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Jia W, Li N, Wang J, Gong X, Ouedraogo SY, Wang Y, Zhao J, Grech G, Chen L, Zhan X. Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach. EPMA J 2024; 15:375-404. [PMID: 38841623 PMCID: PMC11148001 DOI: 10.1007/s13167-024-00359-3] [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: 02/03/2024] [Accepted: 04/07/2024] [Indexed: 06/07/2024]
Abstract
Background DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC. Method Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified. Results A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC. Conclusions This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-024-00359-3.
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Affiliation(s)
- Wenshuang Jia
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Na Li
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Jingjing Wang
- Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Xiaoxia Gong
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Serge Yannick Ouedraogo
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Yan Wang
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
- Department of Gynecological Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, 250117 People’s Republic of China
| | - Junkai Zhao
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Godfrey Grech
- Department of Pathology, University of Malta, Msida, Malta
| | - Liang Chen
- Department of Gynecological Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, 250117 People’s Republic of China
| | - Xianquan Zhan
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
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Wang HQ, Li HL, Han JL, Feng ZP, Deng HX, Han X. MMDAE-HGSOC: A novel method for high-grade serous ovarian cancer molecular subtypes classification based on multi-modal deep autoencoder. Comput Biol Chem 2023; 105:107906. [PMID: 37336028 DOI: 10.1016/j.compbiolchem.2023.107906] [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: 11/22/2022] [Revised: 05/10/2023] [Accepted: 06/11/2023] [Indexed: 06/21/2023]
Abstract
High-grade serous ovarian cancer (HGSOC) is a type of ovarian cancer developed from serous tubal intraepithelial carcinoma. The intrinsic differences among molecular subtypes are closely associated with prognosis and pathological characteristics. At present, multi-omics data integration methods include early integration and late integration. Most existing HGSOC molecular subtypes classification methods are based on the early integration of multi-omics data. The mutual interference among multi-omics data is ignored, which affects the effectiveness of feature learning. High-dimensional multi-omics data contains genes unassociated with HGSOC molecular subtypes, resulting in redundant information, which is not conducive to model training. In this paper, we propose a multi-modal deep autoencoder learning method, MMDAE-HGSOC. MiRNA expression, DNA methylation, and copy number variation (CNV) are integrated with mRNA expression data to construct a multi-omics feature space. The multi-modal deep autoencoder network is used to learn the high-level feature representation of multi-omics data. The superposition LASSO (S-LASSO) regression algorithm is proposed to fully obtain the associated genes of HGSOC molecular subtypes. The experimental results show that MMDAE-HGSOC is superior to the existing classification methods. Finally, we analyze the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which are discovered during the gene selection process.
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Affiliation(s)
- Hui-Qing Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
| | - Hao-Lin Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
| | - Jia-Le Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Zhi-Peng Feng
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Hong-Xia Deng
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Xiao Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
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Tan M, Wang S, Li F, Xu H, Gao J, Zhu L. A methylation-driven genes prognostic signature and the immune microenvironment in epithelial ovarian cancer. Carcinogenesis 2022; 43:635-646. [PMID: 35639961 DOI: 10.1093/carcin/bgac048] [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: 02/15/2022] [Revised: 04/22/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
Aberrant gene methylation has been implicated in the development and progression of tumors. In this study, we aimed to identity methylation driven genes involved in epithelial ovarian cancer (EOC) to establish a prognostic signature for patients with EOC. We identified and verified 6 MDGs that are closely related to the prognosis of ovarian cancer. A prognostic risk score model and nomogram for predicting the prognosis of ovarian cancer were constructed based on the six MDGs. It can also effectively reflect the immune environment and immunotherapy response of ovarian cancer. These MDGs have great significance to the implementation of individualized treatment and disease monitoring of ovarian cancer patients.
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Affiliation(s)
- Mingzi Tan
- Department of Gynecology, Cancer Hospital of China Medical University, No.44 Xiaoheyan Road, Dadong District, Shenyang 110042, Liaoning Province, P R China.,Department of Gynecology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang 110042, Liaoning Province, P R China
| | - Shengtan Wang
- Department of Gynecology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, 570011, P.R. China
| | - Feifei Li
- Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Haoya Xu
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Shenyang, 110004, P.R. China
| | - Jian Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Shenyang, 110004, P.R. China
| | - Liancheng Zhu
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 36 Sanhao Street, Shenyang, 110004, P.R. China
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Vahabi N, Michailidis G. Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review. Front Genet 2022; 13:854752. [PMID: 35391796 PMCID: PMC8981526 DOI: 10.3389/fgene.2022.854752] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 02/28/2022] [Indexed: 12/26/2022] Open
Abstract
Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.
