Gene expression profiling of lung cancer serum or health plasma cultured human monocytes-derived dendritic cells
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE84720 | GEO | 2017/03/28
SECONDARY ACCESSION(S): PRJNA330932
REPOSITORIES: GEO
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2010-05-19 | E-GEOD-9240 | biostudies-arrayexpress
Project description:Recently, extra-cellular components, such as serum and exosome, attract more and more attention as biomarker candidates for mammalian health. However, the contribution of different blood components to the signature of respective microRNAs (miRNAs) remains unknown, especially in cattle. In this study we aimed to investigate the miRNAs from bovine sera and exosomes, and to provide insights into their future applications. Blood collected from four healthy dairy cows were used for this study. The sera and exosomes RNAs were extracted using two commonly used commercial kits (Norgen and Invitrogen total RNA isolation kit), respectively. Sera showed higher complexity of miRNome consisting of 328 miRNAs, while significantly less miRNAs (260, p = 0.001) were detected in exosomes. The profile of total detected miRNAs in sera and exosomes was different, while exosomes represented about 78% of total miRNAs expressed in sera, suggesting that exosomes are the major miRNAs carriers in bovine sera. There were 24 and 3 miRNAs (RPM > 5) that exclusively expressed in sera and exosomes, respectively. In addition, 12 miRNAs were differentially expressed between sera and exosomes (FDR < 0.05). Moreover, functional analysis showed that uniquely and highly expressed miRNAs in sera targeted functions of diseases and disorders, while the functions of those in exosomes were enriched in tissue development and lipid metabolism. Our results provide evidence that bovine sera and exosomes miRNomes are different with regarding to miRNA numbers, types and expressions. Based on their distinct profiles, miRNAs from sera and exosomes may reflect different aspects of physiological and pathological conditions in cattle. The functional analysis suggest that sera may be preferable for the purpose of detecting inflammation in cattle, while exosomes may be a better choice for monitoring the status of muscle development and lipid metabolism.
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Project description:In an attempt to further elucidate the pathomechanisms in oral squamous cell carcinoma (OSCC), gene expression profiling to a set of 35 primary OSCCs compared to 6 oral mucosa from healthy non-tumor patients was performed. Keywords: expression profiling Gene expression profiling using a whole-transcriptome chip that contains 35,035 gene-specific 70mere oligonucleotides (Human Oligoset 4.0; Operon) to a set of 35 primary OSCCs and 6 mucosa of healthy non-tumor patients.
2008-01-12 | E-GEOD-10121 | biostudies-arrayexpress
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Project description:The ability to consistently detect cell-free tumor-specific DNA in peripheral blood of patients with metastatic breast cancer provides the opportunity to detect changes in tumor burden and to monitor response to treatment. We developed cMethDNA, a quantitative multiplexed methylation specific PCR assay for a panel of ten genes, discovered through whole genome methylation array analysis of serum DNA. Cancer-specific methylated DNA was detected in training and test sets of recurrent Stage 4 patient sera with a sensitivity of >90% and a specificity of >96%. A core methylation signature was retained in serum, primary and metastatic tissues throughout the course of the disease, and the cMethDNA assay levels reflected patient response to chemotherapy. Together, our data suggest that the cMethDNA assay can provide a sensitive tool to detect minute levels of tumor DNA present in a vast excess of normal DNA in serum. Samples used: 11 cancer sera,,4 pools of normal leukocytes (5 donors per pool), 6 normal breast tissues microdissected, adjacent to tumor, 15 normal breast tissues, organoid preparations
2014-03-01 | E-GEOD-52621 | 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: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.
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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 | GSE79498 | GEO
Project description:Lung cancer is the most common cause of death from cancer worldwide, largely due to frequent late diagnosis. Thus, there is the urgent need to develop new approaches to improve theearly detection of lung cancer, which would greatly affect patient survival. The quantitative protein expression profiles of microvesicles isolated from sera from 46 lung cancer patients and 41 high-risk non-cancer subjects were obtained using a mass spectrometry method based on library matching. We identified 33 differentially expressed proteins that allow discriminating the two groups. Based on serum protein expression profiles, we built a machine learning algorithm , which highlighted a decrease in the levels of Arysulfatase A (ARSA) as the most discriminating factor found in tumors. Our study identified a signature able to discriminate with high specificity and selectivity early lung cancer patients from high-risk healthy subjects. Among the differentially expressed proteins, ARSA emerged as the most promising biomarker for lung cancer early diagnosis.
2023-02-13 | PXD034221 | Pride
Project description:Ovarian cancer is the leading cause of death in gynaecological malignancies in women. However, currently there are no clinical or pathologic parameters available that can reliably predict clinical outcome. In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict disease stage with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs). In the current study we prospectively evaluate models, built on the pilot data set, on a set of 49 new patients. Principal component analysis showed that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the prospective study did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study, only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours but this model was not able to predict resistance to platin-based chemotherapy. We discuss possible reasons for prospective failure of these models and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before this technology could be considered ready for clinical use.
2007-12-31 | E-MEXP-995 | biostudies-arrayexpress