Abstract
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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Background
Kidney renal clear cell carcinoma (KIRC) is the most common and aggressive malignant subtype of renal cell carcinoma that has a poor prognosis and high mortality in an advanced stage due to the lack of useful biomarkers and treatments [1]. Currently, there is a multitude of established treatments for KIRC, such as surgical resection, nonspecific immune approach, targeted therapy against vascular endothelial growth factor, and novel immunotherapy agents. Despite these treatments, about 50% of KIRC patients develop metastases, and the 5-year survival rate of these patients is still lower than 10% [2]. At present, the commonly used clinical prognostic markers of KIRC include the pathological grade system and tumor node metastasis (TNM) stage, microvascular invasion, tumor necrosis, and invasion of the collecting system [3]. These clinicopathological risk factors exhibit valuable but insufficient prediction of prognosis and estimation for subsets of KIRC patients. Previous researches have established some prognostic models and nomograms that incorporate necrosis, blood tests such as lactate dehydrogenase, hemoglobin, platelets, and calcium levels, prior nephrectomy, symptoms, and performance status [https://www.gencodegenes.org/). Then, the gene symbols were annotated based on the Homo_sapiens.GRCh38.84.chr.gtf file, which was downloaded from the Ensembl database (https://asia.ensembl.org/index.html).
Identification of differentially expressed genes (DEGs)
We compared the KIRC samples and adjacent normal samples to identify DEGs by using DESeq 2 R package (Version 1.27.19; http://www.bioconductor.org/packages/devel/bioc/html/DESeq2.html) with a rigorous threshold as |log2-fold change (FC)| > 2.0 and FDR < 0.01 [21]. Then a heat map and volcano plot were drawn by using the heatmap R package (Version 1.0.1; https://www.rdocumentation.org/packages/pheatmap) and ggpubr R package (Version 0.2.4; https://www.rdocumentation.org/packages/ggpubr) in R software (Version 3.6.0; https://www.r-project.org/), to visualize the hierarchical clustering analysis of the identified DEGs.
Construction of the ceRNA network
The lncATLAS database (http://lncatlas.crg.eu/) was used to identify the DElncRNAs located in the cytoplasm [22]. Then the DEmiRNAs which potentially interacted with DElncRNAs located in the cytoplasm were predicted using the miRcode (http://www.mircode.org/), a comprehensive searchable map of putative microRNA target sites in the long noncoding transcriptome [23]. Subsequently, the target DEmRNAs of DEmiRNA were predicted using miRDB (http://mirdb.org/), miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/index.php) and TargetScan (http://www.targetscan.org/vert_72/) databases [24,25,26]. After that, Cytoscape software (Version 3.7.2; http://www.cytoscape.org/) was utilized to visualize and construct the ceRNA network [27].
Functional enrichment analysis
The pathway and functional enrichment analysis were carried out by utilizing KO-Based Annotation System (KOBAS) (Version 3.0; http://kobas.cbi.pku.edu.cn/) and the Database for Annotation, Visualization and Integrated Discovery (DAVID) (Version: 6.8; https://david.ncifcrf.gov/), to investigate the potential biological implications of the ceRNA network [ In conclusion, we successfully constructed a novel ceRNA regulatory network, which narrowed the scope of predicting prognostic biomarkers and therapeutic targets for KIRC. Besides, we identified and validated an LPM which is based on three lncRNAs involved in the ceRNA network, and it has independent and great prognostic value for KIRC patients.Conclusions
Availability of data and materials
All data generated or analyzed during this study are included in this published article and its additional information files.
Abbreviations
- KIRC:
-
Kidney renal clear cell carcinoma
- lncRNA:
-
Long noncoding RNA
- ceRNA:
-
Competitive endogenous RNA
- DElncRNAs:
-
Differentially expressed lncRNAs
- LPM:
-
lncRNA prognostic model
- OS:
-
Overall survival
- TCGA:
-
The Cancer Genome Atlas
- LASSO:
-
Least absolute shrinkage and selection operator
- K-M:
-
Kaplan–Meier
- ROS:
-
Receiver operating characteristic
- DSS:
-
Disease-specific survival
- C-index:
-
Concordance index
- DCA:
-
Decision curve analysis
- GSEA:
-
Gene set enrichment analysis
- GTEx:
-
Genotype-Tissue Expression
- GEPIA2:
-
Gene Expression Profiling Interactive Analysis
- CI:
-
Confidence interval
- AUC:
-
Area under the ROC curve
- HR:
-
Hazard ratio
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ZD initiated the study and organized; ZD, ZS and Hu XP designed and carried out bioinformatics analyses, statistical analyses, drew figures and drafted the manuscript; ZS and Hu XP participated in modifying the manuscript. All authors read and approved the final manuscript.
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This study was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). All patients signed the informed consent, and this study was approved by the ethics committees of Bei**g Chao-Yang Hospital.
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Supplementary information
Additional file 1: Table S1.
List of primers used for RT-PCR. Table S2. Differentially expressed lncRNAs, miRNAs, and mRNAs between KIRC samples and adjacent normal samples. Table S3. The subcellular distribution of lncRNAs in the ceRNA network. Table S4. The interactions of the ceRNA network in KIRC. Table S5. Seventeen lncRNAs associated with overall survival in KIRC. Table S6. The results of GSEA. Table S7. The potential binding sites of three lncRNAs (LINC00443, LINC00460 and MIAT) on the targeted DEmiRNAs in the ceRNA network.
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Zhang, D., Zeng, S. & Hu, X. Identification of a three-long noncoding RNA prognostic model involved competitive endogenous RNA in kidney renal clear cell carcinoma. Cancer Cell Int 20, 319 (2020). https://doi.org/10.1186/s12935-020-01423-4
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DOI: https://doi.org/10.1186/s12935-020-01423-4