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Affiliation(s)
- Nasim Vahabi
- Informatics Institute, University of Florida, Gainesville, FL, United States
| | - George Michailidis
- Informatics Institute, University of Florida, Gainesville, FL, United States
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Zhou M, Hong S, Li B, Liu C, Hu M, Min J, Tang J, Hong L. Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer. Front Genet 2021; 12:675197. [PMID: 34567062 PMCID: PMC8458765 DOI: 10.3389/fgene.2021.675197] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 08/23/2021] [Indexed: 12/20/2022] Open
Abstract
Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC). Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features. Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC. Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.
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Affiliation(s)
- Min Zhou
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shasha Hong
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bingshu Li
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Cheng Liu
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ming Hu
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jie Min
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jianming Tang
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Li Hong
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
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Huang Z, Li B, Guo Y, Wu L, Kou F, Yang L. Signatures of Multi-Omics Reveal Distinct Tumor Immune Microenvironment Contributing to Immunotherapy in Lung Adenocarcinoma. Front Immunol 2021; 12:723172. [PMID: 34539658 PMCID: PMC8446514 DOI: 10.3389/fimmu.2021.723172] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/17/2021] [Indexed: 12/29/2022] Open
Abstract
Background Lung adenocarcinoma (LUAD) contains a variety of genomic and epigenomic abnormalities; the effective tumor markers related to these abnormalities need to be further explored. Methods Clustering analysis was performed based on DNA methylation (MET), DNA copy number variation (CNV), and mRNA expression data, and the differences in survival and tumor immune microenvironment (TIME) between subtypes were compared. Further, we evaluated the signatures in terms of both prognostic value and immunological characteristics. Results There was a positive correlation between MET and CNV in LUAD. Integrative analysis of multi-omics data from 443 samples determined molecular subtypes, iC1 and iC2. The fractions of CD8+ T cells and activated CD4+ T cells were higher, the fraction of Tregs was lower, and the expression level of programmed death-ligand 1 (PD-L1) was higher in iC2 with a poor prognosis showing a higher TIDE score. We selected PTTG1, SLC2A1, and FAM83A as signatures of molecular subtypes to build a prognostic risk model and divided patients into high-risk group and low-risk group representing poor prognosis and good prognosis, respectively, which were validated in 180 patients with LUAD. Further, the low-risk group with lower TIDE score had more infiltrating immune cells. In 100 patients with LUAD, the high-risk group with an immunosuppressive state had a higher expression of PD-L1 and lower counts of CD8+ T cells and dendritic cells. Conclusions These findings demonstrated that combined multi-omics data could determine molecular subtypes with significant differences of prognosis and TIME in LUAD and suggested potent utility of the signatures to guide immunotherapy.
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Affiliation(s)
- Ziqi Huang
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
| | - Baihui Li
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
| | - Yan Guo
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
| | - Lei Wu
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
| | - Fan Kou
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
| | - Lili Yang
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
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Ding Q, Sun Y, Shang J, Li F, Zhang Y, Liu JX. NMFNA: A Non-negative Matrix Factorization Network Analysis Method for Identifying Modules and Characteristic Genes of Pancreatic Cancer. Front Genet 2021; 12:678642. [PMID: 34367241 PMCID: PMC8340025 DOI: 10.3389/fgene.2021.678642] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 06/03/2021] [Indexed: 01/15/2023] Open
Abstract
Pancreatic cancer (PC) is a highly fatal disease, yet its causes remain unclear. Comprehensive analysis of different types of PC genetic data plays a crucial role in understanding its pathogenic mechanisms. Currently, non-negative matrix factorization (NMF)-based methods are widely used for genetic data analysis. Nevertheless, it is a challenge for them to integrate and decompose different types of genetic data simultaneously. In this paper, a non-NMF network analysis method, NMFNA, is proposed, which introduces a graph-regularized constraint to the NMF, for identifying modules and characteristic genes from two-type PC data of methylation (ME) and copy number variation (CNV). Firstly, three PC networks, i.e., ME network, CNV network, and ME-CNV network, are constructed using the Pearson correlation coefficient (PCC). Then, modules are detected from these three PC networks effectively due to the introduced graph-regularized constraint, which is the highlight of the NMFNA. Finally, both gene ontology (GO) and pathway enrichment analyses are performed, and characteristic genes are detected by the multimeasure score, to deeply understand biological functions of PC core modules. Experimental results demonstrated that the NMFNA facilitates the integration and decomposition of two types of PC data simultaneously and can further serve as an alternative method for detecting modules and characteristic genes from multiple genetic data of complex diseases.
