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

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).

Design, dynamic docking, synthesis, and in vitro validation of a novel DNA gyrase B inhibitor.

Methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-intermediate-resistant Staphylococcus aureus (VRSA) are among the WHO's high priority pathogens. Among these two, MRSA is the most globally documented pathogen that necessitates the pressing demand for new classes of anti-MRSA drugs. Bacterial gyrase targeted therapeutics are unique strategies to overcome cross-resistance as they are present only in bacteria and absent in higher eukaryotes. The GyrB subunit is essential for the catalytic functions of the bacterial enzyme DNA Gyrase, thereby constituting a promising druggable target. The current study performed a structure-based virtual screening to designing GyrB target-specific candidate molecules. The de novo ligand design of novel hit molecules was performed using a rhodanine scaffold. Through a systematic in silico screening process, the hit molecules were screened for their synthetic accessibility, drug-likeness and pharmacokinetics properties in addition to its target specific interactions. Of the 374 hit molecules obtained through de novo ligand design, qsl-304 emerged as the most promising ligand. The molecular dynamic simulation studies confirmed the stable interaction between the key residues and qsl-304. qsl-304 was synthesized through a one-step chemical synthesis procedure, and the in vitro activity was proven, with an IC50 of 31.23 µg/mL against the novobiocin resistant clinical isolate, Staphylococcus aureus sa-P2003. Further studies on time-kill kinetics showed the bacteriostatic nature with the diminished recurrence of resistance. The on-target gyrB inhibition further proved the efficacy of qsl-304.Communicated by Ramaswamy H. Sarma.