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Xu YF, Wang GY, Zhang MY, Yang JG. Hub genes and their key effects on prognosis of Burkitt lymphoma. World J Clin Oncol 2023; 14:357-372. [PMID: 37970111 PMCID: PMC10631346 DOI: 10.5306/wjco.v14.i10.357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/06/2023] [Accepted: 09/18/2023] [Indexed: 10/24/2023] Open
Abstract
BACKGROUND Burkitt lymphoma (BL) is an exceptionally aggressive malignant neoplasm that arises from either the germinal center or post-germinal center B cells. Patients with BL often present with rapid tumor growth and require high-intensity multi-drug therapy combined with adequate intrathecal chemotherapy prophylaxis, however, a standard treatment program for BL has not yet been established. It is important to identify biomarkers for predicting the prognosis of BLs and discriminating patients who might benefit from the therapy. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets. AIM To identify hub genes and perform gene ontology (GO) and survival analysis in BL. METHODS Gene expression profiles and clinical traits of BL patients were collected from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression modules, and the cytoHubba tool was used to find the hub genes. Then, the hub genes were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes analysis. Additionally, a Protein-Protein Interaction network and a Genetic Interaction network were constructed. Prognostic candidate genes were identified through overall survival analysis. Finally, a nomogram was established to assess the predictive value of hub genes, and drug-gene interactions were also constructed. RESULTS In this study, we obtained 8 modules through WGCNA analysis, and there was a significant correlation between the yellow module and age. Then we identified 10 hub genes (SRC, TLR4, CD40, STAT3, SELL, CXCL10, IL2RA, IL10RA, CCR7 and FCGR2B) by cytoHubba tool. Within these hubs, two genes were found to be associated with OS (CXCL10, P = 0.029 and IL2RA, P = 0.0066) by survival analysis. Additionally, we combined these two hub genes and age to build a nomogram. Moreover, the drugs related to IL2RA and CXCL10 might have a potential therapeutic role in relapsed and refractory BL. CONCLUSION From WGCNA and survival analysis, we identified CXCL10 and IL2RA that might be prognostic markers for BL.
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Affiliation(s)
- Yan-Feng Xu
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Guan-Yun Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Ming-Yu Zhang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Ji-Gang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
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2
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Dey H, Vasudevan K, Doss C. GP, Kumar SU, El Allali A, Alsamman AM, Zayed H. Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma. Front Med (Lausanne) 2023; 10:1154417. [PMID: 37081847 PMCID: PMC10110863 DOI: 10.3389/fmed.2023.1154417] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/20/2023] [Indexed: 04/07/2023] Open
Abstract
Introduction Osteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease. Methods In our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks. Results Our study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc. Conclusion To develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.
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Affiliation(s)
- Hrituraj Dey
- Department of Biotechnology, School of Applied Sciences, REVA University, Bangalore, India
| | - Karthick Vasudevan
- Department of Biotechnology, School of Applied Sciences, REVA University, Bangalore, India
| | - George Priya Doss C.
- Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology (VIT), Vellore, India
| | - S. Udhaya Kumar
- Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology (VIT), Vellore, India
| | - Achraf El Allali
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir, Morocco
| | - Alsamman M. Alsamman
- Agriculture Genetic Engineering Research Institute (AGERI), Agriculture Research Center (ARC), Giza, Egypt
- International Center for Agricultural Research in the Dry Areas (ICARDA), Giza, Egypt
| | - Hatem Zayed
- Department of Biomedical Sciences College of Health Sciences, QU Health, Qatar University, Doha, Qatar
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3
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Ahmed MM, Shafat Z, Tazyeen S, Ali R, Almashjary MN, Al-Raddadi R, Harakeh S, Alam A, Haque S, Ishrat R. Identification of pathogenic genes associated with CKD: An integrated bioinformatics approach. Front Genet 2022; 13:891055. [PMID: 36035163 PMCID: PMC9403320 DOI: 10.3389/fgene.2022.891055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 06/28/2022] [Indexed: 11/23/2022] Open
Abstract
Chronic kidney disease (CKD) is defined as a persistent abnormality in the structure and function of kidneys and leads to high morbidity and mortality in individuals across the world. Globally, approximately 8%–16% of the population is affected by CKD. Proper screening, staging, diagnosis, and the appropriate management of CKD by primary care clinicians are essential in preventing the adverse outcomes associated with CKD worldwide. In light of this, the identification of biomarkers for the appropriate management of CKD is urgently required. Growing evidence has suggested the role of mRNAs and microRNAs in CKD, however, the gene expression profile of CKD is presently uncertain. The present study aimed to identify diagnostic biomarkers and therapeutic targets for patients with CKD. The human microarray profile datasets, consisting of normal samples and treated samples were analyzed thoroughly to unveil the differentially expressed genes (DEGs). After selection, the interrelationship among DEGs was carried out to identify the overlapping DEGs, which were visualized using the Cytoscape program. Furthermore, the PPI network was constructed from the String database using the selected DEGs. Then, from the PPI network, significant modules and sub-networks were extracted by applying the different centralities methods (closeness, betweenness, stress, etc.) using MCODE, Cytohubba, and Centiserver. After sub-network analysis we identified six overlapped hub genes (RPS5, RPL37A, RPLP0, CXCL8, HLA-A, and ANXA1). Additionally, the enrichment analysis was undertaken on hub genes to determine their significant functions. Furthermore, these six genes were used to find their associated miRNAs and targeted drugs. Finally, two genes CXCL8 and HLA-A were common for Ribavirin drug (the gene-drug interaction), after docking studies HLA-A was selected for further investigation. To conclude our findings, we can say that the identified hub genes and their related miRNAs can serve as potential diagnostic biomarkers and therapeutic targets for CKD treatment strategies.