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Affiliation(s)
- Qian Ding
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Yan Sun
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Feng Li
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao, China
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Yu P, Tong L, Song Y, Qu H, Chen Y. Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma. J Cell Mol Med 2021; 25:6388-6402. [PMID: 34060213 PMCID: PMC8256358 DOI: 10.1111/jcmm.16619] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/26/2021] [Accepted: 03/30/2021] [Indexed: 12/17/2022] Open
Abstract
Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso-Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.
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Affiliation(s)
- Ping Yu
- Department of Medical OncologyThe First Hospital of China Medical UniversityShenyangChina
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning ProvinceThe First Hospital of China Medical UniversityShenyangChina
- Liaoning Province Clinical Research Center for CancerShenyangChina
| | - Linlin Tong
- Department of Medical OncologyThe First Hospital of China Medical UniversityShenyangChina
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning ProvinceThe First Hospital of China Medical UniversityShenyangChina
- Liaoning Province Clinical Research Center for CancerShenyangChina
| | - Yujia Song
- Department of Medical OncologyThe First Hospital of China Medical UniversityShenyangChina
| | - Hui Qu
- Department of Medical OncologyThe First Hospital of China Medical UniversityShenyangChina
| | - Ying Chen
- Department of Medical OncologyThe First Hospital of China Medical UniversityShenyangChina
- Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning ProvinceThe First Hospital of China Medical UniversityShenyangChina
- Liaoning Province Clinical Research Center for CancerShenyangChina
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Shen Y, Xiong W, Gu Q, Zhang Q, Yue J, Liu C, Wang D. Multi-Omics Integrative Analysis Uncovers Molecular Subtypes and mRNAs as Therapeutic Targets for Liver Cancer. Front Med (Lausanne) 2021; 8:654635. [PMID: 34109194 PMCID: PMC8183685 DOI: 10.3389/fmed.2021.654635] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 04/06/2021] [Indexed: 01/22/2023] Open
Abstract
Objective: This study aimed to systematically analyze molecular subtypes and therapeutic targets of liver cancer using integrated multi-omics analysis. Methods: DNA copy number variations (CNVs), simple nucleotide variations (SNVs), methylation, transcriptome as well as corresponding clinical information for liver carcinoma were retrieved from The Cancer Genome Atlas (TCGA). Multi-omics analysis was performed to identify molecular subtypes of liver cancer via integrating CNV, methylation as well as transcriptome data. Immune scores of two molecular subtypes were estimated using tumor immune estimation resource (TIMER) tool. Key mRNAs were screened and prognosis analysis was performed, which were validated using RT-qPCR. Furthermore, mutation spectra were analyzed in the different subtypes. Results: Two molecular subtypes (iC1 and iC2) were conducted for liver cancer. Compared with the iC2 subtype, the iC1 subtype had a worse prognosis and a higher immune score. Two key mRNAs (ANXA2 and CHAF1B) were significantly related to liver cancer patients' prognosis, which were both up-regulated in liver cancer tissues in comparison to normal tissues. Seventeen genes with p < 0.01 differed significantly for SNV loci between iC1 and iC2 subtypes. Conclusion: Our integrated multi-omics analyses provided new insights into the molecular subtypes of liver cancer, helping to identify novel mRNAs as therapeutic targets and uncover the mechanisms of liver cancer.
