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Feng Y, Jiang Y, Wen T, Meng F, Shu X. Identifying Potential Prognostic Markers for Muscle-Invasive Bladder Urothelial Carcinoma by Weighted Gene Co-Expression Network Analysis. Pathol Oncol Res 2019; 26:1063-1072. [PMID: 31011911 DOI: 10.1007/s12253-019-00657-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 04/01/2019] [Indexed: 12/21/2022]
Abstract
Muscle-invasive bladder urothelial carcinoma (MIBC) is characterized as a genetic heterogeneous cancer with a high percentage of recurrence and worse prognosis. Identify the prognostic potentials of novel genes for muscle-invasive urothelial bladder cancer could at least provide important information for early detection and clinical treatment. Weighted gene co-expression network analysis (WGCNA) algorithm, a powerful systems biology approach, was utilized to extract co-expressed gene networks from mRNA expression dataset to construct transcriptional modules in MIBC samples, which was associated with demographic and clinical traits of MIBC patients. The potential prognostic markers of MIBC were screened out in the discovery dataset and verified in an independent external validation dataset. A total of 8 co-expression modules were detected through the WGCNA algorithm in the discovery datasets based on 401 MIBC samples. One transcriptional module enriched in cell development was observed to be correlated with the MIBC prognosis in the discovery datasets (HR = 1.48, 95%CI = 1.04-2.11) and independently verified in an external dataset (HR = 3.59, 95%CI = 1.09-11.79). High expression of hub genes including discoidin domain receptor tyrosine kinase 2 (DDR2), PDZ and LIM domain 3 (PDLIM3), zinc finger protein 521 (ZNF521), methionine sulfoxide reductase B3 (MSRB3) were significantly associated with the unfavorable survival of MIBC patients. We identified and validated four novel potential biomarkers associated with prognosis of MIBC patients by constructing genes co-expression networks. The discovery of these genetic markers may provide a new target for the development of MIBC chemotherapeutic drugs.
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Affiliation(s)
- Yueyi Feng
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China
| | - Yiqing Jiang
- Department of General Surgery, Harrison International Peace Hospital, Hengshui, 053000, China
| | - Tao Wen
- Medical Research Centre, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China
| | - Fang Meng
- Centre of Systems Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
- Suzhou Institute of Systems Medicine, Suzhou, 215123, China.
| | - Xiaochen Shu
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China.
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52
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Bai Y, Xiong L, Zhu M, Yang Z, Zhao J, Tang H. Co-expression network analysis identified KIF2C in association with progression and prognosis in lung adenocarcinoma. Cancer Biomark 2019; 24:371-382. [DOI: 10.3233/cbm-181512] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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53
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Mack KL, Phifer-Rixey M, Harr B, Nachman MW. Gene Expression Networks Across Multiple Tissues Are Associated with Rates of Molecular Evolution in Wild House Mice. Genes (Basel) 2019; 10:E225. [PMID: 30889893 DOI: 10.3390/genes10030225] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Revised: 03/10/2019] [Accepted: 03/11/2019] [Indexed: 01/13/2023] Open
Abstract
Interactions between genes can influence how selection acts on sequence variation. In gene regulatory networks, genes that affect the expression of many other genes may be under stronger evolutionary constraint than genes whose expression affects fewer partners. While this has been studied for individual tissue types, we know less about the effects of regulatory networks on gene evolution across different tissue types. We use RNA-sequencing and genomic data collected from Mus musculus domesticus to construct and compare gene co-expression networks for 10 tissue types. We identify tissue-specific expression and local regulatory variation, and we associate these components of gene expression variation with sequence polymorphism and divergence. We found that genes with higher connectivity across tissues and genes associated with a greater number of cross-tissue modules showed significantly lower genetic diversity and lower rates of protein evolution. Consistent with this pattern, “hub” genes across multiple tissues also showed evidence of greater evolutionary constraint. Using allele-specific expression, we found that genes with cis-regulatory variation had lower average connectivity and higher levels of tissue specificity. Taken together, these results are consistent with strong purifying selection acting on genes with high connectivity within and across tissues.
