Article Data

  • Views 803
  • Dowloads 163

Original Research

Open Access

N6-methyladenosine-related genes contribute to malignant progression, have clinical prognostic and neoadjuvant treatments response impact for breast cancer

  • Jun Shen1
  • Hongfang Ma2
  • Yongxia Chen3
  • Cong Chen1
  • Zhaoqing Li1
  • Jianguo Shen1,*,

1Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016 Hangzhou, Zhejiang, China

2Department of Plastic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016 Hangzhou, Zhejiang, China

3Laboratory of Cancer Biology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016 Hangzhou, Zhejiang, China

DOI: 10.22514/ejgo.2023.024 Vol.44,Issue 2,April 2023 pp.67-78

Submitted: 18 July 2022 Accepted: 19 August 2022

Published: 15 April 2023

*Corresponding Author(s): Jianguo Shen E-mail: drshenjianguo@zju.edu.cn

Abstract

N6-methyladenosine (m6A) methylation dysregulation contributes to tumorigenesis and breast cancer development. This study intends to conduct a comprehensive analysis for determining the clinical significance of m6A-related genes and establishing m6A-related gene-based risk signature to predict the clinical outcomes and neoadjuvant treatments response for breast cancer patients. The m6Avar database was utilized for downloading the m6A-regulated genes. The Cancer Genome Atlas (TCGA) database was utilized for downloading breast cancer patients’ RNA-Seq data and clinicopathological information. For determining the differentially expressed m6A-related gene, a one-way analysis of variance (ANOVA) was conducted. The interaction and correlation of m6A-related genes were evaluated using search tool for the retrieval of interacting genes/proteins (STRING) and Spearmen test. For determining clusters of breast cancer patients with different clinical outcomes, a consensus clustering analysis was conducted. We screened differentially expressed genes and functional enrichment pathways between subgroups utilizing gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG). We constructed and verified a prognostic signature utilizing Cox regression analysis as well as a least absolute shrinkage and selection operator (LASSO) regression model. 286 genes were detected as significantly differentially expressed in different stages, including 3 m6A RNA methylation regulators, Wilms tumor 1-associating protein (WTAP), YT521-B homology (YTH) domain containing 2 (YTHDC2), and YTH domain family 2 (YTHDF2). A 13-gene prognostic signature was constructed and could predict the overall survival and the neoadjuvant treatments response in breast cancer patients. We categorized breast cancer patients into four groups based on m6A-associated RNAs expression. Significant differences were found in the overall survivals among the four clusters of patients. The biological processes and the key signaling pathways closely related to breast cancer have a close connection to the four clusters. This study confirmed that the m6A-related genes expression levels were highly associated with prognosis and neoadjuvant treatment response in breast cancer and constructed an effective m6A-related gene-based risk signature for predicting the prognosis of patients with breast cancer.


Keywords

Breast cancer; N6-methyladenosine methylation; Prognostic signature; Overall survival; Neoadjuvant treatment


Cite and Share

Jun Shen,Hongfang Ma,Yongxia Chen,Cong Chen,Zhaoqing Li,Jianguo Shen. N6-methyladenosine-related genes contribute to malignant progression, have clinical prognostic and neoadjuvant treatments response impact for breast cancer. European Journal of Gynaecological Oncology. 2023. 44(2);67-78.

References

[1] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA: a Cancer Journal for Clinicians. 2020; 70: 7–30.

[2] Dai D, Jin H, Wang X. Nomogram for predicting survival in triple-negative breast cancer patients with histology of infiltrating duct carcinoma: a population-based study. American Journal of Cancer Research. 2018; 8: 1576–1585.

[3] Lee SK, Yang JH, Woo SY, Lee JE, Nam SJ. Nomogram for predicting invasion in patients with a preoperative diagnosis of ductal carcinoma in situ of the breast. The British Journal of Surgery. 2013;100:1756–1763.

[4] Zheng H, Luo L, Zhao W. Factors associated with level III lymph nodes positive and survival analysis of its dissection in patients with breast cancer. Laparoscopic, Endoscopic and Robotic Surgery. 2020; 3: 43–47.

[5] Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. The Lancet Oncology. 2015; 16: e173–e180.

[6] Cava C, Bertoli G, Castiglioni I. Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential. BMC Systems Biology. 2015; 9: 62.

[7] He L, Li H, Wu A, Peng Y, Shu G, Yin G. Functions of N6-methyladenosine and its role in cancer. Molecular Cancer. 2019;18:176.

[8] Ke S, Alemu EA, Mertens C, Gantman EC, Fak JJ, Mele A, et al. A majority of m6A residues are in the last exons, allowing the potential for 3’ UTR regulation. Genes and Development. 2015; 29: 2037–2053.

[9] Dai D, Wang H, Zhu L, Jin H, Wang X. N6-methyladenosine links RNA metabolism to cancer progression. Cell Death & Disease. 2018; 9: 124.

[10] Batista PJ. The RNA modification N6-methyladenosine and its implications in human disease. Genomics, Proteomics & Bioinformatics. 2017; 15: 154–163.

[11] Maity A, Das B. N6-methyladenosine modification in mRNA: machinery, function and implications for health and diseases. Federation of European Biochemical Societies Journal. 2016; 283: 1607–1630.

[12] Wu L, Wu D, Ning J, Liu W, Zhang D. Changes of N6-methyladenosine modulators promote breast cancer progression. BMC Cancer. 2019; 19: 326.

