Department of Urology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, Shaanxi, China.
Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.
Lipids Health Dis. 2023 Mar 14;22(1):39. doi: 10.1186/s12944-023-01804-4.
Prostate cancer (PCa), the second most prevalent solid tumor among men worldwide, has caused greatly increasing mortality in PCa patients. The effects of lipid metabolism on tumor growth have been explored, but the mechanistic details of the association of lipid metabolism disorders with PCa remain largely elusive.
The RNA sequencing data of the GSE45604 and The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) datasets were extracted from the Gene Expression Omnibus (GEO) and UCSC Xena databases, respectively. The Molecular Signatures Database (MSigDB) was utilized to identify lipid metabolism-related genes. The limma R package was used to identify differentially expressed lipid metabolism-related genes (DE-LMRGs) and differentially expressed microRNAs (DEMs). Moreover, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), and support vector machine-recursive feature elimination (SVM-RFE) were applied to select signature miRNAs and construct a lipid metabolism-related diagnostic model. The expression levels of selected differentially expressed lipid metabolism-related miRNAs (DE-LMRMs) in PCa and benign prostate hyperplasia (BPH) specimens were verified using quantitative real-time polymerase chain reaction (qRT‒PCR). Furthermore, a transcription factor (TF)-miRNA‒mRNA network was constructed. Eventually, Kaplan‒Meier (KM) curves were plotted to illustrate the associations between signature miRNA-related mRNAs and TFs and overall survival (OS) along with biochemical recurrence-free survival (BCR).
Forty-seven LMRMs were screened based on the correlation analysis of 29 DE-LMRGs and 56 DEMs, in which 27 LMRMs were stably expressed in the GSE45604 dataset. Subsequently, receiver operating characteristic (ROC) curves and machine learning methods were employed to develop a lipid metabolism-related diagnostic signature, which may be of diagnostic value for PCa patients. qRT‒PCR results showed that all seven key DE-LMRMs were differentially expressed between PCa and BPH tissues. Eventually, a TF-miRNA‒mRNA network was constructed.
These results suggested that 7 key diagnostic miRNAs were closely related to PCa pathological processes and provided new targets for the diagnosis and treatment of PCa. Moreover, CLIC6 and SCNN1A linked to miR-200c-3p had good prognostic potential and provided valuable insights into the pathogenesis of PCa.
前列腺癌（PCa）是全球男性中第二大常见实体肿瘤，导致 PCa 患者死亡率大大增加。脂质代谢对肿瘤生长的影响已被探索，但脂质代谢紊乱与 PCa 之间关联的机制细节在很大程度上仍难以捉摸。
从基因表达综合数据库（GEO）中提取 GSE45604 和癌症基因组图谱-前列腺腺癌（TCGA-PRAD）数据集的 RNA 测序数据，分别从 UCSC Xena 数据库中提取。利用分子特征数据库（MSigDB）鉴定脂质代谢相关基因。使用 limma R 包鉴定差异表达的脂质代谢相关基因（DE-LMRGs）和差异表达的 microRNAs（DEMs）。此外，应用最小绝对收缩和选择算子（LASSO）、极端梯度提升（XGBoost）和支持向量机递归特征消除（SVM-RFE）来选择特征 microRNAs 并构建脂质代谢相关诊断模型。使用定量实时聚合酶链反应（qRT-PCR）验证 PCa 和良性前列腺增生（BPH）标本中选定的差异表达脂质代谢相关 microRNAs（DE-LMRMs）的表达水平。此外，构建转录因子（TF）-microRNA-mRNA 网络。最后，绘制 Kaplan-Meier（KM）曲线以说明特征 microRNA 相关 mRNAs 和 TF 与总生存（OS）和生化无复发生存（BCR）之间的关联。
基于 29 个 DE-LMRGs 和 56 个 DEMs 的相关性分析，筛选出 47 个 LMRMs，其中 27 个 LMRMs在 GSE45604 数据集稳定表达。随后，采用受试者工作特征（ROC）曲线和机器学习方法构建脂质代谢相关诊断特征，可能对 PCa 患者具有诊断价值。qRT-PCR 结果表明，PCa 和 BPH 组织中所有 7 个关键 DE-LMRMs 的表达均存在差异。最终，构建了 TF-microRNA-mRNA 网络。
这些结果表明，7 个关键诊断 microRNAs 与 PCa 病理过程密切相关，为 PCa 的诊断和治疗提供了新的靶点。此外，与 miR-200c-3p 相关的 CLIC6 和 SCNN1A 具有良好的预后潜力，为 PCa 的发病机制提供了有价值的见解。