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Deng T, Liang A, Liang S, Ma X, Lu X, Duan A, Pang C, Hua G, Liu S, Campanile G, Salzano A, Gasparrini B, Neglia G, Liang X, Yang L. Integrative Analysis of Transcriptome and GWAS Data to Identify the Hub Genes Associated With Milk Yield Trait in Buffalo. Front Genet 2019; 10:36. [PMID: 30804981 PMCID: PMC6371051 DOI: 10.3389/fgene.2019.00036] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 01/18/2019] [Indexed: 01/05/2023] Open
Abstract
The mammary gland is the production organ in mammals that is of great importance for milk production and quality. However, characterization of the buffalo mammary gland transcriptome and identification of the valuable candidate genes that affect milk production is limited. Here, we performed the differential expressed genes (DEGs) analysis of mammary gland tissue on day 7, 50, 140, and 280 after calving and conducted gene-based genome-wide association studies (GWAS) of milk yield in 935 Mediterranean buffaloes. We then employed weighted gene co-expression network analysis (WGCNA) to identify specific modules and hub genes related to milk yield based on gene expression profiles and GWAS data. The results of the DEGs analysis showed that a total of 1,420 DEGs were detected across different lactation points. In the gene-based analysis, 976 genes were found to have genome-wide association (P ≤ 0.05) that could be defined as the nominally significant GWAS geneset (NSGG), 9 of which were suggestively associated with milk yield (P < 10−4). Using the WGCNA analysis, 544 and 225 genes associated with milk yield in the turquoise module were identified from DEGs and NSGG datasets, respectively. Several genes (including BNIPL, TUBA1C, C2CD4B, DCP1B, MAP3K5, PDCD11, SRGAP1, GDPD5, BARX2, SCARA3, CTU2, and RPL27A) were identified and considered as the hub genes because they were involved in multiple pathways related to milk production. Our findings provide an insight into the dynamic characterization of the buffalo mammary gland transcriptome, and these potential candidate genes may be valuable for future functional characterization of the buffalo mammary gland.
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Affiliation(s)
- Tingxian Deng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China.,Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Aixin Liang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Shasha Liang
- Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Xiaoya Ma
- Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Xingrong Lu
- Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Anqin Duan
- Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Chunying Pang
- Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Guohua Hua
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Shenhe Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Giuseppe Campanile
- Department of Veterinary Medicine and Animal Productions, University of Naples Federico II, Naples, Italy
| | - Angela Salzano
- Department of Veterinary Medicine and Animal Productions, University of Naples Federico II, Naples, Italy
| | - Bianca Gasparrini
- Department of Veterinary Medicine and Animal Productions, University of Naples Federico II, Naples, Italy
| | - Gianluca Neglia
- Department of Veterinary Medicine and Animal Productions, University of Naples Federico II, Naples, Italy
| | - Xianwei Liang
- Guangxi Provincial Key Laboratory of Buffalo Genetics, Breeding and Reproduction Technology, Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning, China
| | - Liguo Yang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
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Abstract
BACKGROUND Transcriptional dysregulation is one of the most important features of cancer genesis and progression. Applying gene expression dysregulation information to predict the development of cancers is useful for cancer diagnosis. However, previous studies mainly focused on the relationship between a single gene and cancer. Prognostic prediction using combined gene models remains limited. MATERIALS AND METHODS Gene expression profiles were downloaded from The Cancer Genome Atlas and the data sets were randomly divided into training data sets and test data sets. A six-gene signature associated with head and neck squamous cell carcinoma (HNSCC) and overall survival (OS) was identified according to a training cohort by using weighted gene correlation network analysis and least absolute shrinkage and selection operator Cox regression. The test data set and gene expression omnibus (GEO) data set were used to validate this signature. RESULTS We identified six candidate genes, namely, FOXL2NB, PCOLCE2, SPINK6, ULBP2, KCNJ18, and RFPL1, and, using a six-gene model, predicted the risk of death of head and neck squamous cell carcinoma in The Cancer Genome Atlas. At a selected cutoff, patients were clustered into low- and high-risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics of OS, disease-specific survival (DSS), and progression-free survival (PFS) were as high as 0.766, 0.731, and 0.623, respectively. Then, the test data set and the GEO data set were used to evaluate our model, and we found that the OS time in the high-risk group was significantly shorter than in the low-risk group in both data sets, and the receiver operating characteristics of test data set were 0.669, 0.675, and 0.614, respectively. Furthermore, univariate and multivariate Cox regression analyses showed that the risk score was independent of clinicopathological features. CONCLUSION The six-gene model could predict the OS of HNSCC patients and improve therapeutic decision-making.
