Yu Qianqian, Wang Yunxiao, Fu Ting, Han Dongyu, Wang Linlin, Zhao Lin, Xu Yongle
National Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Hospital of China Medical University, Units of Medical Laboratory , Chinese Academy of Medical Sciences, Shenyang, 110001, PR China.
Department of Gynecology and Obstetrics, The Fifth People's Hospital of Shunde (Longjiang Hospital of Shunde District), Foshan, PR China.
Transl Oncol. 2025 Jun;56:102333. doi: 10.1016/j.tranon.2025.102333. Epub 2025 Apr 16.
Ovarian cancer represents a malignancy characterized by high incidence and mortality rates, necessitating further elucidation of its underlying mechanisms. We conducted an analysis using bulk transcriptomic data of ovarian cancer and normal ovarian tissues, as well as single-cell sequencing data according to publicly available databases. Through calculation of Gene Set Variation Analysis (GSVA) scores for TNF family genes, weighted gene co-expression network analysis (WGCNA) for hub genes identification, and subsequent Gene Ontology (GO) enrichment analysis, we delineated pathways crucial in ovarian cancer pathogenesis. Furthermore, differential expression gene analysis facilitated the identification of genes with pronounced expression levels in tumor tissues and their intersection with hub genes, followed by GO analyses across molecular functions (MF), cellular components (CC), and biological processes (BP). Utilizing multivariable Cox regression and LASSO analyses, we constructed a prognostic model comprising 14 genes (GFPT2, PDE4B, PODNL1, TGFBI, CSF1R, PTGIS, SFRP2, COL5A2, TRAC, SLAMF7, VCAN, GBP1P1, C2, TRBV28). Both training and validation sets demonstrated robust diagnostic and prognostic capabilities. Clinical information and immune cell infiltration analyses were further conducted based on the model. In the single-cell sequencing analysis, reducing dimensional complexity and classifying cell types were performed, followed by exploration of gene expression patterns within each subtype and investigation of temporal expression variations across cell subtypes. Biological functional exploration and drug sensitivity analyses were also conducted. Our study contributes novel insights and theoretical foundations for prognosis, treatment, and development of drugs in patients.
卵巢癌是一种发病率和死亡率都很高的恶性肿瘤,有必要进一步阐明其潜在机制。我们根据公开可用的数据库,使用卵巢癌和正常卵巢组织的批量转录组数据以及单细胞测序数据进行了分析。通过计算TNF家族基因的基因集变异分析(GSVA)分数、进行加权基因共表达网络分析(WGCNA)以识别枢纽基因,并随后进行基因本体(GO)富集分析,我们描绘了在卵巢癌发病机制中至关重要的途径。此外,差异表达基因分析有助于识别在肿瘤组织中表达水平显著的基因及其与枢纽基因的交集,随后在分子功能(MF)、细胞成分(CC)和生物学过程(BP)方面进行GO分析。利用多变量Cox回归和LASSO分析,我们构建了一个包含14个基因(GFPT2、PDE4B、PODNL1、TGFBI、CSF1R、PTGIS、SFRP2、COL5A2、TRAC、SLAMF7、VCAN、GBP1P1、C₂、TRBV28)的预后模型。训练集和验证集均显示出强大的诊断和预后能力。基于该模型进一步进行了临床信息和免疫细胞浸润分析。在单细胞测序分析中,进行了降维和细胞类型分类,随后探索了每个亚型内的基因表达模式,并研究了跨细胞亚型的时间表达变化。还进行了生物学功能探索和药物敏感性分析。我们的研究为患者的预后、治疗和药物开发提供了新的见解和理论基础。