Identification of small molecule inhibitors against Lin28/let-7 to suppress tumor progression and its alleviation role in LIN28-dependent glucose metabolism.

The reciprocal suppression of an RNA-binding protein LIN28 (human abnormal cell lineage 28) and miRNA Let-7 (Lethal 7) is considered to have a prime role in hepatocellular carcinoma (HCC). Though targeting this inhibition interaction is effective for therapeutics, it causes other unfavorable effects on glucose metabolism and increased insulin resistance. Hence, this study aims to identify small molecules targeting Lin28/let-7 interaction along with additional potency to improve insulin sensitivity. Of 22,14,996 small molecules screened by high throughput virtual screening, 6 molecules, namely 41354, 1558, 12437, 23837, 15710, and 8319 were able to block the LIN28 interaction with let-7 and increase the insulin sensitivity via interacting with PPARγ (peroxisome proliferator-activated receptors γ). MM-GBSA (Molecular Mechanics-Generalized Born Surface Area) analysis is used to re-score the binding affinity of docked complexes. Upon further analysis, it is also seen that these molecules have superior ADME (Absorption, Distribution, Metabolism, and Excretion) properties and form stable complexes with the targets for a significant period in a biologically simulated environment (Molecular Dynamics simulation) for 100 ns. From our results, we hypothesize that these identified 6 small molecules can be potential candidates for HCC treatment and the glucose metabolic disorder caused by the HCC treatment.

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).