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Affiliation(s)
- Mohd Murshad Ahmed
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Zoya Shafat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Safia Tazyeen
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Rafat Ali
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
- Department of Biosciences, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - Majed N. Almashjary
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rajaa Al-Raddadi
- Community Medicine Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Steve Harakeh
- King Fahd Medical Research Center, and Yousef Abdullatif Jameel Chair of Prophetic Medicine Application, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Aftab Alam
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
| | - Romana Ishrat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
- *Correspondence: Romana Ishrat,
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Qureshi N, Chi J, Qian Y, Huang Q, Duan S. Looking for the Genes Related to Lung Cancer From Nasal Epithelial Cells by Network and Pathway Analysis. Front Genet 2022; 13:942864. [PMID: 35923697 PMCID: PMC9340151 DOI: 10.3389/fgene.2022.942864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/13/2022] [Indexed: 12/05/2022] Open
Abstract
Previous studies have indicated that the airway epithelia of lung cancer-associated injury can extend to the nose and it was associated with abnormal gene expression. The aim of this study was to find the possible lung cancer-related genes from the nasal epithelium as bio-markers for lung cancer detection. WGCNA was performed to calculate the module-trait correlations of lung cancer based on the public microarray dataset, and their data were processed by statistics of RMA and t-test. Four specific modules associated with clinical features of lung cancer were constructed, including blue, brown, yellow, and light blue. Of which blue or brown module showed strong connection to genetic connectivity. From the brown module, it was found that HCK, NCF1, TLR8, EMR3, CSF2RB, and DYSF are the hub genes, and from the blue module, it was found that SPEF2, ANKFN1, HYDIN, DNAH5, C12orf55, and CCDC113 are the pivotal genes corresponding to the grade. These genes can be taken as the bio-markers to develop a noninvasive method of diagnosing early lung cancer.
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Affiliation(s)
| | | | | | | | - Shaoyin Duan
- Department of Medical Imaging, Zhongshan Hospital, School of Medicine, Xiamen University, Xiamen, China
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5
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Bhardwaj A, Josse C, Van Daele D, Poulet C, Chavez M, Struman I, Van Steen K. Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses. Sci Rep 2022; 12:11027. [PMID: 35773268 PMCID: PMC9247075 DOI: 10.1038/s41598-022-14592-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 06/09/2022] [Indexed: 11/22/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.
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Affiliation(s)
- Archana Bhardwaj
- GIGA-R Centre, BIO3 - Medical Genomics, University of Liège, Avenue de L'Hôpital, 11, 4000, Liège, Belgium.
| | - Claire Josse
- Laboratory of Human Genetics, GIGA Research, University Hospital (CHU), Liège, Belgium
- Medical Oncology Department, CHU Liège, Liège, Belgium
| | - Daniel Van Daele
- Department of Gastro-Enterology, University Hospital (CHU), Liège, Belgium
| | - Christophe Poulet
- Laboratory of Human Genetics, GIGA Research, University Hospital (CHU), Liège, Belgium
- Laboratory of Rheumatology, GIGA-R, University Hospital (CHULiege), Liège, Belgium
| | - Marcela Chavez
- Department of Medicine, Division of Hematology, University Hospital (CHU), Liège, Belgium
| | - Ingrid Struman
- GIGA-R Centre, Laboratory of Molecular Angiogenesis, University of Liège, Liège, Belgium
| | - Kristel Van Steen
- GIGA-R Centre, BIO3 - Medical Genomics, University of Liège, Avenue de L'Hôpital, 11, 4000, Liège, Belgium
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Hub Gene and Its Key Effects on Diffuse Large B-Cell Lymphoma by Weighted Gene Coexpression Network Analysis. BIOMED RESEARCH INTERNATIONAL 2021; 2021:8127145. [PMID: 34873574 PMCID: PMC8643236 DOI: 10.1155/2021/8127145] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 10/11/2021] [Accepted: 10/28/2021] [Indexed: 12/28/2022]
Abstract
Diffuse large B-cell lymphoma (DLBC) is a kind of tumor with rapid progress and poor prognosis. Therefore, it is necessary to explore new biomarkers or therapeutic targets to assist in diagnosis or treatment. This study is aimed at screening hub genes by weighted gene coexpression network analysis (WGCNA) and exploring the significance of overall survival (OS) in DLBC patients. Statistical data using WGCNA to analyze mRNA expression in DLBC patients came from The Cancer Genome Atlas (TCGA) dataset. After analyzing with clinical information, the biological functions of hub genes were detected. Survival analysis, Cox regression detection, and correlation analysis of the hub genes were carried out. The potential function of the hub gene related to prognosis was predicted by gene set enrichment analysis (GSEA). The results showed that APOE, CTSD, LGALS2, and TMEM176B expression in normal tissues was significantly higher than that in cancer tissues (P < 0.01). Survival analysis showed that patients with high APOE and CTSD were associated with better OS (P < 0.01). APOE and CTSD genes were mainly enriched in the regulation of ROS and oxidative stress. The two hub genes related to the prognosis of DLBC were identified and verified based on WGCNA. Survival analysis showed that the overexpression of APOE and CTSD in DLBC might be beneficial to the prognosis. These findings identified vital pathways and genes that may become new therapeutic targets and contribute to prognostic indicators.
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7
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Lamere AT. Inference of Gene Coexpression Networks from Bulk-Based RNA-Sequencing Data. Methods Mol Biol 2021; 2328:13-23. [PMID: 34251617 DOI: 10.1007/978-1-0716-1534-8_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Gene coexpression networks (GCNs) are useful tools for inferring gene functions and understanding biological processes when properly constructed. Traditional microarray analysis is being more frequently replaced by bulk-based RNA-sequencing as a method for quantifying gene expression. This new technology requires improved statistical methods for generating GCNs. This chapter explores several popular methods for constructing GCNs using bulk-based RNA-Seq data, such as distribution-based methods and normalization techniques, implemented using the statistical programming language R.
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Affiliation(s)
- Alicia T Lamere
- Mathematics Department, Bryant University, Smithfield, RI, USA.
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8
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Liu D, Zhou B, Liu R. A transcriptional co-expression network-based approach to identify prognostic biomarkers in gastric carcinoma. PeerJ 2020; 8:e8504. [PMID: 32095347 PMCID: PMC7025707 DOI: 10.7717/peerj.8504] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 01/03/2020] [Indexed: 12/14/2022] Open
Abstract
Background Gastric carcinoma is a very diverse disease. The progression of gastric carcinoma is influenced by complicated gene networks. This study aims to investigate the actual and potential prognostic biomarkers related to survival in gastric carcinoma patients to further our understanding of tumor biology. Methods A weighted gene co-expression network analysis was performed with a transcriptome dataset to identify networks and hub genes relevant to gastric carcinoma prognosis. Data was obtained from 300 primary gastric carcinomas (GSE62254). A validation dataset (GSE34942 and GSE15459) and TCGA dataset confirmed the results. Gene ontology, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA) were performed to identify the clusters responsible for the biological processes and pathways of this disease. Results A brown transcriptional module enriched in the organizational process of the extracellular matrix was significantly correlated with overall survival (HR = 1.586, p = 0.005, 95% CI [1.149–2.189]) and disease-free survival (HR = 1.544, p = 0.008, 95% CI [1.119–2.131]). These observations were confirmed in the validation dataset (HR = 1.664, p = 0.006, 95% CI [1.155–2.398] in overall survival). Ten hub genes were identified and confirmed in the validation dataset from this brown module; five key biomarkers (COL8A1, FRMD6, TIMP2, CNRIP1 and GPR124 (ADGRA2)) were identified for further research in microsatellite instability (MSI) and epithelial-tomesenchymal transition (MSS/EMT) gastric carcinoma molecular subtypes. A high expression of these genes indicated a poor prognosis. Conclusion A transcriptional co-expression network-based approach was used to identify prognostic biomarkers in gastric carcinoma. This method may have potential for use in personalized therapies, however, large-scale randomized controlled clinical trials and replication experiments are needed before these key biomarkers can be applied clinically.
