Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network Identified lncRNA EPB41L4A-AS1 as a Potential Biomarker in Non-small Cell Lung Cancer

doi:10.3389/fgene.2020.511676

. 2020 Sep 18:11:511676.

doi: 10.3389/fgene.2020.511676. eCollection 2020.

Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network Identified lncRNA EPB41L4A-AS1 as a Potential Biomarker in Non-small Cell Lung Cancer

Meiqi Wang¹, Sihan Zheng^{1

2}, Xi Li³, Yu Ding⁴, Mingyan Zhang¹, Lin Lin³, Hao Xu⁵, Yue Cheng¹, Xiaonan Zhang¹, Hui Xu¹, Shijun Li³

Affiliations

¹ Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China.
² College of Laboratory Medicine, Dalian Medical University, Dalian, China.
³ Department of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
⁴ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
⁵ Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

PMID: 33193600
PMCID: PMC7530329
DOI: 10.3389/fgene.2020.511676

Free PMC article

Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network Identified lncRNA EPB41L4A-AS1 as a Potential Biomarker in Non-small Cell Lung Cancer

Meiqi Wang et al. Front Genet. 2020.

Free PMC article

. 2020 Sep 18:11:511676.

doi: 10.3389/fgene.2020.511676. eCollection 2020.

Authors

Meiqi Wang¹, Sihan Zheng^{1

2}, Xi Li³, Yu Ding⁴, Mingyan Zhang¹, Lin Lin³, Hao Xu⁵, Yue Cheng¹, Xiaonan Zhang¹, Hui Xu¹, Shijun Li³

Affiliations

¹ Department of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, China.
² College of Laboratory Medicine, Dalian Medical University, Dalian, China.
³ Department of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
⁴ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
⁵ Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

PMID: 33193600
PMCID: PMC7530329
DOI: 10.3389/fgene.2020.511676

Abstract

Background: Recent evidence has indicated that long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) to modulate mRNAs expression by sponging microRNAs (miRNAs). However, the specific mechanism and function of lncRNA-miRNA-mRNA regulatory network in non-small cell lung cancer (NSCLC) remains unclear.

Materials and methods: We constructed a lung cancer related lncRNA-mRNA network (LCLMN) by integrating differentially expressed genes (DEGs) with miRNA-target interactions. We further performed topological feature analysis and random walk with restart (RWR) analysis of LCLMN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to investigate the target DEGs in LCLMN. The expression levels of significant lncRNAs in NSCLC were validated by quantitative real-time PCR (RT-qPCR). The prognostic value of the potential lncRNA was evaluated by Kaplan-Meier analysis.

Results: A total of 33 lncRNA nodes, 580 mRNA nodes and 2105 edges were identified from LCLMN. Based on functional enrichment analysis and co-expression analysis, lncRNA EPB41L4A-AS1 was demonstrated to be correlated with the tumorigenesis of NSCLC. RT-qPCR results confirmed that the expression levels of lncRNA EPB41L4A-AS1 in NSCLC tissues were downregulated compared with adjacent non-cancerous tissues. Kaplan-Meier analysis showed that high expression of lncRNA EPB41L4A-AS1 was associated with better overall survival (OS) in NSCLC patients. Further investigation identified that high expression levels of COL4A3BP, CDS2, PURA, PDCD6IP, and TMEM245 were also correlated with better OS in NSCLC patients.

Conclusion: In this study, we constructed a lncRNA-miRNA-mRNA ceRNA network to investigate potential prognostic biomarkers for NSCLC. We found that lncRNA EPB41L4A-AS1 could function as a regulator in the pathogenesis of NSCLC.

Keywords: LncRNA-miRNA-mRNA regulatory network; competing endogenous RNAs; long non-coding RNAs; non-small cell lung cancer; overall survival.

