Predicting the clinical behavior of ovarian cancer from gene expression profiles

2006 ◽  
Vol 16 (Suppl 1) ◽  
pp. 147-151 ◽  
Author(s):  
F. De Smet ◽  
N. L.M.M. Pochet ◽  
K. Engelen ◽  
T. Van Gorp ◽  
P. Van Hummelen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Figo Stage ◽  
Stage I ◽  
Stage Iii ◽  
Support Vector ◽  
Test Accuracy ◽  
Advanced Stage

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.

scholarly journals Real-World Treatment Patterns, Survival, and Costs for Ovarian Cancer in Canada: A Retrospective Cohort Study Using Provincial Administrative Data

2021 ◽  
Vol 8 (2) ◽  
pp. 114-121
Author(s):  
Manjusha Hurry ◽  
Shazia Hassan ◽  
Soo Jin Seung ◽  
Ryan Walton ◽  
Ashlie Elnoursi ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Health System ◽  
Advanced Disease ◽  
Stage Iv ◽  
Treatment Patterns ◽  
Stage I ◽  
Stage Iii ◽  
Advanced Stage ◽  
Poor Survival ◽  
Stage Disease

**Background:** In 2020, approximately 3100 Canadian women were diagnosed with ovarian cancer (OC), with 1950 women dying of this disease. Prognosis for OC remains poor, with 70% to 75% of cases diagnosed at an advanced stage and an overall 5-year survival of 46%. Current standard of care in Canada involves a combination of cytoreductive surgery and platinum-based chemotherapy. **Objective:** There are few studies reporting current OC costs. This study sought to determine patient characteristics and costs to the health system for OC in Ontario, Canada. **Methods:** Women diagnosed with OC in Ontario between 2010 and 2017 were identified. The cohort was linked to provincial administrative databases to capture treatment patterns, survival, and costs. Overall total and mean cost per patient (unadjusted) were reported in 2017 Canadian dollars, using a macro-based costing methodology called GETCOST. It is programmed to determine the costs of short-term and long-term episodes of health-care resources utilized. **Results:** Of the 2539 OC patients included in the study, the mean age at diagnosis was 60.4±11.35 years. The majority were diagnosed with stage III disease (n=1247). The only treatment required for 74% of stage I patients and 54% of stage II patients was first-line (1L) platinum chemotherapy; in advanced stages (III/IV) 24% and 20%, respectively, did not receive further treatment after 1L therapy. The median overall survival (mOS) for the whole cohort was 5.13 years. Survival was highest in earlier stage disease (mOS not reached in stage I/II), and dropped significantly in advanced stage patients (stage III: mOS=4.09 years; stage IV: mOS=3.47 years). Overall mean costs in patients stage I were CAD $58 099 compared to CAD $124 202 in stage IV. **Discussion:** The majority of OC patients continue to be diagnosed with advanced disease, which is associated with poor survival and increased treatment costs. Increased awareness and screening could facilitate diagnosis of earlier stage disease and reduce high downstream costs for advanced disease. **Conclusion:** Advanced OC is associated with poor survival and increased costs, mainly driven by hospitalizations or cancer clinic visits. The introduction of new targeted therapies such as olaparib could impact health system costs, by offsetting higher downstream costs while also improving survival.

IGF axis gene expression patterns are prognostic of survival in epithelial ovarian cancer

2007 ◽  
Vol 14 (3) ◽  
pp. 781-790 ◽  
Author(s):  
Dimitrios Spentzos ◽  
Stephen A Cannistra ◽  
Franck Grall ◽  
Douglas A Levine ◽  
Kamana Pillay ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Signaling Pathway ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Prognostic Models ◽  
Igf I ◽  
Igf Receptor ◽  
Igf Signaling

