Dataset Information

The protein mass spectrometry dataset for ovarian cancer research

ABSTRACT: 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.

ORGANISM(S): Homo Sapiens

SUBMITTER: Xulan Li

PROVIDER: PXD075600 | iProX | Wed Apr 22 00:00:00 BST 2026

REPOSITORIES: iProX

ACCESS DATA

Publications

Ensemble machine learning algorithms leveraged on serum proteomics for enhanced early detection of ovarian cancer.

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

Scientific reports 20260508

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 pepti ...[more]

PMID: 42103875

Similar Datasets

Project description:Background. Genome-wide expression changes are associated with development of chemoresistance in patients with ovarian cancer (OVCA); the BCL2 antagonist of cell death (BAD) apoptosis pathway may play a role in clinical outcome. Methods. We analyzed specimens and/or genomic data from 1,406 patients and 116 cancer cell lines. Genome-wide expression changes and cisplatin-resistance were evaluated in OVCA cell lines subjected to a total of 144 (cisplatin)-treatment/recovery cycles. Pathway analysis was performed on genes associated with increasing cisplatin-resistance. BAD protein phosphorylation was studied in patient samples and cell lines, and small interfering RNAs (siRNA) used to explore the pathway as a therapeutic target. We evaluated the influence of BAD-pathway expression on chemosensitivity and/or clinical outcome using genomic data from 60 human cancer cell lines and ovarian, breast, colon, and brain cancers from 1,258 patients. Results. The BAD pathway was associated with evolution of OVCA cell line cisplatin-resistance (P<0.001) and resistance of 7 human cancer cell types to 8 cytotoxic agents (P<0.05). OVCA chemoresistance was associated with BAD protein phosphorylation, and targeted siRNA modulation produced corresponding changes in chemosensitivity. Expression of a 47-gene BAD-pathway signature was associated with survival of 1,258 patients with ovarian, breast, colon, and brain cancer. The OVCA BAD-pathway signature survival advantage was independent of surgical cytoreductive status. Conclusions. The BAD apoptosis pathway influences the sensitivity of human cancers to a variety of chemotherapies, likely via modulation of BAD-phosphorylation. The pathway has clinical relevance as a potential biomarker of therapeutic response, patient survival, and as a promising therapeutic target. Twenty-eight (28) advanced-stage serous epithelial ovarian cancers were resected at the time of primary surgery from patients who would receive platinum-based therapy. The tumors were arrayed on Affymetrix HG-U133A GeneChips. The samples were analyzed with respect to the BAD pathway for correlation to overall survival and cisplatin response.

Dataset Information

The protein mass spectrometry dataset for ovarian cancer research

Publications

Ensemble machine learning algorithms leveraged on serum proteomics for enhanced early detection of ovarian cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets