Proteomics reveal serum biomarkers for early diagnosis in premature ovarian insufficiency
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ABSTRACT:
ORGANISM(S): Homo Sapiens (human)
SUBMITTER: Xiaomin Hu
PROVIDER: PXD078636 | JPOST Repository | Wed May 27 00:00:00 GMT+01:00 2026
REPOSITORIES: jPOST
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Project description:Proteomics reveal serum biomarkers for early diagnosis in premature ovarian insufficiency
2026-05-10 | PXD078220 |
Project description:We aim to explore the common gene between proteomics and transcriptomics and pathway mechanism related to layer classification. Our results will provide several potential biomarkers in early diagnosis of ovarian cancer.Transcriptomics methods were performed to compare the differences between early ovarian cancer specimens (n = 3, stage Ⅰ / Ⅱ) and advanced ovarian cancer specimens (n = 6, stage Ⅲ / Ⅳ), and the similarities and differences of the data obtained were analyzed.
2021-11-30 | GSE161015 | GEO
Project description:Objective: The immune system is a key player in fighting cancer. Thus, we seeked to identify a molecular ‘immune response signature’ indicating the presence of epithelial ovarian cancer (EOC) and to combine this with a serum protein biomarker panel to increase the specificity and sensitivity for earlier detection of EOC. Methods: Comparing the expression of 32,000 genes in a leukocytes fraction from 48 EOC patients and 20 controls, three uncorrelated shrunken centroid models were selected, comprised of 7, 14, and 6 genes. A second selection step using RT-qPCR data and significance analysis of microarrays yielded 13 genes which were finally used in 343 samples (90 healthy, 6 cystadenoma, 8 low malignant potential tumor, and 239 EOC patients). Using new 65 controls and 224 EOC patients the abundances of six plasma proteins was determined and used in combination with the expression values from the 13 genes for diagnosis of EOC. Results: Combined diagnostic models using either each five gene expression and plasma protein abundance values or 13 gene expression and six plasma protein abundance values can discriminate controls from patients with EOC with Receiver Operator Characteristics Area Under the Curve values of 0.998 and bootstrap .632+ validated classification errors of 3.1% and 2.8%, respectively. The sensitivities were 97.8% and 95.6%, respectively, at a set specificity of 99.6%. Conclusion: The combination of expression based and plasma protein based biomarkers in one diagnostic model increases the sensitivity and the specificity significantly. Such a diagnostic test may allow earlier diagnosis of epithelial ovarian cancer. The expression of a blood cell fraction from 20 healthy controls and 48 patients with epithelial ovarian cancer was compared.
2011-09-01 | E-GEOD-31682 | biostudies-arrayexpress
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:We used MethylCap-seq and RRBS to profile methylomes of purified human ovarian granulosa cells. Genomic DNA methylation patterns in ovarian granulosa cells were compared between two groups of women: i) oocyte donors (n=20) who were young (age 26 ± 2.2 years) and had robust response to ovarian stimulation during assisted reproductive technology (ART) (mean number of oocytes retrieved = 25); versus ii) poor responders (n=20) who were older (age 40 ± 2.3 years) and responded poorly to ovarian stimulation during ART (oocytes retrieved ≤4 and peak estradiol level ≤ 1000 pg/ml). The first group served as healthy control. The second group represented the majority of women in their early 40s who have the natural age-related decline of ovarian functions and therefore respond poorly to ovarian stimulation during ART. We compared DNA methylomes in ovarian granulosa cells from oocyte donors versus poor responders using two approaches: MethylCap-seq for broader genomic coverage, and RRBS for absolute quantification. Due to very limited amount of materials available from each poor responder, samples containing equal amounts of granulosa cell DNA were pooled from 10 individuals in each group. A second set of experiments pooling granulosa cell DNA samples from independent donor and poor responder groups (ten individuals each) was then performed.
2015-10-14 | GSE63470 | GEO
Project description:Objective: The immune system is a key player in fighting cancer. Thus, we seeked to identify a molecular ‘immune response signature’ indicating the presence of epithelial ovarian cancer (EOC) and to combine this with a serum protein biomarker panel to increase the specificity and sensitivity for earlier detection of EOC. Methods: Comparing the expression of 32,000 genes in a leukocytes fraction from 48 EOC patients and 20 controls, three uncorrelated shrunken centroid models were selected, comprised of 7, 14, and 6 genes. A second selection step using RT-qPCR data and significance analysis of microarrays yielded 13 genes which were finally used in 343 samples (90 healthy, 6 cystadenoma, 8 low malignant potential tumor, and 239 EOC patients). Using new 65 controls and 224 EOC patients the abundances of six plasma proteins was determined and used in combination with the expression values from the 13 genes for diagnosis of EOC. Results: Combined diagnostic models using either each five gene expression and plasma protein abundance values or 13 gene expression and six plasma protein abundance values can discriminate controls from patients with EOC with Receiver Operator Characteristics Area Under the Curve values of 0.998 and bootstrap .632+ validated classification errors of 3.1% and 2.8%, respectively. The sensitivities were 97.8% and 95.6%, respectively, at a set specificity of 99.6%. Conclusion: The combination of expression based and plasma protein based biomarkers in one diagnostic model increases the sensitivity and the specificity significantly. Such a diagnostic test may allow earlier diagnosis of epithelial ovarian cancer.
