He X, Feng W. Identification and Validation of NK Marker Genes in Ovarian Cancer by scRNA-seq Combined with WGCNA Algorithm.
Mediators Inflamm 2023;
2023:6845701. [PMID:
37144238 PMCID:
PMC10154100 DOI:
10.1155/2023/6845701]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/01/2022] [Accepted: 03/18/2023] [Indexed: 05/06/2023] Open
Abstract
Background
As an innate immune system effector, natural killer cells (NK cells) play a significant role in tumor immunotherapy response and clinical outcomes.
Methods
In our investigation, we collected ovarian cancer samples from TCGA and GEO cohorts, and a total of 1793 samples were included. In addition, four high-grade serous ovarian cancer scRNA-seq data were included for screening NK cell marker genes. Weighted gene coexpression network analysis (WGCNA) identified core modules and central genes associated with NK cells. The "TIMER," "CIBERSORT," "MCPcounter," "xCell," and "EPIC" algorithms were performed to predict the infiltration characteristics of different immune cell types in each sample. The LASSO-COX algorithm was employed to build risk models to predict prognosis. Finally, drug sensitivity screening was performed.
Results
We first scored the NK cell infiltration of each sample and found that the level of NK cell infiltration affected the clinical outcome of ovarian cancer patients. Therefore, we analyzed four high-grade serous ovarian cancer scRNA-seq data, screening NK cell marker genes at the single-cell level. The WGCNA algorithm screens NK cell marker genes based on bulk RNA transcriptome patterns. Finally, a total of 42 NK cell marker genes were included in our investigation. Among which, 14 NK cell marker genes were then used to develop a 14-gene prognostic model for the meta-GPL570 cohort, dividing patients into high-risk and low-risk subgroups. The predictive performance of this model has been well-verified in different external cohorts. Tumor immune microenvironment analysis showed that the high-risk score of the prognostic model was positively correlated with M2 macrophages, cancer-associated fibroblast, hematopoietic stem cell, stromal score, and negatively correlated with NK cell, cytotoxicity score, B cell, and T cell CD4+Th1. In addition, we found that bleomycin, cisplatin, docetaxel, doxorubicin, gemcitabine, and etoposide were more effective in the high-risk group, while paclitaxel had a better therapeutic effect on patients in the low-risk group.
Conclusion
By utilizing NK cell marker genes in our investigation, we developed a new feature that is capable of predicting patients' clinical outcomes and treatment strategies.
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