ArticleBMC medical genomics2022
Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer.
Article in BMC medical genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 14 citations in OpenAlex.
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- Causal inference of glucocorticoid signaling in non-small cell lung cancer: integrating Mendelian randomization, single-cell transcriptomics, and imaging data.NPJ precision oncology · 2026Article
- Review
- PRSS3 is a potential prognostic biomarker for lung adenocarcinoma.Translational cancer research · 2025Article
- FAT1 mutation-related signature predicts survival risk and tumor immunogenicity in lung adenocarcinoma.Frontiers in genetics · 2025Article
- A novel prognostic signature based on smoking-associated genes for predicting prognosis and immune microenvironment in NSCLC smokers.Cancer cell international · 2024Article
- Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring.Biomedicines · 2023Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80-85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).
methodsNSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan-Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature.
resultsIn total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC.
conclusionOur study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.
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