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Affiliation(s)
- Yi Shen
- Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Xiong
- Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qi Gu
- Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qin Zhang
- Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jia Yue
- Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Changsong Liu
- Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Duan Wang
- Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Ma M, Chen Y, Chong X, Jiang F, Gao J, Shen L, Zhang C. Integrative analysis of genomic, epigenomic and transcriptomic data identified molecular subtypes of esophageal carcinoma. Aging (Albany NY) 2021; 13:6999-7019. [PMID: 33638948 PMCID: PMC7993659 DOI: 10.18632/aging.202556] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 12/29/2020] [Indexed: 12/16/2022]
Abstract
Esophageal cancer (EC) involves many genomic, epigenetic and transcriptomic disorders, which play key roles in the heterogeneous progression of cancer. However, the study of EC with multi-omics has not been conducted. This study identified a high consistency between DNA copy number variations and abnormal methylations in EC by analyzing genomics, epigenetics and transcriptomics data and investigating mutual correlations of DNA copy number variation, methylation and gene expressions, and stratified copy number variation genes (CNV-Gs) and methylation genes (MET-Gs). The methylation, CNVs and expression profiles of CNV-Gs and MET-Gs were analyzed by consistent clustering using iCluster integration, here, we determined three subtypes (iC1, iC2, iC3) with different molecular traits, prognostic characteristics and tumor immune microenvironment features. We also identified 4 prognostic genes (CLDN3, FAM221A, GDF15 and YBX2) differentially expressed in the three subtypes, and could therefore be used as representative biomarkers for the three subtypes of EC. In conclusion, by performing comprehensive analysis on genomic, epigenetic and transcriptomic regulations, the current study provided new insights into the multilayer molecular and pathological traits of EC, and contributed to the precision medication for EC patients.
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Affiliation(s)
- Mingyang Ma
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Yang Chen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Xiaoyi Chong
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Fangli Jiang
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Jing Gao
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Cheng Zhang
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing 100142, China
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11
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Song Y, Yang K, Sun T, Tang R. Development and validation of prognostic markers in sarcomas base on a multi-omics analysis. BMC Med Genomics 2021; 14:31. [PMID: 33509178 PMCID: PMC7841904 DOI: 10.1186/s12920-021-00876-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 01/13/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND In sarcomas, the DNA copy number and DNA methylation exhibit genomic aberrations. Transcriptome imbalances play a driving role in the heterogeneous progression of sarcomas. However, it is still unclear whether abnormalities of DNA copy numbers are systematically related to epigenetic DNA methylation, thus, a comprehensive analysis of sarcoma occurrence and development from the perspective of epigenetic and genomics is required. METHODS RNASeq, copy number variation (CNV), methylation data, clinical follow-up information were obtained from The Cancer Genome Atlas (TCGA) and GEO database. The association between methylation and CNV was analyzed to further identify methylation-related genes (MET-Gs) and CNV abnormality-related genes (CNV-Gs). Subsequently DNA copy number, methylation, and gene expression data associated with the MET-Gs and CNV-Gs were integrated to determine molecular subtypes and clinical and molecular characteristics of molecular subtypes. Finally, key biomarkers were determined and validated in independent validation sets. RESULTS A total of 5354 CNV-Gs and 4042 MET-Gs were screened and showed a high degree of consistency. Four molecular subtypes (iC1, iC2, iC3, and iC4) with different prognostic significances were identified by multiomics cluster analysis, specifically, iC2 had the worst prognosis and iC4 indicated an immune-enhancing state. Three potential prognostic markers (ENO1, ACVRL1 and APBB1IP) were determined after comparing the molecular characteristics of the four molecular subtypes. The expression of ENO1 gene was significantly correlated with CNV, and was noticeably higher in iC2 subtype with the worst prognosis than any other subtypes. The expressions of ACVRL1 and APBB1IP were negatively correlated with methylation, and were high-expressed in the iC4 subtype with the most favorable prognosis. In addition, the number of silent/nonsilent mutations and neoantigens in iC2 subtype were significantly more than those in iC1/iC3/iC4 subtype, and the same trend was also observed in CNV Gain/Loss. CONCLUSION The current comprehensive analysis of genomic and epigenomic regulation provides new insights into multilayered pathobiology of sarcomas. Four molecular subtypes and three prognostic markers developed in this study improve the current understanding of the molecular mechanisms underlying sarcoma.
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Affiliation(s)
- Yongchun Song
- Department of Oncology Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, China
| | - Kui Yang
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China
| | - Tuanhe Sun
- Department of Oncology Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, China
| | - Ruixiang Tang
- Department of Oncology Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, China.