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Affiliation(s)
- Danqi Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Boting Zhou
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Rangru Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.,Key Laboratory of Tropical Diseases and Translational Medicine of the Ministry of Education & Hainan Provincial Key Laboratory of Tropical Medicine, Hainan Medical College, Haikou, People's Republic of China
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9
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Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy. THE PHARMACOGENOMICS JOURNAL 2020; 20:705-716. [PMID: 32042095 DOI: 10.1038/s41397-020-0161-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 01/24/2020] [Accepted: 01/29/2020] [Indexed: 12/12/2022]
Abstract
Diffuse Large B-cell Lymphoma (DLBCL), a heterogeneous disease, is influenced by complex network of gene interactions. Most previous studies focused on individual genes, but ignored the importance of intergenic correlations. In current study, we aimed to explore the association between gene networks and overall survival (OS) of DLBCL patients treated with CHOP-based chemotherapy (cyclophosphamide combination with doxorubicin, vincristine and prednisone). Weighted gene co-expression network analysis was conducted to obtain insights into the molecular characteristics of DLBCL. Ten co-expression gene networks (modules) were identified in training dataset (n = 470), and their associations with patients' OS after chemotherapy were tested. The results were validated in four independent datasets (n = 802). Gene ontology (GO) biological function enrichment analysis was conducted with Metascape. Three modules (purple, brown and red), which were enriched in T-cell immune, cell-cell adhesion and extracellular matrix (ECM), respectively, were found to be related to longer OS. Higher expression of several hub genes within these three co-expression modules, for example, LCP2 (HR = 0.77, p = 5.40 × 10-2), CD2 (HR = 0.87, p = 6.31 × 10-2), CD3D (HR = 0.83, p = 6.94 × 10-3), FYB (HR = 0.82, p = 1.40 × 10-2), GZMK (HR = 0.92, p = 1.19 × 10-1), FN1 (HR = 0.88, p = 7.06 × 10-2), SPARC (HR = 0.82, p = 2.06 × 10-2), were found to be associated with favourable survival. Moreover, the associations of the modules and hub genes with OS in different molecular subtypes and different chemotherapy groups were also revealed. In general, our research revealed the key gene modules and several hub genes were upregulated correlated with good survival of DLBCL patients, which might provide potential therapeutic targets for future clinical research.
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Delgado-Chaves FM, Gómez-Vela F, García-Torres M, Divina F, Vázquez Noguera JL. Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach. Genes (Basel) 2019; 10:E962. [PMID: 31766738 PMCID: PMC6947459 DOI: 10.3390/genes10120962] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 10/28/2019] [Accepted: 10/31/2019] [Indexed: 12/22/2022] Open
Abstract
Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis. For this reason, lung cancer biomarkers that can be easily measured are highly demanded in biomedical research. In this work, we present an application of gene co-expression networks in the modelling of lung cancer gene regulatory networks, which ultimately served to the discovery of new biomarkers. For this, a robust GN inference was performed from microarray data concomitantly using three different co-expression measures. Results identified a major cluster of genes involved in SRP-dependent co-translational protein target to membrane, as well as a set of 28 genes that were exclusively found in networks generated from cancer samples. Amongst potential biomarkers, genes N C K A P 1 L and D M D are highlighted due to their implications in a considerable portion of lung and bronchus primary carcinomas. These findings demonstrate the potential of GN reconstruction in the rational prediction of biomarkers.
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Affiliation(s)
- Fernando M. Delgado-Chaves
- Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain; (F.M.D.-C.); (M.G.-T.); (F.D.)
| | - Francisco Gómez-Vela
- Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain; (F.M.D.-C.); (M.G.-T.); (F.D.)
| | - Miguel García-Torres
- Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain; (F.M.D.-C.); (M.G.-T.); (F.D.)
| | - Federico Divina
- Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain; (F.M.D.-C.); (M.G.-T.); (F.D.)
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Villalba M, Redin E, Exposito F, Pajares MJ, Sainz C, Hervas D, Guruceaga E, Diaz-Lagares A, Cirauqui C, Redrado M, Valencia K, de Andrea C, Jantus-Lewintre E, Camps C, Lopez-Lopez R, Lahoz A, Montuenga L, Pio R, Sandoval J, Calvo A. Identification of a novel synthetic lethal vulnerability in non-small cell lung cancer by co-targeting TMPRSS4 and DDR1. Sci Rep 2019; 9:15400. [PMID: 31659178 PMCID: PMC6817908 DOI: 10.1038/s41598-019-51066-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 07/29/2019] [Indexed: 12/23/2022] Open
Abstract
Finding novel targets in non-small cell lung cancer (NSCLC) is highly needed and identification of synthetic lethality between two genes is a new approach to target NSCLC. We previously found that TMPRSS4 promotes NSCLC growth and constitutes a prognostic biomarker. Here, through large-scale analyses across 5 public databases we identified consistent co-expression between TMPRSS4 and DDR1. Similar to TMPRSS4, DDR1 promoter was hypomethylated in NSCLC in 3 independent cohorts and hypomethylation was an independent prognostic factor of disease-free survival. Treatment with 5-azacitidine increased DDR1 levels in cell lines, suggesting an epigenetic regulation. Cells lacking TMPRSS4 were highly sensitive to the cytotoxic effect of the DDR1 inhibitor dasatinib. TMPRSS4/DDR1 double knock-down (KD) cells, but not single KD cells suffered a G0/G1 cell cycle arrest with loss of E2F1 and cyclins A and B, increased p21 levels and a larger number of cells in apoptosis. Moreover, double KD cells were highly sensitized to cisplatin, which caused massive apoptosis (~40%). In vivo studies demonstrated tumor regression in double KD-injected mice. In conclusion, we have identified a novel vulnerability in NSCLC resulting from a synthetic lethal interaction between DDR1 and TMPRSS4.
