N6-methylandenosine-related lncRNAs have the prognostic predictive ability for patients with endometrial cancer

Endometrial carcinoma (EC) ranks as the fourth most common cancer among women, with increasing morbidity and mortality rates in recent years. Identifying reliable biomarkers for prognosis and therapeutic targets is crucial for improving the outcomes of EC patients. In this study, we identified 1560 m6A-related lncRNAs using Pearson correlation coefficient based on the cancer genome atlas datasets and GENCODE annotation. Through univariate Cox regression analysis, 187 m6A-related lncRNAs were found to be related to EC prognosis. Using least absolute shrinkage and selection operator Cox analysis, we constructed an m6A-related lncRNA prognostic signature (m6A-LPS) comprising 12 m6A-related lncRNAs. The m6A-LPS demonstrated robust prognostic ability, and the nomogram incorporating m6A-LPS, age, and pathological grade effectively predicted overall survival of EC patients. Furthermore, EC patients in the low-risk group exhibited higher immune cell infiltration and lower tumor purity. A competing endogenous RNA (ceRNA) network was constructed to elucidate the potential function of these m6A-related prognostic lncRNAs. Our study establishes the m6A-LPS as a stable and reliable prognostic tool for EC patients. These findings may contribute to the identification of novel biomarkers and therapeutic targets, offering insights into the molecular mechanisms underlying EC pathogenesis.

Authors

Institutions

Publication Details

Journal
Medicine
Published
2025-12-19
DOI
https://doi.org/10.1097/md.0000000000046657
Primary Topic
RNA modifications and cancer
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

N6-methylandenosine-related lncRNAs have the prognostic predictive ability for patients with endometrial cancer

Danna Su, Ling Shuai, Rokhaya Día, Jiajie She et al.
Medicine
RNA modifications and cancer
article

N6-methylandenosine-related lncRNAs have the prognostic predictive ability for patients with endometrial cancer

Danna Su, Ling Shuai, Rokhaya Día, Jiajie She, Yan Guo, Dongdong Li, Xuemin Liu, Xiaofeng Ye, Qingyu Hu
article en

Abstract

Endometrial carcinoma (EC) ranks as the fourth most common cancer among women, with increasing morbidity and mortality rates in recent years. Identifying reliable biomarkers for prognosis and therapeutic targets is crucial for improving the outcomes of EC patients. In this study, we identified 1560 m6A-related lncRNAs using Pearson correlation coefficient based on the cancer genome atlas datasets and GENCODE annotation. Through univariate Cox regression analysis, 187 m6A-related lncRNAs were found to be related to EC prognosis. Using least absolute shrinkage and selection operator Cox analysis, we constructed an m6A-related lncRNA prognostic signature (m6A-LPS) comprising 12 m6A-related lncRNAs. The m6A-LPS demonstrated robust prognostic ability, and the nomogram incorporating m6A-LPS, age, and pathological grade effectively predicted overall survival of EC patients. Furthermore, EC patients in the low-risk group exhibited higher immune cell infiltration and lower tumor purity. A competing endogenous RNA (ceRNA) network was constructed to elucidate the potential function of these m6A-related prognostic lncRNAs. Our study establishes the m6A-LPS as a stable and reliable prognostic tool for EC patients. These findings may contribute to the identification of novel biomarkers and therapeutic targets, offering insights into the molecular mechanisms underlying EC pathogenesis.

MedicineVol. 104(51)
Hong Kong University of Science and Technology (HK), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN), University of Hong Kong - Shenzhen Hospital (CN), Shenzhen Second People's Hospital (CN)
Openalex Percentile: Top 62%
RNA modifications and cancer
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.