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12
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Xu D, Wang Y, Liu X, Zhou K, Wu J, Chen J, Chen C, Chen L, Zheng J. Development and clinical validation of a novel 9-gene prognostic model based on multi-omics in pancreatic adenocarcinoma. Pharmacol Res 2020; 164:105370. [PMID: 33316381 DOI: 10.1016/j.phrs.2020.105370] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 10/27/2020] [Accepted: 12/08/2020] [Indexed: 02/07/2023]
Abstract
The prognoses of patients with pancreatic adenocarcinoma (PAAD) remain poor due to the lack of biomarkers for early diagnosis and effective prognosis prediction. RNA sequencing, single nucleotide polymorphism, and copy number variation data were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to identify prognosis-related genes. GISTIC 2.0 was used to identify significantly amplified or deleted genes, and Mutsig 2.0 was used to analyze the mutation data. The Lasso method was used to construct a risk prediction model. The Rms package was used to evaluate the overall predictive performance of the signature. Finally, Western blot and polymerase chain reaction were performed to evaluate gene expression. A total of 54 candidate genes were obtained after integrating the genomic mutated genes and prognosis-related genes. The Lasso method was used to ascertain 9 characteristic genes, including UNC13B, TSPYL4, MICAL1, KLHDC7B, KLHL32, AIM1, ARHGAP18, DCBLD1, and CACNA2D4. The 9-gene signature model was able to help stratify samples at risk in the training and external validation cohorts. In addition, the overall predictive performance of our model was found to be superior to that of other models. KLHDC7B, AIM1, DCBLD1, TSPYL4, and MICAL1 were significantly highly expressed in tumor tissues compared to normal tissues. ARHGAP18 and CACNA2D4 had no difference in expression between tumor and normal tissues. UNC13B and KLHL32 expression in the normal group was higher than in the tumor group. The 9-gene signature constructed in this study can be used as a novel prognostic marker to predict the survival of patients with pancreatic adenocarcinoma.
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Affiliation(s)
- Dafeng Xu
- Department of Hepatobiliary and Pancreatic Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570311, China
| | - Yu Wang
- Geriatrics Center, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570311, China
| | - Xiangmei Liu
- Department of Hepatobiliary and Pancreatic Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570311, China
| | - Kailun Zhou
- Department of Hepatobiliary and Pancreatic Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570311, China
| | - Jincai Wu
- Department of Hepatobiliary and Pancreatic Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570311, China
| | - Jiacheng Chen
- Department of Hepatobiliary and Pancreatic Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570311, China
| | - Cheng Chen
- Department of Hepatobiliary and Pancreatic Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570311, China
| | - Liang Chen
- Department of Hepatobiliary and Pancreatic Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570311, China
| | - Jinfang Zheng
- Department of Hepatobiliary and Pancreatic Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, 570311, China.
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13
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Erdogan OS, Tuncer SB, Kilic S, Odemis DA, Turkcan GK, Celik B, Avsar M, Yazici H. Genome-wide methylation profiles in monozygotic twins with discordance for ovarian carcinoma. Oncol Lett 2020; 20:357. [PMID: 33133257 PMCID: PMC7590432 DOI: 10.3892/ol.2020.12221] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 08/19/2020] [Indexed: 12/24/2022] Open
Abstract
Ovarian cancer is a disease that is generally diagnosed at an advanced stage, and has poor survival. Monozygotic (MZ) twins are considered to be good research models for investigating the epigenetic changes associated with diseases. In the present study, the involvement of epigenetic mechanisms in ovarian cancer etiology were evaluated using the MZ twin model. Whole-genome methylation patterns were investigated in a BRCA1 gene mutation-carrying family comprising MZ twins, only one of whom had ovarian cancer, and other healthy siblings. Whole-genome methylation patterns were assessed in peripheral blood DNA using Infinium MethylationEPIC BeadChips on an Illumina iScan device. The hypermethylated and hypomethylated genes were detected between cases and controls in four different comparison groups in order to evaluate the differences in methylation levels according to cancer diagnosis and BRCA mutation status. The obtained results showed that the differential methylations in 12 different genes, namely PR/SET domain 6, cytochrome B5 reductase 4, ZNF714, OR52M1, SEMA4D, CHD1L, CAPZB, clustered mitochondria homolog, RB-binding protein 7, chromatin repair factor, ankyrin repeat domain 23, RIB43A domain with coiled-coils 1 and C6orf227, were associated with ovarian cancer. Biological functional analysis of the genes detected in the study using the PANTHER classification system revealed that they have roles in biological processes including ‘biologic adhesion’, ‘regulation’, ‘cellular components organization’, ‘biogenesis’, ‘immune system functioning’, ‘metabolic functioning’ and ‘localization’. Overall, the present study suggested that epigenetic differences, such as methylation status, could be used as a non-invasive biological markers for the early diagnosis and follow-up of ovarian cancer.
