Analysis of TCGA (The Cancer Genome Atlas) Data for Prognosis, Risk Categorization, and Survival of AML Patients Using Bioinformatics - Rishi, Bhavika; Zaman, Shamsuz; Siraj, Fouzia; Vishmaya, Sree; Dhanda, Himanshu; Kaur, Manpreet; Swain, Sandeep; Noor, Baseer; Chaudhry, Sumita; Jain, Ankur; Prabhakar, Prashant; Singh, Amitabh; Misra, Aroonima (Indian Journal of Medical and Paediatric Oncology, 2025)
Introduction Acute myeloid leukemia (AML) is a leading cause of mortality among Indian children and adults, driven by diverse genetic and epigenetic abnormalities. Limited access to genomic sequencing in India due to resource constraints has hindered a comprehensive understanding of prognostic factors specific to this population.Objectives This study aims to analyze publicly available genomic data using statistical and bioinformatics tools to identify key prognostic markers relevant to Indian AML patients.Materials and Methods The study utilized tumor/normal pair data from 200 adult de novo AML patients, obtained from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression database, analyzed using cBioPortal. Statistical and bioinformatics tools were employed to assess the impact of existing prognostic targets on disease response and to identify variables with clinical relevance and practical testing feasibility.Results Analysis of the TCGA-AML data set identified high-frequency gene mutations (≥ 10%) and well-defined cytogenetic subtypes, including t(8;21)(q22;q22), NPM1 mutations, and CEBPA mutations as key factors for future prognostic evaluation. These findings will contribute to the development of a prognostic scoring system using R programming in future.Conclusion This study offers insights into the cytogenetic and mutational landscape of AML in the Indian population, identifying critical genetic and cytogenetic markers with the potential to enhance prognostication, guide treatment strategies, and inform transplant decisions. Using R tools like limma and edgeR, differential expression analysis identified five key genes—NPM1, FLT3, IDH2, RUNX1, and STAG2—as significantly upregulated in AML. Notably, STAG2 emerges as a novel marker with potential prognostic significance, warranting validation in larger Indian cohorts. These findings may help uncover novel therapeutic targets not currently recognized in Western populations, paving the way for a more tailored and personalized approach to treatment in India. By leveraging global genomic databases, this research addresses regional gaps in knowledge. Future work should focus on validating these findings through large-scale studies in Indian cohorts to ensure their broader applicability and impact.
Keywords: bioinformatics; TCGA data; AML genetics; prognosis in AML; risk categorization