Ovarian Cancer prognosis: Different methylation in patients with PFS <3 years vs. >3 years
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE62851 | GEO | 2015/11/16
SECONDARY ACCESSION(S): PRJNA265843
REPOSITORIES: GEO
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Project description:We investigated whether prognostic information is reflected in the expression patterns of ovarian carcinoma samples. RNA obtained from seven FIGO stage I without recurrence, seven platin-sensitive advanced-stage (III or IV), and six platin-resistant advanced-stage ovarian tumors was hybridized on a complementary DNA microarray with 21,372 spotted clones. The results revealed that a considerable number of genes exhibit nonaccidental differential expression between the different tumor classes. Principal component analysis reflected the differences between the three tumor classes and their order of transition. Using a leave-one-out approach together with least squares support vector machines, we obtained an estimated classification test accuracy of 100% for the distinction between stage I and advanced-stage disease and 76.92% for the distinction between platin-resistant versus platin-sensitive disease in FIGO stage III/IV. These results indicate that gene expression patterns could be useful in clinical management of ovarian cancer.<br> KEYWORDS: clinical, FIGO stage, microarrays, ovarian cancer, platin resistance.
2007-02-10 | E-MEXP-979 | biostudies-arrayexpress
Project description:Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Targeted therapies in second line treatment gain more and more importance in managing recurrent or progressive carcinomas, particularly in ovarian cancer, a cancer entity characterized by a very high recurrence rate. One step ahead, it is necessary to define new therapeutic targets and to select patients who might benefit from these therapies already in first line settings. A robust molecular subclassification could provide both, an adequate patient selection and potential new targets. Notably, the validation of such a subclassification is of outstanding importance to obtain a reliable basis for a specific clinical decision and a rational for the expensive development of new targeted therapies. This work provides a comprehensive basis for both. The expression values of 204 epithelial ovarian cancer tissues are determined and used for the validation of a subclassification approach (Cancer Sci. 2009 Aug;100(8):1421-8.)
2014-01-01 | E-GEOD-49997 | biostudies-arrayexpress
Project description:Epithelial ovarian cancer (EOC) is the main subtype of ovarian cancer. In this study, we found that Ependymin-related 1 (EPDR1) was significantly downregulated in EOC tissues and low EPDR1 expression was associated with International Federation of Gynecology and Obstetrics (FIGO) stage, metastasis and poor prognosis. We confirmed that EPDR1 overexpression dramatically inhibited EOC cells proliferation, migration and invasion in vitro and in vivo. Mechanistically, EPDR1 suppressed EOC tumorigenesis and progression, at least in part, through inhibiting PI3K/AKT pathway. Furthermore, the expression and function were regulated by miR-429, as demonstrated by luciferase reporter assays and rescue experiments. In conclusion, our study demonstrated that EPDR1, negatively regulated by miR-429, played an important role in EOC development via PI3K/AKT pathway. The miR-429/EPDR1 axis might provide promising therapeutic targets for individualized treatment of EOC patients in future.
2021-11-18 | GSE188918 | GEO
Project description:Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Targeted therapies in second line treatment gain more and more importance in managing recurrent or progressive carcinomas, particularly in ovarian cancer, a cancer entity characterized by a very high recurrence rate. One step ahead, it is necessary to define new therapeutic targets and to select patients who might benefit from these therapies already in first line settings. A robust molecular subclassification could provide both, an adequate patient selection and potential new targets. Notably, the validation of such a subclassification is of outstanding importance to obtain a reliable basis for a specific clinical decision and a rational for the expensive development of new targeted therapies. This work provides a comprehensive basis for both.