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Affiliation(s)
- Ozge Sukruoglu Erdogan
- Department of Basic Oncology, Cancer Genetics Division, Institute of Oncology, Istanbul University, Istanbul 34093, Turkey
| | - Seref Bugra Tuncer
- Department of Basic Oncology, Cancer Genetics Division, Institute of Oncology, Istanbul University, Istanbul 34093, Turkey
| | - Seda Kilic
- Department of Basic Oncology, Cancer Genetics Division, Institute of Oncology, Istanbul University, Istanbul 34093, Turkey
| | - Demet Akdeniz Odemis
- Department of Basic Oncology, Cancer Genetics Division, Institute of Oncology, Istanbul University, Istanbul 34093, Turkey
| | - Gozde Kuru Turkcan
- Department of Basic Oncology, Cancer Genetics Division, Institute of Oncology, Istanbul University, Istanbul 34093, Turkey
| | - Betul Celik
- Department of Basic Oncology, Cancer Genetics Division, Institute of Oncology, Istanbul University, Istanbul 34093, Turkey
| | - Mukaddes Avsar
- Department of Basic Oncology, Cancer Genetics Division, Institute of Oncology, Istanbul University, Istanbul 34093, Turkey
| | - Hulya Yazici
- Department of Basic Oncology, Cancer Genetics Division, Institute of Oncology, Istanbul University, Istanbul 34093, Turkey
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14
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Xu X, Gong C, Wang Y, Hu Y, Liu H, Fang Z. Multi-omics analysis to identify driving factors in colorectal cancer. Epigenomics 2020; 12:1633-1650. [PMID: 32573269 DOI: 10.2217/epi-2020-0073] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Aim: We aim to identify driving genes of colorectal cancer (CRC) through multi-omics analysis. Materials & methods: We downloaded multi-omics data of CRC from The Cancer Genome Atlas dataset. Integrative analysis of single-nucleotide variants, copy number variations, DNA methylation and differentially expressed genes identified candidate genes that carry CRC risk. Kernal genes were extracted from the weighted gene co-expression network analysis. A competing endogenous RNA network composed of CRC-related genes was constructed. Biological roles of genes were further investigated in vitro. Results: We identified LRRC26 and REP15 as novel prognosis-related driving genes for CRC. LRRC26 hindered tumorigenesis of CRC in vitro. Conclusion: Our study identified novel driving genes and may provide new insights into the molecular mechanisms of CRC.
