ArticleScientific reports2023
Quantifiable peptide library bridges the gap for proteomics based biomarker discovery and validation on breast cancer.
Article in Scientific reports, 2023. 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, 16 citations in OpenAlex.
- From Machine Learning-Enhanced Proteomics to a Validated Diagnostic Model: A Pipeline for Breast Cancer Biomarker Discovery via Independent and Transcriptomic Corroboration.Bioengineering (Basel, Switzerland) · 2026Article
- Proteomic Biomarker Discovery in Breast Cancer: Advances, Challenges, and Translational Prospects.Journal of biochemical and molecular toxicology · 2026Review
- Integrated triplex LFIA platform for decentralized molecular subtyping of breast cancer.RSC advances · 2026Article
- Multicenter Assessment of Postoperative MastocheckCancers · 2026Article
- Rapid peptide analysis in dried bloodspots to identify novel markers for newborn screening for congenital hypothyroidism.Scientific reports · 2026Article
- Identification of serum peptide biomarkers for cholangiocarcinoma diagnosis and staging via MALDI-TOF MS and LC-MS/MS.British journal of cancer · 2026Article
- Article
- The Potential of Nanopore Technologies in Peptide and Protein Sensing for Biomarker Detection.Biosensors · 2025Review
- Serum peptide biomarkers by MALDI-TOF MS coupled with machine learning for diagnosis and classification of hepato-pancreato-biliary cancers.Scientific reports · 2025Article
- Association of circulating immuno-oncology biomarkers with breast cancer risk: insights from two prospective cohorts.NPJ precision oncology · 2025Article
- Circulating collagen type I fragments as specific biomarkers of cardiovascular outcome risk: Where are the opportunities?Matrix biology : journal of the International Society for Matrix Biology · 2025Review
- Discovery of novel serum peptide biomarkers for cholangiocarcinoma recurrence through MALDI-TOF MS and LC-MS/MS peptidome analysis.Scientific reports · 2025Article
- In Silico Analysis of Non-Conventional Oxidative Stress-Related Enzymes and Their Potential Relationship with Carcinogenesis.Antioxidants (Basel, Switzerland) · 2024Article
- Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies.ACS pharmacology & translational science · 2024Review
- Transcriptome Analysis in Mexican Adults with Acute Lymphoblastic Leukemia.International journal of molecular sciences · 2024Article
- Article
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Authors and funding
10 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mass spectrometry (MS) based proteomics is widely used for biomarker discovery. However, often, most biomarker candidates from discovery are discarded during the validation processes. Such discrepancies between biomarker discovery and validation are caused by several factors, mainly due to the differences in analytical methodology and experimental conditions. Here, we generated a peptide library which allows discovery of biomarkers in the equal settings as the validation process, thereby making the transition from discovery to validation more robust and efficient. The peptide library initiated with a list of 3393 proteins detectable in the blood from public databases. For each protein, surrogate peptides favorable for detection in mass spectrometry was selected and synthesized. A total of 4683 synthesized peptides were spiked into neat serum and plasma samples to check their quantifiability in a 10 min liquid chromatography-MS/MS run time. This led to the PepQuant library, which is composed of 852 quantifiable peptides that cover 452 human blood proteins. Using the PepQuant library, we discovered 30 candidate biomarkers for breast cancer. Among the 30 candidates, nine biomarkers, FN1, VWF, PRG4, MMP9, CLU, PRDX6, PPBP, APOC1, and CHL1 were validated. By combining the quantification values of these markers, we generated a machine learning model predicting breast cancer, showing an average area under the curve of 0.9105 for the receiver operating characteristic curve.
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