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Affiliation(s)
- Maria Villalba
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Department of Pathology, Anatomy and Physiology, School of Medicine, University of Navarra, Pamplona, Spain.,CIBERONC, ISC-III, Madrid, Spain
| | - Esther Redin
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Department of Pathology, Anatomy and Physiology, School of Medicine, University of Navarra, Pamplona, Spain
| | - Francisco Exposito
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Department of Pathology, Anatomy and Physiology, School of Medicine, University of Navarra, Pamplona, Spain.,CIBERONC, ISC-III, Madrid, Spain
| | - Maria Jose Pajares
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Department of Pathology, Anatomy and Physiology, School of Medicine, University of Navarra, Pamplona, Spain.,CIBERONC, ISC-III, Madrid, Spain
| | - Cristina Sainz
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - David Hervas
- Data Science, Bioestatistics and Bioinformatics, Health Research Institute La Fe, Valencia, Spain
| | - Elizabeth Guruceaga
- Bioinformatics Platform, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Angel Diaz-Lagares
- CIBERONC, ISC-III, Madrid, Spain.,Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), University Clinical Hospital of Santiago (CHUS), Santiago de Compostela, Spain
| | - Cristina Cirauqui
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Miriam Redrado
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Karmele Valencia
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,CIBERONC, ISC-III, Madrid, Spain.,Department of Biochemistry and Genetics, School of Science, University of Navarra, Pamplona, Spain
| | - Carlos de Andrea
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Department of Pathology, Anatomy and Physiology, School of Medicine, University of Navarra, Pamplona, Spain.,CIBERONC, ISC-III, Madrid, Spain
| | - Eloisa Jantus-Lewintre
- CIBERONC, ISC-III, Madrid, Spain.,Molecular Oncology Laboratory, General University Hospital Research Foundation, Valencia, Spain.,Department of Biotechnology, Universitat Politecnica de Valencia, Valencia, Spain
| | - Carlos Camps
- CIBERONC, ISC-III, Madrid, Spain.,Molecular Oncology Laboratory, General University Hospital Research Foundation, Valencia, Spain.,Department of Medicine Universitat de Valencia, Valencia, Spain
| | - Rafael Lopez-Lopez
- CIBERONC, ISC-III, Madrid, Spain.,Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), University Clinical Hospital of Santiago (CHUS), Santiago de Compostela, Spain
| | - Agustin Lahoz
- Biomarkers and Precision Medicine Unit, Health Research Institute la Fe, Valencia, Spain
| | - Luis Montuenga
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Department of Pathology, Anatomy and Physiology, School of Medicine, University of Navarra, Pamplona, Spain.,CIBERONC, ISC-III, Madrid, Spain
| | - Ruben Pio
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,CIBERONC, ISC-III, Madrid, Spain.,Department of Biochemistry and Genetics, School of Science, University of Navarra, Pamplona, Spain
| | - Juan Sandoval
- Biomarkers and Precision Medicine Unit, Health Research Institute la Fe, Valencia, Spain.
| | - Alfonso Calvo
- IDISNA and Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain. .,Department of Pathology, Anatomy and Physiology, School of Medicine, University of Navarra, Pamplona, Spain. .,CIBERONC, ISC-III, Madrid, Spain.
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12
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Competing Endogenous RNA and Coexpression Network Analysis for Identification of Potential Biomarkers and Therapeutics in association with Metastasis Risk and Progression of Prostate Cancer. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2019; 2019:8265958. [PMID: 31467637 PMCID: PMC6701351 DOI: 10.1155/2019/8265958] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 04/11/2019] [Accepted: 06/19/2019] [Indexed: 02/06/2023]
Abstract
Prostate cancer (PCa) is the most frequently diagnosed malignant neoplasm in men. Despite the high incidence, the underlying pathogenic mechanisms of PCa are still largely unknown, which limits the therapeutic options and leads to poor prognosis. Herein, based on the expression profiles from The Cancer Genome Atlas (TCGA) database, we investigated the interactions between long noncoding RNA (lncRNA) and mRNA by constructing a competing endogenous RNA network. Several competing endogenous RNAs could participate in the tumorigenesis of PCa. Six lncRNA signatures were identified as potential candidates associated with stage progression by the Kolmogorov-Smirnov test. In addition, 32 signatures from the coexpression network had potential diagnostic value for PCa lymphatic metastasis using machine learning algorithms. By targeting the coexpression network, the antifungal compound econazole was screened out for PCa treatment. Econazole could induce growth restraint, arrest the cell cycle, lead to apoptosis, inhibit migration, invasion, and adhesion in PC3 and DU145 cell lines, and inhibit the growth of prostate xenografts in nude mice. This systematic characterization of lncRNAs, microRNAs, and mRNAs in the risk of metastasis and progression of PCa will aid in the identification of candidate prognostic biomarkers and potential therapeutic drugs.
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Guo Y, Ma J, Xiao L, Fang J, Li G, Zhang L, Xu L, Lai X, Pan G, Chen Z. Identification of key pathways and genes in different types of chronic kidney disease based on WGCNA. Mol Med Rep 2019; 20:2245-2257. [PMID: 31257514 PMCID: PMC6691232 DOI: 10.3892/mmr.2019.10443] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 03/15/2019] [Indexed: 02/07/2023] Open
Abstract
Chronic kidney disease (CKD) is a highly heterogeneous nephrosis that occurs when the structure and function of the kidney is damaged. Gene expression studies have been widely used to elucidate various biological processes; however, the gene expression profile of CKD is currently unclear. The present study aimed to identify diagnostic biomarkers and therapeutic targets using renal biopsy sample data from patients with CKD. Gene expression data from 30 patients with CKD and 21 living donors were analyzed by weighted gene co-expression network analysis (WGCNA), in order to identify gene networks and profiles for CKD, as well as its specific characteristics, and to potentially uncover diagnostic biomarkers and therapeutic targets for patients with CKD. In addition, functional enrichment analysis was performed on co-expressed genes to determine modules of interest. Four co-expression modules were constructed from the WGCNA. The number of genes in the constructed modules ranged from 269 genes in the Turquoise module to 60 genes in the Yellow module. All four co-expression modules were correlated with CKD clinical traits (P<0.05). For example, the Turquoise module, which mostly contained genes that were upregulated in CKD, was positively correlated with CKD clinical traits, whereas the Blue, Brown and Yellow modules were negatively correlated with clinical traits. Functional enrichment analysis revealed that the Turquoise module was mainly enriched in genes associated with the ‘defense response’, ‘mitotic cell cycle’ and ‘collagen catabolic process’ Gene Ontology (GO) terms, implying that genes involved in cell cycle arrest and fibrogenesis were upregulated in CKD. Conversely, the Yellow module was mainly enriched in genes associated with ‘glomerulus development’ and ‘kidney development’ GO terms, indicating that genes associated with renal development and damage repair were downregulated in CKD. The hub genes in the modules were acetyl-CoA carboxylase α, cyclin-dependent kinase 1, Wilm's tumour 1, NPHS2 stomatin family member, podocin, JunB proto-oncogene, AP-1 transcription factor subunit, activating transcription factor 3, forkhead box O1 and v-abl Abelson murine leukemia viral oncogene homolog 1, which were confirmed to be significantly differentially expressed in CKD biopsies. Combining the eight hub genes enabled a high capacity for discrimination between patients with CKD and healthy subjects, with an area under the receiver operating characteristic curve of 1.00. In conclusion, this study provided a framework for co-expression modules of renal biopsy samples from patients with CKD and living donors, and identified several potential diagnostic biomarkers and therapeutic targets for CKD.
