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Cui H, Ma R, Hu T, Xiao GG, Wu C. Bioinformatics Analysis Highlights Five Differentially Expressed Genes as Prognostic Biomarkers of Cervical Cancer and Novel Option for Anticancer Treatment. Front Cell Infect Microbiol 2022; 12:926348. [PMID: 35782114 PMCID: PMC9247199 DOI: 10.3389/fcimb.2022.926348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
Cervical cancer is one of the most common gynecological malignancies and is related to human papillomavirus (HPV) infection, especially high-risk type HPV16 and HPV18. Aberrantly expressed genes are involved in the development of cervical cancer, which set a genetic basis for patient prognosis. In this study, we identified a set of aberrantly expressed key genes from The Cancer Genome Atlas (TCGA) database, which could be used to accurately predict the survival rate of patients with cervical squamous cell carcinoma (CESC). A total of 3,570 genes that are differentially expressed between normal and cancerous samples were analyzed by the algorithm of weighted gene co-expression network analysis (WGCNA): 1,606 differentially expressed genes (DEGs) were upregulated, while 1,964 DEGs were downregulated. Analysis of these DEGs divided them into 7 modules including 76 hub genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis revealed a significant increase of genes related to cell cycle, DNA replication, p53 signaling pathway, cGMP-PKG signaling pathway, and Fanconi anemia (FA) pathway in CESC. These biological activities are previously reported to associate with cervical cancer or/and HPV infection. Finally, we highlighted 5 key genes (EMEMP2, GIMAP4, DYNC2I2, FGF13-AS1, and GIMAP1) as robust prognostic markers to predict patient’s survival rate (p = 3.706e-05) through univariate and multivariate regression analyses. Thus, our study provides a novel option to set up several biomarkers for cervical cancer prognosis and anticancer drug targets.
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Affiliation(s)
- Hongtu Cui
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Ruilin Ma
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Tao Hu
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Gary Guishan Xiao
- School of Pharmaceutical Science and Technology, Dalian University of Technology, Dalian, China
| | - Chengjun Wu
- School of Biomedical Engineering, Dalian University of Technology, Dalian, China
- *Correspondence: Chengjun Wu,
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Feng Q, Wang J, Cui N, Liu X, Wang H. Autophagy-related long non-coding RNA signature for potential prognostic biomarkers of patients with cervical cancer: a study based on public databases. ANNALS OF TRANSLATIONAL MEDICINE 2022; 9:1668. [PMID: 34988177 PMCID: PMC8667135 DOI: 10.21037/atm-21-5156] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 10/29/2021] [Indexed: 12/14/2022]
Abstract
Background Metastasis and recurrence are the main causes of death from cervical cancer (CC), thus it is important to identify more effective biomarkers to improve its prognosis. The purpose of our research was to determine the potential role of autophagy-related long non-coding RNA (lncRNA) in CC and to construct an autophagy-related lncRNA signature for survival of CC. Methods The lncRNAs in CC were downloaded from The Cancer Genome Atlas (TCGA) database, and autophagy-related lncRNAs were identified through the co-expression of lncRNA genes and autophagy genes. Several autophagy-related lncRNAs with prognostic value (AC012306.2, AL109976.1, ATP2A1-AS1, ILF3-DT, Z83851.2, STARD7-AS1, AC099343.2, AC008771.1, DBH-AS1, and AC097468.3) were identified using univariate and multivariate Cox regression analyses and a prognostic signature was established. The signature effect was detected by univariate Cox regression analysis [hazard ratio (HR) =1.665; 95% confidence interval (CI): 1.331–2.082; P<0.001] and multivariate Cox regression analysis (HR =1.738; 95% CI: 1.359–2.223; P<0.001). A nomogram was drawn by risk score and clinical features. Results The prognostic signature could predict the survival of CC by survival-receiver operating characteristic (ROC) curve [area under the curve (AUC) =0.810]. A nomogram was drawn by risk score and clinical features, and its c-index and calibration curve demonstrated that the prognostic signature could independently predict the prognosis of CC (P<0.001). Gene set enrichment analysis (GSEA) confirmed that the genes were significantly enriched in cancer- and autophagy-related pathways (P<0.05). Conclusions This 10 autophagy-related lncRNA signature has prognostic potential for CC. More important roles in the CC biology of these lncRNAs may be identified with further study.
