Table 9_Identification and experimental validation of prognostic genes related to cytochrome c in breast cancer.xlsx

Breast cancer (BC) is one of the most prevalent malignant diseases affecting women. Cytochrome c (Cyt c) plays a critical role in various pathological processes, however, its precise mechanism in BC remains unclear. This study aimed to identify prognostic genes linked to Cyt c in BC and explore their underlying mechanisms. Transcriptome data related to BC were initially obtained from TCGA and GEO database. Prognostic genes were identified through differential expression analysis, univariate Cox regression, and LASSO analysis. A risk model was subsequently developed and validated. Additionally, enrichment analysis, immune microenvironment analysis, and the construction of a TFs-mRNA network were conducted. Finally, the expression levels of prognostic genes were examined in both tumor and normal tissue samples, with confirmation through RT-qPCR. Eight prognostic genes (CETP, CLEC11A, CYP2A6, CYP2A7, GZMB, HGF, LDHC, and PLAU) were identified. The risk model demonstrated that low-risk individuals have significantly higher survival rates. GSEA results indicated that seven of the prognostic genes are notably enriched in the “cytokine-cytokine receptor interaction” pathway. Transcription factors, such as ATF3 and RUNX1, were found to regulate these prognostic genes. Furthermore, immune cell profiles revealed significant differences between high-risk and low-risk groups. Bioinformatics and RT-qPCR analyses confirmed that CETP and HGF are upregulated in normal tissues, while CLEC11A and PLAU showed higher expression in BC tissues. This study identified eight Cyt c-related prognostic genes and developed a risk model, offering new insights into personalized treatment and prognosis for BC.

乳腺癌（Breast Cancer, BC）是影响女性的最常见恶性疾病之一。细胞色素c（Cytochrome c, Cyt c）在多种病理过程中发挥关键作用，但其在乳腺癌中的具体调控机制仍不明确。本研究旨在筛选乳腺癌中与细胞色素c相关的预后基因，并探究其潜在作用机制。研究初始从癌症基因组图谱（The Cancer Genome Atlas, TCGA）和基因表达综合数据库（Gene Expression Omnibus, GEO）获取乳腺癌相关转录组数据。通过差异表达分析、单变量Cox回归分析及最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）回归分析筛选预后基因。随后构建并验证了预后风险模型。此外，本研究还开展了富集分析、免疫微环境分析，并构建了转录因子-信使RNA（Transcription Factors-mRNA, TFs-mRNA）调控网络。最后，通过实时定量聚合酶链反应（Reverse Transcription Quantitative Polymerase Chain Reaction, RT-qPCR）验证了预后基因在肿瘤与正常组织样本中的表达水平。本研究最终筛选得到8个与细胞色素c相关的预后基因，分别为CETP、CLEC11A、CYP2A6、CYP2A7、GZMB、HGF、LDHC及PLAU。风险模型分析结果显示，低风险组患者的总体生存率显著高于高风险组。基因集富集分析（Gene Set Enrichment Analysis, GSEA）结果表明，其中7个预后基因显著富集于“细胞因子-细胞因子受体相互作用”通路。研究发现ATF3、RUNX1等转录因子可调控上述预后基因的表达。此外，免疫细胞浸润分析显示高低风险组间的免疫细胞浸润谱存在显著差异。生物信息学分析及RT-qPCR验证结果证实，CETP与HGF在正常组织中呈高表达，而CLEC11A及PLAU在乳腺癌组织中表达水平更高。本研究成功筛选出8个乳腺癌相关细胞色素c预后基因并构建了预后风险模型，为乳腺癌的个性化治疗及预后评估提供了新的研究思路。