Comprehensive analysis of differentially expressed profiles of lncRNAs, mRNAs, and miRNAs in laryngeal squamous cell carcinoma in order to construct a ceRNA network and identify potential biomarkers

Wang³

et al. 2020

Bioscience Reports

Cervical cancer (CC) is ranked as the fourth most common cancer that occurs in women universally, which normally causes pain in the lower belly. Plenty of studies have stated that the expression of long non-coding RNAs (lncRNAs) is linked to the cellular development of many kinds of cancers. DSCAM-AS1 has been reported to act as an oncogene in other cancer types and the aim of our study was to uncover the function and regulatory mechanism of DSCAM-AS1 in CC. In this research, our findings presented that DSCAM-AS1 expression was up-regulated in CC cells. DSCAM-AS1 led to the development of CC by enhancing cell proliferation, migration and invasion ability. DSCAM-AS1 was verified to combine with miR-877-5p and down-regulate the expression of miR-877-5p. Results also showed that ATXN7L3 was a downstream target gene of miR-877-5p and it was unfavorably modulated by miR-877-5p. Enhanced expression of ATXN7L3 counterbalanced the DSCAM-AS1 knockdown effect on the progression of CC. This was the first time to analyze the underlying regulatory mechanism of the oncogenic DSCAM-AS1. Our findings clarified that DSCAM-AS1 played as an oncogenic lncRNA by targeting miR-877-5p/ATXN7L3 axis to promote CC progression, which may provide insights into the prevention of CC.

Section: Discussionmentioning

confidence: 99%

Long non-coding RNA DSCAM-AS1 contributes to the tumorigenesis of cervical cancer by targeting miR-877-5p/ATXN7L3 axis

Liang

Wang³

et al. 2020

Bioscience Reports

“…27 Notably, the integrated ceRNA network analysis with large-scale high-throughput sequence data greatly contributes to explore underlying molecular mechanisms of various cancers, including LSCC. 12,28 In this current study, we identified 2957 DEmRNAs, 61DElncRNAs and 23 DEmiRNAs, and constructed a lncRNA-miRNA-mRNA ceRNA network by analyzing the expression profiles of LSCC and normal adjacent. Firstly, functional enrichment analysis of DEmRNAs showed that they were mainly enriched in many GO terms associated with extracellular matrix organization, and several significant KEGG pathways such as DNA replication and cell cycle.…”

Section: Discussionmentioning

confidence: 99%

LncRNA-Associated ceRNA Network Reveals Novel Potential Biomarkers of Laryngeal Squamous Cell Carcinoma

Jing

Guo

Technol Cancer Res Treat

et al. 2020

Objective: This study aims to construct a systematic mRNA-miRNA-lncRNA network to identify novel lncRNAs and miRNAs biomarkers for laryngeal squamous cell carcinoma (LSCC). Methods: The mRNA, miRNA and lncRNA expression profiles of LSCC were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed mRNAs, miRNAs and lncRNAs (DEmRNAs, DEmiRNAs and DElncRNAs) were screened between LSCC tissues and controls. Functional analysis of DEmRNAs, DEmRNAs targeted by DEmiRNAs and DEmRNAs targeted by DElncRNAs were respectively performed. The miRWalk, starbase and DIANA-LncBase were respectively used to predict DEmiRNAs-DEmRNAs, DElncRNAs-DEmRNAs and DElncRNAs-DEmiRNAs pairs. ceRNA network was built by DEmiRNAs-DEmRNAs and DElncRNAs-DEmiRNAs pairs. LncRNA subcellular localization was predicted using lncLocator. Using published The Cancer Genome Atlas (TCGA) and external datasets (GSE127165 and GSE133632), we also validated the expression of key DElncRNAs and DEmiRNAs in ceRNA network. The diagnostic and prognostic value of candidate genes was evaluated by ROC curve analysis and survival analysis, respectively. Results: There were 5 mRNA datasets, 3 miRNA datasets and 2 lncRNA datasets in this study. Totally, 2957 DEmRNAs, 61 DElncRNAs and 23 DEmiRNAs were identified. Functional analysis of DEmRNAs shows that they were significantly enriched in cancer-related pathways, such as DNA replication and extracellular matrix organization. There were 11 DEmiRNAs, 17 DElncRNAs and 967 DEmRNAs in the ceRNA network. Notably, up-regulated lncRNA DGCR5-down-regulated has-miR-338-3p/has-miR-139-5p pairs in this network were experimentally validated. Moreover, down-regulated AL121839.2, down-regulated LINC02147, up-regulated AC079328.2, up-regulated AC004943.2 and up-regulated HMGA2-AS1 were located in the cytoplasm. AL121839.2 and LINC02147 interacted with has-miR-1246. AC004943.2, AC079328.2 and HMGA2-AS1 targeted has-miR-3185, has-miR-3137 and has-miR-582-5p, respectively. Based on the TCGA and external datasets (GSE127165 and GSE133632), DGCR5 and AC004943.2 were significantly up-regulated while AL121839.2 and LINC02147, has-miR-338-3p, has-miR-139-5p and has-miR-582-5p were significantly down-regulated, which were consistent with our integration analysis. DGCR5, AL121839.2, LINC02147, AC004943.2, has-miR-338-3p, has-miR-139-5p and has-miR-582-5p could predict the occurrence of LSCC. Survival analysis suggested that only, AL121839.2 has potential prognostic value for LSCC. Conclusion: This study provided novel insights into the ceRNA network and uncovered novel lncRNAs and miRNAs with diagnostic value in LSCC.