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Affiliation(s)
- Yuhe Guo
- Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, P.R. China
| | - Junjie Ma
- Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, P.R. China
| | - Lanyan Xiao
- Department of Center Laboratory, The Third Affiliated Hospital, Sun Yat‑sen University, Guangzhou, Guangdong 510700, P.R. China
| | - Jiali Fang
- Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, P.R. China
| | - Guanghui Li
- Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, P.R. China
| | - Lei Zhang
- Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, P.R. China
| | - Lu Xu
- Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, P.R. China
| | - Xingqiang Lai
- Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, P.R. China
| | - Guanghui Pan
- Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, P.R. China
| | - Zheng Chen
- Department of Organ Transplantation, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, P.R. China
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14
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Zhao Q, Fan C. A novel risk score system for assessment of ovarian cancer based on co-expression network analysis and expression level of five lncRNAs. BMC MEDICAL GENETICS 2019; 20:103. [PMID: 31182053 PMCID: PMC6558878 DOI: 10.1186/s12881-019-0832-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 05/23/2019] [Indexed: 02/07/2023]
Abstract
Background Ovarian cancer (OC) is the most deadly gynaecological cancer, contributing significantly to female cancer-related deaths worldwide. Improving the outlook for OC patients depends on the identification of more reliable prognostic biomarkers for early diagnosis and survival prediction. The various roles of long non-coding RNAs (lncRNAs) in OC have attracted increasing attention. This study aimed to identify a lncRNA-based signature for survival prediction in OC patients. Methods RNA expression data and clinical information from a large number of OC patients were downloaded from a public database. These data were regarded as a training set to construct a weighed gene co-expression network analysis (WGCNA) network, mine stable modules, and screen differentially expressed lncRNAs. The prognostic lncRNAs were screened using univariate Cox regression analysis and the optimal prognosis lncRNA combination was screened using a Cox-PH model. The finalised lncRNA combination was used to construct the risk score system, which was validated and assessed for effectiveness using other independent datasets. Further functional pathway enrichment was performed using gene set enrichment analysis (GSEA). Results A co-expression network was constructed and four stable modules with OC-related biological functions were obtained. A total of 19 lncRNAs significantly related to prognosis of ovarian cancer were obtained using univariate Cox regression analysis, and the 5 prognostic signature lncRNAs GAS5, HCP5, PART1, SNHG11, and SNHG5 were used to establish a risk assessment system. The reliability of the prognostic scoring system was further confirmed using validation sets, which indicated that the risk assessment system could be used as an independent prognostic factor. Pathway enrichment analysis revealed that the network modules related to the above five prognostic genes were significantly associated with cell local adhesion, cancer signaling pathways, JAK-STAT signalling, and endogenous cell receptor interaction. Conclusions The risk score system established in this study could provide a novel reliable method to identify individuals at high risk of OC. In addition, the five prognostic lncRNAs identified here are promising potential prognostic biomarkers that could help to elucidate the pathogenesis of OC.
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Affiliation(s)
- Qian Zhao
- Department of Gynecology & Obstetrics, Chengdu Women's & Children's Central Hospital, No.1617 Riyue Avenue, Chengdu, 610091, Sichuan Province, China.
| | - Conghong Fan
- Department of Gynecology & Obstetrics, Chengdu Women's & Children's Central Hospital, No.1617 Riyue Avenue, Chengdu, 610091, Sichuan Province, China
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15
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Fan X, Wang Y, Tang XQ. Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection. BMC Bioinformatics 2019; 20:197. [PMID: 31074380 PMCID: PMC6509866 DOI: 10.1186/s12859-019-2739-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background Lung adenocarcinoma is the most common type of lung cancer, with high mortality worldwide. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, DNA methylation, and miRNA quantification. However, the hub genes, which can be served as bio-markers for discriminating cancer and healthy individuals, are not well screened. Result Here we present a new method for extracting gene predictors, aiming to obtain the least predictors without losing the efficiency. We firstly analyzed three different expression microarrays and constructed multi-interaction network, since the individual expression dataset is not enough for describing biological behaviors dynamically and systematically. Then, we transformed the undirected interaction network to directed network by employing Granger causality test, followed by the predictors screened with the use of the stepwise character selection algorithm. Six predictors, including TOP2A, GRK5, SIRT7, MCM7, EGFR, and COL1A2, were ultimately identified. All the predictors are the cancer-related, and the number is very small fascinating diagnosis. Finally, the validation of this approach was verified by robustness analyses applied to six independent datasets; the precision is up to 95.3% ∼ 100%. Conclusion Although there are complicated differences between cancer and normal cells in gene functions, cancer cells could be differentiated in case that a group of special genes expresses abnormally. Here we presented a new, robust, and effective method for extracting gene predictors. We identified as low as 6 genes which can be taken as predictors for diagnosing lung adenocarcinoma.
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Affiliation(s)
- Xuemeng Fan
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Yaolai Wang
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Xu-Qing Tang
- School of Science, Jiangnan University, Wuxi, 214122, China. .,Wuxi Engineering Research Center for Biocomputing, Wuxi, 214122, China.