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Affiliation(s)
- Saisai Tian
- Department of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, People's Republic of China,
| | - Guofeng Meng
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China,
| | - Weidong Zhang
- Department of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, People's Republic of China,
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China,
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56
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Wang Y, Chen L, Wang G, Cheng S, Qian K, Liu X, Wu CL, Xiao Y, Wang X. Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis. J Cell Physiol 2018; 234:10225-10237. [PMID: 30417363 DOI: 10.1002/jcp.27692] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 10/09/2018] [Indexed: 02/06/2023]
Abstract
Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein-protein interaction (PPI) network analysis. After verification of TCGA's ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.
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Affiliation(s)
- Yejinpeng Wang
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Liang Chen
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Gang Wang
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China.,Human Genetics Resource Preservation Center of Hubei Province, Wuhan, China.,Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Songtao Cheng
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Kaiyu Qian
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China.,Human Genetics Resource Preservation Center of Hubei Province, Wuhan, China.,Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xuefeng Liu
- Greenebaum Cancer Center, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Chin-Lee Wu
- Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yu Xiao
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China.,Human Genetics Resource Preservation Center of Hubei Province, Wuhan, China.,Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xinghuan Wang
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Medical Research Institute, Wuhan University, Wuhan, China
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57
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Simonetti G, Bruno S, Padella A, Tenti E, Martinelli G. Aneuploidy: Cancer strength or vulnerability? Int J Cancer 2018; 144:8-25. [PMID: 29981145 PMCID: PMC6587540 DOI: 10.1002/ijc.31718] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 06/05/2018] [Accepted: 06/14/2018] [Indexed: 12/12/2022]
Abstract
Aneuploidy is a very rare and tissue‐specific event in normal conditions, occurring in a low number of brain and liver cells. Its frequency increases in age‐related disorders and is one of the hallmarks of cancer. Aneuploidy has been associated with defects in the spindle assembly checkpoint (SAC). However, the relationship between chromosome number alterations, SAC genes and tumor susceptibility remains unclear. Here, we provide a comprehensive review of SAC gene alterations at genomic and transcriptional level across human cancers and discuss the oncogenic and tumor suppressor functions of aneuploidy. SAC genes are rarely mutated but frequently overexpressed, with a negative prognostic impact on different tumor types. Both increased and decreased SAC gene expression show oncogenic potential in mice. SAC gene upregulation may drive aneuploidization and tumorigenesis through mitotic delay, coupled with additional oncogenic functions outside mitosis. The genomic background and environmental conditions influence the fate of aneuploid cells. Aneuploidy reduces cellular fitness. It induces growth and contact inhibition, mitotic and proteotoxic stress, cell senescence and production of reactive oxygen species. However, aneuploidy confers an evolutionary flexibility by favoring genome and chromosome instability (CIN), cellular adaptation, stem cell‐like properties and immune escape. These properties represent the driving force of aneuploid cancers, especially under conditions of stress and pharmacological pressure, and are currently under investigation as potential therapeutic targets. Indeed, promising results have been obtained from synthetic lethal combinations exploiting CIN, mitotic defects, and aneuploidy‐tolerating mechanisms as cancer vulnerability.
