ArticleGenetics research2023
Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma.
Article in Genetics research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Development of a risk model for outcome prediction and treatment guidance in lung adenocarcinoma based on hypoxia, glycolysis, and lymph node metastasis-related genes.Discover oncology · 2026Article
- Functional roles of Keratin 6A in disease pathogenesis across cancer and skin disorders.Experimental biology and medicine (Maywood, N.J.) · 2026Review
- Tensins in Cancer: Integration of Their Domain Functions, Context-Dependent Regulation and Biomarker Potential.Biology · 2025Review
- Transcriptomic Signatures in TP53 Positive and Negative Tumor Samples in NSCLC.Current gene therapy · 2025Article
- Exploring the promise of regulator of G Protein Signaling 20: insights into potential mechanisms and prospects across solid cancers and hematological malignancies.Cancer cell international · 2024Review
- Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD. Methods: RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465. Results: A prognostic model based on eight LNM-related genes (ANGPTL4, BARX2, GPR98, KRT6A, PTPRH, RGS20, TCN1, and TNS4) was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues. Conclusion: Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications.
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