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Affiliation(s)
- Xi Xu
- Department of Pathology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Chaoju Gong
- Central Laboratory, The Municipal Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221106, PR China
| | - Yunfeng Wang
- Institute for Integrative Biology of the Cell, UMR 9198, CNRS, Commissariat à l'Energie Atomique et aux Énergies Alternatives (CEA), Université Paris-Sud, 91198 Gif-sur-Yvette, Palaiseau, 91120, France
| | - Yanyan Hu
- Central Laboratory, Sanmen People's Hospital of Zhejiang Province, Sanmen, 317100, PR China
| | - Hong Liu
- Zhejiang Normal University - Jinhua People's Hospital Joint Center for Biomedical Research, Jinhua, 321004, PR China.,The Affiliated Hospital of Jinhua Polytechnic College, Jinhua, 321000, PR China
| | - Zejun Fang
- Central Laboratory, Sanmen People's Hospital of Zhejiang Province, Sanmen, 317100, PR China.,Central Laboratory, Sanmenwan Branch, The First Affiliated Hospital, College of Medicine, Zhejiang University, Sanmen, 317100, PR China
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15
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Ding Q, Dong S, Wang R, Zhang K, Wang H, Zhou X, Wang J, Wong K, Long Y, Zhu S, Wang W, Ren H, Zeng Y. A nine-gene signature related to tumor microenvironment predicts overall survival with ovarian cancer. Aging (Albany NY) 2020; 12:4879-4895. [PMID: 32208363 PMCID: PMC7138578 DOI: 10.18632/aging.102914] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 03/02/2020] [Indexed: 12/13/2022]
Abstract
Mounting evidence suggests that immune cell infiltration within the tumor microenvironment (TME) is a crucial regulator of carcinogenesis and therapeutic efficacy in ovarian cancer (OC). In this study, 593 OC patients from TCGA were divided into high and low score groups based on their immune/stromal scores resulting from analysis utilizing the ESTIMATE algorithm. Differential expression analysis revealed 294 intersecting genes that influencing both the immune and stromal scores. Further Cox regression analysis identified 34 differentially expressed genes (DEGs) as prognostic-related genes. Finally, the nine-gene signature was derived from the prognostic-related genes using a Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. This nine-gene signature could effectively distinguish the high-risk patients in the training (TCGA database) and validation (GSE17260) cohorts (all p < 0.01). A time-dependent receiver operating characteristic (ROC) analysis showed that the nine-gene signature had a reasonable predictive accuracy (AUC = 0.707, AUC =0.696) in both cohorts. In addition, this nine-gene signature is associated with immune infiltration in TME by Gene Set Variation Analysis (GSVA), and can be used to predict the survival of patients with OC.
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Affiliation(s)
- Qi Ding
- Translational Medicine Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China.,Engineering Technology Research Center for Diagnosis-Treatment and Application of Tumor Liquid Biopsy, Changsha, China
| | - Shanshan Dong
- Translational Medicine Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China.,Engineering Technology Research Center for Diagnosis-Treatment and Application of Tumor Liquid Biopsy, Changsha, China
| | - Ranran Wang
- Translational Medicine Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China.,Engineering Technology Research Center for Diagnosis-Treatment and Application of Tumor Liquid Biopsy, Changsha, China
| | - Keqiang Zhang
- The Fifth Department of Gynecological Oncology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China
| | - Hui Wang
- Key Laboratory of Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiao Zhou
- Translational Medicine Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China.,Engineering Technology Research Center for Diagnosis-Treatment and Application of Tumor Liquid Biopsy, Changsha, China
| | - Jing Wang
- The Fifth Department of Gynecological Oncology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China
| | - Kee Wong
- Engineering Technology Research Center for Diagnosis-Treatment and Application of Tumor Liquid Biopsy, Changsha, China
| | - Ying Long
- Translational Medicine Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China
| | - Shuai Zhu
- Translational Medicine Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China
| | - Weigang Wang
- The Fifth Department of Gynecological Oncology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China
| | - Huayi Ren
- Translational Medicine Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China
| | - Yong Zeng
- Translational Medicine Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China.,Engineering Technology Research Center for Diagnosis-Treatment and Application of Tumor Liquid Biopsy, Changsha, China
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16
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Kong L, Liu P, Zheng M, Xue B, Liang K, Tan X. Multi-omics analysis based on integrated genomics, epigenomics and transcriptomics in pancreatic cancer. Epigenomics 2020; 12:507-524. [PMID: 32048534 DOI: 10.2217/epi-2019-0374] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Aim: Integrated analysis of genomics, epigenomics, transcriptomics and clinical information contributes to identify specific molecular subgroups and find novel biomarkers for pancreatic cancer. Materials & methods: The DNA copy number variation, the simple nucleotide variation, methylation and mRNA data of pancreatic cancer patients were obtained from The Cancer Genome Atlas. Four molecular subgroups (iC1, iC2, iC3 and iC4) of pancreatic cancer were identified by integrating analysis. Results: The iC1 subgroup harbors better prognosis, higher immune score, lesser DNA copy number variation mutations and better genomic stability compared with iC2, iC3 and iC4 subgroups. Three new genes (GRAP2, ICAM3 and A2ML1) correlated with prognosis were identified. Conclusion: Integrated multi-omics analysis provides fresh insight into molecular classification of pancreatic cancer, which may help discover new prognostic biomarkers and reveal the underlying mechanism of pancreatic cancer.
