Molecular network-based identification of competing endogenous RNAs and mRNA signatures that predict survival in prostate cancer

Xu, Ning; Wu, Yupeng; Yin, Hubin; Xue, Xue-Yi; Gou, Xin

doi:10.1186/s12967-018-1637-x

Cited by 48 publications

(52 citation statements)

References 40 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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“…These findings provided indirect evidence for the hypothesis that CYFIP2 participated in the pathogenic mechanism of BCC. HOXB5 is a member of the homeobox (HOX) gene B cluster and plays key roles in several cancers [30,31]. Lee et al suggested that HOXB5 increased cell proliferation and invasiveness in estrogen receptor-(ER-) positive breast cancer [32].…”

Section: Discussionmentioning

confidence: 99%

Identification of Potential Biomarkers Associated with Basal Cell Carcinoma

Liu

Bian

2020

BioMed Research International

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Purpose. This work is aimed at identifying several molecular markers correlated with the diagnosis and development of basal cell carcinoma (BCC). Methods. The available microarray datasets for BCC were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between BCC and healthy controls. Afterward, the functional enrichment analysis and protein-protein interaction (PPI) network analysis of these screened DEGs were performed. An external validation for the DEG expression level was also carried out, and receiver operating characteristic curve analysis was used to evaluate the diagnostic values of DEGs. Result. In total, five microarray datasets for BCC were downloaded and 804 DEGs (414 upregulated and 390 downregulated genes) were identified. Functional enrichment analysis showed that these genes including CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1 were closely correlated with the cell process and PTCH1 played central roles in the BCC signaling pathway. Moreover, EGFR was a hub gene in the PPI network. The expression changes of six genes (CYFIP2, HOXB5, FOXN3, PTPN3, MARCKSL1, and FAS) were validated by an external GSE74858 dataset analysis. Finally, ROC analysis revealed that CYFIP2, HOXB5, PTPN3, MARCKSL1, PTCH1, and CDC20 could distinguish BCC and healthy individuals. Conclusion. Nine gene signatures (CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1) may serve as promising targets for BCC detection and development.

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“…It is reported that GPC2 could promote proliferation of neuroblastoma cells as a result of MYCN binding to a motif of the promoter of GPC2 and gain of chromosome 7q (37). GPC2 can also be used an effective prognostic indicator for prostate cancer and neuroblastoma (37)(38)(39). GPC3 inhibits cell cycle in renal cancer cells 786-O and ACHN at G1 phase (40).…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of Glypican family genes in early-stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy and possible mechanisms

Liu

Liao

Wang

et al. 2020

Preprint

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Background: This study explored the prognostic significance of Glypican (GPC) family genes in patients with pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy using data from The Cancer Genome Atlas (TCGA).Methods: A total of 112 PDAC patients from TCGA were included in the analysis.The relationship between overall survival and the expression of GPC family genes as well as basic clinical characteristics was analyzed using the Kaplan-Meier method with the log-rank test. Joint effects survival analysis was performed to further examine the relationship between GPC genes and prognosis. A prognosis nomogram was established based on clinical characteristics and prognosis-related genes. Prognosis-related genes were investigated by genome-wide co-expression analysis and gene set enrichment analysis (GSEA) to identify potential underlying mechanisms.Results: High expression of GPC2, GPC3, and GPC5 was significantly associated with favorable survival (log-rank P = 0.031, 0.021, and 0.028, respectively; adjusted P value = 0.005, 0.022, and 0.020, respectively), and joint effects analysis of these genes was effective for prognosis prediction. The prognosis nomogram was applied to predict the survival probability using the total score calculated. Genome-wide co-expression and GSEA analyses suggested that the GPC2 mechanisms affecting prognosis involved sequence-specific DNA binding, protein transport, cell differentiation and oncogenic signatures (KRAS, RAF, STK33, and VEGFA). GPC3 may be related to cell adhesion, angiogenesis, inflammatory response, signaling pathways like Ras, Rap1, PI3K-Akt, chemokine, GPCR, and signatures like cyclin D1, p53, PTEN. GPC5 may be involved in transcription factor complex, TFRC1, oncogenic signatures (HOXA9 and BMI1), gene methylation, phospholipid metabolic process, glycerophospholipid metabolism, cell cycle, and EGFR pathway.Conclusion: GPC2, GPC3, and GPC5 expression may serve as prognostic indicators in PDAC, and the combination of these genes showed a higher efficiency for prognosis prediction.

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Molecular network-based identification of competing endogenous RNAs and mRNA signatures that predict survival in prostate cancer

Cited by 48 publications

References 40 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

Identification of Potential Biomarkers Associated with Basal Cell Carcinoma

Prognostic value of Glypican family genes in early-stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy and possible mechanisms

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