ArticleOncotarget2016
Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A Computational Recognition Analysis of Promising Prognostic Biomarkers in Breast, Colon and Lung Cancer Patients.International journal of molecular sciences · 2025Article
- Pan-cancer analysis of PSCA that is associated with immune infiltration and affects patient prognosis.PloS one · 2024Article
- Searching for New Molecular Targets for Oral Squamous Cell Carcinoma with a View to Clinical Implementation of Precision Medicine.Journal of personalized medicine · 2022Review
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- Identification of oral squamous cell carcinoma markers MUC2 and SPRR1B downstream of TANGO.Journal of cancer research and clinical oncology · 2021Article
- The Role of AKR1B10 in Physiology and Pathophysiology.Metabolites · 2021Review
- Identification of molecular signatures associated with early relapse after complete resection of lung adenocarcinomas.Scientific reports · 2021Article
- Article
- INSL4 as prognostic marker for proliferation and invasiveness in Non-Small-Cell Lung Cancer.Journal of Cancer · 2021Article
- TFF-1 Functions to Suppress Multiple Phenotypes Associated with Lung Cancer Progression.OncoTargets and therapy · 2021Article
- Neuropsychiatric Adverse Events of Montelukast: An Analysis of Real-World Datasets and drug-gene Interaction Network.Frontiers in pharmacology · 2021Article
- Transcriptomic analysis of micropapillary high grade T1 urothelial bladder cancer.Scientific reports · 2020Article
- Upregulation of lncRNA ZFAS1 promotes lung adenocarcinoma progression by sponging miR-1271-5p and upregulating FRS2.Thoracic cancer · 2020Article
- Silencing of LINC00461 enhances radiosensitivity of lung adenocarcinoma cells by down-regulating HOXA10 via microRNA-195.Journal of cellular and molecular medicine · 2020Article
- PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients.Frontiers in cell and developmental biology · 2020Article
- CLCA2 expression is associated with survival among African American women with triple negative breast cancer.PloS one · 2020Article
- Identification of potential diagnostic and therapeutic target genes for lung squamous cell carcinoma.Oncology letters · 2019Article
- Comprehensive clinical implications of homeobox A10 in 3,199 cases of non-small cell lung cancer tissue samples combining qRT-PCR, RNA sequencing and microarray data.American journal of translational research · 2019Article
- Targeted next-generation sequencing reveals recurrence-associated genomic alterations in early-stage non-small cell lung cancer.Oncotarget · 2018Article
- Prognostic significance of AKR1B10 in patients with resected lung adenocarcinoma.Thoracic cancer · 2018Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Risk assessment and treatment choice remains a challenge in early non-small-cell lung cancer (NSCLC). The aim of this study was to identify novel genes involved in the risk of early relapse (ER) compared to no relapse (NR) in resected lung adenocarcinoma (AD) patients using a combination of high throughput technology and computational analysis. We identified 18 patients (n.13 NR and n.5 ER) with stage I AD. Frozen samples of patients in ER, NR and corresponding normal lung (NL) were subjected to Microarray technology and quantitative-PCR (Q-PCR). A gene network computational analysis was performed to select predictive genes. An independent set of 79 ADs stage I samples was used to validate selected genes by Q-PCR.From microarray analysis we selected 50 genes, using the fold change ratio of ER versus NR. They were validated both in pool and individually in patient samples (ER and NR) by Q-PCR. Fourteen increased and 25 decreased genes showed a concordance between two methods. They were used to perform a computational gene network analysis that identified 4 increased (HOXA10, CLCA2, AKR1B10, FABP3) and 6 decreased (SCGB1A1, PGC, TFF1, PSCA, SPRR1B and PRSS1) genes. Moreover, in an independent dataset of ADs samples, we showed that both high FABP3 expression and low SCGB1A1 expression was associated with a worse disease-free survival (DFS).Our results indicate that it is possible to define, through gene expression and computational analysis, a characteristic gene profiling of patients with an increased risk of relapse that may become a tool for patient selection for adjuvant therapy.
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