[13] Cai X, Wang X, Cao C, Gao Y, Zhang S, Yang Z, et al. HBXIP-elevated methyltransferase METTL3 promotes the progression of breast cancer via inhibiting tumor suppressor let-7g. Cancer Letters. 2018; 415: 11–19.

[14] Liu L, Liu X, Dong Z, Li J, Yu Y, Chen X, et al. N6-methyladenosine-related genomic targets are altered in breast cancer tissue and associated with poor survival. Journal of Cancer. 2019; 10: 5447–5459.

[15] Zhang C, Zhi WI, Lu H, Samanta D, Chen I, Gabrielson E, et al. Hypoxia-inducible factors regulate pluripotency factor expression by ZNF217-and ALKBH5-mediated modulation of RNA methylation in breast cancer cells. Oncotarget. 2016; 7: 64527–64542.

[16] Lan Q, Liu PY, Haase J, Bell JL, Hüttelmaier S, Liu T. The critical role of RNA m6A methylation in cancer. Cancer Research. 2019; 79: 1285–1292.

[17] Lence T, Paolantoni C, Worpenberg L, Roignant J. Mechanistic insights into m6A RNA enzymes. Gene regulatory mechanisms BBA. Gene regulatory mechanisms. 2019; 1862: 222–229.

[18] Yang Y, Hsu PJ, Chen Y, Yang Y. Dynamic transcriptomic m6A decoration: writers, erasers, readers and functions in RNA metabolism. Cell Research. 2018; 28: 616–624.

[19] Chen Y, Peng C, Chen J, Chen D, Yang B, He B, et al. WTAP facilitates progression of hepatocellular carcinoma via m6A-HuR-dependent epigenetic silencing of ETS1. Molecular Cancer. 2019; 18: 127.

[20] Tanabe A, Tanikawa K, Tsunetomi M, Takai K, Ikeda H, Konno J, et al. RNA helicase YTHDC2 promotes cancer metastasis via the enhancement of the efficiency by which HIF-1α mRNA is translated. Cancer Letters. 2016; 376: 34–42.

[21] Chen J, Sun Y, Xu X, Wang D, He J, Zhou H, et al. YTH domain family 2 orchestrates epithelial-mesenchymal transition/proliferation dichotomy in pancreatic cancer cells. Cell Cycle. 2017; 16: 2259–2271.

[22] Luo S, Tong L. Molecular basis for the recognition of methylated adenines in RNA by the eukaryotic YTH domain. Proceedings of the National Academy of Sciences. 2014; 111: 13834–13839.

[23] Verret B, Cortes J, Bachelot T, Andre F, Arnedos M. Efficacy of PI3K inhibitors in advanced breast cancer. Annals of Oncology. 2019; 30: x12–x20.

[24] Teoh SL, Das S. The role of microRNAs in diagnosis, prognosis, metastasis and resistant cases in breast cancer. Current Pharmaceutical Design. 2017; 23: 1845–1859.

[25] Makena MR, Rao R. Subtype specific targeting of calcium signaling in breast cancer. Cell Calcium. 2020; 85: 102109.

[26] He S, Lu Y, Liu X, Huang X, Keller ET, Qian C, et al. Wnt3a: functions and implications in cancer. Chinese Journal of Cancer. 2015; 34: 50.

[27] Sorrentino A, Federico A, Rienzo M, Gazzerro P, Bifulco M, Ciccodicola A, et al. PR/SET domain family and cancer: novel insights from the cancer genome atlas. International Journal of Molecular Sciences. 2018; 19: 3250.

[28] Liu L, Chen Z, Shi W, Liu H, Pang W. Breast cancer survival prediction using seven prognostic biomarker genes. Oncology Letters. 2019; 18: 2907–2916.

[29] Ettahar A, Ferrigno O, Zhang M, Ohnishi M, Ferrand N, Prunier C, et al. Identification of PHRF1 as a tumor suppressor that promotes the TGF-β cytostatic program through selective release of TGIF-driven PML inactivation. Cell Reports. 2013; 4: 530–541.

[30] Chen WX, Lou M, Cheng L, Qian Q, Xu LY, Sun L, et al. Bioinformatics analysis of potential therapeutic targets among ARHGAP genes in breast cancer. Oncology Letters. 2019; 18: 6017–6025.

[31] Yao J, Yao X, Tian T, Fu X, Wang W, Li S, et al. ABCB5-ZEB1 axis promotes invasion and metastasis in breast cancer cells. Oncology Research. 2017; 25: 305–316.



Abstracted / indexed in

Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,500 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.

Biological Abstracts Easily discover critical journal coverage of the life sciences with Biological Abstracts, produced by the Web of Science Group, with topics ranging from botany to microbiology to pharmacology. Including BIOSIS indexing and MeSH terms, specialized indexing in Biological Abstracts helps you to discover more accurate, context-sensitive results.

Google Scholar Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines.

JournalSeek Genamics JournalSeek is the largest completely categorized database of freely available journal information available on the internet. The database presently contains 39226 titles. Journal information includes the description (aims and scope), journal abbreviation, journal homepage link, subject category and ISSN.

Current Contents - Clinical Medicine Current Contents - Clinical Medicine provides easy access to complete tables of contents, abstracts, bibliographic information and all other significant items in recently published issues from over 1,000 leading journals in clinical medicine.

BIOSIS Previews BIOSIS Previews is an English-language, bibliographic database service, with abstracts and citation indexing. It is part of Clarivate Analytics Web of Science suite. BIOSIS Previews indexes data from 1926 to the present.

Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.

Submission Turnaround Time

Conferences

Top