ArticleCancer biomarkers : section A of Disease markers2020
Survival prediction for patients with lung adenocarcinoma: A prognostic risk model based on gene mutations.
Article in Cancer biomarkers : section A of Disease markers, 2020. 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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Who cites it
7 citing papers in PubMed.
- Genetic characterization of primary and metastatic high-grade serous ovarian cancer tumors reveals distinct features associated with survival.Communications biology · 2023Article
- A radiomics nomogram prediction for survival of patients with "driver gene-negative" lung adenocarcinomas (LUAD).La Radiologia medica · 2023Article
- Ribonucleotide reductase regulatory subunit M2 (RRM2) as a potential sero-diagnostic biomarker in non-small cell lung cancer.PloS one · 2023Article
- Identification of mutational signature for lung adenocarcinoma prognosis and immunotherapy prediction.Journal of molecular medicine (Berlin, Germany) · 2022Article
- A prognostic predictive model constituted with gene mutations ofAnnals of translational medicine · 2021Article
- Article
- Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer.Frontiers in molecular biosciences · 2020Article
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7 authors.
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Abstract
backgroundLung adenocarcinoma is the most common type of lung cancer, and it is one of the most aggressive and rapidly fatal tumor types.
objectiveTo identify a signature mutation genes for prognostic prediction of lung adenocarcinoma.
methodsFour hundred and sixty-two lung adenocarcinoma cases were screened out and downloaded from TCGA database. Mutation data of 18 targeted genes were detected by MuTect. LASSO-COX model was used to screen gene loci, and then a prognosis model was established. Afterwards, 40 clinical patients of lung adenocarcinoma were collected to verify the mutation features and the predictive function of the above prognostic model. The mutations of above 18 genes were sequenced with targeted next generation sequencing (NGS) and analyzed with GATK and MuTect.
resultsTP53 (282, 32.38%), NF1 (82, 9.41%) and EGFR (80, 9.18%) were the top 3 most frequent mutation genes. A total of 7 variables were screened out after lasso-COX analysis (tumor stage, age, diagnostic type, SMARCA4, GNAS, PTCH2, TSC2). SMARCA4, GNAS and TSC2 were a gene mutation signature to predict a poor prognosis.
conclusionsWe established a prognostic model for lung adenocarcinoma, and further concluded that SMARCA4, GNAS and TSC2 were a gene signature which plays a prognostic role.
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