Competitive Endogenous RNA (ceRNA) Regulation Network of lncRNA–miRNA–mRNA in wilms tumor

doi:10.21203/rs.2.11185/v1

Download PDF

Research article

Competitive Endogenous RNA (ceRNA) Regulation Network of lncRNA–miRNA–mRNA in wilms tumor

https://doi.org/10.21203/rs.2.11185/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 16 Dec, 2019

Read the published version in BMC Medical Genomics →

You are reading this older preprint version

Read the latest preprint version →

Background

Competitive endogenous RNA (ceRNA) have revealed a new mechanism of interaction between RNAs. However, such comprehension of the ceRNA regulatory network in wilms tumor remains limited.

Methods

Raw RNA sequencing profiles regarding mRNAs, miRNAs and lncRNAs on wilms tumor samples and normal samples were obtained from Therapeutically Applicable Research to Generate Effective Treatment (TARGET). EdgeR package was applied to identify differentially expressed lncRNAs, miRNAs and mRNAs. Functional enrichment analysis were conducted via DAVID database and the ClusterProfile R package. The lncRNA–miRNA–mRNA interaction ceRNA network was established in Cytoscape according to the identified lncRNAs–miRNAs and miRNAs–mRNAs interactions. Subsequently, correlation between ceRNA network and overall survival prognosis were analyzed.

Results

A total of 2,037 lncRNAs, 154 miRNAs and 3,609 mRNAs were identified as differentially expressed RNAs in wilms tumor. 205 lncRNAs, 26 miRNAs and 143 mRNAs were included in ceRNA regulatory network. Analysis results showed that 14 out of the 205 lncRNAs, 1 out of 26 miRNAs and 8 out of 143 mRNAs were associated with overall survival in wilms tumor patients (P < 0.05).

Conclusions

CeRNA networks played an important role in wilms tumor. This might provide effective bioinformatics basis and novel insights for further understanding of the mechanisms underlying wilms tumor.

Epigenetics & Genomics

Wilms tumor

Long noncoding RNA

Prognosis

Competing endogenous RNA network

MicroRNAs

Wilms tumor (WT) is the most common type of pediatric renal malignancy. WT have a poor prognosis although 5-year overall survival rate is constantly improved with the advancement of disease-associated therapies ¹. Chemotherapy, surgery and radiation therapy are the main treatment strategies for WT. However, 50% of children who have a recurrence after these treatments go on to die from this tumor ^2,3. N ovel therapeutic treatment targeting specific mechanisms of WT is still lacking.

Previous studies demonstrated that that numerous key Long non-coding RNA (lncRNA) , microRNA (miRNA) and mRNAs are closely related with pathogenesis of WT, such as LINC00473⁴, miR-483-5p⁵, miR-195⁴ and HACE1⁶. However, there were little reports in prognosis biomarkers in WT. If WT patients who were more likely to have a poor prognosis according to these prognosis biomarker result could be identified, clinician might apply more aggressive treatment. if WT patients with low risk could be identified, treatment with less morbidity could be administered. Prognosis biomarkers and targeted cure in WT were required to be identified in order to improve the clinical outcomes.

In the last decade, complexity of the human genome could be revealed by advanced RNA sequencing analysis⁷. Under such circumstance, competing endogenous RNA (ceRNA) hypothesis was presented which demonstrated that lncRNAs could communicate with common miRNA response elements with miRNA to construct anintricate interplay network and ultimately crosstalk with mRNA. The involvement of ceRNA regulatory network tumor initiation and progression was validated in previous studies^8,9. However, specific ceRNA regulatory network (lncRNA–miRNA–mRNA) in WT still remained unelucidated.

In present study, ceRNA regulatory network (205 mRNAs, 26 lncRNAs and 143 miRNAs) was constructed to promote the understanding how lncRNAs sponged miRNA to regulated gene expression in WT. Subsequently, survival analysis and functional analysis were used to promote new understanding of the role of ceRNA regulatory network in WT carcinogenesis. The present study might give insight into molecular mechanism which participate in the progression and tumorigenesis of WT.

