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Advancing Plasma Proteomics for the Discovery of Ovarian Cancer Biomarkers

ABSTRACT: The development of ultrasensitive proteomic methods for the detection of proteins — potential tumor biomarkers — remains one of the key challenges in modern biomedicine. Based on data obtained using high-sensitivity technologies and the integration of proteomic and metabolomic layers of genome-encoded information, an expanded list of proteins associated with ovarian cancer progression has been compiled. A panel of ovarian cancer–specific markers, previously unreported in the scientific literature, has been identified. A total of 371 proteins were found to be potentially involved in the pathological process, with 33% detected exclusively by ultrasensitive methods, and 26% discovered through metabolomic associations. Among these, 6% correspond to proteins already recognized as ovarian cancer–specific. Nine potential biomarkers are discovered for the first time, including immunoglobulin variable region proteins (IGLV7-46, IGHV3-15, IGLV2-18, IGHV1-2), as well as ATRN, CPN1, APOF, TGM3, and CRNN, which participate in diverse biological processes such as cellular transport, enzymatic activity, lipid metabolism, and the maintenance of tissue structural integrity. The identified biomarkers are present in blood at concentrations ranging from 10⁻¹² to 10⁻⁶ mol/L. The proposed approach overcomes the sensitivity limitations of conventional proteomic methods and may be adapted for the discovery of candidate markers in other multifactorial diseases.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Gordeeva Arina

LAB HEAD: Arina Gordeeva

PROVIDER: PXD068982 | Pride | 2025-10-06

REPOSITORIES: Pride

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			Action	DRS
	231123UNU_Arina01_20231123192355.raw	Raw
	231123UNU_Arina02.raw	Raw
	231123UNU_Arina02_20231123223626.raw	Raw
	231123UNU_Arina03.raw	Raw
	231123UNU_Arina03_20231124014854.raw	Raw

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Project description:The paper describes a model on the detection of cancer based on cancer and immune biomarkers. Created by COPASI 4.25 (Build 207) This model is described in the article: Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach Raluca Eftimie and and Esraa Hassanein J Transl Med (2018) 16:73 Abstract: Background: Early cancer diagnosis is one of the most important challenges of cancer research, since in many can- cers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers. Methods: Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection. Results: We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solu- tion diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolu- tion of cancer biomarkers, thus allowing us to make predictions on cancer detection times. Conclusions: Combining cancer and immune biomarkers could improve cancer detection times, and any predic- tions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific. Keywords: Ovarian cancer, Mathematical model, CA-125 biomarker, IL-7 biomarker, Cancer detection times This model is hosted on BioModels Database and identified by: MODEL1907050002. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Advancing Plasma Proteomics for the Discovery of Ovarian Cancer Biomarkers

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