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Figures

**FIGURE 1**
The workflow for construction of lung cancer-related lncRNA-mRNA network. According to the re-annotation results, we used SAM test to calculated the DE-mRNAs and DE-lncRNAs in NSCLC and adjacent non-cancerous samples. The lncRNA-miRNA and mRNA-miRNA interactions were obtained from starBase v2.0 database. We merged all the interactions to construct a global triple network. All the differentially expressed mRNAs and lncRNAs were mapped into a global triple network to extract an expression triple network (DE triple network). Then we combined the significant lncRNA-mRNA interactions to construct LCLMN. lncRNA, long non-coding RNA; miRNA, microRNA; mRNA, messenger RNA; DE-lncRNA, differentially expressed lncRNA; DE-mRNA, differentially expressed mRNA.

**FIGURE 2**
The view of global triple network. The blue, red and yellow nodes represented mRNAs, lncRNAs, and miRNAs, respectively. There were 192 miRNAs, 41 lncRNAs, 775 mRNAs, and 24799 edges in the network.

**FIGURE 3**
Topological features of LCLMN. **(A)** The overview of LCLMN. The blue nodes represented lung cancer related differential expressed mRNAs and the red nodes represented lung cancer related differential expressed lncRNAs. The size of nodes represented the degrees of the nodes in the network. **(B)** The node distribution of mRNAs in LCLMN. **(C)** The node distribution of lncRNAs in LCLMN. **(D)** The Venn diagram analyses showing the overlap of the top 10 max nodes with topological features in each dimension (degree, betweenness and closeness). **(E)** mRNAs adjacent to lncRNA EPB41L4A-AS1 in LCLMN. **(F–H)** The gene ontology (GO) enrichment analysis of lncRNA EPB41L4A-AS1. The x-axis was the -log10 of p-value, and p < 0.05 was considered statistically significant. LCLMN, lung cancer-related lncRNA-mRNA network.

**FIGURE 4**
The KEGG pathway related to lncRNA EPB41L4A-AS1. **(A)** RNA transport. **(B)** MAPK signaling pathway. **(C)** Transcriptional misregulation in cancer. **(D)** TNF signaling pathway.

**FIGURE 5**
Co-expression analyses of lncRNAs and their neighbor mRNAs in LCLMN. **(A)** The lncRNA EPB41L4A-AS1-miRNA-mRNA network extracted from global triple network. **(B)** The lncRNA KB-1732A1.1-miRNA-mRNA network extracted from global triple network. **(C)** The lncRNA RP11-390P2.4-miRNA-mRNA network extracted from global triple network.

**FIGURE 6**
Analysis of NSCLC-related lncRNAs from network and random walk with restart analysis. **(A)** The triple network of lncRNA RP11-421L21.3 extracted from global triple network. **(B)** The triple network of lncRNA HOTAIR extracted from global triple network.

**FIGURE 7**
The expression profiles of lncRNA EPB41L4A-AS1, KB-1732A1.1, RP11-390P2.4, RP11-421L21.3, and HOTAIR. **(A–E)** The re-annotation expression level of lncRNA EPB41L4A-AS1, KB-1732A1.1, RP11-390P2.4, RP11-421L21.3, and HOTAIR was evaluated in 20 pairs of NSCLC samples in the microarray. P-values were obtained by paired t-test (P < 0.05). **(F–J)** The expression level of lncRNA EPB41L4A-AS1, KB-1732A1.1, RP11-390P2.4, RP11-421L21.3, and HOTAIR was evaluated by qPCR in 12 pairs of NSCLC samples and corresponding adjacent non-cancerous samples. Data are presented as 2^–ΔΔCt.

**FIGURE 8**
Kaplan-Meier survival curves for lncRNA EPB41L4A-AS1 (225698_at). **(A)** Survival curves were plotted for all NSCLC patients (n = 1144). **(B)** Survival curves were plotted for adenocarcinoma patients (n = 672). **(C)** Survival curves were plotted for squamous cell carcinoma patients (n = 271). **(D)** Survival curves were plotted for gastric cancer patients (n = 631). **(E)** Survival curves were plotted for breast cancer patients (n = 1764). **(F)** Survival curves were plotted for ovarian cancer patients (n = 614). Horizontal axis: overall survival time (months). Vertical axis, survival function; HR, hazard ratio; CI, confidence interval.