Data collection and preprocessing

All the data were retrieved from Therapeutically Applicable Research to Generate Effective Treatment (TARGET:https://ocg.cancer.gov/programs/target) database. All the data (lncRNAs, mRNAs and miRNAs) in present study were publicly available. Ethics committee approval was not required because data in present study was obtained from TARGET database. Among them, miRNA expression data were acquired from 138 samples, including 6 normal samples and 132 WT samples. And mRNA and lncRNA expression data were acquired from 132 samples, including 6 normal samples and 126 WT samples. The differentially expressed lncRNAs (DELs), differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) between WT and normal samples were carried out via EdgeR package in R software (Version 3.2.2). |logFC(fold change)| >1 and false discovery rate (FDR) of <0.05 were defined as cut-off criteria.

Prediction of lncRNA–miRNA and miRNA–mRNA interactions

DELs–DEMs- DEGs interactions were divided into DELs-DEMs and DEMs-DEGs pairs. starBase database ( http://starbase.sysu.edu.cn/) was apllied to change the miRNA sequences. miRcode¹⁰(http://www.mircode.org/) was effective online software which could provide the interactions between lncRNAs and miRNAs. MiRTarBase¹¹ (http://mirtarbase.mbc.nctu.edu.tw), miRDB¹² (http://www.mirdb.org/miRDB/), and TargetScan ¹³(http://www.targetscan.org/) online tools were applied to retrieve and predict target mRNAs of miRNA. Venny diagram was applied to acquire overlapping portion of target miRNAs and mRNAs. Matched DELs– DEMs and DEMs–DEGs interactions were screened for further bioinformatics analysis.

CeRNA network construction and functional enrichment analysis

Cytoscape software (Version 3.6.1) was applied to construct and visualize the DELs–DEMs- DEGs ceRNA network. Cytoscape was an effective software to visualize the molecular interaction networks according to gene expression profiles and annotations. In order to better comprehend the tumorigenesis mechanisms in WT, Gene Ontology functional enrichment analysis (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis (KEGG) of DEGs in ceRNA network were performed via DAVID (the Database for Annotation, Visualization and Integrated Discovery). FDR <0.05 was defined as cut-off criteria.

Survival analysis

The log-rank test and Kaplan-Meier analysis were applied to identify prognostic DELs, DEMs and DEGs signatures. The survival curves were constructed using the ‘survival’ package. Survival analysis was performed according to KaplanMeier univariate survival analysis. P<0.05 were selected as statistical threshold value. All the survival analysis was conducted in R software (version: 3.3.2).

DELs, DEGs and DEMs in WT.

LncRNA, mRNA, and miRNA expression profiles between WT samples and normal samples acquired from TARGET were analyzed in present study. 2,037 DELs, 154 DEMs and 3,609 DEGs, were screened out in present study. 1,247 up-regulated and 790 down-regulated DELs were identified in WT with cut-off threshold of |logFC(fold change)| >1 and false discovery rate (FDR) of <0.05. DELs distribution between WT and normal controls were presented as heatmap plot in Figure 1A. According to the cut-off threshold of |logFC(fold change)| >1 and false discovery rate (FDR) of <0.05, 105 up-regulated and 49 down-regulated DEMs were identified in WT. Heatmap plot of the related DEMs between WT and normal controls is shown in Figure 1B. A total of 1,894 up-regulated and 1,715 down-regulated DEGs were identified in WT. DEGs distribution between WT and normal controls were presented as a heatmap plot in Figure 1C. The top 10 upregulated and downregulated DELs, DEMs and DEGs were showed in Table 1.

CeRNA network construction and Functional enrichment analysis

A dysregulated lncRNA-miRNA-mRNA ceRNA network in WT was constructed according to the interactions of 980 DEL–DEM pairs and 235 DEM–DEG pairs between 205 DELs, 26 DEMs, and 143 DEGs. Finally, lncRNA-miRNA-mRNA ceRNA regulatory network visualized in Cytoscape was presented in Figure 2.

