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Questions {❓}

• How many proteins can be identified in a 2DE gel spot within an analysis of a complex human cancer tissue proteome?
• Non-coding transcript variants of protein-coding genes - what are they good for?
• Ovarian cancer treatment—are we getting warmer?

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         headline:Identification of clinical trait–related lncRNA and mRNA biomarkers with weighted gene co-expression network analysis as useful tool for personalized medicine in ovarian cancer
         description:The pathogenesis and biomarkers of ovarian cancer (OC) remain not well-known in diagnosis, effective therapy, and prognostic assessment in OC personalized medicine. The novel identified lncRNA and mRNA biomarkers from gene co-expression modules associated with clinical traits provide new insight for effective treatment of ovarian cancer. Long non-coding RNAs (lncRNAs) are relevant to tumorigenesis via multiple mechanisms. This study aimed to investigate cancer-specific lncRNAs and mRNAs, and their related networks in OCs. This study comprehensively analyzed lncRNAs and mRNAs with associated competing endogenous RNA (ceRNA) network and lncRNA–RNA binding protein–mRNA network in the OC tissues in the Cancer Genome Atlas, including 2562 cancer-specific lncRNAs (n = 352 OC tissues) and 5000 mRNAs (n = 359 OC tissues). The weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression gene modules and their relationship with clinical traits. The statistically significant difference of identified lncRNAs and mRNAs was confirmed with qRT-PCR in OC cells. An lncRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tissues source site, and vascular invasion, and identified 16 lncRNAs (ACTA2-AS1, CARD8-AS1, HCP5, HHIP-AS1, HOTAIRM1, ITGB2-AS1, LINC00324, LINC00605, LINC01503, LINC01547, MIR31HG, MIR155HG, OTUD6B-AS1, PSMG3-AS1, SH3PXD2A-AS1, and ZBED5-AS1) that were significantly related to overall survival in OC patients. An mRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tumor residual disease, and vascular invasion; and identified 21 hub-mRNA molecules and 11 mRNAs (FBN3, TCF7L1, SBK1, TRO, TUBB2B, PLCG1, KIAA1549, PHC1, DNMT3A, LAMA1, and C10orf82) that were closely linked with OC patients’ overall survival. Moreover, the prognostic model of five-gene signature (OTUD6B-AS1, PSMG3-AS1, ZBED5-AS1, SBK1, and PLCG1) was constructed to predict risk score in OC patients. Furthermore, starBase bioinformatics constructed the lncRNA–miRNA–mRNA and lncRNA–RNA binding protein-mRNA networks in OCs. These new findings showed that lncRNA-related networks in OCs are a useful resource for identification of biomarkers in OCs.
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            Co-expression module
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            WGCNA
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            Prognostic assessment
            Predictive preventive personalized medicine (PPPM)
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      headline:Identification of clinical trait–related lncRNA and mRNA biomarkers with weighted gene co-expression network analysis as useful tool for personalized medicine in ovarian cancer
      description:The pathogenesis and biomarkers of ovarian cancer (OC) remain not well-known in diagnosis, effective therapy, and prognostic assessment in OC personalized medicine. The novel identified lncRNA and mRNA biomarkers from gene co-expression modules associated with clinical traits provide new insight for effective treatment of ovarian cancer. Long non-coding RNAs (lncRNAs) are relevant to tumorigenesis via multiple mechanisms. This study aimed to investigate cancer-specific lncRNAs and mRNAs, and their related networks in OCs. This study comprehensively analyzed lncRNAs and mRNAs with associated competing endogenous RNA (ceRNA) network and lncRNA–RNA binding protein–mRNA network in the OC tissues in the Cancer Genome Atlas, including 2562 cancer-specific lncRNAs (n = 352 OC tissues) and 5000 mRNAs (n = 359 OC tissues). The weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression gene modules and their relationship with clinical traits. The statistically significant difference of identified lncRNAs and mRNAs was confirmed with qRT-PCR in OC cells. An lncRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tissues source site, and vascular invasion, and identified 16 lncRNAs (ACTA2-AS1, CARD8-AS1, HCP5, HHIP-AS1, HOTAIRM1, ITGB2-AS1, LINC00324, LINC00605, LINC01503, LINC01547, MIR31HG, MIR155HG, OTUD6B-AS1, PSMG3-AS1, SH3PXD2A-AS1, and ZBED5-AS1) that were significantly related to overall survival in OC patients. An mRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tumor residual disease, and vascular invasion; and identified 21 hub-mRNA molecules and 11 mRNAs (FBN3, TCF7L1, SBK1, TRO, TUBB2B, PLCG1, KIAA1549, PHC1, DNMT3A, LAMA1, and C10orf82) that were closely linked with OC patients’ overall survival. Moreover, the prognostic model of five-gene signature (OTUD6B-AS1, PSMG3-AS1, ZBED5-AS1, SBK1, and PLCG1) was constructed to predict risk score in OC patients. Furthermore, starBase bioinformatics constructed the lncRNA–miRNA–mRNA and lncRNA–RNA binding protein-mRNA networks in OCs. These new findings showed that lncRNA-related networks in OCs are a useful resource for identification of biomarkers in OCs.
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      dateModified:2019-07-19T00:00:00Z
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         Co-expression module
         lncRNA
         mRNA
         WGCNA
         Diagnosis
         Prognostic assessment
         Predictive preventive personalized medicine (PPPM)
         Biomedicine
         general
         Medicine/Public Health
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      name:Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
      name:Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
      name:State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, People’s Republic of China
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