“…Previous publications have focused in part on ceRNAs in laryngeal squamous cell carcinoma (LSCC) (Kong et al, 2019;Liu & Ye, 2019;Gao et al, 2019;Zhao et al, 2019;Sun et al, 2018;Feng et al, 2016), tongue squamous cell carcinoma (TSCC) (Sturgis & Cinciripini, 2007), and nasopharyngeal carcinoma (NPC) (Fullerton et al, 2020;Agrawal et al, 2011). These studies have identified differentially expressed RNAs by lncRNA and mRNA integrated microarrays of cancer tissue or by immunohistochemistry (Gao et al, 2019;Zhao et al, 2019;Sun et al, 2018;Feng et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

A prognostic long non-coding RNA-associated competing endogenous RNA network in head and neck squamous cell carcinoma

Cao

Wang

et al. 2020

PeerJ

Background This study aimed to develop multi-RNA-based models using a competing endogenous RNA (ceRNA) regulatory network to provide survival risk prediction in head and neck squamous cell carcinoma (HNSCC). Methods All long non-coding RNA (lncRNA), microRNA (miRNA), and mRNA expression data and clinicopathological features related to HNSCC were derived from The Cancer Genome Atlas. Differentially expressed RNAs were calculated using R. Prognostic factors were identified using univariate Cox regression analysis. Functional analysis was performed using GO, KEGG pathways, and PPI network. Based on the results, we derived a risk signature and compared high- and low-risk subgroups using LASSO regression analysis. Survival analysis and the relationship between risk signature and clinicopathological features were performed using log-rank tests and Cox regression analysis. A ceRNA regulatory network was constructed, and prognostic lncRNAs and miRNA expression levels were validated in vitro and in vivo. Results A list of 207 lncRNAs, 18 miRNAs and 362 mRNAs related to overall survival was established. Five lncRNAs (HOTTIP, LINC00460, RMST, SFTA1P, and TM4SF19-AS1), one miRNA (hsa-miR-206), and one mRNA (STC2) were used to construct the ceRNA network. Three prognostic models contained 13 lncRNAs, eight miRNAs, and 17 mRNAs, which correlated with the patient status, disease-free survival (DFS), stage, grade, T stage, N stage, TP53 mutation status, angiolymphatic invasion, HPV status, and extracapsular spread. KEGG pathway analysis revealed significant enrichment of “Transcriptional misregulation in cancer” and “Neuroactive ligand-receptor interaction.” In addition, HOTTIP, LINC00460, miR-206 and STC2 were validated in GTEx data, GEO microarrays and six HNSCC cell lines. Conclusions Our findings clarify the interaction of ceRNA regulatory networks and crucial clinicopathological features. These results show that prognostic biomarkers can be identified by constructing multi-RNA-based prognostic models, which can be used for survival risk prediction in patients with HNSCC.