Xiao Xi, Qing Liangliang, Li Zonglin, Ye Fuxiang, Dong Yajia, Mi Jun, Tian Junqiang
Department of Urology, Lanzhou University Second Hospital, Lanzhou, 730030, China.
Discov Oncol. 2024 Sep 21;15(1):131. doi: 10.1007/s12672-024-00983-5.
Prostate cancer (PCa) represents a significant health challenge for men, and the advancement of the disease often results in a grave prognosis for patients. Therefore, the identification of biomarkers associated with the diagnosis and prognosis of PCa holds paramount importance in patient health management.
The datasets pertaining to PCa were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to investigate the modules specifically associated with the diagnosis of PCa. The hub genes were identified using the LASSO regression analysis. The expression levels of these hub genes were further validated by qRT-PCR experiments. Receiver operating characteristic (ROC) curves and nomograms were employed as evaluative measures for assessing the diagnostic value.
The blue module identified by WGCNA exhibited a strong association with PCa. Six hub genes (SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3) were identified by LASSO regression analysis. Further verification confirmed that these six genes were significantly downregulated in tumor tissues and cells. The six hub genes and the nomogram demonstrated substantial diagnostic value, with area under the curve (AUC) values ranging from 0.754 to 0.961. Moreover, patients with low expression levels of these six genes exhibited elevated T/N pathological stage and Gleason score, implying a more advanced disease state. Meanwhile, their progression-free survival (PFS) was observed to be potentially poorer. Finally, a significant association could be observed between the expression of these genes and the dysregulation of immune cells, along with drug sensitivity.
In summary, our study identified six hub genes, namely SLC14A1, COL4A6, MYOF, FLRT3, KRT15, and LAMB3, which can be utilized to establish a diagnostic model for PCa. The discovery may offer potential molecular targets for clinical diagnosis and treatment of PCa.
前列腺癌（PCa）对男性健康构成重大挑战，疾病进展往往导致患者预后不良。因此，识别与PCa诊断和预后相关的生物标志物在患者健康管理中至关重要。
从基因表达综合数据库（GEO）检索与PCa相关的数据集。进行加权基因共表达网络分析（WGCNA）以研究与PCa诊断特异性相关的模块。使用LASSO回归分析鉴定枢纽基因。通过qRT-PCR实验进一步验证这些枢纽基因的表达水平。采用受试者工作特征（ROC）曲线和列线图作为评估诊断价值的指标。
WGCNA识别出的蓝色模块与PCa密切相关。通过LASSO回归分析鉴定出6个枢纽基因（SLC14A1、COL4A6、MYOF、FLRT3、KRT15和LAMB3）。进一步验证证实这6个基因在肿瘤组织和细胞中显著下调。这6个枢纽基因和列线图显示出显著的诊断价值，曲线下面积（AUC）值在0.754至0.961之间。此外，这6个基因表达水平低的患者T/N病理分期和Gleason评分升高，意味着疾病状态更晚期。同时，观察到他们的无进展生存期（PFS）可能更差。最后，可观察到这些基因的表达与免疫细胞失调以及药物敏感性之间存在显著关联。
总之，我们的研究鉴定出6个枢纽基因，即SLC14A1、COL4A6、MYOF、FLRT3、KRT15和LAMB3，可用于建立PCa诊断模型。这一发现可能为PCa临床诊断和治疗提供潜在分子靶点。
