ArticleProteome science2012
A critical assessment of SELDI-TOF-MS for biomarker discovery in serum and tissue of patients with an ovarian mass.
Article in Proteome science, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Advances in Detection of Kidney Transplant Injury.Molecular diagnosis & therapy · 2019Review
- Complementarity of Matrix- and Nanostructure-Assisted Laser Desorption/Ionization Approaches.Nanomaterials (Basel, Switzerland) · 2019Review
- Elevation of glycoprotein nonmetastatic melanoma protein B in type 1 Gaucher disease patients and mouse models.FEBS open bio · 2016Article
- Proteomic study of benign and malignant pleural effusion.Journal of cancer research and clinical oncology · 2016Article
- Construction of protein profile classification model and screening of proteomic signature of acute leukemia.International journal of clinical and experimental pathology · 2014Article
- Potential serum biomarkers for glioblastoma diagnostic assessed by proteomic approaches.Proteome science · 2014Article
- Article
- The proteomics big challenge for biomarkers and new drug-targets discovery.International journal of molecular sciences · 2012Review
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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backgroundLess than 25% of patients with a pelvic mass who are presented to a gynecologist will eventually be diagnosed with epithelial ovarian cancer. Since there is no reliable test to differentiate between different ovarian tumors, accurate classification could facilitate adequate referral to a gynecological oncologist, improving survival. The goal of our study was to assess the potential value of a SELDI-TOF-MS based classifier for discriminating between patients with a pelvic mass.
methodsOur study design included a well-defined patient population, stringent protocols and an independent validation cohort. We compared serum samples of 53 ovarian cancer patients, 18 patients with tumors of low malignant potential, and 57 patients with a benign ovarian tumor on different ProteinChip arrays. In addition, from a subset of 84 patients, tumor tissues were collected and microdissection was used to isolate a pure and homogenous cell population.
resultsDiagonal Linear Discriminant Analysis (DLDA) and Support Vector Machine (SVM) classification on serum samples comparing cancer versus benign tumors, yielded models with a classification accuracy of 71-81% (cross-validation), and 73-81% on the independent validation set. Cancer and benign tissues could be classified with 95-99% accuracy using cross-validation. Tumors of low malignant potential showed protein expression patterns different from both benign and cancer tissues. Remarkably, none of the peaks differentially expressed in serum samples were found to be differentially expressed in the tissue lysates of those same groups.
conclusionAlthough SELDI-TOF-MS can produce reliable classification results in serum samples of ovarian cancer patients, it will not be applicable in routine patient care. On the other hand, protein profiling of microdissected tumor tissue may lead to a better understanding of oncogenesis and could still be a source of new serum biomarkers leading to novel methods for differentiating between different histological subtypes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.