Skip to main content

Advertisement

SpringerLink
Log in

Landscape and Construction of a Novel N6-methyladenosine-related LncRNAs in Cervical Cancer

  • Gynecologic Oncology: Original Article
  • Published:
Reproductive Sciences Aims and scope Submit manuscript

Abstract

Cervical cancer is a crucial clinical problem with high mortality. Despite much research in therapy, the prognosis of patients with cervical cancer is still not ideal. The data on cervical cancer were downloaded from The Cancer Genome Atlas (TCGA) portal. R language was used to screen out the N6-methyladenosine (m6A)-related lncRNAs (long non-coding RNA). A consensus clustering algorithm was performed to identify m6A-related lncRNAs in cervical cancer; 10 m6A-related lncRNAs showing a significant association with survival were filtrated through a gradually univariate Cox regression model, least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate Cox regression preliminarily. Furthermore, we conducted Kaplan-Meier curves, receiver operating curve (ROC) analyses, and proportional hazards model to quantify the underlying character of the m6A-related model in the prevision of cervical cancer patients. Gene set enrichment analysis (GSEA) was used to explore several pathways significantly. Finally, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was applied to estimate the immune cell infiltration in the profiling. In the present study, 10 m6A-related lncRNAs make up our prediction model. This prediction model can do duty for an independent predictive biomolecular element. Subsequently, we then found that the model was still valid in further validation of the training group and the test group. Our signature was correlated with immune cell infiltration and partial signaling pathway. These lncRNAs played a no negligible biomolecular role in contributing to the prognosis of cervical cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

All data used in this work could be acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/).

Code Availability

Not applicable.

Abbreviations

TCGA:

The Cancer Genome Atlas

m6A:

N6-methyladenosine

LASSO:

least absolute shrinkage and selection operator

ROC:

receiver operating curve

GSEA:

gene set enrichment analysis

CIBERSORT:

cell-type identification by estimating relative subsets of RNA transcripts

HPV:

high-risk human papillomavirus

lncRNAs:

long non-coding RNAs

References

  1. Muñoz N, Castellsagué X, Berrington de González A, Gissmann L. Chapter 1: HPV in the etiology of human cancer. Vaccine. 2006;24(Suppl 3):S3/1–S3/10. https://doi.org/10.1016/j.vaccine.2006.05.115.

    Article  CAS  PubMed  Google Scholar 

  2. Saavedra KP, Brebi PM, Roa JC. Epigenetic alterations in preneoplastic and neoplastic lesions of the cervix. Clin Epigenetics. 2012;4(1):13. https://doi.org/10.1186/1868-7083-4-13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Chi Y, Wang D, Wang J, Yu W, Yang J. Long non-coding RNA in the pathogenesis of cancers. Cells. 2019;8(9):1015. https://doi.org/10.3390/cells8091015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Liu N, Pan T. RNA epigenetics. Transl Res. 2015;165(1):28–35. https://doi.org/10.1016/j.trsl.2014.04.003.

    Article  CAS  PubMed  Google Scholar 

  5. Hui B, Ji H, Xu Y, et al. RREB1-induced upregulation of the lncRNA AGAP2-AS1 regulates the proliferation and migration of pancreatic cancer partly through suppressing ANKRD1 and ANGPTL4. Cell Death Dis. 2019;10(3):207. https://doi.org/10.1038/s41419-019-1384-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. He Y, Hu H, Wang Y, et al. ALKBH5 inhibits pancreatic cancer motility by decreasing long non-coding RNA KCNK15-AS1 methylation [published correction appears in Cell Physiol Biochem. 2019;52(5):1254]. Cell Physiol Biochem. 2018;48(2):838–46. https://doi.org/10.1159/000491915.

    Article  CAS  PubMed  Google Scholar 

  7. Sun T, Wu R, Ming L. The role of m6A RNA methylation in cancer. Biomed Pharmacother. 2019;112:108613. https://doi.org/10.1016/j.biopha.2019.108613.

    Article  CAS  PubMed  Google Scholar 

  8. Xu Q, Wang Y, Huang W. Identification of immune-related lncRNA signature for predicting immune checkpoint blockade and prognosis in hepatocellular carcinoma. Int Immunopharmacol 2021; 92:107333. https://doi.org/10.1016/j.intimp.2020.107333.

