Integrated Analysis of lncRNA–Mediated ceRNA Network in Lung Adenocarcinoma

Wu, Xianxian; Sui, Zhilin; Zhang, Hongdian; Wang, Ying; Yu, Zhentao

doi:10.3389/fonc.2020.554759

Cited by 133 publications

(99 citation statements)

References 47 publications

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“…The decreased probability of the OS of patients with LUAD in GEO is consistent with the findings observed in TCGA-LUAD data. However, we observed no overlapping mRNAs or lncRNAs with a previous study that did similar research on LUAD by constructing a ceRNA network with DEGs using Cytoscape [ 38 ]. This might be caused by the following reasons: the definitions set by the two studies were different (we set ∣log 2 FC | >1.5 instead of ∣log 2 FC | >2); we added two processes before ceRNA network construction, including collecting LUAD-related genes (with differential expression) based on WGCNA and evaluating the prognostic value of genes with univariate Cox regression; when constructing the ceRNA network, besides interactions predicted by the database, two rules (the correlation between the expression of lncRNAs and mRNAs and similar roles of miRNAs in regulating their corresponding lncRNAs and mRNAs) were considered.…”

Section: Discussionsupporting

confidence: 44%

Potential Prognostic Biomarkers of Lung Adenocarcinoma Based on Bioinformatic Analysis

Hou

Cheng

2021

BioMed Research International

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Lung adenocarcinoma (LUAD), which accounts for 60% of non-small-cell lung cancers, is poorly diagnosed and has a low average 5-year survival rate (approximately 20%). It remains the leading cause of cancer-related deaths worldwide. Studies on long noncoding RNAs (lncRNAs) in LUAD-related competing endogenous RNA (ceRNA) networks are limited. We aimed to identify novel prognostic biomarkers for LUAD using bioinformatic tools and data analysis. We systemically integrated differentially expressed genes and clinically significant modules using weighted correlation network analysis. We performed a functional analysis of the collected candidate genes and explored three LUAD-related genes (VWF, PECAM1, and COL1A1) associated with the overall survival rates of patients with LUAD. Based on Cox proportional hazards analysis of candidate mRNAs and lncRNAs together with differentially expressed microRNAs, we constructed ceRNA networks, obtained 12 lncRNAs in the ceRNA networks, and revealed seven novel lncRNAs AC021016.2, AC079630.1, AC116407.1, AC125807.2, AF131215.5, LINC01936, and RHOXF1-AS1. These lncRNAs were found to be associated with overall survival rates and are suitable for the prediction of prognosis by Kaplan-Meier survival and receiver operating characteristic curve analyses. In particular, three lncRNAs—AF131215.5, AC125807.2, and LINC01936—showed an independent prognostic value of overall survival for patients with LUAD. We evaluated the diagnostic capabilities of seven lncRNAs for patients with LUAD using principal component analysis and the Gene Set Variation Analysis index. lncRNAs and crucial genes could be effectively used for distinguishing LUAD tumors from normal tissues in the Gene Expression Omnibus profile. In particular, AC021016.2 showed a significant prognostic value in the validation dataset. Our findings reveal the significance of exploring lncRNAs in cancer-related ceRNAs using bioinformatic strategies.

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Section: Discussionsupporting

confidence: 44%

Potential Prognostic Biomarkers of Lung Adenocarcinoma Based on Bioinformatic Analysis

Hou

Cheng

2021

BioMed Research International

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“…At present, a large number of studies have found that the occurrence and metastasis of lung cancer are affected not only by genes, but also by external factors such as small RNA and long non-coding RNA. A lot of evidence shows that they play an important role in a variety of diseases (28). In recent years, the function of lncRNA in life activities has been continuously discovered with the extensive development of research.…”

Section: Discussionmentioning

confidence: 99%

RETRACTED: LncRNA SNHG19 Promotes the Development of Non-Small Cell Lung Cancer via Mediating miR-137/E2F7 Axis

Zhao

Ning²,

Wang

2021

Front. Oncol.