**FIGURE 9**
Kaplan-Meier survival curves for lncRNA EPB41L4A-AS1 (225698_at). **(A)** Survival curves were plotted for female patients (n = 374). **(B)** Survival curves were plotted for male patients (n = 659). **(C)** Survival curves were plotted for all patients excluding who never smoked (n = 300). **(D)** Survival curves were plotted for patients never smoked (n = 141). **(E)** Survival curves were plotted for patients in stage 1 (n = 449). **(F)** Survival curves were plotted for patients in AJCC stage T1 (n = 224). **(G)** Survival curves were plotted for patients in AJCC stage N1 (n = 102). **(H)** Survival curves were plotted multivariable cox regression analysis (n = 115). Horizontal axis, overall survival time (months); Horizontal axis, overall survival time (months); Vertical axis, survival function; HR, hazard ratio; CI, confidence interval.

**FIGURE 10**
**(A–E)** Kaplan-Meier survival curves for DE-mRNAs correlated to NSCLC patients.

**FIGURE 11**
**(A–E)** Kaplan-Meier survival curves for DE-miRNAs correlated to NSCLC patients.

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[1] Anastasiadou E., Jacob L. S., Slack F. J. (2018). Non-coding RNA networks in cancer. Nat. Rev. Cancer 18 5–18. 10.1038/nrc.2017.99 - DOI - PMC - PubMed

[2] Anastasiadou E., Jacob L. S., Slack F. J. (2018). Non-coding RNA networks in cancer. Nat. Rev. Cancer 18 5–18. 10.1038/nrc.2017.99 - DOI - PMC - PubMed

[3] Beermann J., Piccoli M. T., Viereck J., Thum T. (2016). Non-coding RNAs in development and disease: background, mechanisms, and therapeutic approaches. Physiol. Rev. 96 1297–1325. 10.1152/physrev.00041.2015 - DOI - PubMed

[4] Beermann J., Piccoli M. T., Viereck J., Thum T. (2016). Non-coding RNAs in development and disease: background, mechanisms, and therapeutic approaches. Physiol. Rev. 96 1297–1325. 10.1152/physrev.00041.2015 - DOI - PubMed

[5] Bray F., Ferlay J., Soerjomataram I., Siegel R. L., Torre L. A., Jemal A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68 394–424. 10.3322/caac.21492 - DOI - PubMed

[6] Bray F., Ferlay J., Soerjomataram I., Siegel R. L., Torre L. A., Jemal A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68 394–424. 10.3322/caac.21492 - DOI - PubMed

[7] Cech T. R., Steitz J. A. (2014). The noncoding RNA revolution-trashing old rules to forge new ones. Cell 157 77–94. 10.1016/j.cell.2014.03.008 - DOI - PubMed

[8] Cech T. R., Steitz J. A. (2014). The noncoding RNA revolution-trashing old rules to forge new ones. Cell 157 77–94. 10.1016/j.cell.2014.03.008 - DOI - PubMed

[9] Chen S. S., Peng M., Zhou G. Z., Pu Y. C., Yi M. C., Zhu Y., et al. (2019). Long non-coding RNA HOTAIR regulates the development of non-small cell lung cancer through miR-217/DACH1 signaling pathway. Eur. Rev. Med. Pharmacol. Sci. 23 670–678. 10.26355/eurrev_201901_16905 - DOI - PubMed

[10] Chen S. S., Peng M., Zhou G. Z., Pu Y. C., Yi M. C., Zhu Y., et al. (2019). Long non-coding RNA HOTAIR regulates the development of non-small cell lung cancer through miR-217/DACH1 signaling pathway. Eur. Rev. Med. Pharmacol. Sci. 23 670–678. 10.26355/eurrev_201901_16905 - DOI - PubMed

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Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network Identified lncRNA EPB41L4A-AS1 as a Potential Biomarker in Non-small Cell Lung Cancer

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Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network Identified lncRNA EPB41L4A-AS1 as a Potential Biomarker in Non-small Cell Lung Cancer

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