ArticleScientific reports2022
Identidication of novel biomarkers in non-small cell lung cancer using machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 34 citations in OpenAlex.
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- Integrative bioinformatics identifies NSCLC biomarkers associated with LPS metabolism and circadian disruption.Translational oncology · 2026Article
- The key enzyme PYCR1 in proline metabolism: a dual driver of cancer progression and fibrotic remodeling.Journal of enzyme inhibition and medicinal chemistry · 2025Review
- Inhibitory Effects of Hesperetin and EGCG in the Lung Cancer Progression involves impairment in TOP2A gene expression regulation.Applied biochemistry and biotechnology · 2025Article
- In Search of Ideal Solutions for Cancer Diagnosis: From Conventional Methods to Protein Biomarkers in Liquid Biopsy.Proteomes · 2025Review
- Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer.Journal of applied genetics · 2025Article
- Bioinformatics analysis identifies ZBTB16 as a potential immune biomarker for lung cancer and pan-cancer.Frontiers in genetics · 2025Article
- Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches.PloS one · 2025Article
- Role of four and a half LIM domain protein 1 in tumors (Review).Oncology letters · 2025Review
- Gut metatranscriptomics based de novo assembly reveals microbial signatures predicting immunotherapy outcomes in non-small cell lung cancer.Journal of translational medicine · 2024Article
- Unveiling Potential Targeted Therapeutic Opportunities for Co-Overexpressed Targeting Protein for Xklp2 and Aurora-A Kinase in Lung Adenocarcinoma.Molecular biotechnology · 2024Article
- Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case.BMC medical informatics and decision making · 2024Article
- Differentially Expressed Genes Involved in Primary Resistance to Immunotherapy in Patients with Advanced-Stage Pulmonary Cancer.International journal of molecular sciences · 2024Article
- Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach.Bioinformatics and biology insights · 2024Article
- Advances in Genomic Data and Biomarkers: Revolutionizing NSCLC Diagnosis and Treatment.Cancers · 2023Review
- Roles of tissue-resident immune cells in immunotherapy of non-small cell lung cancer.Frontiers in immunology · 2023Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.
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