ArticleEBioMedicine2018
Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors.
Article in EBioMedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Body Biofluids for Minimally-Invasive Diagnostics: Insights, Challenges, Emerging Technologies, and Clinical Potential.Advanced healthcare materials · 2026Review
- Label-free quantitative urinary proteomics for non-invasive biomarker discovery in endometrial cancer.Frontiers in medicine · 2026Article
- Circulating Molecular Biomarkers for the Diagnosis and Monitoring of NSCLC-A Review.International journal of molecular sciences · 2025Review
- Evolving stratification and biomarker discovery in cancer research with technological advancement of proteomics: 35 years and counting.Bioscience reports · 2025Review
- Inflammasomes in lymphocytes as therapeutic targets.Translational oncology · 2025Article
- Identifying Lipid Metabolism-Related Therapeutic Targets and Diagnostic Markers for Lung Adenocarcinoma by Mendelian Randomization and Machine Learning Analysis.Thoracic cancer · 2025Article
- Urinary Proteome and Exosome Analysis Protocol for the Discovery of Respiratory Diseases Biomarkers.Biomolecules · 2025Article
- Application of a risk score model based on tyrosine-related genes in the prognosis and treatment of patients with lung adenocarcinoma.Frontiers in immunology · 2025Article
- Host and bacterial urine proteomics might predict treatment outcomes for immunotherapy in advanced non-small cell lung cancer patients.Frontiers in immunology · 2025Article
- Urinary Proteome Characterization of Stroke-Prone Spontaneously Hypertensive Rats.International journal of molecular sciences · 2024Article
- Label-based comparative proteomics of oral mucosal tissue to understand progression of precancerous lesions to oral squamous cell carcinoma.Biochemistry and biophysics reports · 2024Article
- A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.Scientific reports · 2024Article
- Circulating tumor DNA methylation detection as biomarker and its application in tumor liquid biopsy: advances and challenges.MedComm · 2024Review
- Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features.Journal of translational medicine · 2024Observational
- FGA influences invasion and metastasis of hepatocellular carcinoma through the PI3K/AKT pathway.Aging · 2024Article
- Urine proteome profile of firefighters with exposure to emergency fire-induced smoke: A pilot study to identify potential carcinogenic effects.The Science of the total environment · 2024Article
- Exosomal Proteomics: Unveiling Novel Insights into Lung Cancer.Aging and disease · 2024Review
- A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes.eLife · 2024Article
- Recent progress in mass spectrometry-based urinary proteomics.Clinical proteomics · 2024Review
3 more citing papers are in PubMed but not listed here.
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Development of noninvasive, reliable biomarkers for lung cancer diagnosis has many clinical benefits knowing that most of lung cancer patients are diagnosed at the late stage. For this purpose, we conducted proteomic analyses of 231 human urine samples in healthy individuals (n=33), benign pulmonary diseases (n=40), lung cancer (n=33), bladder cancer (n=17), cervical cancer (n=25), colorectal cancer (n=22), esophageal cancer (n=14), and gastric cancer (n=47) patients collected from multiple medical centers. By random forest modeling, we nominated a list of urine proteins that could separate lung cancers from other cases. With a feature selection algorithm, we selected a panel of five urinary biomarkers (FTL: Ferritin light chain; MAPK1IP1L: Mitogen-Activated Protein Kinase 1 Interacting Protein 1 Like; FGB: Fibrinogen Beta Chain; RAB33B: RAB33B, Member RAS Oncogene Family; RAB15: RAB15, Member RAS Oncogene Family) and established a combinatorial model that can correctly classify the majority of lung cancer cases both in the training set (n=46) and the test sets (n=14-47 per set) with an AUC ranging from 0.8747 to 0.9853. A combination of five urinary biomarkers not only discriminates lung cancer patients from control groups but also differentiates lung cancer from other common tumors. The biomarker panel and the predictive model, when validated by more samples in a multi-center setting, may be used as an auxiliary diagnostic tool along with imaging technology for lung cancer detection.
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