Table 7_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作用机制仍不明确。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。本研究首先从TCGA及GEO数据库获取乳腺癌相关转录组数据，通过差异表达分析、单因素Cox回归及LASSO回归分析筛选预后基因，随后构建并验证了风险预测模型。此外，本研究还开展了富集分析、免疫微环境分析以及转录因子（Transcription Factors, TFs）-mRNA调控网络的构建工作。最后，检测了预后基因在肿瘤组织与正常组织样本中的表达水平，并通过实时定量聚合酶链反应（RT-qPCR）进行了验证。最终筛选得到8个预后基因（CETP、CLEC11A、CYP2A6、CYP2A7、GZMB、HGF、LDHC及PLAU）。该风险模型显示，低风险组患者的生存率显著更高。基因集富集分析（Gene Set Enrichment Analysis, GSEA）结果显示，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等转录因子可调控这些预后基因。此外，免疫细胞浸润谱分析显示，高风险组与低风险组间存在显著差异。生物信息学分析及实时定量聚合酶链反应验证结果表明，CETP与HGF在正常组织中呈高表达，而CLEC11A与PLAU在乳腺癌组织中表达水平更高。本研究筛选得到8个与细胞色素c相关的乳腺癌预后基因并构建了风险预测模型，为乳腺癌的个性化治疗及预后评估提供了新的研究思路。