数据表2：亚型聚类分析揭示了m6A和铜凋亡相关lncRNA与食管癌预后、免疫微环境和治疗敏感性的相关性

数据集获取（Figshare）

• 源站标题：Data Sheet 2_Subtype cluster analysis unveiled the correlation between m6A- and cuproptosis-related lncRNAs and the prognosis, immune microenvironment, and treatment sensitivity of esophageal cancer.zip
• DOI：10.3389/fimmu.2025.1539630.s003
• 发布日期：2025-02-17
• 文件数：1 个
• 文件总体积：2.94 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Subtype_cluster_analysis_unveiled_the_correlation_between_m6A-_and_cuproptosis-related_lncRNAs_and_the_prognosis_immune_microenvironment_and_treatment_sensitivity_of_esophageal_cancer_zip/28427504

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curl -L -o "Data Sheet 2_Subtype cluster analysis unveiled the correlation between m6A- and cuproptosis-related lncRNAs and the prognosis, immune microenvironment.zip" "https://ndownloader.figshare.com/files/52398290"

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• Data Sheet 2_Subtype cluster analysis unveiled the correlation between m6A- and cuproptosis-related lncRNAs and the prognosis, immune microenvironment.zip（2.94 MB）

文件总体积 2.94 MB（源站 API 实测）。

数据说明

ObjectiveEsophageal cancer (EC) is characterized by a high degree of malignancy and poor prognosis. N6-methyladenosine (m6A), a prominent post-transcriptional modification of mRNA in mammalian cells, plays a pivotal role in regulating various cellular and biological processes. Similarly, cuproptosis has garnered attention for its potential implications in cancer biology. This study seeks to elucidate the impact of m6A- and cuproptosis-related long non-coding RNAs (m6aCRLncs) on the prognosis of patients with EC.
MethodsThe EC transcriptional data and corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database, comprising 11 normal samples and 159 EC samples. Data on 23 m6A regulators and 25 cuproptosis-related genes were sourced from the latest literature. The m6aCRLncs linked to EC were identified through co-expression analysis. Differentially expressed m6aCRLncs associated with EC prognosis were screened using the limma package in R and univariate Cox regression analysis. Subtype clustering was performed to classify EC patients, enabling the investigation of differences in clinical outcomes and immune microenvironment across patient clusters. A risk prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression. Its robustness was evaluated through survival analysis, risk stratification curves, and receiver operating characteristic (ROC) curves. Additionally, the model’s applicability across various clinical features and molecular subtypes of EC patients was assessed. To further explore the model’s utility in predicting the immune microenvironment, single-sample gene set enrichment analysis (ssGSEA), immune cell infiltration analysis, and immune checkpoint differential expression analysis were conducted. Drug sensitivity analysis was performed to identify potential therapeutic agents for EC. Finally, the mRNA expression levels of m6aCRLncs in EC cell lines were validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR).
ResultsWe developed a prognostic risk model based on five m6aCRLncs, namely ELF3-AS1, HNF1A-AS1, LINC00942, LINC01389, and MIR181A2HG, to

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2025.1539630.s003（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Subtype_cluster_analysis_unveiled_the_correlation_between_m6A-_and_cuproptosis-related_lncRNAs_and_the_prognosis_immune_microenvironment_and_treatment_sensitivity_of_esophageal_cancer_zip/28427504）。