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Zhang D, Li Y, Yang S, Wang M, Yao J, Zheng Y, Deng Y, Li N, Wei B, Wu Y, Zhai Z, Dai Z, Kang H. Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients. Cancer Med 2021; 10:8222-8237. [PMID: 34609082 PMCID: PMC8607265 DOI: 10.1002/cam4.4317] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 08/22/2021] [Accepted: 08/31/2021] [Indexed: 12/17/2022] Open
Abstract
Background Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV. Methods The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets. Results A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed. Conclusion Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.
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Affiliation(s)
- Dai Zhang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, The Air Force Medical University, Xi'an, China
| | - Yiche Li
- Department of Tumor Surgery, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Si Yang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Meng Wang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jia Yao
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yi Zheng
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yujiao Deng
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Na Li
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Bajin Wei
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Ying Wu
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Zhen Zhai
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zhijun Dai
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Huafeng Kang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Zhang B, Nie X, Miao X, Wang S, Li J, Wang S. Development and verification of an immune-related gene pairs prognostic signature in ovarian cancer. J Cell Mol Med 2021; 25:2918-2930. [PMID: 33543590 PMCID: PMC7957197 DOI: 10.1111/jcmm.16327] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 12/22/2020] [Accepted: 12/29/2020] [Indexed: 02/06/2023] Open
Abstract
Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17-IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17-IRGP signature noticeably split patients into high- and low-risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll-like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17-IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.
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Affiliation(s)
- Bao Zhang
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
| | - Xiaocui Nie
- Department of Obstetrics and GynecologyShenyang women's and children's hospitalShenyangChina
| | - Xinxin Miao
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
| | - Shuo Wang
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
| | - Jing Li
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
| | - Shengke Wang
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
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A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer. JOURNAL OF ONCOLOGY 2019; 2019:3614207. [PMID: 31885574 PMCID: PMC6925684 DOI: 10.1155/2019/3614207] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 07/17/2019] [Indexed: 12/15/2022]
Abstract
The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan–Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment.
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Zhou J, Yi Y, Wang C, Su C, Luo Y. Identification of a 3-mRNA signature as a novel potential prognostic biomarker in patients with ovarian serous cystadenocarcinoma in G2 and G3. Oncol Lett 2019; 18:3545-3552. [PMID: 31579405 PMCID: PMC6757305 DOI: 10.3892/ol.2019.10701] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 07/03/2019] [Indexed: 12/25/2022] Open
Abstract
The use of mRNAs as biomarkers serves to diagnose, treat, as well as aid the prognosis of cancer. The present study involved an analysis of mRNAs in the cell cycle at the G2 and G3 tumor grades for the prognosis of ovarian serous cystadenocarcinoma (OSC) using 364 clinical samples (G2:G3=42:322). Statistics aided the identification of NPFFR2, XPNPEP2 and CELA3B; the 3-mRNA model that allows for classification of patients into high- and low-risk groups using a median value of 0.9580745. The rates of survival varied (P=0.00149) and the independent detection of stratification of the risk of this disease was validated with success using the 3-mRNA signature, which was demonstrated to be more successful than the weight model. This approach was revealed to provide the prognosis of grade G2 and G3 in patients with OSC compared with factors used traditionally. Compared with traditional factors, this 3-mRNA model was demonstrated to be the only and independent prognostic factor for patients with G2 and G3 stage OSC. A literature survey was also performed in the present study in order to assess the role of the 3 genes and indirectly prove their effectiveness. The establishment of this new genetic model will enhance prospective prognosis and treatment for patients with OSC.
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Affiliation(s)
- Jiahua Zhou
- Pediatric Surgery II Ward, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530000, P.R. China
| | - Yeye Yi
- Department of Obstetrics and Gynecology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530000, P.R. China
| | - Congjun Wang
- Pediatric Surgery II Ward, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530000, P.R. China
| | - Cheng Su
- Pediatric Surgery II Ward, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530000, P.R. China
| | - Yige Luo
- Pediatric Surgery II Ward, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530000, P.R. China
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Guo W, Zhu L, Yu M, Zhu R, Chen Q, Wang Q. A five-DNA methylation signature act as a novel prognostic biomarker in patients with ovarian serous cystadenocarcinoma. Clin Epigenetics 2018; 10:142. [PMID: 30446011 PMCID: PMC6240326 DOI: 10.1186/s13148-018-0574-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2018] [Accepted: 10/28/2018] [Indexed: 12/25/2022] Open
Abstract
Background Ovarian cancer is the most fatal tumor of the female reproductive system and the fifth leading cause of cancer death among women in the USA. The prognosis is poor due to the lack of biomarkers for treatment options. Results The methylation array data of 551 patients with ovarian serous cystadenocarcinoma (OSC) in The Cancer Genome Atlas (TCGA) database were assessed in this study to explore the methylation biomarkers associated with prognosis and improve the prognosis of patients. These patients were divided into training (first two thirds) and validation datasets (remaining one third). A five-DNA methylation signature was found to be significantly associated with the overall survival of patients with OSC using the Cox regression analysis in the training dataset. The Kaplan–Meier analysis showed that the five-DNA methylation signature could significantly distinguish the high- and low-risk patients in both training and validation sets. The receiver operating characteristic (ROC) analysis further confirmed that the five-DNA methylation signature exhibited high sensitivity and specificity to predict the prognostic survival of patients. Also, the five-DNA methylation signature was not only applicable in patients of different ages, stages, histologic grade, and size of residual tumor after surgery but also more accurate in predicting OSC prognosis compared with known biomarkers. Conclusions This five-DNA methylation signature demonstrated the potential of being a novel independent prognostic indicator and served as an important tool for guiding the clinical treatment of OSC to improve outcome prediction and management for patients. Hence, the findings of this study might have potential clinical significance. Electronic supplementary material The online version of this article (10.1186/s13148-018-0574-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wenna Guo
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Liucun Zhu
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Minghao Yu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
| | - Rui Zhu
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Qihan Chen
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China.
| | - Qiang Wang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China.
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