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Affiliation(s)
- Qian Feng
- Department of Reproductive Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jingyuan Wang
- Department of Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Nan Cui
- Department of Reproductive Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xian Liu
- Department of Reproductive Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Haiyan Wang
- Department of Reproductive Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Li M, Tian X, Guo H, Xu X, Liu Y, Hao X, Fei H. A novel lncRNA-mRNA-miRNA signature predicts recurrence and disease-free survival in cervical cancer. Braz J Med Biol Res 2021; 54:e11592. [PMID: 34550275 PMCID: PMC8457683 DOI: 10.1590/1414-431x2021e11592] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 06/17/2021] [Indexed: 11/22/2022] Open
Abstract
Cervical cancer (CC) patients have a poor prognosis due to the high recurrence rate. However, there are still no effective molecular signatures to predict the recurrence and survival rates for CC patients. Here, we aimed to identify a novel signature based on three types of RNAs [messenger RNA (mRNAs), microRNA (miRNAs), and long non-coding RNAs (lncRNAs)]. A total of 763 differentially expressed mRNAs (DEMs), 46 lncRNAs (DELs), and 22 miRNAs (DEMis) were identified between recurrent and non-recurrent CC patients using the datasets collected from the Gene Expression Omnibus (GSE44001; training) and The Cancer Genome Atlas (RNA- and miRNA-sequencing; testing) databases. A competing endogenous RNA network was constructed based on 23 DELs, 15 DEMis, and 426 DEMs, in which 15 DELs, 13 DEMis, and 390 DEMs were significantly associated with disease-free survival (DFS). A prognostic signature, containing two DELs (CD27-AS1, LINC00683), three DEMis (hsa-miR-146b, hsa-miR-1238, hsa-miR-4648), and seven DEMs (ARMC7, ATRX, FBLN5, GHR, MYLIP, OXCT1, RAB39A), was developed after LASSO analysis. The built risk score could effectively separate the recurrence rate and DFS of patients in the high- and low-risk groups. The accuracy of this risk score model for DFS prediction was better than that of the FIGO (International Federation of Gynecology and Obstetrics) staging (the area under receiver operating characteristic curve: training, 0.954 vs 0.501; testing, 0.882 vs 0.656; and C-index: training, 0.855 vs 0.539; testing, 0.711 vs 0.508). In conclusion, the high predictive accuracy of our signature for DFS indicated its potential clinical application value for CC patients.
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Affiliation(s)
- Mengxiong Li
- Department of Obstetrics and Gynecology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Xiaohui Tian
- Department of Obstetrics and Gynecology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Hongling Guo
- Department of Obstetrics and Gynecology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Xiaoyu Xu
- Department of Obstetrics and Gynecology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Yun Liu
- Department of Obstetrics and Gynecology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Xiulan Hao
- Department of Obstetrics and Gynecology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Hui Fei
- Department of Obstetrics and Gynecology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
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Laliscia C, Gadducci A, Mattioni R, Orlandi F, Giusti S, Barcellini A, Gabelloni M, Morganti R, Neri E, Paiar F. MRI-based radiomics: promise for locally advanced cervical cancer treated with a tailored integrated therapeutic approach. TUMORI JOURNAL 2021; 108:376-385. [PMID: 34235995 DOI: 10.1177/03008916211014274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
OBJECTIVE To assess prognostic factors by analyzing clinical and radiomic data of patients with locally advanced cervical cancer (LACC) treated with definitive concurrent cisplatin-based chemoradiotherapy (CCRT) using magnetic resonance imaging (MRI). METHODS We analyzed radiomic features from MRI in 60 women with FIGO (International Federation of Gynecology and Obstetrics) stage IB2-IVA cervical cancer who underwent definitive CCRT 45-50.4 Gy (in 25-28 fractions). Thirty-nine (65.0%) received EBRT sequential boost (4-20 Gy) on primary tumor site and 56 (93.3%) received high-dose-rate brachytherapy boost (6-28 Gy) (daily fractions of 5-7 Gy). Moreover, 71.7% of patients received dose-dense neoadjuvant chemotherapy for 6 cycles. The gross tumor volume was defined on T2-weighted sequences and 29 features were extracted from each MRI performed before and after CCRT, using dedicated software, and their prognostic value was correlated with clinical information. RESULTS In univariate analysis, age ⩾60 years and FIGO stage IB2-IIB had significantly better progression-free survival (PFS) (p = 0.022 and p = 0.009, respectively). There was a trend for significance for worse overall survival (OS) in patients with positive nodes (p = 0.062). In multivariate analysis, only age ⩾60 years and FIGO stage IB2-IIB reached significantly better PFS (p = 0.020 and p = 0.053, respectively). In radiomic dataset, in multivariate analysis, pregray level p75 was significantly associated with PFS (p = 0.047), pre-D3D value with OS (p = 0.049), and preinformation measure of correlation value with local control (p = 0.031). CONCLUSION The combination of clinical and radiomics features can provide information to predict behavior and prognosis of LACC and to make more accurate treatment decisions.
