Abstract
RelevanceThe 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.PurposeLong 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.MethodsThis 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.ResultsAn 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.ConclusionThese new findings showed that lncRNA-related networks in OCs are a useful resource for identification of biomarkers in OCs.
Highlights
Ovarian cancer (OC) is a highly malignant tumor with poor prognosis, which is the most deadly cancer in gynecology [1]
The results showed that no significant difference was found for three Long non-coding RNAs (lncRNAs) (PSMG3-AS1, LINC01547, and ZBED5-AS1) between OC cells (SK-OV3, TOV-21G, and A2780) and control cell IOSE80 (p > 0.05), whereas significant difference was found for nine survivalassociated lncRNAs (ITGB2-AS1, OTUD6B-AS1, LINC00324, LINC01503, HOTAIRM1, SH3PXD2A-AS1, HCP5, MIR31HG, and MIR155HG) (Fig. 8A), and nine survival-associated hub-mRNAs (LAMA1, KIAA1549, TCF7L1, DNMT3A, EFS, SBK1, PLCG1, C10orf82, and TUBB2B) (Fig. 8B) between OC cells and control cells
LncRNA–miRNA–mRNA networks and lncRNA–RNA binding protein-mRNA networks were identified based on those identified lncRNAs and mRNAs in OCs to clarify the molecular mechanisms of lncRNAs regulating mRNAs
Summary
Ovarian cancer (OC) is a highly malignant tumor with poor prognosis, which is the most deadly cancer in gynecology [1]. Most OC patients are often detected in late clinical stages because the OC pathogenesis is concealed without effective characteristics. In less than 30% of patients, OCs were found to be located in the ovary but most of which spread to pelvic and abdominal organs. Most of the patients have no obvious symptoms in the early stage, and the common symptoms of the patients in the late stage include emaciation, bellyache, abdominal distension, pelvic lumps, and ascites [2]. The diagnosis of OCs based on those common biomarkers is still unsatisfactory. Even for two-biomarker (CA125 and HE4) joint detection, the sensitivity is only about 71% [5]. It is an urgent need to explore novel effective tumor molecular biomarkers for early diagnosis, prognosis monitoring, and therapy improvement [7]
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