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16
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RNA-Seq transcriptome analysis shows anti-tumor actions of melatonin in a breast cancer xenograft model. Sci Rep 2019; 9:966. [PMID: 30700756 PMCID: PMC6353949 DOI: 10.1038/s41598-018-37413-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 11/30/2018] [Indexed: 02/07/2023] Open
Abstract
Melatonin is a pleiotropic anti-cancer molecule that controls cancer growth by multiple mechanisms. RNA-Seq can potentially evaluate therapeutic response and its use in xenograft tumor models can differentiate the changes that occur specifically in tumor cells or in the tumor microenvironment (TME). Melatonin actions were evaluated in a xenograft model of triple-negative breast cancer. Balb/c nude mice bearing MDA-MB-231 tumors were treated with melatonin or vehicle. RNA-Seq was performed on the Illumina HiSeq. 2500 and data were mapped against human and mouse genomes separately to differentiate species-specific expression. Differentially expressed (DE) genes were identified and Weighted Gene Co-expression Network Analysis (WGCNA) was used to detect clusters of highly co-expressed genes. Melatonin treatment reduced tumor growth (p < 0.01). 57 DE genes were identified in murine cells, which represented the TME, and were mainly involved in immune response. The WGCNA detected co-expressed genes in tumor cells and TME, which were related to the immune system among other biological processes. The upregulation of two genes (Tnfaip8l2 and Il1f6) by melatonin was validated in the TME, these genes play important roles in the immune system. Taken together, the transcriptomic data suggests that melatonin anti-tumor actions occur through modulation of TME in this xenograft tumor model.
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17
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Guo YZ, Sun HH, Wang XT, Wang MT. Transcriptomic analysis reveals key lncRNAs associated with ribosomal biogenesis and epidermis differentiation in head and neck squamous cell carcinoma. J Zhejiang Univ Sci B 2019; 19:674-688. [PMID: 30178634 DOI: 10.1631/jzus.b1700319] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma (HNSCC)-associated long noncoding RNAs (lncRNAs), and to discover potential lncRNA prognostic biomarkers for HNSCC based on next-generation RNA-seq. METHODS RNA-seq data of 546 samples from patients with HNSCC were downloaded from The Cancer Genome Atlas (TCGA), including 43 paired samples of tumor tissue and adjacent normal tissue. An integrated analysis incorporating differential expression, weighted gene co-expression networks, functional enrichment, clinical parameters, and survival analysis was conducted to discover HNSCC-associated lncRNAs. The function of CYTOR was verified by cell-based experiments. To further identify lncRNAs with prognostic significance, a multivariate Cox proportional hazard regression analysis was performed. The identified lncRNAs were validated with an independent cohort using clinical feature relevance analysis and multivariate Cox regression analysis. RESULTS We identified nine HNSCC-relevant lncRNAs likely to play pivotal roles in HNSCC onset and development. By functional enrichment analysis, we revealed that CYTOR might participate in the multistep pathological processes of cancer, such as ribosome biogenesis and maintenance of genomic stability. CYTOR was identified to be positively correlated with lymph node metastasis, and significantly negatively correlated with overall survival (OS) and disease free survival (DFS) of HNSCC patients. Moreover, CYTOR inhibited cell apoptosis following treatment with the chemotherapeutic drug diamminedichloroplatinum (DDP). HCG22, the most dramatically down-regulated lncRNA in tumor tissue, may function in epidermis differentiation. It was also significantly associated with several clinical features of patients with HNSCC, and positively correlated with patient survival. CYTOR and HCG22 maintained their prognostic values independent of several clinical features in multivariate Cox hazards analysis. Notably, validation either based on an independent HNSCC cohort or by laboratory experiments confirmed these findings. CONCLUSIONS Our transcriptomic analysis suggested that dysregulation of these HNSCC-associated lncRNAs might be involved in HNSCC oncogenesis and progression. Moreover, CYTOR and HCG22 were confirmed as two independent prognostic factors for HNSCC patient survival, providing new insights into the roles of these lncRNAs in HNSCC as well as clinical applications.
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Affiliation(s)
- Yu-Zhu Guo
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | - Hui-Hui Sun
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | - Xiang-Ting Wang
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | - Mei-Ting Wang
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China.,College of Liren, Yanshan University, Qinhuangdao 066004, China
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18
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Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis. PLoS One 2018; 13:e0198445. [PMID: 29879180 PMCID: PMC5991748 DOI: 10.1371/journal.pone.0198445] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 05/18/2018] [Indexed: 12/17/2022] Open
Abstract
Several microRNAs (miRNAs) have been suggested as novel biomarkers for diagnosing gastric cancer (GC) at an early stage, but the single-marker strategy may ignore the co-regulatory relationships and lead to low diagnostic specificity. Thus, multi-target modular diagnostic biomarkers are urgently needed. In this study, a Zsummary and NetSVM-based method was used to identify GC-related hub miRNAs and activated modules from clinical miRNA co-expression networks. The NetSVM-based sub-network consisting of the top 20 hub miRNAs reached a high sensitivity and specificity of 0.94 and 0.82. The Zsummary algorithm identified an activated module (miR-486, miR-451, miR-185, and miR-600) which might serve as diagnostic biomarker of GC. Three members of this module were previously suggested as biomarkers of GC and its 24 target genes were significantly enriched in pathways directly related to cancer. The weighted diagnostic ROC AUC of this module was 0.838, and an optimized module unit (miR-451 and miR-185) obtained a higher value of 0.904, both of which were higher than that of individual miRNAs. These hub miRNAs and module have the potential to become robust biomarkers for early diagnosis of GC with further validations. Moreover, such modular analysis may offer valuable insights into multi-target approaches to cancer diagnosis and treatment.
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Couto CMV, Comin CH, Costa LDF. Effects of threshold on the topology of gene co-expression networks. MOLECULAR BIOSYSTEMS 2018; 13:2024-2035. [PMID: 28770908 DOI: 10.1039/c7mb00101k] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Several developments regarding the analysis of gene co-expression profiles using complex network theory have been reported recently. Such approaches usually start with the construction of an unweighted gene co-expression network, therefore requiring the selection of a suitable threshold defining which pairs of vertices will be connected. We aimed at addressing such an important problem by suggesting and comparing five different approaches for threshold selection. Each of the methods considers a respective biologically-motivated criterion for electing a potentially suitable threshold. A set of 21 microarray experiments from different biological groups was used to investigate the effect of applying the five proposed criteria to several biological situations. For each experiment, we used the Pearson correlation coefficient to measure the relationship between each gene pair, and the resulting weight matrices were thresholded considering several values, generating respective adjacency matrices (co-expression networks). Each of the five proposed criteria was then applied in order to select the respective threshold value. The effects of these thresholding approaches on the topology of the resulting networks were compared by using several measurements, and we verified that, depending on the database, the impact on the topological properties can be large. However, a group of databases was verified to be similarly affected by most of the considered criteria. Based on such results, it can be suggested that when the generated networks present similar measurements, the thresholding method can be chosen with greater freedom. If the generated networks are markedly different, the thresholding method that better suits the interests of each specific research study represents a reasonable choice.
