1. Academic Validation
  2. Identification of N6-Methyladenosine-Associated lncRNAs and Analysis of Prognostic Signature in Breast Cancer

Identification of N6-Methyladenosine-Associated lncRNAs and Analysis of Prognostic Signature in Breast Cancer

  • Biochem Genet. 2024 Jul 23. doi: 10.1007/s10528-024-10889-0.
Yun Gu # 1 Min Xu # 1 Wangfei Wu # 1 Zhifang Ma 2 Weiguang Liu 3
Affiliations

Affiliations

  • 1 Department of Pathology, Nanjing Women and Children's Healthcare Hospital, Women's Hospital of Nanjing Medical University, Tianfei Road 123th, Nanjing, 210004, Jiangsu, China.
  • 2 Department of Pathology, Nanjing Women and Children's Healthcare Hospital, Women's Hospital of Nanjing Medical University, Tianfei Road 123th, Nanjing, 210004, Jiangsu, China. mzfhj522@163.com.
  • 3 Department of Molecular and Cellular Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, 77030, USA. wliu17@mdanderson.org.
  • # Contributed equally.
Abstract

Breast Cancer represents the predominant malignant neoplasm in women, posing significant threats to both life and health. N6-methyladenosine (m6A) methylation, the most prevalent RNA modification, plays a crucial role in Cancer development. This study aims to delineate the prognostic implications of m6A-associated long non-coding RNAs (m6AlncRNAs) and identify potential m6AlncRNA candidates as novel therapeutic targets for breast Cancer. Through univariate COX, Least Absolute Shrinkage and Selection Operator and multiple COX regression analysis, m6AlncRNA was analyzed and a risk-prognosis model was constructed. Kaplan-Meier analysis, principal component analysis and nomogram were used to evaluate the risk model. Finally, we screened candidate lncRNAs and validated them in breast Cancer cell lines. m6AlncRNAs were stratified into three subtypes, and their associations with survival outcomes and immune infiltrating capacities were systematically analyzed. Subsequently, breast Cancer patients were stratified into high and low-risk groups based on median risk scores, revealing distinct clinical characteristics, tumor immunoinvasive profiles, tumor mutation burden, and survival probabilities. Additionally, a prognostic model was established, highlighting three promising candidate lncRNAs: ECE1-AS1, NDUFA6-DT, and COL4A2-AS1. This study investigated the prognostic implications of m6A-associated long non-coding RNAs (m6AlncRNAs) and developed a prognostic risk model to identify three potential m6AlncRNA candidates. These findings provide valuable insights into the potential application of these m6AlncRNAs in guiding immunotherapeutic strategies for breast Cancer.

Keywords

Breast cancer; Prognosis signature; lncRNAs; m6A.

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