Liu Zong-Yan, Huang Ruo-Hui
Department of Pharmacy, Ganzhou People's Hospital (Ganzhou Hospital-Nanfang Hospital, Southern Medical University), Ganzhou, Jiangxi, 341000, China.
Department of Urology, First Affiliated Hospital of Gannan Medical University, Gan Zhou, Jiang xi, 341000, China.
Heliyon. 2024 May 9;10(9):e30766. doi: 10.1016/j.heliyon.2024.e30766. eCollection 2024 May 15.
Prostate cancer (PCa) is the most common malignancy of the male urinary system. Mitophagy, as a type of autophagy, can remove damaged mitochondria in cells. Mitophagy-related genes (MRGs) have been shown to play critical roles in the development of PCa. To this end, based on the comprehensive analysis of RNA-seq and scRNA-seq data of PCa samples and their controls, this paper identified PCa subtypes and constructed a prognostic model. In this paper, we downloaded scRNA-seq and RNA-seq data from Gene Expression Omnibus (GEO) and TCGA database. Based on the R package "Seurat" to process the scRNA-seq data, a total of five cell types were identified. Each cell population was scored based on the R package "AUCell" and using the intersection genes between MRGs and each cell population. The B cell population was then identified as a high-scoring cell population. Differentially expressed genes in RNA-seq data were identified based on the R package "limma" and intersected with previously intersected genes. Then, based on univariate Cox regression analysis and Lasso-Cox regression analysis, the prognostic genes were screened, and the risk model was constructed (composed of ADH5, CAT, BCAT2, DCXR, OGT, and FUS). The model is validated on internal and external test sets. Independent prognostic analysis identified age, N stage, and risk score as independent prognostic factors. This paper's risk models and prognostic genes can provide a reference for developing novel therapeutic targets for PCa.
前列腺癌（PCa）是男性泌尿系统最常见的恶性肿瘤。线粒体自噬作为一种自噬类型，可清除细胞内受损的线粒体。线粒体自噬相关基因（MRGs）已被证明在PCa的发展中起关键作用。为此，基于对PCa样本及其对照的RNA测序和单细胞RNA测序数据的综合分析，本文确定了PCa亚型并构建了一个预后模型。在本文中，我们从基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据库下载了单细胞RNA测序和RNA测序数据。基于R包“Seurat”处理单细胞RNA测序数据，共鉴定出五种细胞类型。根据R包“AUCell”并使用MRGs与每个细胞群体之间的交集基因对每个细胞群体进行评分。然后将B细胞群体鉴定为高评分细胞群体。基于R包“limma”鉴定RNA测序数据中的差异表达基因，并与先前的交集基因进行交集。然后，基于单变量Cox回归分析和套索-考克斯回归分析筛选预后基因，并构建风险模型（由ADH5、CAT、BCAT2、DCXR、OGT和FUS组成）。该模型在内部和外部测试集上进行了验证。独立预后分析确定年龄、N分期和风险评分是独立的预后因素。本文的风险模型和预后基因可为开发PCa的新型治疗靶点提供参考。