GO and KEGG analysis were also performed to reveal the functions of the 16 DEGs which were included in the ceRNA network. For “biological processes (BP)”, the top five terms were response to mechanical stimulus, G1/S transition of mitotic cell cycle, cell cycle G1/S phase transition, muscle cell proliferation, mesenchymal cell differentiation; for the “cellular component (CC)”, the top five terms were transcription factor complex, cyclin-dependent protein kinase holoenzyme complex, nuclear chromatin, nuclear transcription factor complex, Flemming body; The top five “molecular function (MF)” terms were transcription factor activity and RNA polymerase II proximal promoter sequence-specific DNA binding, transcriptional activator activity and RNA polymerase II transcription regulatory region sequence-specific DNA binding, proximal promoter sequence-specific DNA binding, transcriptional activator activity and RNA polymerase II proximal promoter sequence-specific DNA binding, transcriptional repressor activity and RNA polymerase II transcription regulatory region sequence-specific DNA binding (Table 2 ). Additionally, KEGG pathway analysis showed that DEGs were enriched in 30 pathways, such as Cell cycle, Small cell lung cancer, p53 signaling pathway, MicroRNAs in cancer, Cellular senescence (Table 2 and Figure 3).

Survival analysis

Lapland-Meier univariate survival analysis was performed via combined information of clinical information and gene expression profiles of 205 lncRNAs, 26 miRNAs and 1 43 mRNAs in the ceRNA network in WT samples. According to analysis results, 14 lncRNAs (AC005609.1, AC135178.1, ADAMTS9-AS1, AL391832.1, AL445228.2, DENND5B-AS1, DLEU2, GRM7-AS3, LINC00303, LINC00473, MEG3, MYB-AS1, NRG1-IT1, RMST) among 205 DELs were closely related to overall survival of WT patients (Figure 4). For AC005609.1, AC135178.1, AL391832.1, AL445228.2, DENND5B-AS1, DLEU2, GRM7-AS3, LINC00303 and LINC00473, low expression was related to a high overall survival rate in WT patients. High expression of ADAMTS9-AS1, MEG3, MYB-AS1, NRG1-IT1, and RMST were related with high survival rates in WT patients. In addition, 1 miRNA (hsa-mir-200a) among 26 DEMs were closely related to prognosis of WT patients (Figure 5). High expression of hsa-mir-200a were related with high survival rates in WT patients. 8 mRNAs (CDCA4, CEP55, DEPDC1, KIAA0922, OSR1, PHF19, PLEKHA8, ZBTB4) among 143 DEGs were closely related to overall survival of WT patients (Figure 6). For CDCA4, CEP55, DEPDC1, OSR1, PHF19 and PLEKHA8, low expression was related to a high overall survival rate in WT patients. High expression of KIAA0922 and ZBTB4 were significantly related with high survival rates in WT patients.

WT is a type of pediatric renal malignancy. Although overall survival in WT patients is constantly improved, disease recurrence and poor prognosis are still the main cause of cancer-related death in childhood ¹. Dysregulated genes are considered as the major cause of tumorigenesis of WT. Recently, more and more attentions are paid to the crucial roles of ceRNA network in gene expression regulation at three levels (transcription, post-transcription and translation). Previous study reported regulatory role of ceRNA networks in proliferation, metastasis and invasion of cancer^14,15. To better comprehend how ceRNA regulatory network affected WT, a large-scale WT data from TARGET database were analyzed and dysregulated ceRNA regulatory network in WT were constructed successfully. A dysregulated ceRNA network in WT was constructed according to the interactions of 980 DEL–DEM pairs and 235 DEM–DEG pairs between 205 DELs, 26 DEMs, and 143 DEGs. In addition, Kaplan-Meier curve analysis was also applied to identify prognosis biomarker in WT. And 14 differentially expressed lncRNAs (AC005609.1, AC135178.1, ADAMTS9-AS1, AL391832.1, AL445228.2, DENND5B-AS1, DLEU2, GRM7-AS3, LINC00303, LINC00473, MEG3, MYB-AS1, NRG1-IT1, RMST), 1 differentially expressed miRNA (hsa-mir-200a) and 8 differentially expressed mRNAs (CDCA4, CEP55, DEPDC1, KIAA0922, OSR1, PHF19, PLEKHA8, ZBTB4) were showed to be significantly associated with overall survival rate in WT.