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20
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Hu W, Yang Y, Li X, Zheng S. Pan-organ transcriptome variation across 21 cancer types. Oncotarget 2018; 8:6809-6818. [PMID: 28036280 PMCID: PMC5351671 DOI: 10.18632/oncotarget.14303] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 12/05/2016] [Indexed: 12/31/2022] Open
Abstract
It is widely accepted that some messenger RNAs are evolutionarily conserved across species, both in sequence and tissue-expression specificity. To date, however, little effort has been made to exploit the transcriptome divergence between cancer and adjacent normal tissue at the pan-organ level. In this work, a transcriptome sequencing dataset from 675 normal-tumor pairs, representing 21 solid organs in The Cancer Genome Atlas, is used to evaluate expression evolution. The results show that in most cancer types, gene expression divergence and organ-specificity are reduced in cancer tissue compared to adjacent normal tissue. Furthermore, we observe that all cancers share cell cycle dysregulation through interrogating differentially expressed protein coding genes. Meanwhile, weighted correlation network analysis is used to detect of the gene module structure variation between cancer and adjacent normal tissue. And modules consisting of tightly co-regulated genes in cancer change substantially compared with those in adjacent normal tissue. We thus assume that the destruction of a coordinated regulatory network might result in tumorigenesis and tumor progression. Our results provide new insights into the complex cancer biology and shed light on the mysterious regulation mode for cancer.
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Affiliation(s)
- Wangxiong Hu
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Yanmei Yang
- Key Laboratory of Reproductive and Genetics, Ministry of Education, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang 310006, China
| | - Xiaofen Li
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
| | - Shu Zheng
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China.,Research Center for Air Pollution and Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310009, China
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21
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The Bioinformatic Analysis of the Dysregulated Genes and MicroRNAs in Entorhinal Cortex, Hippocampus, and Blood for Alzheimer's Disease. BIOMED RESEARCH INTERNATIONAL 2017; 2017:9084507. [PMID: 29359159 PMCID: PMC5735586 DOI: 10.1155/2017/9084507] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 08/15/2017] [Accepted: 08/29/2017] [Indexed: 02/08/2023]
Abstract
Aim The incidence of Alzheimer's disease (AD) has been increasing in recent years, but there exists no cure and the pathological mechanisms are not fully understood. This study aimed to find out the pathogenesis of learning and memory impairment, new biomarkers, potential therapeutic targets, and drugs for AD. Methods We downloaded the microarray data of entorhinal cortex (EC) and hippocampus (HIP) of AD and controls from Gene Expression Omnibus (GEO) database, and then the differentially expressed genes (DEGs) in EC and HIP regions were analyzed for functional and pathway enrichment. Furthermore, we utilized the DEGs to construct coexpression networks to identify hub genes and discover the small molecules which were capable of reversing the gene expression profile of AD. Finally, we also analyzed microarray and RNA-seq dataset of blood samples to find the biomarkers related to gene expression in brain. Results We found some functional hub genes, such as ErbB2, ErbB4, OCT3, MIF, CDK13, and GPI. According to GO and KEGG pathway enrichment, several pathways were significantly dysregulated in EC and HIP. CTSD and VCAM1 were dysregulated significantly in blood, EC, and HIP, which were potential biomarkers for AD. Target genes of four microRNAs had similar GO_terms distribution with DEGs in EC and HIP. In addtion, small molecules were screened out for AD treatment. Conclusion These biological pathways and DEGs or hub genes will be useful to elucidate AD pathogenesis and identify novel biomarkers or drug targets for developing improved diagnostics and therapeutics against AD.
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22
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Li B, Liu J, Yu Y, Wang P, Zhang Y, Ni X, Liu Q, Zhang X, Wang Z, Wang Y. Network-Wide Screen Identifies Variation of Novel Precise On-Module Targets Using Conformational Modudaoism. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 7:16-25. [PMID: 28925077 PMCID: PMC5784734 DOI: 10.1002/psp4.12253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 08/30/2017] [Accepted: 09/13/2017] [Indexed: 01/30/2023]
Abstract
Modular targeting is promising in drug research at the network level, but it is challenging to quantificationally identify the precise on-modules. Based on a proposed Modudaoism (MD), we defined conserved MD (MDc) and varied MD (MDv) to quantitatively evaluate the conformational and energy variations of modules, and thereby identify the conserved and discrepant allosteric modules (AMs). Compared to the Zsummary , MDc/MDv got an optimized result of module preserved ratio and modular structure. In the mice anti-ischemic networks, 3, 5, and 1 conserved AMs as well as 4, 1, and 3 on-modules of baicalin (BA), jasminoidin (JA), and ursodeoxycholic acid (UA) were identified by MDc and MDv, 5 unique AMs and their characteristic actions were revealed. Besides, co-immunoprecipitation (Co-IP) experiments validated the representative modular structure. MDc/MDv method can quantitatively define the conformational variations of modules and screen the precise on-modules network-wide, which may provide a promising strategy for drug discovery.
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Affiliation(s)
- Bing Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.,Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.,ShanXi Buchang Pharmaceutical Co., Ltd., Heze, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanan Yu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Pengqian Wang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yingying Zhang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, China
| | - Qiong Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaoxu Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yongyan Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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23
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Pan JQ, Zhang YQ, Wang JH, Xu P, Wang W. lncRNA co-expression network model for the prognostic analysis of acute myeloid leukemia. Int J Mol Med 2017; 39:663-671. [PMID: 28204819 PMCID: PMC5360362 DOI: 10.3892/ijmm.2017.2888] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 02/02/2017] [Indexed: 12/01/2022] Open
Abstract
Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy with great variability of prognostic behaviors. Previous studies have reported that long non-coding RNAs (lncRNAs) play an important role in AML and may thus be used as potential prognostic biomarkers. However, thus use of lncRNAs as prognostic biomarkers in AML and their detailed mechanisms of action in this disease have not yet been well characterized. For this purpose, in the present study, the expression levels of lncRNAs and mRNAs were calculated using the RNA-seq V2 data for AML, following which a lncRNA-lncRNA co-expression network (LLCN) was constructed. This revealed a total of 8 AML prognosis-related lncRNA modules were identified, which displayed a significant correlation with patient survival (p≤0.05). Subsequently, a prognosis-related lncRNA module pathway network was constructed to interpret the functional mechanism of the prognostic modules in AML. The results indicated that these prognostic modules were involved in the AML pathway, chemokine signaling pathway and WNT signaling pathway, all of which play important roles in AML. Furthermore, the investigation of lncRNAs in these prognostic modules suggested that an lncRNA (ZNF571-AS1) may be involved in AML via the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway by regulating KIT and STAT5. The results of the present study not only provide potential lncRNA modules as prognostic biomarkers, but also provide further insight into the molecular mechanisms of action of lncRNAs.
