{"database":"iProX","file_versions":[],"scores":null,"additional":{"omics_type":["Proteomics"],"submitter":["Xulan Li"],"species":["Homo Sapiens"],"full_dataset_link":["http://www.iprox.org/page/project.html?id=IPX0015658000"],"submitter_email":["lixuelan1225@126.com"],"submitter_affiliation":["The First Affiliated Hospital of Xi'an Jiaotong University"],"sample_protocol":[""],"repository":["iProX"],"data_protocol":[""],"pubmed_abstract":["Ovarian cancer poses a significant clinical challenge due to its asymptomatic onset and poor prognosis, highlighting the critical need for effective early detection strategies. This study developed a framework that integrates serum proteomic profiling with machine learning algorithms. Serum samples from 188 patients and 208 healthy controls were analysed via Matrix-Assisted Laser Desorption/Ionization Time - of - Flight (MALDI - TOF) mass spectrometry, revealing 43 differentially expressed peptides (17 upregulated, 26 downregulated). An ensemble pipeline incorporating eight machine learning algorithms showed favorable discriminatory ability in the study cohort, with the area under the receiver operating characteristic curve (AUC) of the integrated models approaching 1.00 for distinguishing ovarian cancer samples from healthy controls. We identified three consensus biomarkers [mass-to-charge ratio (m/z) = 4211.41, 2881.50, 2662.15] through feature importance analysis, and their diagnostic reliability was supported by receiver operating characteristic curves optimization and decision curve analysis. Interpretability approaches integrating Shapley values and LIME indicated that the model's high performance (AUC ≈ 1) was driven by robust multi-dimensional feature contributions. Cross-referencing with existing datasets suggested the PDE11A as a potential diagnostic biomarker. Collectively, this ensemble machine learning algorithms leveraged on serum proteomics shows promising potential for early detection of ovarian cancer, offering a strategy to mitigate the limitations of single-analyte biomarkers."],"pubmed_title":["Ensemble machine learning algorithms leveraged on serum proteomics for enhanced early detection of ovarian cancer."],"pubmed_authors":["Ding Jie J, Dong Xin X, Cao Li L, Zhao Lihong L, Ding Jiangbo J, Wang Bingju B, Li Yue Y, Liu Zefeng Z, Han Lin L, Li Wen W, Zhang Wei W, Wang Xiaofei X, Guo Bo B, Huang Chen C, Li Xuelan X, Guo Aihong A, Song Lingqin L"],"additional_accession":[]},"is_claimable":false,"name":"The protein mass spectrometry dataset for ovarian cancer research","description":"This project provides a dataset for proteomic research on ovarian cancer based on mass spectrometry technology, including the raw MS/MS data of 396 samples from ovarian cancer patients and healthy controls. This data aims to provide important resources for discovering novel biomarkers of ovarian cancer, elucidating the disease mechanism, and developing new strategies for diagnosis and treatment.","dates":{"publication":"Wed Apr 22 00:00:00 BST 2026"},"accession":"PXD075600","cross_references":{"TAXONOMY":["9606"],"pubmed":["42103875"]}}