2011-09-02 | GSE31682 | 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: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:Epithelial ovarian cancer (EOC) are a heterogeneous collection of malignancies, each with their own developmental origins, clinical behavior and molecular profile. With less than 5% of EOC cases, mucinous ovarian carcinoma is a rare form with a poor prognosis with a 5-year survival of 11% for advanced stages (III/IV). Clinically, due to the large size and heterogeneity of mu-cinous tumors, Malignant forms are difficult to distinguish from Borderline (15%) and benign (80%) forms with a better prognosis. Improving their diagnosis is therefore a challenge with regard to the risk of under-treating a malignant form or of unnecessarily undertaking radical surgical excision. The involvement of microRNAs (miRNAs) in cancer and their potential as biomarkers of diagnosis are becoming increasingly recognized. In this study, miRNA expres-sion profiling generates using FFPE patient samples followed by qRT-PCR validation allows to identify 10 down-regulated and 5 up-regulated miRNAs between Malignant and Borderline tumors. To overcome issues linked to data normalization and to improve the accuracy of the results, a ratio analysis combining up-regulated and down-regulated miRNAs was used. Alt-hough 21/50 miRNA expression ratio were significantly different between Malignant and Bor-derline tumor samples, any ratio could perfectly discriminate the two groups. However, a combination of 14 miRNAs expression ratio (double ratio) showed high discriminatory poten-tial, with 100% of accuracy in distinguishing Malignant and Borderline ovarian tumors, and suggests that miRNAs may hold significant clinical potential as a diagnostic tool. Epithelial ovarian cancer (EOC) are a heterogeneous collection of malignancies, each with their own developmental origins, clinical behavior and molecular profile. With less than 5% of EOC cases, mucinous ovarian carcinoma is a rare form with a poor prognosis with a 5-year survival of 11% for advanced stages (III/IV). Clinically, due to the large size and heterogeneity of mu-cinous tumors, Malignant forms are difficult to distinguish from Borderline (15%) and benign (80%) forms with a better prognosis. Improving their diagnosis is therefore a challenge with regard to the risk of under-treating a malignant form or of unnecessarily undertaking radical surgical excision. The involvement of microRNAs (miRNAs) in cancer and their potential as biomarkers of diagnosis are becoming increasingly recognized. In this study, miRNA expres-sion profiling generates using FFPE patient samples followed by qRT-PCR validation allows to identify 10 down-regulated and 5 up-regulated miRNAs between Malignant and Borderline tumors. To overcome issues linked to data normalization and to improve the accuracy of the results, a ratio analysis combining up-regulated and down-regulated miRNAs was used. Alt-hough 21/50 miRNA expression ratio were significantly different between Malignant and Bor-derline tumor samples, any ratio could perfectly discriminate the two groups. However, a combination of 14 miRNAs expression ratio (double ratio) showed high discriminatory poten-tial, with 100% of accuracy in distinguishing Malignant and Borderline ovarian tumors, and suggests that miRNAs may hold significant clinical potential as a diagnostic tool.
2023-10-19 | GSE245725 | GEO
Project description:Spermidine is involved in extensive cellular processes, including mammalian oocyte development. Wheat germ is a natural source of polyamines and contains high concentrations of spermidine. No studies have assessed the effects and mechanisms of action of wheat germ spermidine in regulating mammalian ovarian functions. Herein, a dietary experiment was conducted in mice administered wheat germ extract (WGE) for 90 days. Dietary WGE significantly increased spermidine absorption and affected the metabolism of the other polyamines (spermine and putrescine) in mouse intestines. Dietary intake of 50 g/kg WGE significantly increased the number of mouse pups per litter (p < 0.01), and the secreted levels of the sex hormones E2 and P4 (p < 0.05). Furthermore, compared with a normal diet, WGE feeding led to significantly greater ovarian reserves and fewer atretic follicles (p < 0.001). Finally, co-relationship analysis revealed gut microbes that significantly correlated with sex hormone levels. In summary, our results confirm that wheat germ spermidine plays a beneficial role in mouse ovarian function. Moreover, changes in the structure of the gut microbiota are likely to be crucial for improving ovarian function.
2024-10-28 | MTBLS11115 | MetaboLights