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Aboughaleb IH, Matboli M, Shawky SM, El-Sharkawy YH. Integration of transcriptomes analysis with spectral signature of total RNA for generation of affordable remote sensing of Hepatocellular carcinoma in serum clinical specimens. Heliyon 2021; 7:e06388. [PMID: 33748469 PMCID: PMC7972971 DOI: 10.1016/j.heliyon.2021.e06388] [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: 07/29/2020] [Revised: 01/08/2021] [Accepted: 02/25/2021] [Indexed: 12/24/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a major global health problem with about 841,000 new cases and 782,000 deaths annually, due to lacking early biomarker/s, and centralized diagnosis. Transcriptomes research despite its infancy has proved excellence in its implementation in identifying a coherent specific cancer RNAs differential expression. However, results are sometimes overlapped by other cancer types which negatively affecting specificity, plus the high cost of the equipment used. Hyperspectral imaging (HSI) is an advanced tool with unique, spectroscopic features, is an emerging tool that has widely been used in cancer detection. Herein, a pilot study has been performed for HCC diagnosis, by exploiting HIS properties and the analysis of the transcriptome for the development of non-invasive remote HCC sensing. HSI data cube images of the sera extracted total RNA have been analyzed in HCC, normal subject, liver benign tumor, and chronic HCV with cirrhotic/non-cirrhotic liver groups. Data analyses have revealed a specific spectral signature for all groups and can be easily discriminated; at the computed optimum wavelength. Moreover, we have developed a simple setup based on a commercial laser pointer for sample illumination and a Smartphone CCD camera, with HSI consistent data output. We hypothesized that RNA differential expression and its spatial organization/folding are the key players in the obtained spectral signatures. To the best of our knowledge, we are the first to use HSI for sensing cancer based on total RNA in serum, using a Smartphone CCD camera/laser pointer. The proposed biosensor is simple, rapid (2 min), and affordable with specificity and sensitivity of more than 98% and high accuracy.
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Affiliation(s)
| | - Marwa Matboli
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Sherif M. Shawky
- Center of Genomics, Helmy Medical Institute, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 6th of October City, 12578 Giza, Egypt
- Misr University for Science and Technology, Faculty of Pharmacy, Biochemistry Department, Al-Motamayez District. P.O.BOX: 77, 6thOctober City, Giza, Egypt
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Jiang L, Zhao L, Bi J, Guan Q, Qi A, Wei Q, He M, Wei M, Zhao L. Glycolysis gene expression profilings screen for prognostic risk signature of hepatocellular carcinoma. Aging (Albany NY) 2019; 11:10861-10882. [PMID: 31790363 PMCID: PMC6932884 DOI: 10.18632/aging.102489] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 11/17/2019] [Indexed: 12/20/2022]
Abstract
Metabolic changes are the markers of cancer and have attracted wide attention in recent years. One of the main metabolic features of tumor cells is the high level of glycolysis, even if there is oxygen. The transformation and preference of metabolic pathways is usually regulated by specific gene expression. The aim of this study is to develop a glycolysis-related risk signature as a biomarker via four common cancer types. Only hepatocellular carcinoma was shown the strong relationship with glycolysis. The mRNA sequencing and chip data of hepatocellular carcinoma, breast invasive carcinoma, renal clear cell carcinoma, colorectal adenocarcinoma were included in the study. Gene set enrichment analysis was performed, profiling three glycolysis-related gene sets, it revealed genes associated with the biological process. Univariate and multivariate Cox proportional regression models were used to screen out prognostic-related gene signature. We identified six mRNAs (DPYSL4, HOMER1, ABCB6, CENPA, CDK1, STMN1) significantly associated with overall survival in the Cox proportional regression model for hepatocellular carcinoma. Based on this gene signature, we were able to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis showed that prognostic power of this six gene signature is independent of clinical variables. Further, we validated this data in our own 55 paired hepatocellular carcinoma and adjacent tissues. The results showed that these proteins were highly expressed in hepatocellular carcinoma tissues compared with adjacent tissue. The survival time of high-risk group was significantly shorter than that of low-risk group, indicating that high-risk group had poor prognosis. We calculated the correlation coefficients between six proteins and found that these six proteins were independent of each other. In conclusions, we developed a glycolysis-related gene signature that could predict survival in hepatocellular carcinoma patients. Our findings provide novel insight to the mechanisms of glycolysis and it is useful for identifying patients with hepatocellular carcinoma with poor prognoses.
