Prognostic value of genes associated with metastasis and propionate metabolism in rectal cancer.

Background: Research indicates that alterations in propionate metabolic pathways play a critical role in cancer development and invasion. Postoperative metastatic recurrence remains a major cause of mortality in patients with rectal cancer. However, propionate metabolism-related genes (PMRGs) in rectal cancer remain insufficiently characterized. Therefore, this study aimed to identify prognostic biomarkers associated with lymph node metastasis and propionate metabolism and construct a risk‑prediction model for rectal cancer via bioinformatic analyses. Methods: The Cancer Genome Atlas-Rectum Adenocarcinoma (TCGA-READ) and GSE87211 datasets, together with a curated PMRGs gene set, were used in this study. Pearson correlation analysis was performed to assess associations between overlapping genes (differentially expressed genes between READ and normal tissues, as well as between N0 and N1-N2 stages) and PMRGs, leading to the identification of candidate genes. Functional enrichment analyses were subsequently conducted to characterize the biological roles of these candidates. Prognostic biomarkers were identified using univariate Cox regression combined with least absolute shrinkage and selection operator (LASSO) regression, and a prognostic model was constructed accordingly. Independent prognostic validation was then performed. In addition, immune checkpoint profiling and immunotherapy response analyses were conducted across risk subgroups. Single-gene Gene Set Enrichment Analysis (GSEA) was applied to elucidate the pathways associated with the identified biomarkers. Finally, drug sensitivity analyses were performed. Results: A total of 157 candidate genes were identified through the analytical pipeline. Functional enrichment analysis indicated that these genes were primarily involved in inflammatory response regulation and tumor necrosis factor (TNF) signaling pathways. Five prognostic biomarkers were subsequently identified and incorporated into a predictive model. External validation using the GSE87211 cohort confirmed the robustness of the model. Risk score and disease status were identified as independent prognostic factors. Six immune checkpoint molecules exhibited differential expression between risk groups. Correlation analyses revealed that the risk score was positively associated with most immune checkpoint genes. Single-gene GSEA demonstrated that the biomarkers were mainly enriched in ribosomal biogenesis and cell adhesion molecule-related pathways. Furthermore, 51 therapeutic agents exhibited significantly different half-maximal inhibitory concentration (IC Conclusions: This study identified five biomarkers (