2014-01-01 | GSE49997 | GEO
Project description:In the last decades platinum-based neo-adjuvant chemotherapy (NACT) has been recognized as a reliable therapeutic strategy in patients with un-resectable advanced epithelial ovarian cancer (EOC). However, the molecular changes induced by NACT at miRNA level, and their prognostic role has not been clarified until now. In order to uncover miRNAs that are altered in EOC tumor which received NACT, we performed whole-miRNA analysis on 82 FIGO Stage IIIC-IV high-grade serous (HGS) tumors, whose samples had been collected at complete primary debulking (PDS) and at interval-debulking surgery (IDS) after fter 4 courses of NACT.
2016-01-20 | E-MTAB-3696 | biostudies-arrayexpress
Project description:Background: It has been shown that based on gene expression profiles, subgroups within epithelial ovarian cancers (EOC) can be identified. We studied a well characterized series of ovarian carcinomas from patients treated at our institute using gene expression profiling to better define clinically significant subgroups. Methods: Gene expression profiling was performed using RNA of 90 primary fresh frozen EOC samples representing all histological subtypes and stages (FIGO I-IV). Patients underwent either primary or interval debulking surgery and if indicated taxane-based chemotherapy. Pathology was reviewed for all cased and complete follow-up, including treatment response and recurrences was available for all patients. Results: Unsupervised and supervised analysis of gene expression data showed distinct subtypes correlating with histology. Mucinous carcinoma was the most distinct subtype based on gene expression profile. No significant differences in gene expression profile between high and low grade serous carcinomas could be observed. No gene expression signatures associated with survival or treatment response could be identified. Conclusion: Histological subtypes of ovarian adenocarcinomas are characterized by distinct gene expression profiles. In order to find signatures correlated to outcome of treatment it is essential that gene expression profiling studies are performed in histological homogeneous groups.
2023-05-18 | GSE61883 | GEO
Project description:Ovarian cancer is the fifth most lethal malignancy in women, and epithelial ovarian cancer (EOC) is the most common histological type. Due to the absence of specific symptoms and diagnostic biomarkers, greater than 70% of EOC patients are diagnosed with clinical stage Federation International of Gynecology and Obstetrics (FIGO) III or IV, which has a five-year survival rate of only 20% to 30%. Thus, it is crucial to identify novel molecular biomarkers and therapeutic targets for EOC. NDC80 kinetochore complex component (NUF2) is upregulated and plays an important role in various human cancers. Our results showed that NUF2 was significantly upregulated in EOC tissues. Downregulation of NUF2 decreased cell proliferation, migration, invasion and tumor growth in nude mice. However, the mechanism of NUF2 in EOC remains unclear. The mechanisms by which NUF2 regulates EOC progression were detected by RNA sequencing. Thus, OVCAR3 cells after transfection with shNC (control) and shNUF2 (NUF2i) were added puromycin for establishing stable cell lines. RNA-seq was performed to analyze the differentially expressed genes in OVCAR3 cells after NUF2 knockdown.
2023-01-11 | GSE213611 | GEO
Project description:miRNA expression from breast tumor tissue obtained from two groups of breast cancer (BC) patients: one group formed by women younger than 36 years and the other group by women older than 65 years. We selected breast tumor tissue samples from 21 women under 36 years old and 12 women above 65 years old, without BRCA mutation and without metastases. We selected a pool of samples in which all the subtypes were representated, in young patients as well as in older patients, to avoid expression biass. We add three samples from breast tissue of cancer-free women to minimize the age-related expression biass. Our aim was to compare miRNA expression between young women and older women, considering normal tissue as a reference.
2014-09-12 | E-GEOD-48088 | biostudies-arrayexpress
Project description:Ovarian cancer (OC) is the most lethal gynecological carcinoma because of the lack of early diagnostic markers and effective drug targets. Discovery of new therapeutic targets in OC to improve the treatment outcome is urgently needed. Patients with recurrent EOC after initial therapy have few treatment options that greatly compromises their quality of life and life expectancy. Discovery of new therapeutic targets in EOC to improve the treatment outcome is urgently needed. Proteomic analysis is one of the approaches to identify therapeutic targets in OC.
2019-09-09 | PXD013811 | 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