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Affiliation(s)
- Longyang Jiang
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China.,Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China
| | - Lan Zhao
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China.,Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China
| | - Jia Bi
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China.,Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China
| | - Qiutong Guan
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China.,Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China
| | - Aoshuang Qi
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China.,Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China
| | - Qian Wei
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China.,Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China
| | - Miao He
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China.,Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China
| | - Minjie Wei
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China.,Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China
| | - Lin Zhao
- Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China.,Liaoning Key Laboratory of Molecular Targeted Anti-Tumor Drug Development and Evaluation, China Medical University, Shenyang North New Area, Shenyang 110122, Liaoning, China
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Kim S, Choi S, Yoon JH, Kim Y, Lee S, Park T. Drug response prediction model using a hierarchical structural component modeling method. BMC Bioinformatics 2018; 19:288. [PMID: 30367591 PMCID: PMC6101092 DOI: 10.1186/s12859-018-2270-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Component-based structural equation modeling methods are now widely used in science, business, education, and other fields. This method uses unobservable variables, i.e., "latent" variables, and structural equation model relationships between observable variables. Here, we applied this structural equation modeling method to biologically structured data. To identify candidate drug-response biomarkers, we first used proteomic peptide-level data, as measured by multiple reaction monitoring mass spectrometry (MRM-MS), for liver cancer patients. MRM-MS is a highly sensitive and selective method for proteomic targeted quantitation of peptide abundances in complex biological samples. RESULTS We developed a component-based drug response prediction model, having the advantage that it first combines collapsed peptide-level data into protein-level information, facilitating subsequent biological interpretation. Our model also uses an alternating least squares algorithm, to efficiently estimate both coefficients of peptides and proteins. This approach also considers correlations between variables, without constraint, by a multiple testing problem. Using estimated peptide and protein coefficients, we selected significant protein biomarkers by permutation testing, resulting in our model for predicting liver cancer response to the tyrosine kinase inhibitor sorafenib. CONCLUSIONS Using data from a cohort of liver cancer patients, we then "fine-tuned" our model to successfully predict drug responses, as demonstrated by a high area under the curve (AUC) score. Such drug response prediction models may eventually find clinical translation in identifying individual patients likely to respond to specific therapies.
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Affiliation(s)
- Sungtae Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826 South Korea
| | - Sungkyoung Choi
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826 South Korea
| | - Jung-Hwan Yoon
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, 03080 South Korea
| | - Youngsoo Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080 South Korea
| | - Seungyeoun Lee
- Department of Mathematics and Statistics, Sejong University, Seoul, 05006 South Korea
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826 South Korea
- Department of Statistics, Seoul National University, Seoul, 08826 South Korea
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Urgard E, Reigo A, Reinmaa E, Rebane A, Metspalu A. Human basonuclin 2 up-regulates a cascade set of interferon-stimulated genes with anti-cancerous properties in a lung cancer model. Cancer Cell Int 2017; 17:18. [PMID: 28184177 PMCID: PMC5294813 DOI: 10.1186/s12935-017-0394-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2016] [Accepted: 02/01/2017] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Human basonuclin 2 (BNC2) acts as a tumor suppressor in multiple cancers in an as yet unidentified manner. The role and expression of the BNC2 gene in lung cancer has not yet been investigated. METHODS BNC2 expression was studied in the A549 and BEAS-2B cell lines, as well as in lung cancer tissue. Illumina array analysis and a viability assay were used to study the effects of transient transfection of BNC2 in A549 cells. Ingenuity pathway analysis and g:Profiler were applied to identify affected pathways and networks. RT-qPCR was used to validate the array results. RESULTS We showed the reduced mRNA expression of BNC2 in non-small cell lung cancer tissue and lung cancer cell line A549 compared to non-cancerous lung tissue and BEAS-2B cells, respectively. Further array analysis demonstrated that the transfection of BNC2 into A549 cells resulted in the increased expression of 139 genes and the down-regulation of 13 genes. Pathway analysis revealed that half of the up-regulated genes were from the interferon/signal transducer and activator of transcription signaling pathways. The differential expression of selected sets of genes, including interferon-stimulated and tumor suppressor genes of the XAF1 and OAS families, was confirmed by RT-qPCR. In addition, we showed that the over-expression of BNC2 inhibited the proliferation of A549 cells. CONCLUSION Our data suggest that human BNC2 is an activator of a subset of IFN-regulated genes and might thereby act as a tumor suppressor.
