ArticleFrontiers in oncology2021
Identification of a N6-Methyladenosine (m6A)-Related lncRNA Signature for Predicting the Prognosis and Immune Landscape of Lung Squamous Cell Carcinoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Identification and validation of an m1A-score model to predict outcomes and immunomodulation in lung squamous cell carcinoma by integrated analysis of single-cell and bulk RNA sequencing.Discover oncology · 2026Article
- Development and validation of an m6A and autophagy related lncRNAs signature for predicting survival and modulating the immune microenvironment in esophageal squamous cell carcinoma.Frontiers in immunology · 2026Article
- Mechanistic and therapeutic insights into the function of N6-methyladenosine in arthritic diseases.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Review
- Subtype cluster analysis unveiled the correlation between m6A- and cuproptosis-related lncRNAs and the prognosis, immune microenvironment, and treatment sensitivity of esophageal cancer.Frontiers in immunology · 2025Article
- Prognostic signature and immune landscape of 5-methylcytosine-related long non-coding RNAs in gastric cancer.Heliyon · 2024Article
- Article
- Non-coding RNA-Mediated N6-Methyladenosine (mNon-coding RNA research · 2024Review
- Establishment of a prognosis prediction model for lung squamous cell carcinoma related to PET/CT: basing on immunogenic cell death-related lncRNA.BMC pulmonary medicine · 2023Article
- Novel insights into the multifaceted roles of mBiomarker research · 2023Review
- Construction and validation of a nomogram based on N6-Methylandenosine-related lncRNAs for predicting the prognosis of non-small cell lung cancer patients.Cancer medicine · 2023Article
- Targeting immune cell types of tumor microenvironment to overcome resistance to PD-1/PD-L1 blockade in lung cancer.Frontiers in pharmacology · 2023Review
- The lncRNA epigenetics: The significance of m6A and m5C lncRNA modifications in cancer.Frontiers in oncology · 2023Review
- Identification and validation of N6-methyladenosine (m6A)-related lncRNAs signature for predicting the prognosis of laryngeal carcinoma, especially for smoking patients.Frontiers in genetics · 2023Article
- A novel pyroptosis-related lncRNA prognostic signature associated with the immune microenvironment in lung squamous cell carcinoma.BMC cancer · 2022Article
- Article
- Identification and Validation of an m6A-Related LncRNA Signature to Predict Progression-Free Survival in Colorectal Cancer.Pathology oncology research : POR · 2022Article
- mDisease markers · 2022Article
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5 authors.
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Abstract
backgroundm6A-related lncRNAs emerged as potential targets for tumor diagnosis and treatment. This study aimed to identify m6A-regulated lncRNAs in lung squamous cell carcinoma (LUSC) patients. MATERIALS AND
methodsRNA sequencing and the clinical data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The m6A-related lncRNAs were identified by using Pearson correlation assay. Univariate and multivariate Cox regression analyses were utilized to construct a risk model. The performance of the risk model was validated using Kaplan-Meier survival analysis and receiver operating characteristics (ROC). Immune estimation of LUSC was downloaded from TIMER, and the correlations between the risk score and various immune cells infiltration were analyzed using various methods. Differences in immune functions and expression of immune checkpoint inhibitors and m6A regulators between high-risk and low-risk groups were further explored. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were utilized to explore the biological functions of AL122125.1.
resultsA total of 351 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs demonstrated prognostic values. A further multivariate Cox regression assay constructed a risk model consisting of two lncRNAs (AL122125.1 and HORMAD2-AS1). The Kaplan-Meier analysis and area under the curve indicated that this risk model could be used to predict the prognosis of LUSC patients. The m6A-related lncRNAs were immune-associated. There were significant correlations between risk score and immune cell infiltration, immune functions, and expression of immune checkpoint inhibitors. Meanwhile, there were significant differences in the expression of m6A regulators between the high- and low-risk groups. Moreover, GO and KEGG analyses revealed that the upregulated expression of AL122125.1 was tumor-related.
conclusionIn this study, we constructed an m6A-related lncRNA risk model to predict the survival of LUSC patients. This study could provide a novel insight to the prognosis and treatment of LUSC patients.
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