Survival Analysis on Identified Genes in Acute Myeloid Leukemia Based on TCGA Database

Abstract

Objective: This study aims to develop a comprehensive prognostic model for acute myeloid leukemia (AML) by integrating genomic and clinical factors. AML is a prevalent malignant bone marrow disorder with a significant impact on adult populations. Despite existing knowledge about certain prognostic genes, a holistic model considering both genomic and clinical variables for assessing overall survival is lacking. This research endeavors to fill this gap by analyzing gene expression profiles and clinical attributes from The Cancer Genome Atlas (TCGA) database, with a focus on determining the influence of these factors on AML patient survival by incorporating disease-associated genes sourced from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. Methods: We conducted an analysis of complete gene expression profiles and clinical data from 173 AML patients within the TCGA database. Utilizing advanced statistical techniques, we explored the relationships between gene expression levels, clinical features, and patient survival. Disease-related genes identified from the KEGG pathway database were integrated into the analysis to enhance the model’s predictive power. Cox proportional hazards regression and machine learning algorithms were employed to develop and optimize the prognostic model. Results: Our analysis revealed substantial insights into the impact of gene expression patterns and clinical attributes on the survival of AML patients. By incorporating disease-associated genes from the KEGG pathway database, we observed a notable enhancement in the model’s ability to predict survival outcomes. The optimized prognostic model successfully integrated both genomic and clinical factors, providing a more accurate assessment of AML patient survival. Conclusion: This study underscores the significance of combining genomic and clinical factors in predicting survival outcomes for AML patients. Our comprehensive prognostic model, enriched by disease-related genes from the KEGG pathway database, offers an innovative approach to enhancing the accuracy of survival predictions. By shedding light on the intricate interplay between gene expression profiles and clinical attributes, this research contributes to a deeper understanding of AML prognosis and paves the way for more effective personalized treatment strategies.