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Affiliation(s)
- Concetta Laliscia
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
| | - Angiolo Gadducci
- Department of Experimental and Clinical Medicine, Division of Gynecology and Obstetrics, University of Pisa, Pisa, Italy
| | - Roberto Mattioni
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
| | - Francesca Orlandi
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
| | - Sabina Giusti
- Department of New Technologies and Translational Research, Division of Radiology, University of Pisa, Pisa, Italy
| | - Amelia Barcellini
- National Center of Oncological Hadrontherapy (Fondazione CNAO), Pavia, Italy
| | - Michela Gabelloni
- Department of New Technologies and Translational Research, Division of Radiology, University of Pisa, Pisa, Italy
| | - Riccardo Morganti
- Department of Clinical and Experimental Medicine, Section of Statistics, University of Pisa, Pisa, Italy
| | - Emanuele Neri
- Department of New Technologies and Translational Research, Division of Radiology, University of Pisa, Pisa, Italy
| | - Fabiola Paiar
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
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Mao Y, Chen R, Xia M, Guo P, Zeng F, Huang J, He M. Identification of an immune-based mRNA-lncRNA signature for overall survival in cervical squamous cell carcinoma. Future Oncol 2021; 17:2365-2380. [PMID: 33724869 DOI: 10.2217/fon-2020-1153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Aim: To better predict the survival of cervical squamous cell carcinoma (CESC) patients, we aimed to construct a signature according to different immune infiltration. Methods: We downloaded the RNA sequences of CESC patients from the Cancer Genome Atlas database. By using single-sample gene set enrichment analysis, we separated the samples into high- and low-immunity groups. Then we separated the samples into training and testing datasets and performed the following analyses: univariate, least absolute shrinkage and selection operator analysis, multivariate Cox regression analyses and weighted gene coexpression network analysis using R software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes studies were performed using the Database for Annotation, Visualization and Integrated Discovery website. Results & conclusion: We finally identified a signature with three mRNAs and two lncRNAs: ADGRG5, HSH2D, ZMAT4, RBAKDN and LINC00200. In short, our study constructed an mRNA-lncRNA signature related to immune infiltration to better predict the survival of CESC patients.
