Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers
Abstract
:1. Introduction
2. Protein Biomarkers Associated with OC
3. Candidate Markers for OC Diagnosis in Circulatory Fluids
4. Proteomic Profiling of Solid Tumors and Clinically Relevant Protein Markers
5. Identification of PTMs in Ovarian Cancer and Their Clinical Implications
6. Ovarian Cancer Drug Resistance and Proteomics
7. High-Density Protein Microarrays and OC Biomarkers
8. Perspective on Existing Biomarker Panels: Their Utility, Limitations, and Future Scope
9. Conclusions and Outlook
Author Contributions
Funding
Conflicts of Interest
References
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Marker(s) | Gene ID (If Applicable) | Source | Type (Circulatory/Tumor-Specific | Utility (Early/Late-Stage Pre/Post-Menopausal) | Platform & Study Design | Reference |
---|---|---|---|---|---|---|
CA-125 | MUC16 | Serum/Plasma | Serum marker-high molecular weight glycoprotein | Monitoring response to chemotherapy and disease activity in clinical trials. | Immunoassays from patient sera using OC125 and M11 antibodies | [18,19,20,21] |
HE4 | WFDC2 | Serum/Plasma | HE4 is also a secreted glycoprotein that is overexpressed in OCs | FDA approved biomarker for monitoring disease activity | Immunoassays from patient sera | [19,20] |
MCSF and LPA | CSF1 | Blood/Tumor tissue ascites | Components of the tumor microenvironment | LPA is elevated in the blood, tumor tissue, and ascites. LPA also influences tumor-associated macrophages, which can be used as a therapeutic target | Metanalysis from several studies mostly based on the immunoassay-based determination of markers | [22] |
CART analysis: CA-125, OVX1, LASA, CA 15-3, CA 72-4) | MUC16, ovx1, MUC1 | Serum | Circulatory markers as well as tumor microenvironment components | CART analysis (classification and regression tree analysis), uses the sequential analysis of marker concentrations with 5 markers (CA-125, OVX1, LASA, CA 15-3, CA 72-4) to yield a sensitivity of 90.6% and a specificity of 93.2% | Initial discovery-based studies using radioimmunoassay. Multiple marker analysis performed on ANN based machine learning algorithms | [23,24,25] |
A three-panel marker: Apolipoprotein I TransthyretinInter-α-trypsin inhibitor heavy chain H4 (cleavage fragment) | APOA1, TTR, ITIH4 | Serum | Components of the circulatory biofluids | Useful for detection of early-stage patients, exhibits higher sensitivity (74%) over CA125 alone (52%) | The study employed SELDI-TOF technology with the ProteinChip Biomarker System (Ciphergen Biosystems) | [25,26] |
CT45 | CT45A1, CT45A | Tumor tissue (FFPE blocks) | Tumor marker | Reported to be an independent prognostic factor that is associated with a doubling of disease-free survival in advanced-stage HGSCs | Quantitative proteomics on FFPE tumor samples derived from 25 chemotherapy-naive patients with advanced-stage HGSCs | [27] |
MUCIN-16, SPINT1, TACSTD2, CLEC6A, ICOSLG, MSMB, PROK1, CDH3, WFDC2, KRT19, and FR-alpha | MUCIN-16, SPINT1, TACSTD2, CLEC6A, ICOSLG, MSMB, PROK1, CDH3, WFDC2, KRT19, and FOLR | Plasma | Circulatory markers | Potentially useful for improved diagnosis of adnexal ovarian mass and identification of potential cases for specialized referrals | PEA was implemented utilizing oligonucleotide antibody probes to measure protein abundance | [17] |
Marker(s) | Source | PTM Details/Drug Resistance/Other | Platform | Reference |
---|---|---|---|---|
FAK, PTK2B | Ovarian cell lines | Phosphorylated | Protein microarrays: HuProt arrays | [83] |
POSTN, SERPINA1, HYO1 | HGSC tumor tissues | Glycosylation | SPEG for glycosite analysis & intact glycopeptides for investigation of IGPs followed by LC MS/MS | [72] |
TGFBI, OPN | Ovarian cell lines | Drug resistance against cisplatin and paclitaxel | Protein microarray: Affymetrix GeneChip Human Genome U219 microarrays | [91] |
COL5A2, LPL | Exosomes derived from normal human ovarian surface & cancer cell line | Elevated levels seen in exosomes derived from cancer cells | Exosome isolation followed by LC MS/MS | [92] |
HSPA1 (Hsp70) | Tumor effusions from HGSCs | Methylation status of Lys561 | LC MS/MS analysis | [84] |
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Mukherjee, S.; Sundfeldt, K.; Borrebaeck, C.A.K.; Jakobsson, M.E. Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers. Proteomes 2021, 9, 25. https://doi.org/10.3390/proteomes9020025
Mukherjee S, Sundfeldt K, Borrebaeck CAK, Jakobsson ME. Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers. Proteomes. 2021; 9(2):25. https://doi.org/10.3390/proteomes9020025
Chicago/Turabian StyleMukherjee, Shuvolina, Karin Sundfeldt, Carl A. K. Borrebaeck, and Magnus E. Jakobsson. 2021. "Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers" Proteomes 9, no. 2: 25. https://doi.org/10.3390/proteomes9020025