Long noncoding RNAs (lncRNAs) are defined as noncoding RNAs longer than 200 nucleotides¹⁶. lncRNAs were showed to be involved in a variety of biological regulatory functions including metastasis and tumorigenesis of cancer^17,18. A total of 1,247 up-regulated and 790 down-regulated DELs were identified in present study. 205 DELs were included in construction of ceRNA network. And 14 out of the 205 DELs were associated with overall survival in WT patients (P < 0.05). Some differentially expressed lncRNAs in our analysis result have been investigated in WT: for example, LINC00473 was capable to decrease miR-195 expression level and inhibit miR-195’s function in WT¹⁹. Dysregulated lncRNA signature LINC00473/miR-195/IKKα was showed to act as pro-tumour pathogenesis role in WT¹⁹. The above-mentioned molecular experiments partially supported our analysis result in present study. Such identified lncRNAs might serve as prognosis biomarkers and therapeutic targets in WT. In previous studies, RMST might inhibit cell proliferation, invasion, migration, and enhance cell apoptosis, and regulate cell cycle to acted as a tumor suppressor in triple-negative breast cancer²⁰. MEG3, a myeloid-related lncRNA, played a tumor suppressor role in various solid neoplasms^21,22. Dleu2 could control miR-16-1 to regulate proliferation, invasion and migration of laryngeal cancer ²³. ADAMTS9-AS1 were related to overall survival of breast cancer patients²⁴, bladder cancer²⁵ and colon adenocarcinoma²⁶. Few studies, however, have explored the relationship between above-mentioned DELs and tumorigenesis of WT. Additionally, little is known about the regulatory role of LINC00303, GRM7-AS3, DLEU2, DENND5B-AS1, AL445228.2, AL391832.1, AC135178.1 and AC005609.1 in cancer. Therefore, further studies are needed to illuminate the molecular and biological mechanism of these DELs in WT.

MicroRNAs are single-stranded and 18-25 nucleotide-long noncoding RNA which targets mRNAs to control gene expression ²⁷. DEMs in WT, including 105 up-regulated and 49 down-regulated DEMs, were summarized in present study. In present study, ceRNA network contained 14 differentially expressed miRNAs. However, only one miRNA (miR-200a) were related to overall survival in WT patients. MiR-200a was an important member of miR - 200 family. It had been reported that miR-200a was involved in several biological processes to control progression of cancer^28,29. MiR-200a was showed to target FOXA1 and acts as a tumor suppressor on the survival, proliferation and invasion of glioma cells ²⁹. And miR-200a might inactivate BRD4-mediated AR signaling to inhibit progression of prostate cancer ²⁸. Moreover, previous studies reported that miR-200a is associated with the development and occurrence of esophageal cancer, breast cancer and endometrial cancer, breast cancer, and esophageal cancer by targeting specific genes, such as CRMP‐1, EPHA2 and PTEN^30-32. However, there is no research to clearly elucidate the role of miR-200a in WT. The understanding of miR-200a in the progression of WT is limited which require more targeted molecular studies to confirm its role.

