ArticleBioMed research international2016
Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 21 citations in OpenAlex.
- Hybrid Approach to Classifying Histological Subtypes of Non-small Cell Lung Cancer (NSCLC): Combining Radiomics and Deep Learning Features from CT Images.Journal of imaging informatics in medicine · 2025Article
- Targeting the COMMD4-H2B protein complex in lung cancer.British journal of cancer · 2023Article
- An immune-related nomogram model that predicts the overall survival of patients with lung adenocarcinoma.BMC pulmonary medicine · 2022Article
- COMMD1, from the Repair of DNA Double Strand Breaks, to a Novel Anti-Cancer Therapeutic Target.Cancers · 2021Article
- Lung cancer histology classification from CT images based on radiomics and deep learning models.Medical & biological engineering & computing · 2021Article
- A Holistic Performance Comparison for Lung Cancer Classification Using Swarm Intelligence Techniques.Journal of healthcare engineering · 2021Article
- Defining COMMD4 as an anti-cancer therapeutic target and prognostic factor in non-small cell lung cancer.British journal of cancer · 2020Article
- Construction of subtype-specific prognostic gene signatures for early-stage non-small cell lung cancer using meta feature selection methods.Oncology letters · 2019Article
- Identification of monotonically differentially expressed genes for non-small cell lung cancer.BMC bioinformatics · 2019Article
- Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures.BioMed research international · 2019Review
- A longitudinal feature selection method identifies relevant genes to distinguish complicated injury and uncomplicated injury over time.BMC medical informatics and decision making · 2018Article
- Article
- Corrigendum to "Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm".BioMed research international · 2018Article
- Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients.Oncology letters · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among non-small cell lung cancer (NSCLC), adenocarcinoma (AC), and squamous cell carcinoma (SCC) are two major histology subtypes, accounting for roughly 40% and 30% of all lung cancer cases, respectively. Since AC and SCC differ in their cell of origin, location within the lung, and growth pattern, they are considered as distinct diseases. Gene expression signatures have been demonstrated to be an effective tool for distinguishing AC and SCC. Gene set analysis is regarded as irrelevant to the identification of gene expression signatures. Nevertheless, we found that one specific gene set analysis method, significance analysis of microarray-gene set reduction (SAMGSR), can be adopted directly to select relevant features and to construct gene expression signatures. In this study, we applied SAMGSR to a NSCLC gene expression dataset. When compared with several novel feature selection algorithms, for example, LASSO, SAMGSR has equivalent or better performance in terms of predictive ability and model parsimony. Therefore, SAMGSR is a feature selection algorithm, indeed. Additionally, we applied SAMGSR to AC and SCC subtypes separately to discriminate their respective stages, that is, stage II versus stage I. Few overlaps between these two resulting gene signatures illustrate that AC and SCC are technically distinct diseases. Therefore, stratified analyses on subtypes are recommended when diagnostic or prognostic signatures of these two NSCLC subtypes are constructed.
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