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ObjectiveNon-small cell lung cancer (NSCLC) is a common malignant tumor, which has high incidence and low the 5-year survival rate. Long non-coding RNAs (lncRNAs) play critical roles in carcinoma occurrence and metastasis. Herein, our aim was to investigate the effects of lncRNA SNHG19 in NSCLC progression.Materials and MethodsLong non-coding RNA Small Nucleolar RNA Host Gene 19 (lncRNA SNHG19) expression level was measured by bioinformatics and qRT-PCR. Edu, Transwell, and scratch assays were performed to explore the role of si-SNHG19 or SNHG19 on NSCLC progression. Luciferase assay was used to verify the relationship between SNHG19/E2F7 and miR-137. The experiment of Xenograft was used for exploring the function of SNHG19 in vivo.ResultsSNHG19 was upregulated in cancer tissues, patients plasma and cell lines of NSCLC. Knockdown of SNHG19 inhibited cell proliferation, migration, and invasion. Luciferase assay confirmed that SNHG19 regulated E2F7 expression via interacting with miR-137. Overexpression of SNHG19 accelerated NSCLC tumor progression via miR-137/E2F7 axis both in vitro and in vivo.ConclusionsOur results clarified the SNHG19 function for the first time, and SNHG19 promoted the progression of NSCLC, which was mediated by the miR-137/E2F7 axis. This study might provide new understanding and targets for NSCLC diagnosis and treatment.

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“…Compared with several previous studies, our approach has the following improvements. First, for constructing the ceRNA network, we used only negatively correlated lncRNAs/mRNAs-miRNAs regulatory pairs, which fits well the definition of the ceRNA network compared to the previous method without this distinction Fan et al, 2020;Wu et al, 2020). Second, we applied 1,000 rounds of lasso-Cox regression model fittings to identify the optimal lncRNA-based set, which is deemed to be more robust than the conventional Cox model utilized in a previous study (Zheng et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

A Seven-Long Non-coding RNA Signature Improves Prognosis Prediction of Lung Adenocarcinoma: An Integrated Competing Endogenous RNA Network Analysis

Han²,

Xu³

et al. 2021

Front. Genet.

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Early and precise prediction is an important way to reduce the poor prognosis of lung adenocarcinoma (LUAD) patients. Nevertheless, the widely used tumor, node, and metastasis (TNM) staging system based on anatomical information only often could not achieve adequate performance on foreseeing the prognosis of LUAD patients. This study thus aimed to examine whether the long non-coding RNAs (lncRNAs), known highly involved in the tumorigenesis of LUAD through the competing endogenous RNAs (ceRNAs) mechanism, could provide additional information to improve prognosis prediction of LUAD patients. To prove the hypothesis, a dataset consisting of both RNA sequencing data and clinical pathological data, obtained from The Cancer Genome Atlas (TCGA) database, was analyzed. Then, differentially expressed RNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) were identified and a lncRNA–miRNA–mRNA ceRNA network was constructed based on those differentially expressed RNAs. Functional enrichment analysis revealed that this ceRNA network was highly enriched in some cancer-associated signaling pathways. Next, lasso-Cox model was run 1,000 times to recognize the potential survival-related combinations of the candidate lncRNAs in the ceRNA network, followed by the “best subset selection” to further optimize these lncRNA-based combinations, and a seven-lncRNA prognostic signature with the best performance was determined. Based on the median risk score, LUAD patients could be well distinguished into high-/low-risk subgroups. The Kaplan–Meier survival curve showed that LUAD patients in the high-risk group had significantly shorter overall survival than those in the low-risk group (log-rank test P = 4.52 × 10–9). The ROC curve indicated that the clinical genomic model including both the TNM staging system and the signature had a superior performance in predicting the patients’ overall survival compared to the clinical model with the TNM staging system only. Further stratification analysis suggested that the signature could work well in the different strata of the stage, gender, or age, rendering it to be a wide application. Finally, a ceRNA subnetwork related to the signature was extracted, demonstrating its high involvement in the tumorigenesis mechanism of LUAD. In conclusion, the present study established a lncRNA-based molecular signature, which can significantly improve prognosis prediction for LUAD patients.

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Integrated Analysis of lncRNA–Mediated ceRNA Network in Lung Adenocarcinoma

Cited by 133 publications

References 47 publications

Potential Prognostic Biomarkers of Lung Adenocarcinoma Based on Bioinformatic Analysis

Potential Prognostic Biomarkers of Lung Adenocarcinoma Based on Bioinformatic Analysis

RETRACTED: LncRNA SNHG19 Promotes the Development of Non-Small Cell Lung Cancer via Mediating miR-137/E2F7 Axis

A Seven-Long Non-coding RNA Signature Improves Prognosis Prediction of Lung Adenocarcinoma: An Integrated Competing Endogenous RNA Network Analysis

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