To further investigate related cellular mechanisms in WT, GO and KEGG analysis of

           1
           43
            DEGs in the ceRNA network was performed. GO analysis result shows that that 
        DEGs were mainly enriched in mechanical stimulus, G1/S transition of mitotic cell cycle, cell cycle G1/S phase
           transition, muscle cell proliferation, mesenchymal cell differentiation. KEGG analysis result indicated that
         DEGs in ceRNA network were mainly enriched in Cell cycle, Small cell lung cancer, p53 signaling pathway, MicroRNAs in cancer, Cellular senescence. In recent years, many studies have found the same research findings. PI3K-AKT-p53&nbsp;signaling&nbsp;pathway
        was involved in the tumorigenesis of WT which might represent a potential treatment
        in the future <sup>33</sup>. MiRNAs, 
        single-stranded and 18-25 nucleotide-long noncoding RNA<sup>27</sup>, 
           were involved in regulate the proliferation, cell cycle and apoptosis of WT<sup>34,35</sup>. Cellular&nbsp;senescence&nbsp;was reported to responsible for restricted proliferation in
        WT, which had&nbsp;been linked&nbsp;to increased p21 expression and was independent of p53 expression<sup>36</sup>.&nbsp;The above-mentioned related researches and experiments partially&nbsp;supported our GO and KEGG analysis result in present study.</p>

The major limitation of present study is that tumor tissue and blood verification of these differentially expressed lncRNAs, miRNAs, mRNA and relative pathways is lacking. Further targeted studies related to this ceRNA network still need to be designed to verify and investigate these valuable ncRNAs in the progression of WT.

In summary, differentially expressed lncRNAs, mRNAs, and miRNAs were identified and functional lncRNA-miRNA-mRNA ceRNA regulatory network for WT tumorigenesis was successfully constructed. Significantly altered lncRNAs, mRNAs and miRNAs might serve as prognosis biomarkers and therapeutic targets for tumorigenesis of WT. The ceRNA regulatory network might illuminate inner molecular mechanism which was involved in progression and tumorigenesis of WT.

WT：Wilms tumor

ceRNA ： Competitive endogenous RNA

TARGET ： Therapeutically Applicable Research to Generate Effective Treatment

lncRNA：Long non-coding RNA

miRNA：microRNA

DELs：differentially expressed lncRNAs

DEMs：differentially expressed miRNAs

DEGs：differentially expressed genes

FDR：false discovery rate

GO：Gene Ontology

KEGG：Kyoto Encyclopedia of Genes and Genomes pathway

DAVID：The Database for Annotation, Visualization and Integrated Discovery

Funding

The present study was supported by research start-up fee for the eighth affiliated hospital, Sun Yat-sen University (grant no.zdbykyqdf005).

Availability of data and materials

The datasets used and analyzed during the current study download from Therapeutically Applicable Research to Generate Effective Treatment database(https://ocg.cancer.gov/programs/target).

Authors’ contributions

Zhaohui He, Fucai Tang and Zechao Lu conceived the study; Zhaohui He and Fucai Tang conducted the work; Zhibiao Li, weijia Wu and Haifeng Duan obtaining and analyzed the data; Fucai Tang, Zechao Lu and Jiaming Wang wrote the paper. All the authors listed have read and approved the manuscript.

Ethics approval and consent to participate

The present study, the data download from Therapeutically Applicable Research to Generate Effective Treatment database, therefore, this article does not contain any studies with human participants or animals performed by any of the authors. thus no ethical approval and patient consent are required.

Consent for publication

Not applicable.

Conflicts of Interests

All authors declare that they have no conflict of interest to state.

1. Routh JC, Grundy PE, Anderson JR, Retik AB, Kurek KC. B7-h1 as a biomarker for therapy failure in patients with favorable histology Wilms tumor. J Urol. 2013;189(4):1487-1492.

2. Malogolowkin M, Cotton CA, Green DM, et al. Treatment of Wilms tumor relapsing after initial treatment with vincristine, actinomycin D, and doxorubicin. A report from the National Wilms Tumor Study Group. Pediatr Blood Cancer. 2008;50(2):236-241.

3. Cone EB, Dalton SS, Van Noord M, Tracy ET, Rice HE, Routh JC. Biomarkers for Wilms Tumor: A Systematic Review. J Urol. 2016;196(5):1530-1535.