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Affiliation(s)
- Lingming Kong
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Peng Liu
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Mingjun Zheng
- Department of Obstetrics & Gynecology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Busheng Xue
- Department of Pediatrics, Children's Cancer Research Center, Kinderklinik München Schwabing, School of Medicine, Technical University of Munich, Munich 80804, Germany
| | - Keke Liang
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Xiaodong Tan
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
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17
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Zheng M, Hu Y, Gou R, Liu O, Nie X, Li X, Liu Q, Hao Y, Liu J, Lin B. Identification of immune-enhanced molecular subtype associated with BRCA1 mutations, immune checkpoints and clinical outcome in ovarian carcinoma. J Cell Mol Med 2020; 24:2819-2831. [PMID: 31995855 PMCID: PMC7077593 DOI: 10.1111/jcmm.14830] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/26/2019] [Accepted: 11/06/2019] [Indexed: 12/15/2022] Open
Abstract
Ovarian carcinoma has the highest mortality among the malignant tumours in gynaecology, and new treatment strategies are urgently needed to improve the clinical status of ovarian carcinoma patients. The Cancer Genome Atlas (TCGA) cohort were performed to explore the immune function of the internal environment of tumours and its clinical correlation with ovarian carcinoma. Finally, four molecular subtypes were obtained based on the global immune‐related genes. The correlation analysis and clinical characteristics showed that four subtypes were all significantly related to clinical stage; the immune scoring results indicated that most immune signatures were upregulated in C3 subtype, and the majority of tumour‐infiltrating immune cells were upregulated in both C3 and C4 subtypes. Compared with other subtypes, C3 subtype had a higher BRCA1 mutation, higher expression of immune checkpoints, and optimal survival prognosis. These findings of the immunological microenvironment in tumours may provide new ideas for developing immunotherapeutic strategies for ovarian carcinoma.
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Affiliation(s)
- Mingjun Zheng
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China.,Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Munich, Germany
| | - Yuexin Hu
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Rui Gou
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Ouxuan Liu
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Xin Nie
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Xiao Li
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Qing Liu
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Yingying Hao
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Juanjuan Liu
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Bei Lin
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
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18
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Lin W, Ye H, You K, Chen L. Up-regulation of circ_LARP4 suppresses cell proliferation and migration in ovarian cancer by regulating miR-513b-5p/LARP4 axis. Cancer Cell Int 2020; 20:5. [PMID: 31911757 PMCID: PMC6945592 DOI: 10.1186/s12935-019-1071-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 12/12/2019] [Indexed: 01/08/2023] Open
Abstract
Background Ovarian cancer (OC) is a common fatal malignant tumor of female reproductive system worldwide. Growing studies have proofed that circular RNAs (circRNAs) engage in the regulation of various types of cancers. However, the underlying biological functions and effect mechanism of circular RNA_LARP4 (circ_LARP4) in OC have not been explored. Methods Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was used to detect the expression of circ_LARP4 in OC cells. The function of circ_LARP4 was measured by cell counting kit-8 (CCK-8), colony formation assay and transwell assay. RNA immunoprecipitation (RIP) assay and luciferase reporter assays assessed the binding correlation between miR-513b-5p and circ_LARP4 (or LARP4). Results The expression of circ_LARP4 in OC cells was much lower than that in human normal ovarian epithelial cells. Overexpressing circ_LARP4 impaired cell proliferation, invasion and migration abilities. Circ_LARP4 worked as a competing endogenous RNA (ceRNA) to sponge miR-513b-5p. Furthermore, LARP4 was indirectly modulated by circ_LARP4 as the downstream target of miR-513b-5p, as well as the host gene of circ_LARP4. Conclusion Circ_LARP4 could hamper cell proliferation and migration by sponging miR-513b-5p to regulate the expression of LARP4. This research may provide some referential value to OC treatment.![]()
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Affiliation(s)
- Wumei Lin
- Department of Gynecology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan 2 Road, Guangzhou, 510080 Guangdong China
| | - Haiyan Ye
- Department of Gynecology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan 2 Road, Guangzhou, 510080 Guangdong China
| | - Keli You
- Department of Gynecology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan 2 Road, Guangzhou, 510080 Guangdong China
| | - Le Chen
- Department of Gynecology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan 2 Road, Guangzhou, 510080 Guangdong China
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