Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma

Liao, Xiwen; Wang, Xiangkun; Huang, Ketuan; Han, Chuangye; Jun, Deng; Yu, Tingdong; Yang, Chengkun; Huang, Rui; Liu, Xiaoguang; Liu, Yu; Zhu, Guangzhi; Su, Hao; Zeng, Xianmin; Han, Bowen; Han, Quanfa; Liu, Zheng-Qian; Zhou, Xin; Gong, Yizhen; Liu, Zhengtao; Huang, Jianlv; Winkler, Cheryl A.; O’Brien, Stephen J.; Ye, Xinping; Peng, Tao

doi:10.7150/jca.29986

Cited by 23 publications

(16 citation statements)

References 90 publications

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“…Most lncRNAs and miRNAs may function as one member of the ceRNA regulatory networks. In fact, several reports have suggested that the ceRNA networks are related to the occurrence and progression of tumors, such as hepatocellular carcinoma [28], prostate cancer [29] and glioblastoma [30]. However, there have been few reports regarding the ceRNA regulatory networks and their involvement in drug resistance except for chemoesistance to osteosarcoma [23] and cisplatin-resistance to epithelial ovarian cancer [31].…”

Section: Discussionmentioning

confidence: 99%

Analysis of lncRNA, miRNA and mRNA-associated ceRNA networks and identification of potential drug targets for drug-resistant non-small cell lung cancer

Kong¹,

Hu²,

Yuan³

et al. 2020

J. Cancer

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Background: Drug resistance to chemotherapeutic drugs or targeted medicines is an obstacle encountered in the treatment of non-small-cell lung cancer (NSCLC). However, the mechanisms of competing endogenous RNA (ceRNA) on the drug resistance in NSCLC are rarely reported. In this paper, the comprehensive expression profiles of lncRNAs and mRNAs in drug-resistant NSCLC cells were obtained by RNA sequencing. Methods: The dysregulated lncRNAs, miRNAs and mRNAs in drug-resistant NSCLC cell lines were identified by RNA-sequencing and bioinformatics methods. Results: A total of 39 dysregulated lncRNAs and 650 dysregulated mRNAs were identified between drug-resistant NSCLC cell lines and their parental cell lines. Additionally, 33 lncRNA-miRNA-mRNA pathways in the ceRNA network in drug-resistant NSCLC were constructed through bioinformatics methods and ceRNA regulatory rules. These comprised 12 dysregulated lncRNAs, five dysregulated miRNAs, and eight dysregulated mRNAs. In addition, lncRNA ATP2B1/miR-222-5p/TAB2 and lncRNA HUWE1/miR-222-5p/TAB2 were identified as potential ceRNA networks involved in drug resistance to NSCLC. Conclusions: The current study provides a promising therapeutic strategy against the lncRNA-miRNA-mRNA ceRNA regulatory network for NSCLC treatment and deepens our comprehension of the ceRNA regulatory mechanisms related to drug resistance to NSCLC.

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Section: Discussionmentioning

confidence: 99%

Analysis of lncRNA, miRNA and mRNA-associated ceRNA networks and identification of potential drug targets for drug-resistant non-small cell lung cancer

Kong¹,

Hu²,

Yuan³

et al. 2020

J. Cancer

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“…Knockdown of CBX2 inhibited the proliferation and increased apoptosis of HCC cells. Using the TCGA dataset, Liao et al [42] also identified CBX2 as one member of a prognostic signature for HCC patients, with lower OS in patients having high expression of CBX2 by KM survival curve analysis. Li et al [43] performed immunohistochemistry in 132 matched tissues to demonstrate CDC20 expression was upregulated in HCC tissues.…”

