Transcriptomic Profiling for Prognostic Model Construction and Biomarker Discovery in Colorectal Cancer

Colorectal cancer (CRC), a leading global malignancy with high morbidity and mortality, urgently requires improved predictive models and biomarkers to address its heterogeneous progression and suboptimal survival outcomes. While advances in screening have been made, the five-year survival rate remains low—particularly for advanced-stage patients—highlighting the need for precision-driven strategies. This study leverages next-generation sequencing (NGS) and bioinformatics to systematically analyze transcriptomic profiles of CRC patients and healthy controls. By integrating RNA-seq data with machine learning algorithms, we aim to identify tumor-specific differentially expressed genes (DEGs) and construct a multivariate predictive model capable of stratifying tumor subtypes and forecasting disease progression. Additionally, our approach explores dynamic interactions within the tumor microenvironment, with emphasis on immune modulation mechanisms that may inform novel immunotherapy frameworks. The proposed model seeks to address CRC heterogeneity by unifying multi-dimensional molecular data, ultimately advancing early diagnosis accuracy and personalized therapeutic interventions. This integrative methodology underscores the transformative potential of NGS-driven bioinformatics in refining prognostic tools and improving clinical outcomes for CRC patients.