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Affiliation(s)
- Giorgia Simonetti
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna and Institute of Hematology "L. e A. Seràgnoli", Bologna, Italy
| | - Samantha Bruno
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna and Institute of Hematology "L. e A. Seràgnoli", Bologna, Italy
| | - Antonella Padella
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna and Institute of Hematology "L. e A. Seràgnoli", Bologna, Italy
| | - Elena Tenti
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna and Institute of Hematology "L. e A. Seràgnoli", Bologna, Italy
| | - Giovanni Martinelli
- Scientific Directorate, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
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58
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Xiong Y, Yuan L, Chen L, Zhu Y, Zhang S, Liu X, Xiao Y, Wang X. Identifying a Novel Biomarker TOP2A of Clear Cell Renal Cell Carcinoma (ccRCC) Associated with Smoking by Co-Expression Network Analysis. J Cancer 2018; 9:3912-3922. [PMID: 30410595 PMCID: PMC6218786 DOI: 10.7150/jca.25900] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 08/26/2018] [Indexed: 12/16/2022] Open
Abstract
Although it is well known that smoking is one of pathogenesis of clear cell renal cell carcinoma (ccRCC), the underlying molecular mechanism is still unclear. In our study, the microarray dataset GSE46699 is analyzed by weighted gene co-expression network analysis (WGCNA). Then we identify 15 co-expressed gene modules in which the lightcyan module (R2 = 0.30) is the most significant. Combined with the protein-protein interaction (PPI) network and WGCNA, two hub genes are identified. Meanwhile, linear regression analyses indicate that TOP2A has a higher connection with smoking in ccRCC, survival analysis proved that overexpression of TOP2A in ccRCC could lead to shorter survival time. Furthermore, bioinformatical analyses based on GSE46699 and GSE2109 as well as qRT-PCR experiment show similar results that TOP2A is significantly up-regulated in smoking ccRCC compared to non-smoking ccRCC samples. In addition, Functional analysis, pathway enrichment analysis and gene set enrichment analysis (GSEA) indicate that high expression of TOP2A is related to cell cycle and p53 signaling pathway in ccRCC samples. Moreover, in vitro experiments revealed that TOP2A induced cell cycle arrest at G2 phase and proliferation inhibition via p53 phosphorylation. Taken together, by using WGCNA, we have identified a novel biomarker named TOP2A, which could affect the development of smoking-related ccRCC by regulating cell cycle and p53 signaling pathway.
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Affiliation(s)
- Yaoyi Xiong
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Lushun Yuan
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Liang Chen
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yuan Zhu
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China.,Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.,Human Genetics Resource Preservation Center of Hubei Province, Wuhan, China
| | - Shanshan Zhang
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China.,Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.,Human Genetics Resource Preservation Center of Hubei Province, Wuhan, China
| | - Xuefeng Liu
- Department of Pathology, Lombardi Comprehensive Cancer Center, Georgetown University Medical School, Washington DC, USA
| | - Yu Xiao
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China.,Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.,Human Genetics Resource Preservation Center of Hubei Province, Wuhan, China
| | - Xinghuan Wang
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
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Liu WT, Wang Y, Zhang J, Ye F, Huang XH, Li B, He QY. A novel strategy of integrated microarray analysis identifies CENPA, CDK1 and CDC20 as a cluster of diagnostic biomarkers in lung adenocarcinoma. Cancer Lett 2018; 425:43-53. [PMID: 29608985 DOI: 10.1016/j.canlet.2018.03.043] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 03/06/2018] [Accepted: 03/27/2018] [Indexed: 01/15/2023]
Abstract
