Genome‐wide screening and cohorts validation identifying novel lncRNAs as prognostic biomarkers for clear cell renal cell carcinoma

Zhang, Chuanjie; Huang, Da; Liu, Ao; Xu, Yu; Na, Rong; Xu, Danfeng

doi:10.1002/jcb.29478

Cited by 12 publications

(8 citation statements)

References 43 publications

(64 reference statements)

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“…More importantly, we not only constructed a 13-lncRNA-based risk score model with moderate accuracy, but also identified six independent adverse prognostic lncRNAs for patients with ccRCC, including lncRNA AC009654.1, AC012615.3, AC092490.2, AL357507.1, LINC01234 and LINC01956. It was similar to a recent study which suggested that lncRNA AC009654.1, AC092490.2, LINC00524, LINC01234 and LINC01885 were significantly associated with ccRCC prognosis (Zhang et al, 2020). The expression levels of the six lncRNAs above were upregulated in ccRCC tissues and the high expression levels of them predicted a worse OS in ccRCC patients.…”

Section: Discussionsupporting

confidence: 90%

Long non-coding RNA LINC01234 regulates proliferation, migration and invasion via HIF-2α pathways in clear cell renal cell carcinoma cells

Yang¹,

Liu²,

Zhao³

et al. 2020

PeerJ

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Long non-coding RNAs (lncRNAs) have been proved to have an important role in different malignancies including clear cell renal cell carcinoma (ccRCC). However, their role in disease progression is still not clear. The objective of the study was to identify lncRNA-based prognostic biomarkers and further to investigate the role of one lncRNA LINC01234 in progression of ccRCC cells. We found that six adverse prognostic lncRNA biomarkers including LINC01234 were identified in ccRCC patients by bioinformatic analysis using The Cancer Genome Atlas database. LINC01234 knockdown impaired cell proliferation, migration and invasion in vitro as compared to negative control. Furthermore, the epithelial-mesenchymal transition was inhibited after LINC01234 knockdown. Additionally, LINC01234 knockdown impaired hypoxia-inducible factor-2a (HIF-2α) pathways, including a suppression of the expression of HIF-2α, vascular endothelial growth factor A, epidermal growth factor receptor, c-Myc, Cyclin D1 and MET. Together, these datas showed that LINC01234 was likely to regulate the progression of ccRCC by HIF-2α pathways, and LINC01234 was both a promising prognostic biomarker and a potential therapeutic target for ccRCC.

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Section: Discussionsupporting

confidence: 90%

Long non-coding RNA LINC01234 regulates proliferation, migration and invasion via HIF-2α pathways in clear cell renal cell carcinoma cells

Yang¹,

Liu²,

Zhao³

et al. 2020

PeerJ

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“…Thus, we firstly identified DEGs as candidate genes for the ceRNA network by using the DESeq 2 method, which has better statistical power in sensitivity and precision than edgeR and DESeq. To the best of our knowledge, our study is the first to use the DESeq 2 method in constructing the ceRNA network in KIRC, resulting in an inconsistent finding in the DEGs screen with other studies [35,43]. Since the lncRNA-miRNA-mRNA interaction in the ceRNA network only presented in the cytoplasm [44], we excluded the lncR-NAs that only located in the nucleus to enhance the accuracy of prediction.…”

Section: Discussionmentioning

confidence: 99%

Identification of a three-long noncoding RNA prognostic model involved competitive endogenous RNA in kidney renal clear cell carcinoma