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Affiliation(s)
- Jia-Qi Pan
- Department of Hematology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China
| | - Yan-Qing Zhang
- Department of Hematology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China
| | - Jing-Hua Wang
- Department of Hematology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China
| | - Ping Xu
- Department of Hematology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China
| | - Wei Wang
- Department of Hematology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China
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OncoBinder facilitates interpretation of proteomic interaction data by capturing coactivation pairs in cancer. Oncotarget 2017; 7:17608-15. [PMID: 26872056 PMCID: PMC4951236 DOI: 10.18632/oncotarget.7305] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Accepted: 01/29/2016] [Indexed: 11/25/2022] Open
Abstract
High-throughput methods such as co-immunoprecipitationmass spectrometry (coIP-MS) and yeast 2 hybridization (Y2H) have suggested a broad range of unannotated protein-protein interactions (PPIs), and interpretation of these PPIs remains a challenging task. The advancements in cancer genomic researches allow for the inference of "coactivation pairs" in cancer, which may facilitate the identification of PPIs involved in cancer. Here we present OncoBinder as a tool for the assessment of proteomic interaction data based on the functional synergy of oncoproteins in cancer. This decision tree-based method combines gene mutation, copy number and mRNA expression information to infer the functional status of protein-coding genes. We applied OncoBinder to evaluate the potential binders of EGFR and ERK2 proteins based on the gastric cancer dataset of The Cancer Genome Atlas (TCGA). As a result, OncoBinder identified high confidence interactions (annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) or validated by low-throughput assays) more efficiently than co-expression based method. Taken together, our results suggest that evaluation of gene functional synergy in cancer may facilitate the interpretation of proteomic interaction data. The OncoBinder toolbox for Matlab is freely accessible online.
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25
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Feng Y, Wang X. Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes. Mol Med Rep 2017; 15:1252-1262. [PMID: 28098893 PMCID: PMC5367356 DOI: 10.3892/mmr.2017.6124] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Accepted: 11/21/2016] [Indexed: 01/20/2023] Open
Abstract
In order to investigate commonly disturbed genes and pathways in various brain regions of patients with Parkinson's disease (PD), microarray datasets from previous studies were collected and systematically analyzed. Different normalization methods were applied to microarray datasets from different platforms. A strategy combining gene co-expression networks and clinical information was adopted, using weighted gene co-expression network analysis (WGCNA) to screen for commonly disturbed genes in different brain regions of patients with PD. Functional enrichment analysis of commonly disturbed genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Co-pathway relationships were identified with Pearson's correlation coefficient tests and a hypergeometric distribution-based test. Common genes in pathway pairs were selected out and regarded as risk genes. A total of 17 microarray datasets from 7 platforms were retained for further analysis. Five gene coexpression modules were identified, containing 9,745, 736, 233, 101 and 93 genes, respectively. One module was significantly correlated with PD samples and thus the 736 genes it contained were considered to be candidate PD-associated genes. Functional enrichment analysis demonstrated that these genes were implicated in oxidative phosphorylation and PD. A total of 44 pathway pairs and 52 risk genes were revealed, and a risk gene pathway relationship network was constructed. Eight modules were identified and were revealed to be associated with PD, cancers and metabolism. A number of disturbed pathways and risk genes were unveiled in PD, and these findings may help advance understanding of PD pathogenesis.
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Affiliation(s)
- Yinling Feng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, Sichuan 400016, P.R. China
| | - Xuefeng Wang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, Sichuan 400016, P.R. China
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Yan W, Xue W, Chen J, Hu G. Biological Networks for Cancer Candidate Biomarkers Discovery. Cancer Inform 2016; 15:1-7. [PMID: 27625573 PMCID: PMC5012434 DOI: 10.4137/cin.s39458] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 06/06/2016] [Accepted: 06/16/2016] [Indexed: 12/16/2022] Open
Abstract
Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.
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Affiliation(s)
- Wenying Yan
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Wenjin Xue
- Department of Electrical Engineering, Technician College of Taizhou, Taizhou, Jiangsu, China
| | - Jiajia Chen
- School of Chemistry, Biology and Material Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Guang Hu
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
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Zhu J, Zhang B, Song W, Zhang X, Wang L, Yin B, Zhu F, Yu C, Li H. A literature review on the role of miR-370 in disease. GENE REPORTS 2016. [DOI: 10.1016/j.genrep.2016.02.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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28
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Correlating transcriptional networks with pathological complete response following neoadjuvant chemotherapy for breast cancer. Breast Cancer Res Treat 2015; 151:607-18. [PMID: 25981901 DOI: 10.1007/s10549-015-3428-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 05/11/2015] [Indexed: 12/20/2022]
Abstract
We aimed to investigate the association between gene co-expression modules and responses to neoadjuvant chemotherapy in breast cancer by using a systematic biological approach. The gene expression profiles and clinico-pathological data of 508 (discovery set) and 740 (validation set) patients with breast cancer who received neoadjuvant chemotherapy were analyzed. Weighted gene co-expression network analysis was performed and identified seven co-regulated gene modules. Each module and gene signature were evaluated with logistic regression models for pathological complete response (pCR). The association between modules and pCR in each intrinsic molecular subtype was also investigated. Two transcriptional modules were correlated with tumor grade, estrogen receptor status, progesterone receptor status, and chemotherapy response in breast cancer. One module that constitutes upregulated cell proliferation genes was associated with a high probability for pCR in the whole (odds ratio (OR) = 5.20 and 3.45 in the discovery and validation datasets, respectively), luminal B, and basal-like subtypes. The prognostic potentials of novel genes, such as MELK, and pCR-related genes, such as ESR1 and TOP2A, were identified. The upregulation of another gene co-expression module was associated with weak chemotherapy responses (OR = 0.19 and 0.33 in the discovery and validation datasets, respectively). The novel gene CA12 was identified as a potential prognostic indicator in this module. A systems biology network-based approach may facilitate the discovery of biomarkers for predicting chemotherapy responses in breast cancer and contribute in developing personalized medicines.
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