Integrated bulk RNA and single-cell RNA sequencing to identify and validate exercise-related genes for predicting the prognosis of invasive ductal carcinoma

Background As the predominant subtype of breast cancer, invasive ductal carcinoma (IDC) is characterized by its aggressive invasive behavior and strong metastatic capacity. Exercise has been shown to confer multiple benefits in cancer prevention. This research sought elucidate the exercise-related mechanisms in IDC, emphasizing risk stratification therapeutic implications. Methods IDC-related datasets downloaded were from the gene expression omnibus (GEO) and the cancer genome atlas (TCGA) databases. Differential expression analysis, Cox univariable survival analysis, and machine learning methods were used to select exercise-related genes (ERGs) and construct a risk model. Subsequently, the prognostic evaluations were enhanced through independent survival analysis, nomogram development, enrichment profiling, tumor immune microenvironment assessment, and chemosensitivity testing. Besides, GSE195861 was analyzed to determine key cells and perform pseudo-time and cell communication analyses. Finally, Prognostic ERG gene expression was confirmed by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results A prognostic risk model with 8 prognostic ERGs (TRDN, PGK1, SCG2, CALM2, PHKA1, MLIP, GYPC, and IL16) was constructed and demonstrated a strong prognostic effect. Subsequently, a nomogram was developed according to tumor stage and gender, showing strong predictive power for IDC prognosis. Subsequently, immune cells like immature B cells, pathways like hematopoietic cell lineage, and drug sensitivities to GW-441756 were detected to be linked to the risk stratification of IDC patients. Moreover, pseudo-time analysis revealed a notable correlation between prognostic ERGs' expression about differentiation status of key cells (NK cells and B cells), and cell signaling revealed key cell-macrophage interplay. Importantly, RT-qPCR confirmed that PGK1, SCG2, CALM2, and PHKA1 were abundantly expressed, while GYPC and IL16 were lowly expressed in IDC patients. Conclusion This study highlighted the pivotal role of exercise in IDC progression. A novel IDC-related risk model based on prognostic ERGs was developed and validated, and it exhibited robust predictive efficacy for IDC patient outcomes.