Sanjukta Dasgupta

Artificial Intelligence-Driven Multiomics Integration in Lung Cancer: From Data Convergence to Precision Phenomics.

Lung cancer remains a leading cause of cancer-related mortality worldwide due to its extensive molecular heterogeneity, late-stage diagnosis, and therapeutic resistance. Advances in high-throughput omics technologies have enabled comprehensive characterization of tumors across multiple biological layers, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. However, single-omics analyses provide only fragmented insights into tumor biology, highlighting the need for integrative multiomics approaches. Artificial intelligence (AI), particularly machine learning and deep learning, has emerged as a powerful tool for integrating heterogeneous datasets and uncovering biologically and clinically relevant patterns. This review summarizes recent advances in AI-driven multiomics integration for lung cancer, highlighting its applications in molecular subtyping, biomarker discovery, prognosis prediction, therapeutic response modeling, and precision oncology. We also discuss current challenges, including data heterogeneity, model interpretability, reproducibility, and clinical translation, together with emerging strategies for integrating multimodal data such as radiomics and digital pathology. Finally, we introduce precision phenomics as a unifying framework that links molecular, spatial, functional, and clinical characteristics of tumors to support personalized cancer management. Collectively, AI-driven multiomics integration has the potential to transform lung cancer research and improve patient outcomes.

Deciphering the role of sirtuins in cancer immunoregulation through molecular cross-talk and docking-based interaction

Sustainable bioconversion of sugarcane bagasse into valorizable riboflavin by Microbacterium proteolyticum BWBTDIPO1, characterization and in-silico mechanistic pathway prediction

Strategic bioconversion of lignocellulosic agro-wastes like sugarcane bagasse (SCB) into high-value Riboflavin is a rarely reported phenomenon. The present study explored a Riboflavin-producing bacterial strain, Microbacterium proteolyticum BWBTDIPO1 (GenBank Acc no.: PQ517523), isolated from the dumping area of Kolkata, West Bengal for its caliber to utilize SCB as a source of carbon and energy for the sustainable bio-production of the valuable nutraceutical under submerged fermentation (SmF) conditions. A strong association of bacterial growth (specific growth rate of μ = 0.325 h -1 ) with production of Riboflavin (yield of 397 ± 15.8 mgL - 1, which amounts to 19.85 ± 2.0 mg per gram of SCB after 78 h) was recorded. Yeast extract peptone mineral salt media (YPMSM) was found to be the most suitable medium for the production. UV-Vis spectrophotometry and thin-layer chromatography (TLC) with an R f value of 0.83 confirmed that the metabolite was Riboflavin. The biochemical assays revealed that the strain utilized SCB components, including lignin, cellulose, and hemicellulose. Further confirmation of biomass deconstruction was assessed via FTIR, XRD, and FESEM analysis, where reports ensured successful deconstruction of SCB by the bacterial isolate. To complement the experimental findings, KEGG-based in silico pathway analysis was performed to explore potential metabolic routes associated with lignocellulosic biomass utilization and carbon metabolism. The predicted pathways provide a hypothetical framework for understanding biomass deconstruction and require experimental validation. This is the first report of Riboflavin production from waste SCB via bacterial treatment (M. proteolyticum) , indicating the novel nature of the strain and the methodology employed. Valorizable Riboflavin obtained from waste SCB can be a green alternative to chemical Riboflavin synthesis as well as bulk SCB waste management, promoting the concept of microbe-mediated waste-to-wealth conversion, thus contributing to circular bioeconomy.