The IGF axis has documented growth-promoting effects in various malignancies, but its role in epithelial ovarian cancer (EOC) has not been adequately examined. We studied the expression of the IGF axis genes in relation to outcome in EOC. Microarray expression profiles from 64 patients with advanced-stage EOC were used. Two multi-gene subsets were chosen, one upstream of the IGF receptor (‘IGF family’) and the other downstream of the IGF receptor (‘IGF signaling pathway’), and analyzed in relation to survival. In addition, expression patterns of the two gene subsets were analyzed in relation to favorable and unfavorable prognosis categories identified in a previous study by whole-genome expression profiling. In a gene-by-gene analysis, IGF binding protein 4 and IGF-II receptor gene expression was inversely associated with survival. Using hierarchical clustering, the two multi-gene subsets separated the patient cohort into two groups with different median survival (IGF family: 33 vs 63 months, P=0.02 and IGF signaling pathway: 41 vs 63 months, P=0.05). Furthermore, the two multi-gene subsets were differentially expressed between the previously defined favorable and unfavorable prognosis tumors (Kolmogorov–Smirnov permutation: P=0.0005 and 0.003 for the IGF family and signaling pathway respectively), and individual genes (including IGF-I, IGF-I receptor, and several genes downstream of the receptor) were overexpressed in unfavorable prognosis tumors (permutation P<0.05). The expression patterns of several genes in the IGF axis are associated with survival in EOC, and expression changes of these genes may be underlying previously proposed microarray-derived clinical prognostic models. Future studies are needed to more precisely determine the diagnostic and potential therapeutic significance of these findings.

All Clinically Relevant Leukemia Subtypes Can Be Diagnosed and Classified Based Solely on Gene Expression Profiling with an Accuracy of 95.1%: A Study on 1337 Adult Patients.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 143-143
Author(s):  
Torsten Haferlach ◽  
Alexander Kohlmann ◽  
Susanne Schnittger ◽  
Martin Dugas ◽  
Sylvia Merk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Sensitivity And Specificity ◽  
Diagnostic Tool ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Second Step ◽  
Support Vector ◽  
Therapeutic Decisions

Abstract So far, comprehensive diagnosis of leukemia requires a combination of cytomorphology, immunophenotyping, and genetic methods. We aimed at developing a new diagnostic tool based solely on gene expression profiling to accurately predict all clinically relevant subtypes of leukemia in adults and to distinguish these from normal bone marrow. Therefore, we analyzed samples from 1337 untreated patients at diagnosis and healthy donors using oligonucleotide microarrays. The first series of 937 cases was hybridized to HG-U133A+B microarrays (Affymetrix). The following 13 subgroups were included: 620 AML (42 t(15;17); 38 t(8;21); 49 inv(16); 47 t(11q23); 75 complex aberrant karyotype; 193 normal karyotype; 176 other cytogenetic abn.); 152 ALL (26 Pro-B-ALL/t(11q23); 12 ALL-t(8;14); 32 T-ALL; 82 c-ALL/Pre-B-ALL); 75 CML, 45 CLL, and 45 bone marrows from healthy volunteers or non-leukemia pts. (nBM). For each disease entity the top 100 differentially expressed genes were calculated in a one-versus-all (OVA) approach. Class prediction was performed using support vector machines (SVM). Prediction accuracy was estimated by 10-fold cross validation (CV) and assessed for robustness in a resampling approach. 891 of the 937 samples (95.1%) were correctly classified (10-fold CV). A resampling approach with 2/3 training and 1/3 test cohort (100 runs of SVM) confirmed this high accuracy (median, 93.8%). In particular, a median of 100% sensitivity and specificity was achieved for AML with t(15;17), t(8;21), and inv(16), as well as for Pro-B-ALL/t(11q23), and CLL. The median specificity was at least 99.7% in all subgroups except for AML normal/other (median specificity, 93.7%). In a second step T-ALL cases were separated into cortical and immature ones (accuracy, 84.4%) and c-ALL/Pre-B-ALL into cases with and without t(9;22) (accuracy, 82.9%). The second prospective series comprized 400 unselected cases which were hybridized to the new generation HG-U133 Plus 2.0 microarrays (Affymetrix). To validate the diagnostic accuracy of our approach these cases were processed blinded in parallel to routine diagnostic work-up and classified based on the gene expression signatures discovered in the first series described above. Applying a first classification step as described above the 13 different diagnoses were predicted with an accuracy of 94.5%. Failures were mostly due to misclassification into biologically related subgroups, e.g. AML with del(5q) aberrations classified as AML with complex aberrant karyotype. In the second step (separation of the two T-ALL subtypes, and c-ALL/Pre-B-ALL with or without t(9;22)) accuracies of 100% and 70.6% respectively were achieved. In conclusion, we were able to identify within a routine diagnostic workflow distinct expression profiles for all clinically and prognostically relevant adult leukemia subtypes and their discrimination from nBM based only on gene expression data. Accuracy, sensitivity, and specificity were higher than achieved with each of the gold standard techniques alone used today. Thus, gene expression patterns analyzed by microarrays qualify as a diagnostic tool in a routine setting for leukemia diagnosis and classification and may guide relevant therapeutic decisions.