  9. Aalijahan H, Ghorbian S. Long non-coding RNAs and cervical cancer. Exp Mol Pathol. 2019;106:7–16. https://doi.org/10.1016/j.yexmp.2018.11.010.

    Article  CAS  PubMed  Google Scholar 

  10. Peng L, Yuan X, Jiang B, Tang Z, Li GC. LncRNAs: key players and novel insights into cervical cancer. Tumour Biol. 2016;37(3):2779–88. https://doi.org/10.1007/s13277-015-4663-9.

    Article  CAS  PubMed  Google Scholar 

  11. Tu Z, Wu L, Wang P, et al. N6-Methylandenosine-related lncRNAs are potential biomarkers for predicting the overall survival of lower-grade glioma patients. Front Cell Dev Biol. 2020;8:642. https://doi.org/10.3389/fcell.2020.00642.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Yang X, Zhang S, He C, et al. METTL14 suppresses proliferation and metastasis of colorectal cancer by down-regulating oncogenic long non-coding RNA XIST. Mol Cancer. 2020;19(1):46. https://doi.org/10.1186/s12943-020-1146-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Liu GM, Zeng HD, Zhang CY, Xu JW. Identification of METTL3 as an adverse prognostic biomarker in hepatocellular carcinoma. Dig Dis Sci. 2021;66(4):1110–26. https://doi.org/10.1007/s10620-020-06260-z.

    Article  CAS  PubMed  Google Scholar 

  14. Liu T, Han Z, Li H, Zhu Y, Sun Z, Zhu A. LncRNA DLEU1 contributes to colorectal cancer progression via activation of KPNA3. Mol Cancer. 2018;17:118. https://doi.org/10.1186/s12943-018-0873-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Pang B, Sui S, Wang Q, Wu J, Yin Y, Xu S. Upregulation of DLEU1 expression by epigenetic modification promotes tumorigenesis in human cancer. J Cell Physiol. 2019;234:17420–32. https://doi.org/10.1002/jcp.28364.

    Article  CAS  PubMed  Google Scholar 

  16. Xu H, Wang L, Jiang X. Silencing of lncRNA DLEU1 inhibits tumorigenesis of ovarian cancer via regulating miR-429/TFAP2A axis. Mol Cell Biochem. 2021;476(2):1051–61. https://doi.org/10.1007/s11010-020-03971-9.

    Article  CAS  PubMed  Google Scholar 

  17. Li X, Li Z, Liu Z, Xiao J, Yu S, Song Y. Long non-coding RNA DLEU1 predicts poor prognosis of gastric cancer and contributes to cell proliferation by epigenetically suppressing KLF2 [published correction appears in Cancer Gene Ther. 2021 Aug 23]. Cancer Gene Ther. 2018;25(3-4):58–67. https://doi.org/10.1038/s41417-017-0007-9.

    Article  CAS  PubMed  Google Scholar 

  18. Li Y, Shi B, Dong F, Zhu X, Liu B, Liu Y. Long non-coding RNA DLEU1 promotes cell proliferation, invasion, and confers cisplatin resistance in bladder cancer by regulating the miR-99b/HS3ST3B1 axis. Front Genet. 2019;10:280. https://doi.org/10.3389/fgene.2019.00280.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang S, Guan Y, Liu X, Ju M, Zhang Q. Long non-coding RNA DLEU1 exerts an oncogenic function in non-small cell lung cancer. Biomed Pharmacother. 2019;109:985–90. https://doi.org/10.1016/j.biopha.2018.10.175.

    Article  CAS  PubMed  Google Scholar 

  20. Wang C, Xie XX, Li WJ, Jiang DQ. LncRNA DLEU1/microRNA-300/RAB22A axis regulates migration and invasion of breast cancer cells. Eur Rev Med Pharmacol Sci. 2019;23(23):10410–21. https://doi.org/10.26355/eurrev_201912_19680.

    Article  CAS  PubMed  Google Scholar 

  21. Xu X, Liu T, Wu J, Wang Y, Hong Y, Zhou H. Transferrin receptor-involved HIF-1 signaling pathway in cervical cancer. Cancer Gene Ther. 2019;26(11-12):356–65. https://doi.org/10.1038/s41417-019-0078-x.

