PRADclass: Hybrid Gleason Grade-Informed Computational Strategy Identifies Consensus Biomarker Features Predictive of Aggressive Prostate Adenocarcinoma.

Autor:	Balraj AS; Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India., Muthamilselvan S; Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India., Raja R; Department of Pharmaceutical Technology, UCE, Anna University (BIT campus), Trichy, India., Palaniappan A; Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India.
Jazyk:	angličtina
Zdroj:	Technology in cancer research & treatment [Technol Cancer Res Treat] 2024 Jan-Dec; Vol. 23, pp. 15330338231222389.
DOI:	10.1177/15330338231222389
Abstrakt:	Background: 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) Gleason-grade 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/pradclass (online) and https://github.com/apalania/pradclass (command-line interface). Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Databáze:	MEDLINE
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