Lung adenocarcinoma (LAC) is the most lethal cancer and the leading cause of cancer-related death worldwide. The identification of meaningful clusters of co-expressed genes or representative biomarkers may help improve the accuracy of LAC diagnoses. Public databases, such as the Gene Expression Omnibus (GEO), provide rich resources of valuable information for clinics, however, the integration of multiple microarray datasets from various platforms and institutes remained a challenge. To determine potential indicators of LAC, we performed genome-wide relative significance (GWRS), genome-wide global significance (GWGS) and support vector machine (SVM) analyses progressively to identify robust gene biomarker signatures from 5 different microarray datasets that included 330 samples. The top 200 genes with robust signatures were selected for integrative analysis according to "guilt-by-association" methods, including protein-protein interaction (PPI) analysis and gene co-expression analysis. Of these 200 genes, only 10 genes showed both intensive PPI network and high gene co-expression correlation (r > 0.8). IPA analysis of this regulatory networks suggested that the cell cycle process is a crucial determinant of LAC. CENPA, as well as two linked hub genes CDK1 and CDC20, are determined to be potential indicators of LAC. Immunohistochemical staining showed that CENPA, CDK1 and CDC20 were highly expressed in LAC cancer tissue with co-expression patterns. A Cox regression model indicated that LAC patients with CENPA+/CDK1+ and CENPA+/CDC20+ were high-risk groups in terms of overall survival. In conclusion, our integrated microarray analysis demonstrated that CENPA, CDK1 and CDC20 might serve as novel cluster of prognostic biomarkers for LAC, and the cooperative unit of three genes provides a technically simple approach for identification of LAC patients.
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Affiliation(s)
- Wan-Ting Liu
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China
| | - Yang Wang
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China
| | - Jing Zhang
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China
| | - Fei Ye
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China
| | - Xiao-Hui Huang
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China
| | - Bin Li
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China.
| | - Qing-Yu He
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China.
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60
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Yuan L, Zeng G, Chen L, Wang G, Wang X, Cao X, Lu M, Liu X, Qian G, Xiao Y, Wang X. Identification of key genes and pathways in human clear cell renal cell carcinoma (ccRCC) by co-expression analysis. Int J Biol Sci 2018; 14:266-279. [PMID: 29559845 PMCID: PMC5859473 DOI: 10.7150/ijbs.23574] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 01/11/2018] [Indexed: 12/13/2022] Open
Abstract
Human clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidney, and its prognostic is influenced by the progression covering a complex network of gene interactions. In our study, we screened differential expressed genes, and constructed protein-protein interaction (PPI) network and a weighted gene co-expression network to identify key genes and pathways associated with the progression of ccRCC (n = 56). Functional and pathway enrichment analysis demonstrated that upregulated differentially expressed genes (DEGs) were significantly enriched in response to wounding, positive regulation of immune system process, leukocyte activation, immune response and cell activation. Downregulated DEGs were significantly enriched in oxidation reduction, monovalent inorganic cation transport, ion transport, excretion and anion transport. In the PPI network, top 10 hub genes were identified (TOP2A, MYC, ALB, CDK1, VEGFA, MMP9, PTPRC, CASR, EGFR and PTGS2). In co-expression network, 6 ccRCC-related modules were identified. They were associated with immune response, metabolic process, cell cycle regulation, angiogenesis and ion transport. In conclusion, our study illustrated the hub genes and pathways involved in the progress of ccRCC, and further molecular biological experiments are needed to confirm the function of the candidate biomarkers in human ccRCC.
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Affiliation(s)
- Lushun Yuan
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Medical Research Institute, School of Medicine, Wuhan University, Wuhan, China
| | - Guang Zeng
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Liang Chen
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Gang Wang
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Medical Research Institute, School of Medicine, Wuhan University, Wuhan, China.,Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaolong Wang
- Department of Urology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Xinyue Cao
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Mengxin Lu
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xuefeng Liu
- Department of Pathology, Lombardi Comprehensive Cancer Center, Georgetown University Medical School, Washington DC, USA
| | - Guofeng Qian
- Department of Endocrinology, The First Affiliated Hospital of Zhejiang University, Hangzhou, China
| | - Yu Xiao
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China.,Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xinghuan Wang
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Medical Research Institute, School of Medicine, Wuhan University, Wuhan, China
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