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Affiliation(s)
- Egon Urgard
- Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.,Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Anu Reigo
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Eva Reinmaa
- Department of Immunoanalysis, United Laboratories, Tartu University Hospital, Tartu, Estonia
| | - Ana Rebane
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.,Estonian Genome Center, University of Tartu, Tartu, Estonia
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Xu L, Fengji L, Changning L, Liangcai Z, Yinghui L, Yu L, Shanguang C, Jianghui X. Comparison of the Prognostic Utility of the Diverse Molecular Data among lncRNA, DNA Methylation, microRNA, and mRNA across Five Human Cancers. PLoS One 2015; 10:e0142433. [PMID: 26606135 PMCID: PMC4659652 DOI: 10.1371/journal.pone.0142433] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 10/21/2015] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Advances in high-throughput technologies have generated diverse informative molecular markers for cancer outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in human cancers; however, the prognostic utility of such diverse molecular data remains to be explored. MATERIALS AND METHODS We proposed a computational pipeline (IDFO) to predict patient survival by identifying prognosis-related biomarkers using multi-type molecular data (mRNA, microRNA, DNA methylation, and lncRNA) from 3198 samples of five cancer types. We assessed the predictive performance of both single molecular data and integrated multi-type molecular data in patient survival stratification, and compared their relative importance in each type of cancer, respectively. Survival analysis using multivariate Cox regression was performed to investigate the impact of the IDFO-identified markers and traditional variables on clinical outcome. RESULTS Using the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71-0.97) in five cancer types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four cancer types, followed by DNA methylation, mRNA, and then microRNA. We found the incorporating of multi-type molecular data showed similar predictive power to single-type molecular data, but with the exception of the lncRNA + DNA methylation combinations in two cancers. Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables. CONCLUSION Our study provides insight into systematically understanding the prognostic performance of diverse molecular data in both single and aggregate patterns, which may have specific reference to subsequent related studies.
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Affiliation(s)
- Li Xu
- School of life science and biotechnology, Harbin Institute of Technology, Harbin, Hei Longjiang Province, China
- State Key Laboratory of Space Medicine Fundamentals and Application, Space Institute of Southern China, China Astronaut Research and Training Center, Beijing, China
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Liang Fengji
- State Key Laboratory of Space Medicine Fundamentals and Application, Space Institute of Southern China, China Astronaut Research and Training Center, Beijing, China
| | - Liu Changning
- Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Yunnan, China
| | - Zhang Liangcai
- Department of statistics, Rice University, Houston, Texas, United States of America
| | - Li Yinghui
- School of life science and biotechnology, Harbin Institute of Technology, Harbin, Hei Longjiang Province, China
- State Key Laboratory of Space Medicine Fundamentals and Application, Space Institute of Southern China, China Astronaut Research and Training Center, Beijing, China
| | - Li Yu
- School of life science and biotechnology, Harbin Institute of Technology, Harbin, Hei Longjiang Province, China
| | - Chen Shanguang
- School of life science and biotechnology, Harbin Institute of Technology, Harbin, Hei Longjiang Province, China
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
- * E-mail: (XJH); (CSG)
| | - Xiong Jianghui
- State Key Laboratory of Space Medicine Fundamentals and Application, Space Institute of Southern China, China Astronaut Research and Training Center, Beijing, China
- * E-mail: (XJH); (CSG)
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Tan LZ, Song Y, Nelson J, Yu YP, Luo JH. Integrin α7 binds tissue inhibitor of metalloproteinase 3 to suppress growth of prostate cancer cells. THE AMERICAN JOURNAL OF PATHOLOGY 2013; 183:831-40. [PMID: 23830872 DOI: 10.1016/j.ajpath.2013.05.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2013] [Revised: 04/30/2013] [Accepted: 05/13/2013] [Indexed: 12/21/2022]
Abstract
Integrin α7 (ITGA7) is a tumor-suppressor gene that is critical for suppressing the growth of malignant tumors; however, the mechanisms allowing ITGA7 to suppress the growth of cancer cells remain unclear. Herein, we show that ITGA7 binds to tissue inhibitor of metalloproteinase 3 (TIMP3) in prostate cancer cells. The ITGA7-TIMP3 binding led to a decreased protein level of tumor necrosis factor α, cytoplasmic translocation of NF-κB, and down-regulation of cyclin D1. These changes led to an accumulation of cells in G0/G1 and a dramatic suppression of cell growth. Knocking down TIMP3 or ITGA7/TIMP3 binding interference largely abrogated the signaling changes induced by ITGA7, whereas a mutant ITGA7 lacking TIMP3 binding activity had no tumor-suppressor activity. Interestingly, knocking down ITGA7 ligand laminin β1 enhanced ITGA7-TIMP3 signaling and the downstream tumor-suppressor activity, suggesting the existence of a counterbalancing role between extracellular matrix and integrin signaling. As a result, this report demonstrates a novel and critical signaling mechanism of ITGA7, through the TIMP3/NF-κB/cyclin D1 pathway.
