Prognostic value of mitophagy gene PINK1 in triple-negative breast cancer

Zengjian Tian, Shilong Yu, Yongzhao Zhu
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Abstract

Mitochondrial autophagy plays a crucial role in the development, progression, and treatment of triple-negative breast cancer (TNBC). A total of 43 genes related to mitochondria were obtained from the scientific literature in Pubmed. To assess the expression of these genes, they were categorized into high and low-scoring groups using the ssGSEA algorithm. Additionally, the infiltration of immune cells in the combined dataset was evaluated using the CIBERSORTx algorithm. To analyze the expression of the selected genes, one-way Cox regression analysis, along with clinical staging variables, was employed. A multifactorial Cox regression model was constructed, and a forest plot was generated to assess the predictive power of the model on the actual outcomes. The accuracy of the model was confirmed by validating the expression of the PINK1 gene and its model through immunohistochemistry. TNBC cancer was categorized into two molecular subtypes (cluster1 and cluster2) based on the MRGs. Finally, a prognostic model was developed using the PINK1 gene, which demonstrated a considerable degree of accuracy. The validity of the PINK1 gene and its model was confirmed through immunohistochemistry, indirectly indicating that PINK1 might serve as a potential therapeutic target and prognostic biomarker for TNBC.
有丝分裂吞噬基因 PINK1 在三阴性乳腺癌中的预后价值
线粒体自噬在三阴性乳腺癌(TNBC)的发生、发展和治疗中起着至关重要的作用。我们从 Pubmed 上的科学文献中获取了与线粒体相关的 43 个基因。为了评估这些基因的表达情况,使用ssGSEA算法将其分为高分和低分两组。此外,还使用 CIBERSORTx 算法评估了合并数据集中免疫细胞的浸润情况。为了分析所选基因的表达,采用了单向 Cox 回归分析和临床分期变量。构建了一个多因素 Cox 回归模型,并生成了森林图,以评估模型对实际结果的预测能力。通过免疫组化验证了 PINK1 基因的表达及其模型,从而确认了模型的准确性。根据 MRGs 将 TNBC 癌症分为两种分子亚型(cluster1 和 cluster2)。最后,利用 PINK1 基因建立了一个预后模型,该模型显示出相当高的准确性。免疫组化证实了PINK1基因及其模型的有效性,间接表明PINK1可能是TNBC的潜在治疗靶点和预后生物标志物。
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