Comprehesive DNA methylation profiling reveals novel biomarkers and potential pathways in epithelial ovarian cancer.
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1022800 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26257143_1.fastq.gz | Fastqsanger.gz | |||
| SRR26257143_2.fastq.gz | Fastqsanger.gz | |||
| SRR26257144_1.fastq.gz | Fastqsanger.gz | |||
| SRR26257144_2.fastq.gz | Fastqsanger.gz | |||
| SRR26257145_1.fastq.gz | Fastqsanger.gz |
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Project description:Diagnosis of ovarian cancer at an early stage is the most important determinant of survival. Thus, there is a clear need for novel biomarkers to improve diagnostic and prognostics that may better inform on therapeutic strategies. We have conducted a discovery study using label-free quantitative mass spectrometry (LFQ) to identify potential biomarker candidates in urine from individual ovarian cancer patients. LFQ analyses identified 4394 proteins (16397 peptides) in urine samples (n=20), 23 of which were significantly elevated in the malignant patient group compared to patients with benign disease. To validate these changes, we used Parallel Reaction Monitoring (PRM) to investigate their abundance in an independent cohort (n=20) of patient urine samples. Seven of the ten proteins were significantly enriched in the ovarian cancer patient samples; amongst these were established ovarian cancer markers WFDC2 (HE4) and Mesothelin (MSLN), validating our approach. This is the first application of a LFQ-PRM workflow to identify and validate ovarian cancer-specific biomarkers in urine samples.
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Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.
2017-06-01 | GSE79517 | GEO
Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.
2017-06-01 | GSE79516 | GEO
Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation.
2017-06-01 | GSE79498 | GEO
Project description:Protein biomarkers of early stages of disease are critical for managing the disease and improving outcomes. Unfortunately, discovery of protein biomarkers is hampered by the limited availability of assays for sensitive and reproducible quantification of proteins in complex biological matrices such as blood plasma. In the recent years, Selected Reaction Monitoring (SRM) has emerged as a robust mass spectrometric method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing protein biomarkers for epithelial ovarian cancer, and illustrate the extent to which SRM platform, combined with sound experimental design and statistical analysis, results in robust detection of predictive analytes. First, the biomarker development was guided by a discovery-driven proteomic effort to detect potential N-glycoprotein biomarker candidates in tissue of a genetically stable ovarian cancer mouse model. Next, 65 candidate markers were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive 5-protein signatures for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.
2019-07-02 | MSV000084048 | MassIVE