Epidemiological data of ovarian cancer in Vojvodina and south great plain region in Hungary in 2007-2012 period: CrossBiomark IPA PROJECT HUSRB/1203/214/091

2013 ◽  
Vol 21 (3-4) ◽  
pp. 97-100 ◽  
Author(s):  
Aljosa Mandic ◽  
László Thurzó ◽  
Dejan Nincic ◽  
Milica Zivaljevic ◽  
Tihomir Dugandzija ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Statistical Difference ◽  
Epidemiological Data ◽  
Figo Stage ◽  
Great Plain ◽  
Stage Iii ◽  
Advanced Stage ◽  
Female Population ◽  
Significant Statistical Difference ◽  
Plain Region

Background: Ovarian cancer is among the sixth leading cancers in Vojvodina and the fifth leading cause of cancer death among female population in Vojvodina according to Cancer Registry of Vojvodina in 2010. The majority of ovarian cancers cases are diagnosed at an advanced stage, FIGO stage III-IV with poor prognosis. The aim of the study was to evaluate newly diagnosed ovarian cancer among female population in Vojvodina (Serbia) and South Great Plain region in Hungary in 2007- 2012 period. Methods: The evaluation was based on the data from hospital registries for malignant diseases at the Oncology Institute of Vojvodina and the Department of Oncotherapy, University of Szeged. Results: The majority of patients were diagnosed in advanced disease (FIGO stage III-IV) in both regions. Serous epithelial ovarian cancer was the most common cancer type among studied women in both regions. The average age of women diagnosed with ovarian cancer was 60 years; there was no significant statistical difference related to patients? age in both studied regions. Advanced stage of ovarian cancer investigated in our study showed a moderate descending liner trend with no significant statistical difference. The results from our study were similar when compared with the epidemiological data from the literature. Conclusion: The lack of efficient screening methods is the major obstacle to improve the prognosis of women affected by this disease. Further investigations and introduction of new technologies applied to medical discoveries offers new hope for finding effective screening policies.

Screening and identification of key biomarkers and related transcription factors in ovarian cancer by integrated bioinformatics analysis

2020 ◽  
Author(s):  
Wenqiong Qin ◽  
Qiang Yuan ◽  
Yi Liu ◽  
Ying Zeng ◽  
Dandan Ke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Molecular Mechanisms ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Transcriptional Factor ◽  
Ppi Network ◽  
Hub Genes

Abstract Background Ovarian tumors are the most malignant tumors of all gynecological tumors, and although multiple efforts have been made to elucidate the pathogenesis, the molecular mechanisms of ovarian cancer remain unclear. Methods In this study, we used bioinformatics to identify genes involved in the carcinogenesis and progression of ovarian cancer. Three microarray datasets (GSE14407, GSE29450, and GSE54388) were downloaded from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. For a more in-depth understanding of the DEGs, functional and pathway enrichment analyses were performed and a protein-protein interaction (PPI) network was constructed. The associated transcriptional factor (TFs) regulation network of the DEGs was also constructed. Kaplan Meier-plotter, Gene Expression Profiling Interactive Analysis (GEPIA), the Human Protein Atlas (HPA) database and the Oncomine database were implemented to validated hub genes. Results A total of 514 DEGs were detected after the analysis of the three gene expression profiles, including 171 upregulated and 343 downregulated genes. Nine hub genes ( CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, TTK) were obtained from the PPI network. Survival analysis showed that high expression levels of seven hub genes ( CCNB1, BUB1, BUB1B, CCNA2, AURKA, CDK1, and RRM2) were associated with worse overall survival (OS). All of seven hub genes were discovered highly expressed in ovarian cancer samples compared to normal ovary samples in GEPIA. Immunostaining results from the HPA database suggested that the expressions of CCNB1, CCNA2, AURKA, and CDK1 proteins were increased in ovarian cancer tissues, and Oncomine analysis indicated that the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 have associated with patient clinicopathological information. From the gene-transcriptional factor network, key transcriptional factors, such as POLR2A, ZBTB11, KLF9, and ELF1, were identified with close interactions with these hub genes. Conclusion We identified six significant DEGs with poor prognosis in ovarian cancer, which could be of potential biomarkers for ovarian cancer patients.