Zhang

Zeng

2020

Cancer Cell Int

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Background: Long noncoding RNA (lncRNA) is generally identified as competing endogenous RNA (ceRNA) that plays a vital role in the pathogenesis of kidney renal clear cell carcinoma (KIRC), the most common subtype of renal cell carcinoma with poor prognosis and unclear pathogenesis. This study established a novel ceRNA network and thus identified a three-lncRNA prognostic model in KIRC patients. Methods: Differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) database. The lncATLAS was applied to determine the differentially expressed lncRNAs (DElncRNAs) of the cytoplasm. The miRcode, miRDB, miRTarBase, and TargetScan databases were utilized to predict the interactions of DElncRNAs, DEmiRNAs, and DEmRNAs. Cytoscape was used to construct the ceRNA network. Then, a lncRNA prognostic model (LPM) was constructed based on ceRNA-related lncRNA that was significantly related to overall survival (OS), and its predictive ability was evaluated. Moreover, an LPM-based nomogram model was constructed. The significantly different expression of genes in the LPM was validated in an independent clinical cohort (N = 21) by quantitative RT-PCR. Results: A novel ceRNA regulatory network, including 73 lncRNAs, 8 miRNAs, and 21 mRNAs was constructed. Functional enrichment analysis indicated that integral components of membrane and PI3K-Akt signaling pathway represented the most significant GO terms and pathway, respectively. The LPM established based on three lncRNAs (MIAT, LINC00460, and LINC00443) of great prognostic value from the ceRNA network was proven to be independent of conventional clinical parameters to differentiate patients with low or high risk of poor survival, with the AUC of 1-, 5-and 10-year OS were 0.723, 0.714 and 0.826 respectively. Furthermore, the nomogram showed a better predictive value in KIRC patients than individual prognostic parameters. The expression of MIAT and LINC00460 was significantly upregulated in the KIRC samples, while the expression of LINC00443 was significantly downregulated compared with the adjacent normal samples in the clinical cohort, TCGA, and GTEx. Conclusion: This LPM based on three-lncRNA could serve as an independent prognostic factor with a tremendous predictive ability for KIRC patients, and the identified novel ceRNA network may provide insight into the prognostic biomarkers and therapeutic targets of KIRC.

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“…However, due to the heterogenous nature of kidney cancer (Sankin et al, 2014), single biomarker lacks ample credibility to predict patients' outcomes, thereby, an RS that is composed of more than one biomarker is necessary. There were a few former articles about ccRCC and RS models which were based on gene expression (Zeng et al, 2019;Li et al, 2018), lncRNA expression (Liu et al, 2018;Zhang et al, 2019) or miRNA expression (Luo et al, 2019;Fritz et al, 2014), because of the tremendous amount of genes and various RNAs the models of these studies were diverse. Currently, as the importance of m6A cut a striking figure, despite the fact that the first report of m6A was as early in 1974 (Desrosiers, Friderici & Rottman, 1974), it was not until recent days that the mechanism of the m6A was uncovered (Roundtree et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Identification of a three-m6A related gene risk score model as a potential prognostic biomarker in clear cell renal cell carcinoma

Zhao¹,

Tao²,

Chen³

2020

PeerJ

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Background Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent malignancies worldwide, N6-methyladenosine (m6A) has been shown to play important roles in regulating gene expression and phenotypes in both health and disease. Here, our purpose is to construct a m6A-regulrator-based risk score (RS) for prediction of the prognosis of ccRCC. Methods We used clinical and expression data of m6A related genes from The Cancer Genome Atlas (TCGA) dataset and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to develop an RS to predict survival of patients with ccRCC, and analyzed correlations between RS and other clinical indicators such as age, grade and stage. Validation of this RS was then engaged in another cohort, E-MTAB-1980 from the ArrayExpress dataset. Finally, we used quantitative real-time PCR to analyze the expression profile of genes consists of the RS. Results A three-gene RS including METTL3, METTL14 and HNRNPA2B1 which can predict overall survival (OS) of ccRCC patients from TCGA. After applying this RS into the validation cohort from Arrayexpress, we found that it successfully reproduced the result; furthermore, the results of PCR validation were in line with our analysis. Conclusion To sum up, our study has identified an RS composed of m6A related genes that may predict the prognosis of ccRCC patients, which might be helpful for future therapeutic strategies. Our results call for further experimental studies for validations.

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Genome‐wide screening and cohorts validation identifying novel lncRNAs as prognostic biomarkers for clear cell renal cell carcinoma

Cited by 12 publications

References 43 publications

Long non-coding RNA LINC01234 regulates proliferation, migration and invasion via HIF-2α pathways in clear cell renal cell carcinoma cells

Long non-coding RNA LINC01234 regulates proliferation, migration and invasion via HIF-2α pathways in clear cell renal cell carcinoma cells

Identification of a three-long noncoding RNA prognostic model involved competitive endogenous RNA in kidney renal clear cell carcinoma

Identification of a three-m6A related gene risk score model as a potential prognostic biomarker in clear cell renal cell carcinoma

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