Mining featured biomarkers associated with prostatic carcinoma based on bioinformatics

Guanying PiaoSchool of Life Sciences, University of Science and Technology of China HefeiChina;Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences ShanghaiChinaCorrespondence[email protected]

Jiarui WuSchool of Life Sciences, University of Science and Technology of China HefeiChina;Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences ShanghaiChina

Abstract

Objective: To analyze the differentially expressed genes and identify featured biomarkers from prostatic carcinoma.

Methods: The software “Significance Analysis of Microarray” (SAM) was used to identify the differentially coexpressed genes (DCGs). The DCGs existed in two datasets were analyzed by GO (Gene Ontology) functional annotation.

Results: A total of 389 DCGs were obtained. By GO analysis, we found these DCGs were closely related with the acinus development, TGF-β receptor and signal transduction pathways. Furthermore, five featured biomarkers were discovered by interaction analysis.

Conclusion: These important signal pathways and oncogenes may provide potential therapeutic targets for prostatic carcinoma.

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Mining featured biomarkers associated with prostatic carcinoma based on bioinformatics

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Mining featured biomarkers associated with prostatic carcinoma based on bioinformatics

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