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Affiliation(s)
- Yifang Mao
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, PR China
| | - Run Chen
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, PR China
| | - Meng Xia
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, PR China
| | - Peng Guo
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, PR China
| | - Feitianzhi Zeng
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, PR China
| | - Jiaming Huang
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, PR China
| | - Mian He
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, 510080, PR China
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Chen Q, Hu L, Huang D, Chen K, Qiu X, Qiu B. Six-lncRNA Immune Prognostic Signature for Cervical Cancer. Front Genet 2020; 11:533628. [PMID: 33173530 PMCID: PMC7591729 DOI: 10.3389/fgene.2020.533628] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 09/21/2020] [Indexed: 12/13/2022] Open
Abstract
Background This study searched for immune-related long noncoding RNAs (lncRNAs) to predict the prognosis of patients with cervical cancer. Method We obtained immunologically relevant lncRNA expression profiles and clinical follow-up data from cervical cancer patients from The Cancer Genome Atlas database and the Molecular Signatures Database. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The immune prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator Cox regression, prognosis was analyzed by Kaplan-Meier curves between different groups, and the accuracy of the prognostic model was assessed by receiver operating characteristic-area under the curve (ROC-AUC) analysis. Results A six-lncRNA immune prognostic signature (LIPS) was constructed to predict the prognosis of cervical cancer. The six lncRNAs are as follows: AC009065.8, LINC01871, MIR210HG, GEMIN7-AS1, GAS5-AS1, and DLEU1. A ROC-AUC analysis indicated that the model could predict the prognosis of cervical cancer patients in different subgroups. A Kaplan-Meier analysis showed that patients with high risk scores had a poor prognosis; these results were equally meaningful in the subgroup analyses. Risk scores differed depending on the clinical pathology and tumor grade and were independent risk factors for cervical cancer prognosis. Gene set enrichment analysis revealed an association between the LIPS and the immune response, Wnt signaling pathway, and TGF beta signaling pathway. Conclusion Our study shows that the six-LIPS can predict the prognosis of cervical cancer and contribute to decisions regarding the immunotherapeutic strategy.
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Affiliation(s)
- Qian Chen
- Department of Research, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Lang Hu
- Guangxi Medical University Cancer Hospital, Nanning, China
| | - Dongping Huang
- Department of Nutrition, School of Public Health, Guangxi Medical University, Nanning, China
| | - Kaihua Chen
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Xiaoqiang Qiu
- Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, China
| | - Bingqing Qiu
- Department of Nuclear Medicine, Guangxi Medical University Cancer Hospital, Nanning, China
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Xia Q, Jin H, Zhang X, Yan W, Meng D, Ding B, Cao J, Li D, Wang S. Prognosis prediction signature of seven immune genes based on HPV status in cervical cancer. Int Immunopharmacol 2020; 88:106935. [PMID: 32889244 DOI: 10.1016/j.intimp.2020.106935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/20/2020] [Accepted: 08/22/2020] [Indexed: 12/24/2022]
Abstract
Cervical cancer (CC) has a high incidence and mortality rate, with a low 5-year survival rate, and human papillomavirus (HPV) is one of its carcinogenic risks. However, little evidence exists on the impact of HPV infection on the survival of patients with CC. In the present study, the CC cohort and immune genes were downloaded from the TCGA database and the ImmPort database, respectively. Subsequently, the Gene Set Enrichment Analysis was performed and found that HPV status was involved in multiple immune signaling pathways, which revealed that HPV infection might play critical roles in the immune response. Then seven prognostic immune genes were identified according to HPV status in CC. Using the seven immune genes, we established an immune risk score (IRS) signature and the Kaplan-Meier curve showed that high IRS was significantly correlated with poor prognosis of CC in both the training sets (HR = 2.32, 95% CI = 1.66-3.33; AUC = 0.712) and the validation sets (HR = 1.38, 95% CI = 1.02-1.85 and AUC = 0.583 in TCGA-HNSCC; HR = 2.58, 95% CI = 1.364-4.893, AUC = 0.676 in GSE44001). A nomogram of IRS combined with clinical features was established, and further analyses demonstrated that the power of the nomogram to predict the prognosis of CC was more reliable than that of a single independent factor. In conclusion, this study provided a more comprehensive understanding of the correlation between HPV and immune mechanisms as well as a novel signature that can effectively predict the prognosis of CC patients.
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Affiliation(s)
- Qianqian Xia
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Hua Jin
- Clinical Laboratory, Affiliated Tumor Hospital of Nantong University (Nantong Tumor Hospital), Nantong, China
| | - Xing Zhang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
| | - Wenjing Yan
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
| | - Dan Meng
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Bo Ding
- Department of Gynecology and Obstetrics, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jian Cao
- Department of Gynecology, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Dake Li
- Department of Gynecology, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China.
| | - Shizhi Wang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
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