Screening and prognostic value of potential biomarkers for ovarian cancer

Huiqin Li; Ming Li; Chunhui Tang; Liang Xu

doi:10.21037/atm-21-2627

Screening and prognostic value of potential biomarkers for ovarian cancer

Ann Transl Med. 2021 Jun;9(12):1007. doi: 10.21037/atm-21-2627.

Authors

Huiqin Li^{1

2}, Ming Li³, Chunhui Tang¹, Liang Xu⁴

Affiliations

¹ Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Nantong, China.
² Maternal and Child Health and Family Planning Service Center of Chongchuan District, Nantong, China.
³ Department of Laboratory Medicine, People's Hospital of Binhai County, Yancheng, China.
⁴ Department of Surgery, Changshu Affiliated Hospital of Nanjing University of Chinese Medicine, Changshu Traditional Chinese Medicine Hospital, Changshu, China.

Abstract

Background: Ovarian cancer is a common gynecological malignant tumor that greatly threatens women's health, so we screened potential biomarkers of ovarian cancer and analyzed their prognostic value.

Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to analyze the ovarian cancer-related genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of ovarian cancer-related genes. The survival-related genes were screened out through the least absolute shrinkage and selection operator (LASSO) method. Multivariate Cox regression model and stepwise regression analysis were performed to construct the risk model. The receiver operating characteristic (ROC) and the area under the ROC curve (AUC) were used to evaluate the prediction accuracy of risk score model. Finally, gene set enrichment analysis (GSEA) and immune cell infiltration analysis were performed to investigate the biological function and immune cell infiltration.

Results: A total of 111 genes were found to have common effects on survival. These genes were mainly involved in metabolism, protein phosphorylation and immune-related signaling pathways. Seven risk genes (AP3D1, DCAF10, FBXO16, LRFN4, PTPN2, SAYSD1, ZNF426) were screened out. Among these genes, AP3D1 and LRFN4 are risk genes and DCAF10, FBXO16, PTPN2, SAYSD1, and ZNF426 are protective genes. These findings suggest that risk status may be an independent prognostic factor. The risk score had a high predictive value for the prognosis of ovarian cancer. In addition, GSEA revealed that the biological function of genes expressed in patients at a high risk was mostly related to immune-related function. The contents of CD4⁺ T cells, macrophages, myeloid dendritic cells (mDC) and neutrophils were high in samples at a high risk for ovarian cancer.

Conclusions: The abnormal expression of AP3D1, DCAF10, FBXO16, LRFN4, PTPN2, SAYSD1 and ZNF426 is highly related to the progression of ovarian cancer. These seven genes can be used as independent prognostic markers of ovarian cancer. This study not only adds evidence to the pathogenesis of ovarian cancer but also provides scientific basis for judging the prognosis of ovarian cancer.

Keywords: Gene Expression Omnibus (GEO); The Cancer Genome Atlas (TCGA); marker; ovarian cancer; prognosis.