Design and development of AI based computational tools for identifying predictive biomarkers and signaling pathways for blood cancer

Abstract

Blood cancer has emerged as a growing concern over the past decade, necessitating early detection for timely and effective treatment. Traditional methods of diagnosing blood cancers involve a series of pathological tests and consultations with medical experts, a process that is not only time-consuming but also financially burdensome. The advent of genomic data analysis offers a promising avenue for understanding the pathogenesis of blood cancers, providing valuable insights into crucial biomarkers that could serve as potential therapeutic targets, ultimately impeding the progression of the disease. In the scope of this study, we have delved into the genomic intricacies of two prominent blood cancer types: Chronic Lymphocytic Leukemia (CLL) and Multiple Myeloma (MM). The treatment decisions for CLL and MM rely heavily on patient symptoms and are underpinned by the genetic anomalies in the patient s genome. Here, we have undertaken a comprehensive omics data analysis, employing novel pipelines and methodologies developed in-house. Our objective has been to unearth the genetic aberrations that underlie these diseases development and identify pivotal biomarkers that hold promise as therapeutic targets for each category of haematological malignancy. Our first objective was to identify clinically relevant small non-coding RNAs (sncRNAs) in CLL through a comprehensive genome-wide study of RNASeq data. This analysis revealed a distinct pattern of dysregulated miRNAs in the CLL cohort. Among these, three miRNAs were up-regulated (hsa-mir-1295a, hsa-mir-155, and hsa-mir-4524a), while five miRNAs were down-regulated (hsa-mir-30a, hsa-mir-423, hsa-mir-486*, hsa-let-7e, and hsa-mir-744). Moreover, our investigation identified seven novel miRNA sequences with elevated expression in CLL, including tRNAs, piRNAs (piRNA-30799, piRNA-36225), and snoRNAs (SNORD43). Notably, we observed a significant correlation between the increased expression of hsa-mir-4524a and a shorter time to first treatment (TTFT) (HR: 1.916,