4. Zhu S, Fu W, Zhang L, et al. LINC00473 antagonizes the tumour suppressor miR-195 to mediate the pathogenesis of Wilms tumour via IKKalpha. Cell proliferation. 2018;51(1).

5. Liu K, He B, Xu J, et al. miR-483-5p Targets MKNK1 to Suppress Wilms' Tumor Cell Proliferation and Apoptosis In Vitro and In Vivo. Medical science monitor : international medical journal of experimental and clinical research. 2019;25:1459-1468.

6. Jia W, Deng Z, Zhu J, et al. Association Between HACE1 Gene Polymorphisms and Wilms' Tumor Risk in a Chinese Population. Cancer investigation. 2017;35(10):633-638.

7. Schwartz MH, Wang H, Pan JN, et al. Microbiome characterization by high-throughput transfer RNA sequencing and modification analysis. Nature communications. 2018;9(1):5353.

8. Xu Y, Chen J, Yang Z, Xu L. Identification of RNA Expression Profiles in Thyroid Cancer to Construct a Competing Endogenous RNA (ceRNA) Network of mRNAs, Long Noncoding RNAs (lncRNAs), and microRNAs (miRNAs). Medical science monitor : international medical journal of experimental and clinical research. 2019;25:1140-1154.

9. Liu Q, Deng J, Wei X, Yuan W, Ma J. Integrated analysis of competing endogenous RNA networks revealing five prognostic biomarkers associated with colorectal cancer. Journal of cellular biochemistry. 2019.

10. A J, DS M, E L. miRcode: a map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics (Oxford, England). 2012;28(15):2062-2063.

11. CH C, S S, CD Y, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic acids research. 2018;46(null):D296-D302.

12. N W, X W. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic acids research. 2015;43(null):D146-152.

13. K P, KB K. miRTar Hunter: a prediction system for identifying human microRNA target sites. Molecules and cells. 2013;35(3):195-201.

14. S Z, L D, X L, H F. Identification of biomarkers associated with the recurrence of osteosarcoma using ceRNA regulatory network analysis. International journal of molecular medicine. 2019;undefined(undefined):undefined.

15. W T, C J, Z H, D X, S Z. Comprehensive analysis of dysregulated lncRNAs, miRNAs and mRNAs with associated ceRNA network in esophageal squamous cell carcinoma. Gene. 2019;undefined(undefined):undefined.

16. Bhan A, Soleimani M, Mandal SS. Long Noncoding RNA and Cancer: A New Paradigm. Cancer research. 2017;77(15):3965-3981.

17. Slaby O, Laga R, Sedlacek O. Therapeutic targeting of non-coding RNAs in cancer. The Biochemical journal. 2017;474(24):4219-4251.

18. Sun W, Yang Y, Xu C, Guo J. Regulatory mechanisms of long noncoding RNAs on gene expression in cancers. Cancer genetics. 2017;216-217:105-110.

19. S Z, W F, L Z, et al. LINC00473 antagonizes the tumour suppressor miR-195 to mediate the pathogenesis of Wilms tumour via IKKα. Cell proliferation. 2018;51(1):undefined.

20. L W, D L, X W, et al. Long non-coding RNA (LncRNA) RMST in triple-negative breast cancer (TNBC): Expression analysis and biological roles research. Journal of cellular physiology. 2018;233(10):6603-6612.

21. X Z, K R, Y W, et al. Maternally expressed gene 3 (MEG3) noncoding ribonucleic acid: isoform structure, expression, and functions. Endocrinology. 2010;151(3):939-947.

22. Y L, J L, Y Y, et al. Dysfunction of the WT1-MEG3 signaling promotes AML leukemogenesis via p53-dependent and -independent pathways. Leukemia. 2017;31(12):2543-2551.

23. ZZ X, ZC X, YX S, W L, GL T. Long non-coding RNA Dleu2 affects proliferation, migration and invasion ability of laryngeal carcinoma cells through triggering miR-16-1 pathway. European review for medical and pharmacological sciences. 2018;22(7):1963-1970.

