Title:Identifying a Novel Eight-NK Cell-related Gene Signature for
Ovarian Cancer Prognosis Prediction
Volume: 31
Issue: 12
Author(s): Nan Li, Kai Yu, Delun Huang, Hui Zhou*Dingyuan Zeng*
Affiliation:
- Department of Gynecologic Oncology, Sun Yat-Sen Memorial Hospital of Sun Yatsen
University, Guangzhou, 510120, China
- Reproductive Medicine Center, Liuzhou Maternity and Child Health Care Hospital, Liuzhou, 545001, China
- Liuzhou Institute of Reproduction and Genetics, Liuzhou Maternity and Child Health Care Hospital, Liuzhou,
545001, China
- Guangxi Health Commission Key Laboratory of Birth Cohort Study in Pregnant Women
of Advanced Age, Liuzhou, 545001, China
- The Department of Obstetrics and Gynecology, Liuzhou Maternity
and Child Health Care Hospital, Liuzhou, 545001, China
Keywords:
Natural killer cell, ovarian cancer, prognosis, immune infiltration, tumor stem cells, gene signature.
Abstract:
Background: Ovarian cancer (OVC) is the most common and costly tumor in
the world with unfavorable overall survival and prognosis. This study is aimed to explore
the prognostic value of natural killer cells related genes for OVC treatment.
Methods: RNA-seq and clinical information were acquired from the TCGA-OVC dataset
(training dataset) and the GSE51800 dataset (validation dataset). Genes linked to NK
cells were obtained from the immPort dataset. Moreover, ConsensusClusterPlus facilitated
the screening of molecular subtypes. Following this, the risk model was established
by LASSO analysis, and immune infiltration and immunotherapy were then detected by
CIBERSORT, ssGSEA, ESTIMATE, and TIDE algorithms.
Results: Based on 23 NK cell-related genes with prognosis, TCGA-OVC samples were
classified into two clusters, namely C1 and C2. Of these, C1 had better survival outcomes
as well as enhanced immune infiltration and tumor stem cells. Additionally, it was
more suitable for immunotherapy and was also sensitive to traditional chemotherapy
drugs. The eight-gene prognosis model was constructed and verified via the GSE51800
dataset. Additionally, a high infiltration level of immune cells was observed in low-risk
patients. Low-risk samples also benefited from immunotherapy and chemotherapy drugs.
Finally, a nomogram and ROC curves were applied to validate model accuracy.
Conclusion: The present study identified a RiskScore signature, which could stratify patients
with different infiltration levels, immunotherapy, and chemotherapy drugs. Our
study provided a basis for precisely evaluating OVC therapy and prognosis.