Current issues in molecular biology · 2026
Identification and Prognostic Analysis of Immune-Related Genes Co-Regulated by Key Histone Modifications in Breast Cancer.
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Cao Yanni, Li Xiaohui, Liu Jiangshan, Zhang Junyuan, Xu Kangcheng, Lin Hao, Liu Yuxian
Аннотация
BACKGROUND: Breast cancer (BRCA) is a common malignant tumor that seriously threatens women's health. Studies have shown that histone modifications (HMs) play a vital role in the occurrence and development of BRCA. This study aims to explore the distribution patterns of HMs in the mammary epithelial cell line (HMEC) and breast cancer cell line (MCF-7), and their potential associations with gene expression, patient prognosis, and drug efficacy.
METHODS: First, the distribution of histone modification (HM) signals in HMEC and MCF-7 cell lines was analyzed. Multiple algorithms were then used to predict the effects of different HMs and their modified regions on gene expression in the two cell lines. Based on four key regions identified from this analysis, 268 HM-related immune-related genes (H_IRGs) were screened, followed by functional enrichment and pathway analysis. Subsequently, Cox and LASSO regression analyses were performed on the H_IRGs to construct a risk scoring model.
RESULTS: The random forest algorithm showed the best predictive performance (AUC = 0.92) and identified three key HMs (H3K4me2, H3K27me3, and H3K36me3) and four key regions that strongly influenced gene expression. A risk scoring model was constructed from 11 key IRGs (BCL2A1, PSME2, STC2, ESRRG, CRISP3, IL13RA1, LCN1, EED, CLEC10A, SLURP1, and FGF12). This model effectively predicted patients' survival in both the training and validation cohorts.
CONCLUSIONS: In summary, our research results provide a theoretical basis for the occurrence and development of BRCA, and the 11 key IRGs discovered are expected to become potential biomarkers for BRCA prognostic assessment and treatment response prediction.
METHODS: First, the distribution of histone modification (HM) signals in HMEC and MCF-7 cell lines was analyzed. Multiple algorithms were then used to predict the effects of different HMs and their modified regions on gene expression in the two cell lines. Based on four key regions identified from this analysis, 268 HM-related immune-related genes (H_IRGs) were screened, followed by functional enrichment and pathway analysis. Subsequently, Cox and LASSO regression analyses were performed on the H_IRGs to construct a risk scoring model.
RESULTS: The random forest algorithm showed the best predictive performance (AUC = 0.92) and identified three key HMs (H3K4me2, H3K27me3, and H3K36me3) and four key regions that strongly influenced gene expression. A risk scoring model was constructed from 11 key IRGs (BCL2A1, PSME2, STC2, ESRRG, CRISP3, IL13RA1, LCN1, EED, CLEC10A, SLURP1, and FGF12). This model effectively predicted patients' survival in both the training and validation cohorts.
CONCLUSIONS: In summary, our research results provide a theoretical basis for the occurrence and development of BRCA, and the 11 key IRGs discovered are expected to become potential biomarkers for BRCA prognostic assessment and treatment response prediction.