Section: Discussionmentioning

confidence: 99%

Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients

Bai¹,

Li²,

Li³

et al. 2020

Medicine

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Hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma (HCC), but HBV-HCC related prognosis signature remains rarely investigated. This study was to identify an integrated long non-coding RNAs-messenger RNAs (lncRNA-mRNA) signature for prediction of overall survival (OS) and explore their underlying functions. One RNA-sequencing dataset (training set, n = 95) and one microarray dataset E-TABM-36 (validation set, n = 44) were collected. Least absolute shrinkage and selection operator analysis was performed to identify an lncRNA-mRNA prognosis signature. The OS difference of patients in the high-risk and low-risk risk groups was evaluated by Kaplan–Meier curve. Area under the receiver operating characteristic curve (AUC), Harrell concordance index (C-index) calculation, and multivariate analyses with clinical characteristics were used to determine the prognostic ability. Furthermore, a coexpression network was constructed to interpret the functions. Nine signature genes (3 lncRNAs and 6 mRNAs) were selected to generate the risk score model. Patients belonging to the high-risk group showed a significantly shorter survival than those of the low-risk group. The prediction accuracy of the risk score for 5-year OS was 0.936 and 0.905 for the training set and validation set, respectively. Also, this risk score was independent of various clinical variables for the prognosis prediction. Incorporation of the risk score remarkably increased the predictive power of the routine clinical prognostic factors (vascular invasion status, tumor recurrence status) (AUC = 0.942 vs 0.628; C-index = 0.7997 vs 0.6908). Furthermore, LncRNA insulin-like growth factor 2 antisense RNA (IGF2-AS) and long intergenic non-protein coding RNA 342 (LINC00342) were predicted to exert tumor suppression effects by regulating homeobox D1 (HOXD1) and secreted frizzled related protein 5 (SFRP5), respectively; while lncRNA rhophilin Rho GTPase binding protein 1 antisense RNA 1 (RHPN1-AS1) may possess carcinogenic potential by promoting the transcription of chromobox 2 (CBX2), cell division cycle 20 (CDC20), matrix metallopeptidase 12 (MMP12), stratifin (SFN), tripartite motif containing 16 (TRIM16), and uroplakin 3A (UPK3A). These mRNAs may be associated with cell proliferation or apoptosis related pathways. This study may provide a novel, effective prognostic biomarker, and some therapeutic targets for HBV-HCC patients.

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“…We use prognostic-related snoRNAs as variables into the multivariate Cox proportional hazard regression model, and the Cox regression coefficient (b) as the weight of each snoRNAs included in the signature to compose the risk score. The snoRNAs signature calculation formula was as follows: risk score = Exp of snoRNA 1 × b 1 snoRNA 1 + Exp of snoRNA 2 × b 2 snoRNA 2 + Exp of snoRNA n × b n snoRNA n (Exp: expression) (11)(12)(13). Subsequently, we also used the time-dependent ROC curve, nomogram, and combined effect survival analysis to conduct a comprehensive analysis of the prognostic value of this risk score signature in LGG.…”

Section: Survival Analysis Of Snornas Signaturementioning

confidence: 99%

Integrative Analysis of a Novel Eleven-Small Nucleolar RNA Prognostic Signature in Patients With Lower Grade Glioma

et al. 2021

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ObjectiveThe present study used the RNA sequencing (RNA-seq) dataset to identify prognostic snoRNAs and construct a prognostic signature of The Cancer Genome Atla (TCGA) lower grade glioma (LGG) cohort, and comprehensive analysis of this signature.MethodsRNA-seq dataset of 488 patients from TCGA LGG cohort were included in this study. Comprehensive analysis including function enrichment, gene set enrichment analysis (GSEA), immune infiltration, cancer immune microenvironment, and connectivity map (CMap) were used to evaluate the snoRNAs prognostic signature.ResultsWe identified 21 LGG prognostic snoRNAs and constructed a novel eleven-snoRNA prognostic signature for LGG patients. Survival analysis suggests that this signature is an independent prognostic risk factor for LGG, and the prognosis of LGG patients with a high-risk phenotype is poor (adjusted P = 0.003, adjusted hazard ratio = 2.076, 95% confidence interval = 1.290–3.340). GSEA and functional enrichment analysis suggest that this signature may be involved in the following biological processes and signaling pathways: such as cell cycle, Wnt, mitogen-activated protein kinase, janus kinase/signal transducer and activator of tran-ions, T cell receptor, nuclear factor-kappa B signaling pathway. CMap analysis screened out ten targeted therapy drugs for this signature: 15-delta prostaglandin J2, MG-262, vorinostat, 5155877, puromycin, anisomycin, withaferin A, ciclopirox, chloropyrazine and megestrol. We also found that high- and low-risk score phenotypes of LGG patients have significant differences in immune infiltration and cancer immune microenvironment.ConclusionsThe present study identified a novel eleven-snoRNA prognostic signature of LGG and performed a integrative analysis of its molecular mechanisms and relationship with tumor immunity.

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Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma

Cited by 23 publications

References 90 publications

Analysis of lncRNA, miRNA and mRNA-associated ceRNA networks and identification of potential drug targets for drug-resistant non-small cell lung cancer

Analysis of lncRNA, miRNA and mRNA-associated ceRNA networks and identification of potential drug targets for drug-resistant non-small cell lung cancer

Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients

Integrative Analysis of a Novel Eleven-Small Nucleolar RNA Prognostic Signature in Patients With Lower Grade Glioma

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