Dataset Information

A Novel Classifier Based on Urinary Proteomics for Distinguishing between Benign and Malignant Ovarian Tumors

ABSTRACT: Preoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types.

ORGANISM(S): Homo Sapiens

SUBMITTER: Maowei Ni

PROVIDER: PXD027695 | iProX | Wed Aug 04 00:00:00 BST 2021

REPOSITORIES: iProX

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Publications

A Novel Classifier Based on Urinary Proteomics for Distinguishing Between Benign and Malignant Ovarian Tumors.

Ni Maowei M Zhou Jie J Zhu Zhihui Z Yuan Jingtao J Gong Wangang W Zhu Jianqing J Zheng Zhiguo Z Zhao Huajun H

Frontiers in cell and developmental biology 20210830

<h4>Background</h4>Preoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types.<h4>Methods</h4>In total, 132 urine samples from 73 malignant and 59 benign cases of ovarian carcinoma were d ...[more]

PMID: 34527671

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Project description:Epithelial ovarian cancer is morphologically and clinically heterogeneous. Transcriptional profiling has revealed molecular subtypes (referred to as “C-signatures”) that correlate to biological as well as clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and to investigate similarities to the intrinsic molecular subtypes of breast cancer. Global gene expression profiling was performed using Illumina's HT12 Bead Arrays and applied to 59 fresh-frozen ovarian tumors. SAM analysis revealed enrichment of cell cycel processes among the malignant tumors, in line with malignant tumors being highly proliferative. The borderline tumors were split between the malignant and benign tumor clusters, indicating that borderline tumors have both malignant and benign features. Furthermore, nearest centroid classification was performed applying previously published gene profiles for the ovarian cancer C-signatures and the intrinsic breast cancer subtypes, respectively, and showed significant correlations between the malignant serous tumors and the highly aggressive C1, C2 and C4 ovarian cancer signatures, and the basal-like breast cancer subtype. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer C3 signature. The borderline tumors, on the other hand, correlated significantly to the Luminal A breast cancer subtype. These findings remained when analyzed in a large, independent dataset. The data in this study link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, including an aggressive phenotype, frequent TP53 mutations and a high degree of genomic instability, and suggest that biomarkers and targeted therapies may overlap between these subsets of ovarian and breast cancers. Finally, the link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers.

Project description:Epithelial ovarian cancer is morphologically and clinically heterogeneous. Transcriptional profiling has revealed molecular subtypes (referred to as M-bM-^@M-^\C-signaturesM-bM-^@M-^]) that correlate to biological as well as clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and to investigate similarities to the intrinsic molecular subtypes of breast cancer. Global gene expression profiling was performed using Illumina's HT12 Bead Arrays and applied to 59 fresh-frozen ovarian tumors. SAM analysis revealed enrichment of cell cycel processes among the malignant tumors, in line with malignant tumors being highly proliferative. The borderline tumors were split between the malignant and benign tumor clusters, indicating that borderline tumors have both malignant and benign features. Furthermore, nearest centroid classification was performed applying previously published gene profiles for the ovarian cancer C-signatures and the intrinsic breast cancer subtypes, respectively, and showed significant correlations between the malignant serous tumors and the highly aggressive C1, C2 and C4 ovarian cancer signatures, and the basal-like breast cancer subtype. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer C3 signature. The borderline tumors, on the other hand, correlated significantly to the Luminal A breast cancer subtype. These findings remained when analyzed in a large, independent dataset. The data in this study link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, including an aggressive phenotype, frequent TP53 mutations and a high degree of genomic instability, and suggest that biomarkers and targeted therapies may overlap between these subsets of ovarian and breast cancers. Finally, the link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers. Total RNA obtained from serous ovarian adenocarcinomas, adenomas and borderline tumors. Gene expression profiling using Illumina's HT12 v4 bead arrays. Application of ovarian cancer molecular subtypes and intrinsic breast cancer subtypes using nearest centroid classification. KRAS and BRAF mutation analyses in the malignant and borderline tumors.

Dataset Information

A Novel Classifier Based on Urinary Proteomics for Distinguishing between Benign and Malignant Ovarian Tumors

Publications

A Novel Classifier Based on Urinary Proteomics for Distinguishing Between Benign and Malignant Ovarian Tumors.

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