Frontiers in Genetics (Nov 2023)
Computational profiling and prognostic modeling based on lysosome-related genes in colorectal cancer
Abstract
Background: Despite significant advances over the past decade, patients diagnosed with advanced colorectal cancer (CRC) continue to face unfavorable prognoses. Recent studies have underscored the pivotal role of lysosomes in tumor development and progression. This led us to postulate and develop a novel lysosomal-centric model for predicting CRC risk and therapeutic response.Methods: CRC tissue samples were sourced from the TCGA database, while lysosome-associated genes were collated from the GSEA database. Differentially expressed lysosome-related genes (DE-LRGs) were discerned by contrasting tumor samples with normal tissue. Based on the expression profile of DE-LRGs, patients were stratified into two distinct clusters. Survival disparities between the clusters were delineated using Kaplan-Meier estimators. For tumor microenvironment assessment, we employed ESTIMATE and ssGSEA. Functional pathway enrichment was ascertained using both GSVA and GSEA. Subsequent uni- and multi-variate Cox regression analyses pinpointed risk-associated DE-LRGs. Leveraging these genes, we constructed a novel risk prediction model and derived risk scores. The model’s prognostic capability was externally validated using dataset GSE39084. The mutational landscape across risk categories was evaluated using the Maftools algorithm. The potential efficacy of targeted and immunotherapeutic interventions for each patient cohort was gauged using pRRophetic, CYT, and IMvigor210.Results: We identified 46 DE-LRGs. Tumor Immune MicroEnvironment (TIME) assessment revealed that cluster 2 patients exhibited elevated ESTIMATE, Immunocore, and stromal scores, yet diminished tumor purity relative to cluster 1. Notable differences in immune cell infiltration patterns were observed between clusters, and distinct pathway enrichments were evident. Cluster 2 manifested a pronounced expression of immune checkpoint-related genes. Four DE-LRGs (ATP6V0A4, GLA, IDUA, and SLC11A1) were deemed critical for risk association, leading to the formulation of our novel risk model. The model exhibited commendable predictive accuracy, which was corroborated in an external validation cohort. A palpable survival advantage was observed in high-TMB, low-risk subgroups. Moreover, the low-risk cohort displayed heightened sensitivity to both targeted and immunotherapeutic agents.Conclusion: Our findings underscore the potential of lysosome-associated genes as robust prognostic and therapeutic response markers in CRC patients.
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