24. CN F, L M, N L. Systematic analysis of lncRNA-miRNA-mRNA competing endogenous RNA network identifies four-lncRNA signature as a prognostic biomarker for breast cancer. Journal of translational medicine. 2018;16(1):264.

25. N Z, J H, Y W, et al. Integrated analysis of a competing endogenous RNA network reveals key lncRNAs as potential prognostic biomarkers for human bladder cancer. Medicine. 2018;97(35):e11887.

26. Y X, Z Z, Y Z, L Z, A Z, D P. Comprehensive analysis of differential expression profiles of mRNAs and lncRNAs and identification of a 14-lncRNA prognostic signature for patients with colon adenocarcinoma. Oncology reports. 2018;39(5):2365-2375.

27. Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nature reviews Drug discovery. 2017;16(3):203-222.

28. H G, Z Y, C W, et al. MicroRNA-200a suppresses prostate cancer progression through BRD4/AR signaling pathway. Cancer medicine. 2019;undefined(undefined):undefined.

29. X C, K L, P Y, et al. microRNA-200a functions as a tumor suppressor by targeting FOXA1 in glioma. Experimental and therapeutic medicine. 2019;17(1):221-229.

30. Y Z, Y T, B W, X J. miR-200a-3p promotes the proliferation of human esophageal cancer cells by post-transcriptionally regulating cytoplasmic collapsin response mediator protein-1. International journal of molecular medicine. 2016;38(5):1558-1564.

31. Q W, RL L, JX L, LJ R. MiR-200a and miR-200b target PTEN to regulate the endometrial cancer cell growth in vitro. Asian Pacific journal of tropical medicine. 2017;10(5):498-502.

32. E T, J W, DE F, E A, C W. miR-200a inhibits migration of triple-negative breast cancer cells through direct repression of the EPHA2 oncogene. Carcinogenesis. 2015;36(9):1051-1060.

33. M Z, E X, W S. Andrographolide promotes vincristine-induced SK-NEP-1 tumor cell death via PI3K-AKT-p53 signaling pathway. Drug design, development and therapy. 2016;10(undefined):3143-3152.

34. XS Z, B H, JX Z, N T, CY D. MiR-155-5p affects Wilms' tumor cell proliferation and apoptosis via targeting CREB1. European review for medical and pharmacological sciences. 2019;23(3):1030-1037.

35. H L, D H, S H. Salidroside inhibits the growth, migration and invasion of Wilms' tumor cells through down-regulation of miR-891b. Life sciences. 2019;undefined(undefined):undefined.

36. K T, J K, J K, et al. Senescence Process in Primary Wilms' Tumor Cell Culture Induced by p53 Independent p21 Expression. Journal of Cancer. 2016;7(13):1867-1876.

Due to technical limitations, Tables 1 and 2 are only available as downloads from the supplemental file section.

Table 1. The top 10 up-regulated and down-regulated differentially expressed genes

Table 2. The enriched Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes of differentially expressed genes.

Download PDF

Journal Publication

published 16 Dec, 2019

Read the published version in BMC Medical Genomics →

Review #2 received at journal
13 Sep, 2019
Editorial decision: Major revision
13 Sep, 2019
Reviewer #2 agreed at journal
23 Aug, 2019
Review #1 received at journal
23 Aug, 2019
Reviewer #1 agreed at journal
05 Aug, 2019
Editor assigned by journal
15 Jul, 2019
Reviewers invited by journal
15 Jul, 2019
Submission checks completed at journal
05 Jul, 2019
Editor invited by journal
05 Jul, 2019
First submitted to journal
05 Jun, 2019

You are reading this older preprint version

Read the latest preprint version →

Competitive Endogenous RNA (ceRNA) Regulation Network of lncRNA–miRNA–mRNA in wilms tumor

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Materials and Methods

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Supplementary Files

Status:

Journal Publication

Version 1