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Comprehensive analysis of competitive endogenous RNAs networks reveals potential prognostic biomarkers associated with epithelial ovarian cancer

ABSTRACT: Ovarian cancer (OC) is a major health threat to females, as it has high morbidity and mortality. Evidence has increasingly demonstrated that long non-coding RNAs (lncRNAs) regulate OC progression and they may have value as early diagnostic biomarkers, prognostic biomarkers and/or therapeutic targets. In the present study, the regulatory mechanisms and prognosis associated with cancer-specific lncRNAs and their related competing endogenous (ce)RNA network in OC were investigated. The differential expression profiles and prognostic significance of lncRNAs and mRNAs were systematically explored based on data from 359 OC cases from The Cancer Genome Atlas and 180 healthy individuals from the Genotype-Tissue Expression database. Functional enrichment analyses, RNA-RNA interactome prediction, ceRNA network analysis, correlation analysis and survival analysis were utilized to identify hub lncRNAs and biomarkers associated with OC diagnosis or prognosis. A total of 1,049 differentially expressed lncRNAs and 6,516 differentially expressed mRNAs between OC and healthy tissues were detected. An lncRNA-micro (mi)RNA-mRNA regulatory network in OC was further established, containing 91 lncRNAs, 23 miRNAs and 179 mRNAs. After survival analysis based on the expression of the RNAs in the ceRNA network, 8 lncRNAs, 4 miRNAs and 11 mRNAs that were significantly associated with OC patient survival (P<0.05) were obtained. Using least absolute shrinkage and selection operator-penalized Cox regression, an eight-lncRNA risk score model was generated, which was able to readily discriminate between OC and healthy individuals and predict the survival of patients with OC. In addition, the differential expression of several key lncRNAs and mRNAs was verified by reverse transcription-quantitative PCR and western blot analysis. The current study presents a novel lncRNA-miRNA-mRNA network, which provides insight into the potential pathogenesis of OC and allows the identification of prognostic biomarkers and treatment strategies for OC.

SUBMITTER: Wu W

PROVIDER: S-EPMC8581474 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA-miRNA-mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.

Project description:BackgroundLong non-coding RNA (lncRNA) functions as a competitive endogenous RNA (ceRNA) and plays an important role in the biological processes underlying tumorigenesis. However, studies describing the function of lncRNA in pheochromocytoma and paraganglioma (PCPG) remain largely unknown. Our study aims to construct a regulatory ceRNA network and explore prognostic biomarkers for PCPG through a comprehensive analysis.MethodsPCPG data from The Cancer Genome Atlas (TCGA) were utilized to obtain differentially expressed lncRNAs (DElncRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs). Kaplan-Meier analysis was used to detect prognostic biomarkers and Cytoscape was utilized to construct a regulatory network of ceRNA. Potential lncRNA-miRNA-mRNA axes were inferred by correlation analysis. GO and KEGG pathways were constructed using "clusterProfiler" and "DOSE" R-packages. Immunohistochemistry (IHC) staining was performed to validate differential protein expression levels of genes in the axes. Finally, the GSE19422 dataset and Pan-Cancer data were applied to validate the expression pattern and survival status of mRNAs, respectively.ResultsA total of 334 DElncRNAs, 116 DEmiRNAs, and 3496 DEmRNAs were identified and mainly enriched in hormone secretion, metabolism signaling, metastatic and proliferative pathways. Among these differentially expressed genes, 16 mRNAs, six lncRNAs, and two miRNAs were associated with overall survival of patients with PCPG and sequentially enrolled in the ceRNA network. Two lncRNA-miRNA-mRNA regulatory axes were predicted: AP001486.2/hsa-miR-195-5p/RCAN3 and AP006333.2/hsa-miR-34a-5p/PTPRJ. The GSE19422 dataset and IHC analysis validated that mRNA and protein levels of RCAN3 and PTPRJ were upregulated in PCPG tissues compared with adjacent adrenal gland medulla tissues. Pan-Cancer data showed that the upregulated expression of RCAN3 and PTPRJ was associated with favorable overall survival and disease-free survival.ConclusionA regulatory lncRNA-miRNA-mRNA ceRNA network was successfully constructed and 24 prognostic biomarkers were identified for PCPG patients. These findings may contribute toward a better understanding of the biological mechanism of tumorigenesis and enable further evaluation of the prognosis of patients with PCPG.

Project description:Ovarian cancer (OC) is a highly malignant gynecologic tumor with few treatments available and poor prognosis with the currently available diagnostic markers and interventions. More effective methods for diagnosis and treatment are urgently needed. Although the current evidence implicates ferroptosis in the development and therapeutic responses of various types of tumors, it is unclear to what extent ferroptosis affects OC. To explore the potential of ferroptosis-related genes as biomarkers and molecular targets for OC diagnosis and intervention, this study collected several datasets from The Cancer Genome Atlas-OC (TCGA-OC), analyzed and identified the coexpression profiles of 60 ferroptosis-related genes and two subtypes of OC with respect to ferroptosis and further examined and analyzed the differentially expressed genes between the two subtypes. The results indicated that the expression levels of ferroptosis genes were significantly correlated with prognosis in patients with OC. Single-factor Cox and LASSO analysis identified eight lncRNAs from the screened ferroptosis-related genes, including lncRNAs RP11-443B7.3, RP5-1028K7.2, TRAM2-AS1, AC073283.4, RP11-486G15.2, RP11-95H3.1, RP11-958F21.1, and AC006129.1. A risk scoring model was constructed from the ferroptosis-related lncRNAs and showed good performance in the evaluation of OC patient prognosis. The high- and low-risk groups based on tumor scores presented obvious differences in clinical characteristics, tumor mutation burden, and tumor immune cell infiltration, indicating that the risk score has a good ability to predict the benefit of immunotherapy and may provide data to support the implementation of precise immunotherapy for OC. Although in vivo tests and research are needed in the future, our bioinformatics analysis powerfully supported the effectiveness of the risk signature of ferroptosis-related lncRNAs for prognosis prediction in OC. The findings suggest that these eight identified lncRNAs have great potential for development as diagnostic markers and intervention targets for OC and that patients with high ferroptosis-related lncRNA expression will receive greater benefits from conventional chemotherapy or treatment with ferroptosis inducers.

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Comprehensive analysis of competitive endogenous RNAs networks reveals potential prognostic biomarkers associated with epithelial ovarian cancer

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