ArticlePloS one2023
Identification and comprehensive analysis of super-enhancer related genes involved in epithelial-to-mesenchymal transition in lung adenocarcinoma.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Construction and evaluation of a bladder cancer prognosis model based on super-enhancer-associated genes.Discover oncology · 2026Article
- The single-cell transcriptional landscape of the pediatric cystic fibrosis lung from minimally invasive respiratory specimens.Scientific reports · 2026Article
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Authors and funding
1 author at 1 institution in 1 country.
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Abstract
Lung adenocarcinoma is a disease with a high mortality rate, and its mechanism is still unclear. Super-enhancers play an important role in gene expression and also affect the occurrence and development of lung adenocarcinoma, so more and more people pay attention to them. In order to explore the influence of super-enhancer related genes on tumor development, we identified super-enhancer regulated genes related to Epithelial-to-mesenchymal transition (EMT). By analyzing the single-cell sequencing data and the TCGA database of lung adenocarcinoma, we suggest that the up-regulation of TMSB10 in lung adenocarcinoma and its association with poor prognosis may be due to the regulation of super-enhancers during tumor cell metastasis. Using the TCGA lung adenocarcinoma data set, the samples were divided into TMSB10 high-expression group and low-expression group, and it was found that there were significant differences in immune infiltration between the high-expression group and the low-expression group. We parted 513 samples into eight TMSB10-related molecular subtypes using differentially expressed genes of high and low TMSB10 expression groups. We concentrated on four molecular subtypes with the most significant clusters, each with its own characteristics in terms of Immune cell infiltration, prognosis, or pathological stages. In order to predict the four molecular subtypes, we established a prediction model using random forest, and the external test results showed that the prediction accuracy of the model was 0.87. This study may provide potential help for the study of the mechanism of metastasis and invasion of lung adenocarcinoma cells and personalized treatment of lung adenocarcinoma.
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