    Article  CAS  PubMed  Google Scholar 

  22. Ramos-Solano M, Alvarez-Zavala M, Garcia-Castro B, Jave-Suarez LF, Aguilar-Lemarroy A. Wnt signalling pathway and cervical cancer. Rev Med Inst Mex Seguro Soc. 2015;53(Suppl 2):S218–24.

    PubMed  Google Scholar 

  23. Boromand N, Hasanzadeh M, ShahidSales S, et al. Clinical and prognostic value of the C-Met/HGF signaling pathway in cervical cancer. J Cell Physiol. 2018;233(6):4490–6. https://doi.org/10.1002/jcp.26232.

    Article  CAS  PubMed  Google Scholar 

  24. Rodrigues C, Joy LR, Sachithanandan SP, Krishna S. Notch signalling in cervical cancer. Exp Cell Res. 2019;385(2):111682. https://doi.org/10.1016/j.yexcr.2019.111682.

    Article  CAS  PubMed  Google Scholar 

  25. Liu Y, Li L, Liu Y, et al. RECK inhibits cervical cancer cell migration and invasion by promoting p53 signaling pathway. J Cell Biochem. 2018;119(4):3058–66. https://doi.org/10.1002/jcb.26441.

    Article  CAS  PubMed  Google Scholar 

  26. Xu T, Zeng Y, Shi L, et al. Targeting NEK2 impairs oncogenesis and radioresistance via inhibiting the Wnt1/β-catenin signaling pathway in cervical cancer. J Exp Clin Cancer Res. 2020;39(1):183. https://doi.org/10.1186/s13046-020-01659-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Geng XD, Wang WW, Feng Z, et al. Identification of key genes and pathways in diabetic nephropathy by bioinformatics analysis. J Diabetes Investig. 2019;10(4):972–84. https://doi.org/10.1111/jdi.12986.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ling B, Liao X, Huang Y, et al. Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments. Int J Oncol. 2020;56(1):193–205. https://doi.org/10.3892/ijo.2019.4926.

    Article  CAS  PubMed  Google Scholar 

  29. Zheng MJ, Li X, Hu YX, et al. Identification of molecular marker associated with ovarian cancer prognosis using bioinformatics analysis and experiments. J Cell Physiol. 2019;234(7):11023–36. https://doi.org/10.1002/jcp.27926.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

I would like to express my gratitude to all those who have helped me during the writing of the article. I gratefully acknowledge the help of the financial support of the Henan Joint Construction Project. Thanks for the technical support provided by the central laboratory, and thanks to Professor Wei Ming for his careful revision of the article.

Funding

This work was sponsored by medical science and technology research project in Henan Province [grant numbers: LHGJ20190419]. XL and MW were supported by the Science and Technology Department of Henan Province. None of the funding bodies played any role in the design of the study; collection, analysis, and interpretation of data; or in the writing of the manuscript.

Author information

Author notes

  1. Xin Liu and Weijie Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Blood Transfusion Department, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China

    Xin Liu, Diming Xiao & Ming Wei

  2. Department of Pharmacy Department, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China

    Weijie Zhang

  3. Department of General Practice Department, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China

    Jun Wan

Authors
  1. Xin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weijie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Diming Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XL designed this work. WZ and JW wrote this manuscript. DMX and MW revised the manuscript. All authors approved this manuscript.

Corresponding author

Correspondence to Ming Wei.

Ethics declarations

Ethics Approval and Consent to Participate

The patient data for this study were derived from a publicly available complete patient informed consent data set (TCGA). Our study was submitted to IRB of The Fifth Affiliated Hospital of Zhengzhou University for review, and ethics approval was waived.

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Fig. S1

The flow chart of this study. (PNG 137 kb)

High resolution image (TIF 246 kb)

Fig. S2

Cibersort algorithm calculated the difference of immune infiltration in high and low risk groups. (PNG 79 kb)

High resolution image (TIF 3090 kb)

Table S1

(XLSX 10 kb)

Table S2

(XLSX 9 kb)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Zhang, W., Wan, J. et al. Landscape and Construction of a Novel N6-methyladenosine-related LncRNAs in Cervical Cancer. Reprod. Sci. 30, 903–913 (2023). https://doi.org/10.1007/s43032-022-01074-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s43032-022-01074-y

Keywords

Search

Navigation