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Affiliation(s)
- Lang-Zhu Tan
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15261, USA
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Luo JH, Ding Y, Chen R, Michalopoulos G, Nelson J, Tseng G, Yu YP. Genome-wide methylation analysis of prostate tissues reveals global methylation patterns of prostate cancer. THE AMERICAN JOURNAL OF PATHOLOGY 2013; 182:2028-36. [PMID: 23583283 DOI: 10.1016/j.ajpath.2013.02.040] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2012] [Revised: 01/22/2013] [Accepted: 02/01/2013] [Indexed: 12/15/2022]
Abstract
Altered genome methylation is a hallmark of human malignancies. In this study, high-throughput analyses of concordant gene methylation and expression events were performed for 91 human prostate specimens, including prostate tumor (T), matched normal adjacent to tumor (AT), and organ donor (OD). Methylated DNA in genomic DNA was immunoprecipitated with anti-methylcytidine antibodies and detected by Affymetrix human whole genome SNP 6.0 chips. Among the methylated CpG islands, 11,481 islands were found located in the promoter and exon 1 regions of 9295 genes. Genes (7641) were methylated frequently across OD, AT, and T samples, whereas 239 genes were differentially methylated in only T and 785 genes in both AT and T but not OD. Genes with promoter methylation and concordantly suppressed expression were identified. Pathway analysis suggested that many of the methylated genes in T and AT are involved in cell growth and mitogenesis. Classification analysis of the differentially methylated genes in T or OD produced a specificity of 89.4% and a sensitivity of 85.7%. The T and AT groups, however, were only slightly separated by the prediction analysis, indicating a strong field effect. A gene methylation prediction model was shown to predict prostate cancer relapse with sensitivity of 80.0% and specificity of 85.0%. These results suggest methylation patterns useful in predicting clinical outcomes of prostate cancer.
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Affiliation(s)
- Jian-Hua Luo
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15261, USA
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Characterization of a multiplex, 12-biomarker test for rheumatoid arthritis. J Pharm Biomed Anal 2012; 70:415-24. [DOI: 10.1016/j.jpba.2012.06.003] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2012] [Revised: 06/03/2012] [Accepted: 06/05/2012] [Indexed: 11/18/2022]
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Pennings JL, van Dartel DA, Pronk TE, Hendriksen PJ, Piersma AH. Identification by Gene Coregulation Mapping of Novel Genes Involved in Embryonic Stem Cell Differentiation. Stem Cells Dev 2011; 20:115-26. [DOI: 10.1089/scd.2010.0181] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Jeroen L.A. Pennings
- Laboratory for Health Protection Research, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Dorien A.M. van Dartel
- Laboratory for Health Protection Research, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Health Risk Analysis and Toxicology (GRAT), Nutrition and Toxicology Research Institute Maastricht (NUTRIM), Maastricht University, Maastricht, The Netherlands
| | - Tessa E. Pronk
- Laboratory for Health Protection Research, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Health Risk Analysis and Toxicology (GRAT), Nutrition and Toxicology Research Institute Maastricht (NUTRIM), Maastricht University, Maastricht, The Netherlands
| | - Peter J.M. Hendriksen
- RIKILT Institute of Food Safety, Wageningen University and Research Centre, Wageningen, The Netherlands
| | - Aldert H. Piersma
- Laboratory for Health Protection Research, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Veterinary Faculty, Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
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Identification of novel epithelial ovarian cancer biomarkers by cross-laboratory microarray analysis. ACTA ACUST UNITED AC 2010; 30:354-9. [PMID: 20556581 DOI: 10.1007/s11596-010-0356-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2009] [Indexed: 10/19/2022]
Abstract
The purpose of this study was to pool information in epithelial ovarian cancer by combining studies using Affymetrix expression microarray datasets made at different laboratories to identify novel biomarkers. Epithelial microarray expression information across laboratories was screened and combined after preprocessing raw microarray data, then ANOVA and unpaired T test statistical analysis was performed for identifying differentially expressed genes (DEGs), followed by clustering and pathway analysis for these DEGs. In this work, we performed a combination analysis on microarrays from three different laboratories using gene expression data on ovarian cancer and obtained a list of differential expression profiles identified as potential candidate in aggressiveness of ovarian cancer. The clustering and pathway analysis explored the different molecular basis of different ovarian cancer stages and potential important regulatory pathways in ovarian cancer development. Our results showed that combination of microarray data from different laboratories in the same platforms may overcome biases derived from probe design and technical features, thereby accelerating the identification of trustworthy DEGs, and demonstrating the advantage of integrative analysis in gene expression studies on epithelial ovarian cancer research.
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