Cai Junyan, Qiu Jiayi, Wang Hongliang, Sun Jiacheng, Ji Yanan
Department of Rehabilitation, Affiliated Hospital of Nantong University, Nantong, China.
Medical College, Nantong University, Nantong, China.
Ann Transl Med. 2021 Sep;9(18):1472. doi: 10.21037/atm-21-4606.
Ovarian cancer is one of the most common malignant tumors in female genital organs, and its incidence rate is high. However, the pathogenesis and prognostic markers of ovarian cancer are unclear. This study sought to screen potential markers of ovarian cancer and to explore their prognostic value.
The Cancer Genome Atlas, Gene Expression Omnibus, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were used in this study. The least absolute shrinkage and selection operator (LASSO), multivariate Cox regression and stepwise regression analysis were chosen to screen genes and construct risk model. Gene Set Enrichment Analysis (GSEA) and an immune-infiltration analysis were performed.
One hundred thirty two co-expressed genes were found. They involved in metabolism, protein phosphorylation, mitochondria, and immune signaling pathways. Twelve genes significantly related to the survival of ovarian cancer were identified. Eight risk genes (i.e., , , , , , , , and ) were further screened and used to construct the risk model. The risk status might be an independent prognostic factor of ovarian cancer, and most of the biological functions of genes expressed in high-risk ovarian cancer were related to synapse, adhesion, and immune-related functions. The clusters of CD4+ T cells and M2 macrophages were high in high-risk status samples.
In ovarian cancer, the abnormal expression of 8 genes, including , , , , , , , and , is closely related to ovarian cancer progression, and these genes can serve as independent prognosis markers of ovarian cancer.
卵巢癌是女性生殖器官中最常见的恶性肿瘤之一,发病率较高。然而,卵巢癌的发病机制和预后标志物尚不清楚。本研究旨在筛选卵巢癌的潜在标志物并探讨其预后价值。
本研究使用了癌症基因组图谱、基因表达综合数据库、基因本体论和京都基因与基因组百科全书数据库。采用最小绝对收缩和选择算子(LASSO)、多变量Cox回归和逐步回归分析来筛选基因并构建风险模型。进行了基因集富集分析(GSEA)和免疫浸润分析。
发现了132个共表达基因。它们参与代谢、蛋白质磷酸化、线粒体和免疫信号通路。鉴定出12个与卵巢癌生存显著相关的基因。进一步筛选出8个风险基因(即 、 、 、 、 、 、 和 )并用于构建风险模型。风险状态可能是卵巢癌的独立预后因素,高危卵巢癌中表达的基因的大多数生物学功能与突触、黏附和免疫相关功能有关。高危状态样本中CD4 + T细胞和M2巨噬细胞的簇较高。
在卵巢癌中,包括 、 、 、 、 、 、 和 在内的8个基因的异常表达与卵巢癌进展密切相关,这些基因可作为卵巢癌的独立预后标志物。