ArticleScientific reports2021
Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers.
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Who cites it
124 citing papers in PubMed.
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- Pathomics-based machine learning models for predicting METTL5 expression and prognosis in lung adenocarcinoma.Journal of thoracic disease · 2026Article
- Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.Cell reports methods · 2026Article
- Encounter tool for Shared Decision Making about Adjuvant Treatment of Lung Cancer: Randomized Clinical Trial.Research square · 2026Article
- Molecular characteristics, clonal relatedness and surgical outcomes of pulmonary mixed invasive mucinous and non-mucinous adenocarcinoma: a retrospective cohort study.Molecular biomedicine · 2026Article
- MEAST: a novel framework for analyzing gene set stability in single cell transcriptomics.Scientific reports · 2026Article
- SUSD2 suppresses lung adenocarcinoma tumorigenesis by inducing autophagyEuropean journal of histochemistry : EJH · 2026Article
- Anoikis-Related Genes Signature Contributes to Predicting Prognosis and Response to Immunotherapy in Lung Squamous Cell Carcinoma.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- c-Myc transactivates CFL1 to induce senescence-like phenotype and potentiate the bystander effects for the migration and proliferation in lung cancer cells.Cell death discovery · 2026Article
- Artificial intelligence and machine learning in non-small cell lung cancer: the current state of the science on multi-omic applications.BMC medical research methodology · 2026Review
- Neural Latent Filtering for Gene Discovery in Breast Cancer Subtypes.Biotechnology reports (Amsterdam, Netherlands) · 2026Article
- RPSLearner: A Novel Approach Based on Random Projection and Deep Stacking Learning for Categorizing Non-Small Cell Lung Cancer.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2026Article
- QSOX1: A Mysterious Golgi-Localized Disulfide Bond Catalyst and an Emerging Cancer Regulator.Cancers · 2026Article
- Exploring phenotype-related single-cells through attention-enhanced representation learning.Genome medicine · 2026Article
- USP44 Stabilizes MAOB via Deubiquitination to Inhibit Cisplatin Resistance in Lung Adenocarcinoma.International journal of genomics · 2026Article
- Early prediction of immune checkpoint inhibitor-related pneumonitis in advanced non-small cell lung cancer based on primary tumor delta-radiomics features.Frontiers in immunology · 2026Article
- Integrative multi-omics and mendelian randomization reveal the critical role of pyroptosis in prognosis and therapy of lung squamous cell carcinomas.Frontiers in cell and developmental biology · 2026Article
- Article
- Screening Potential Diagnostic Biomarkers of Lung Adenocarcinoma Related to Immune Infiltration Based on Single-Cell Proteomic Integration Analysis.Canadian respiratory journal · 2026Article
- SNCA Overexpression Induces Apoptosis in Non-Small Cell Lung Cancer via Caspase-Dependent Signaling Pathways.BioMed research international · 2026Article
64 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
Lung cancer is one of the deadliest cancers in the world. Two of the most common subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically different biological signatures, yet they are often treated similarly and classified together as non-small cell lung cancer (NSCLC). LUAD and LUSC biomarkers are scarce, and their distinct biological mechanisms have yet to be elucidated. To detect biologically relevant markers, many studies have attempted to improve traditional machine learning algorithms or develop novel algorithms for biomarker discovery. However, few have used overlapping machine learning or feature selection methods for cancer classification, biomarker identification, or gene expression analysis. This study proposes to use overlapping traditional feature selection or feature reduction techniques for cancer classification and biomarker discovery. The genes selected by the overlapping method were then verified using random forest. The classification statistics of the overlapping method were compared to those of the traditional feature selection methods. The identified biomarkers were validated in an external dataset using AUC and ROC analysis. Gene expression analysis was then performed to further investigate biological differences between LUAD and LUSC. Overall, our method achieved classification results comparable to, if not better than, the traditional algorithms. It also identified multiple known biomarkers, and five potentially novel biomarkers with high discriminating values between LUAD and LUSC. Many of the biomarkers also exhibit significant prognostic potential, particularly in LUAD. Our study also unraveled distinct biological pathways between LUAD and LUSC.
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