason-grade wise linear modeling, followed by five contrasts against controls and ten contrasts between grades; and (ii) Gleasongrade wise network modeling via weighted gene correlation network analysis (WGCNA). Candidate biomarkers were obtained from the above analysis and the consensus found. The consensus biomarkers were used as the feature space to train ML models for classifying a sample as benign, indolent or aggressive. Results: The statistical modeling yielded 77 Gleason grade-salient genes while the WGCNA algorithm yielded 1003 trait-specific key genes in grade-wise significant modules. Consensus analysis of the two approaches identified two genes in Grade-1 (SLC43A1 and PHGR1), 26 genes in Grade-4 (including LOC100128675, PPP1R3C, NECAB1, UBXN10, SERPINA5, CLU, RASL12, DGKG, FHL1, NCAM1, and CEND1), and seven genes in Grade-5 (CBX2, DPYS, FAM72B, SHCBP1, TMEM132A, TPX2, UBE2C). A RandomForest model trained and optimized on these 35 biomarkers for the ternary classification problem yielded a balanced accuracy ∼ 86% on external validation. Conclusions: The consensus of multiple parallel computational strategies has unmasked candidate Gleason grade-specific biomarkers. PRADclass, a validated AI model featurizing these biomarkers achieved good performance, and could be trialed to predict the differentiation of prostate cancers. PRADclass is available for academic use at: [https://apalania.shinyapps.io/](https://apalania.shinyapps.io/)  \npradclass (online) and [https://github.com/apalania/pradclass](https://github.com/apalania/pradclass) (command-line interface).  \nKeywords  \nprostate adenocarcinoma, Gleason grading, cancer aggressiveness, WGCNA-based reconstruction, grade-salient gene, traitspecific key gene, consensus biomarker, machine learning, cancer differentiation, risk stratification  \nAbbreviations  \nAI, Artificial Intelligence; CGC, Cancer Gene Census; CV, cross-validation; DEG, Differentially expressed genes; DRE, Digital rectal examination; GO, Gene Ontology; GS, Gene significance; KEGG, Kyoto Encyclopaedia of Genes and Genomes; ISUP, International Society of Urological Pathology; lfc, log fold-change; ME, Module eigengene; MEG, Monotonically expressed genes; MM, Module membership; MS, Module significance; NCG, Network of Cancer Genes; PCA, Principal Components Analysis; PRAD, Prostate adenocarcinoma; PSA, prostate-specific antigen; RSEM, RNA-Seq by Expectation Maximization; TCGA, The Cancer Genome Atlas; TNM, Tumor, Node, Metastasis; TOM, Topological overlap matrix; SVM, Support Vector Machine; WGCNA, Weighted gene co-expression network analysis.  \nReceived: August 22, 2023; Revised: November 18, 2023; Accepted: December 6, 2023.  \n1 Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, ","cbCaismdZ3Kxuhgt","https://ap.wps.com/l/cbCaismdZ3Kxuhgt","pdf",4257203,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What problem does PRADclass address in prostate cancer research?\",\"answer\":\"PRADclass targets the gap in molecular understanding of why prostate adenocarcinomas progress and become aggressive, enabling early recognition to optimize treatment and reduce mortality.\"},{\"question\":\"How were candidate biomarkers identified?\",\"answer\":\"Using TCGA transcriptomic data, the approach assigns Gleason grade and runs grade-wise linear modeling with multiple contrasts and grade-wise WGCNA network modeling. Consensus biomarkers come from the overlap of both strategies.\"},{\"question\":\"How well does PRADclass perform in classifying patients?\",\"answer\":\"A RandomForest model trained on 35 consensus biomarkers for benign/indolent/aggressive ternary classification achieves about 86% balanced accuracy on external validation.\"}]","PRADclass - Hybrid Gleason Grade-Informed Computational Strategy Identifies Consensus Biomarker Features Predictive of Aggressive Prostate Adenocarcinoma | PDF",1789986477]