ArticleBMC bioinformatics2023
Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Advances in research on RNA methylation and its role in the immune microenvironment of gastrointestinal tumors.Frontiers in cell and developmental biology · 2026Review
- Advances in epigenetics of gastric cancer.Oncology reviews · 2026Review
- Identification of an m7G-Related lncRNA Signature for Prognostic Prediction and Immune Landscape Characterization in Early Gastric Cancer.OncoTargets and therapy · 2026Article
- A Narrative Review of Prognostic Gene Signatures in Oral Squamous Cell Carcinoma Using LASSO Cox Regression.Biomedicines · 2025Review
- Article
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11 authors.
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Abstract
Presenting with a poor prognosis, gastric cancer (GC) remains one of the leading causes of disease and death worldwide. Long non-coding RNAs (lncRNAs) regulate tumor formation and have been long used to predict tumor prognosis. N7-methylguanosine (m7G) is the most prevalent RNA modification. m7G-lncRNAs regulate GC onset and progression, but their precise mechanism in GC is unclear. The objective of this research was the development of a new m7G-related lncRNA signature as a biomarker for predicting GC survival rate and guiding treatment. The Cancer Genome Atlas database helped extract gene expression data and clinical information for GC. Pearson correlation analysis helped point out m7G-related lncRNAs. Univariate Cox analysis helped in identifying m7G-related lncRNA with predictive capability. The Lasso-Cox method helped point out seven lncRNAs for the purpose of establishing an m7G-related lncRNA prognostic signature (m7G-LPS), followed by the construction of a nomogram. Kaplan-Meier analysis, univariate and multivariate Cox regression analysis, calibration plot of the nomogram model, receiver operating characteristic curve and principal component analysis were utilized for the verification of the risk model's reliability. Furthermore, q-PCR helped verify the lncRNAs expression of m7G-LPS in-vitro. The study subjects were classified into high and low-risk groups based on the median value of the risk score. Gene enrichment analysis confirmed the constructed m7G-LPS' correlation with RNA transcription and translation and multiple immune-related pathways. Analysis of the clinicopathological features revealed more progressive features in the high-risk group. CIBERSORT analysis showed the involvement of m7G-LPS in immune cell infiltration. The risk score was correlated with immune checkpoint gene expression, immune cell and immune function score, immune cell infiltration, and chemotherapy drug sensitivity. Therefore, our study shows that m7G-LPS constructed using seven m7G-related lncRNAs can predict the survival time of GC patients and guide chemotherapy and immunotherapy regimens as biomarker.
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