Evidence map›Paper›PMID 41044260›Full record

ArticleScientific reports2025

Identification of prognostic genes associated with phase separation in lung adenocarcinoma and construction of prognostic models.

Hanlin Wang, Qi Zhang, Yiwei Liu, Jiaxin Tang, Xiu Chen, Renquan Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hanlin Wang *Department of Thoracic Surgery, The First Affiliated Hospital of Anhui Medical University, Jixi Road, Shushan District, Hefei City, 230022, Anhui, China.
Qi Zhang *Department of Thoracic Surgery, The First Affiliated Hospital of Anhui Medical University, Jixi Road, Shushan District, Hefei City, 230022, Anhui, China.
Yiwei LiuDepartment of Thoracic Surgery, The First Affiliated Hospital of Anhui Medical University, Jixi Road, Shushan District, Hefei City, 230022, Anhui, China.
Jiaxin TangDepartment of Dermatology, Anhui Provincial Hospital, Hefei, 230001, China.
Xiu ChenDepartment of Thoracic Surgery, The First Affiliated Hospital of Anhui Medical University, Jixi Road, Shushan District, Hefei City, 230022, Anhui, China.
Renquan ZhangDepartment of Thoracic Surgery, The First Affiliated Hospital of Anhui Medical University, Jixi Road, Shushan District, Hefei City, 230022, Anhui, China. zrqahmu@163.com.

Funding

Natural Science Foundation of Anhui Medical Universities KJ2019ZD22Natural Science Foundation of Anhui Province 1808085QH271
6 · The paper itself

Abstract

Lung adenocarcinoma (LUAD) is a common histological subtype of lung cancer, but its prognosis remains poor. Recent studies have suggested that liquid-liquid phase separation-related genes (LRGs) can significantly predict the prognosis of low-grade tumors. Identifying potential LRGs associated with prognosis in LUAD could have significant clinical value for predicting patient outcomes. Data were sourced from public databases. Differentially expressed LRGs (DE-LRGs) were identified through differential expression analysis and by taking intersections between datasets. Regression analysis and the Least Absolute Shrinkage and Selection Operator (Lasso) method were used to shortlist prognostic genes, and a multivariate Cox regression model was developed to create a prognostic risk model. Tumor samples were stratified into high- and low-risk groups based on the median risk score. Independent prognostic analyses and the construction of a nomogram were performed in conjunction with clinical characteristics. Immune characteristics of the two risk groups were also analyzed. Additionally, single-cell RNA sequencing (scRNA-seq) data were used to identify cell clusters and annotate known cell types. A total of 389 DE-LRGs were identified, and 7 prognostic genes were selected to construct the risk model. Patients in the high-risk group exhibited lower survival rates, and the nomogram demonstrated high predictive accuracy. Significant differences were observed in clinical characteristics, immune status, and drug sensitivity between the high- and low-risk groups. Based on scRNA-seq data, 8 distinct cell types were annotated, with the prognostic genes GRIA1 and BCAN showing higher expression levels in fibroblasts and mast cells, respectively. Seven prognostic genes were identified, and the resulting prognostic model accurately predicted the survival outcomes of LUAD patients. This model provides valuable insights for the prognosis and personalized treatment of LUAD patients.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorLung NeoplasmsAgedFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedNomogramsPhase SeparationPrognosisBiomarkers, TumorLiquid-liquid phase separationLung adenocarcinomaPrognosisRisk scoreSingle-cell RNA sequencing

Identifiers

PMID41044260
PMCPMC12494920

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