Systematic prediction of target genes and pathways in cervical cancer from microRNA expression data.

Cervical cancer (CC) is a leading cause of cancer-associated mortality in women; thus, the present study aimed to investigated potential target genes and pathways in patients with CC by utilizing an ensemble method and pathway enrichment analysis. The ensemble method integrated a correlation method [Pearson's correlation coefficient (PCC)], a causal inference method (IDA) and a regression method [least absolute shrinkage and selection operator (Lasso)] using the Borda count election algorithm, forming the PCC, IDA and Lasso (PIL) method. Subsequently, the PIL method was validated to be a feasible approach to predict microRNA (miRNA) targets by comparing predicted miRNA targets against those from a confirmed database. Finally, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was conducted for target genes in the 1,000 most frequently predicted miRNA-mRNA interactions to determine target pathways. A total of 10 target genes were obtained that were predicted >5 times, including secreted frizzled-related protein 4, maternally expressed 3 and NIPA like domain containing 4. Additionally, a total of 17 target pathways were identified, of which cytokine-cytokine receptor interaction (P=8.91×10-7) was the most significantly associated with CC of all pathways. In conclusion, the present study predicted target genes and pathways for patients with CC based on miRNA expression data, the PIL method and pathway analysis. The results of the present study may provide an insight into the pathological mechanisms underlying CC, and provide potential biomarkers for the diagnosis and treatment of this tumor type. However, these biomarkers have yet to be validated; these validations will be performed in future studies.

[1]  Peter Bühlmann,et al.  Predicting causal effects in large-scale systems from observational data , 2010, Nature Methods.

[2]  Cheng-Chang Chang,et al.  Promoter methylation of SFRPs gene family in cervical cancer. , 2009, Gynecologic oncology.

[3]  J. Roa,et al.  Evaluation of ZAR1 and SFRP4 methylation status as potentials biomarkers for diagnosis in cervical cancer: exploratory study phase I , 2014, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.

[4]  Chris Jay,et al.  miRNA profiling for diagnosis and prognosis of human cancer. , 2007, DNA and cell biology.

[5]  Seung Jun Kim,et al.  Molecular Classification of Triple-Negative Breast Cancer , 2016, Journal of breast cancer.

[6]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[7]  Yan-Li Zhao,et al.  Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer , 2016, Comput. Math. Methods Medicine.

[8]  Sung-Liang Yu,et al.  MicroRNA signature predicts survival and relapse in lung cancer. , 2008, Cancer cell.

[9]  Byron D. Ford,et al.  Expression Analysis Systematic Explorer (EASE) analysis reveals differential gene expression in permanent and transient focal stroke rat models , 2006, Brain Research.

[10]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[11]  A. Dharmarajan,et al.  The role of sFRP4, a secreted frizzled-related protein, in ovulation , 2003, Apoptosis.

[12]  Tongbin Li,et al.  miRecords: an integrated resource for microRNA–target interactions , 2008, Nucleic Acids Res..

[13]  Joseph O Deasy,et al.  A microRNA expression signature for cervical cancer prognosis. , 2010, Cancer research.

[14]  Yun Deng,et al.  SFRP4 was overexpressed in colorectal carcinoma , 2010, Journal of Cancer Research and Clinical Oncology.

[15]  Gerhard Nahler,et al.  Pearson Correlation Coefficient , 2020, Definitions.

[16]  Sharon O'Toole,et al.  Gene expression profiling in cervical cancer: identification of novel markers for disease diagnosis and therapy. , 2009, Methods in molecular biology.

[17]  Jiuyong Li,et al.  Inferring microRNA-mRNA causal regulatory relationships from expression data , 2013, Bioinform..

[18]  K. Itahana,et al.  The uric acid transporter SLC2A9 is a direct target gene of the tumor suppressor p53 contributing to antioxidant defense , 2014, Oncogene.

[19]  Peng Qiu,et al.  TCGA-Assembler: open-source software for retrieving and processing TCGA data , 2014, Nature Methods.

[20]  PRO: Patients With Metastatic/Recurrent Cervical Cancer Should Be Treated With Cisplatin Plus Paclitaxel , 2010 .

[21]  W. Tjalma,et al.  Role of human papillomavirus in the carcinogenesis of squamous cell carcinoma and adenocarcinoma of the cervix. , 2005, Best practice & research. Clinical obstetrics & gynaecology.

[22]  Michelangelo Ceci,et al.  Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach , 2014, BMC Bioinformatics.

[23]  Nectarios Koziris,et al.  TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support , 2011, Nucleic Acids Res..

[24]  Hsien-Da Huang,et al.  miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database , 2015, Nucleic Acids Res..

[25]  B. Monk,et al.  Prognostic factors for response to cisplatin-based chemotherapy in advanced cervical carcinoma: a Gynecologic Oncology Group Study. , 2010, Gynecologic oncology.

[26]  M. Maathuis,et al.  Estimating high-dimensional intervention effects from observational data , 2008, 0810.4214.

[27]  D. Bartel.  MicroRNAs: Target Recognition and Regulatory Functions , 2009, Cell.

[28]  T. Speed.  A Correlation for the 21st Century , 2011, Science.

[29]  Martin Reczko,et al.  The database of experimentally supported targets: a functional update of TarBase , 2008, Nucleic Acids Res..

[30]  Eugene Berezikov,et al.  Approaches to microRNA discovery , 2006, Nature Genetics.

[31]  Harsh Dweep,et al.  miRWalk database for miRNA-target interactions. , 2014, Methods in molecular biology.

[32]  W. Creasman.  Adenocarcinoma of the uterine corpus , 1993, Current opinion in obstetrics & gynecology.

[33]  V. Kim,et al.  Regulation of microRNA biogenesis , 2014, Nature Reviews Molecular Cell Biology.

[34]  J. Wrana,et al.  Cancer Cells Hijack PRC2 to Modify Multiple Cytokine Pathways , 2015, PloS one.

[35]  Jiuyong Li,et al.  Ensemble Methods for MiRNA Target Prediction from Expression Data , 2015, PloS one.

[36]  C. Plata-salamán,et al.  Cytokine–cytokine interactions and the brain , 2000, Brain Research Bulletin.

[37]  Fons J. Verbeek,et al.  Comparison and Integration of Target Prediction Algorithms for microRNA Studies , 2010, J. Integr. Bioinform..

[38]  Junpeng Zhang,et al.  From miRNA regulation to miRNA-TF co-regulation: computational approaches and challenges , 2015, Briefings Bioinform..

[39]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[40]  Hsien-Da Huang,et al.  miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions , 2013, Nucleic Acids Res..