Pathology of stage I versus stage III ovarian carcinoma with implications for pathogenesis and screening

2008 ◽  
Vol 18 (3) ◽  
pp. 465-469 ◽  
Author(s):  
A. V. YEMELYANOVA ◽  
J. A. COSIN ◽  
M. A. BIDUS ◽  
C. R. BOICE ◽  
J. D. SEIDMAN
Keyword(s):  
Ovarian Cancer ◽  
Ovarian Carcinoma ◽  
Screening Program ◽  
Ovarian Tumors ◽  
Stage I ◽  
Stage Iii ◽  
Advanced Stage ◽  
Ovarian Carcinomas ◽  
Pathologic Features ◽  
Abdominal Disease

The progression of ovarian carcinoma from stage I when it is confined to the ovaries and curable to disseminated abdominal disease, which is usually fatal, is poorly understood. An accurate understanding of this process is fundamental to designing, testing, and implementing an effective screening program for ovarian cancer. Pathologic features of the primary ovarian tumors in 41 FIGO stage I ovarian carcinomas were compared with those in 40 stage III carcinomas. The primary ovarian tumors in stage I cases, when compared with stage III, respectively, were significantly larger (15.4 versus 9.8 cm), were less frequently bilateral (12% versus 75%), more frequently contained a noninvasive component (88% versus 30%), had a higher proportion of a noninvasive component (42% versus 8%), and were more often nonserous (83% versus 20%) (P< 0.001 for all five comparisons). There are significant pathologic differences between the primary ovarian tumors in stage I and III ovarian carcinomas that are very difficult to explain by a simple temporal progression. These findings along with the growing body of literature suggest that early- and advanced-stage ovarian cancers are in many instances biologically different entities. This knowledge may have significant implications for our understanding of the biology of early- and advanced-stage ovarian cancer and therefore on the development of screening strategies for ovarian cancer.

Identification of biomarkers and functional modules from genomic data in stage-wise breast cancer

2020 ◽  
Vol 15 ◽  
Author(s):  
Athira K ◽  
Vrinda C ◽  
Sunil Kumar P V ◽  
Gopakumar G
Keyword(s):  
Breast Cancer ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Predictive Biomarkers ◽  
Stage I ◽  
Stage Ii ◽  
Stage Iii ◽  
Functional Modules ◽  
Incidence And Mortality ◽  
Multiple Stages

Background: Breast cancer is the most common cancer in women across the world, with high incidence and mortality rates. Being a heterogeneous disease, gene expression profiling based analysis plays a significant role in understanding breast cancer. Since expression patterns of patients belonging to the same stage of breast cancer vary considerably, an integrated stage-wise analysis involving multiple samples is expected to give more comprehensive results and understanding of breast cancer. Objective: The objective of this study is to detect functionally significant modules from gene co-expression network of cancerous tissues and to extract prognostic genes related to multiple stages of breast cancer. Methods: To achieve this, a multiplex framework is modelled to map the multiple stages of breast cancer, which is followed by a modularity optimization method to identify functional modules from it. These functional modules are found to enrich many Gene Ontology terms significantly that are associated with cancer. Result and Discussion: predictive biomarkers are identified based on differential expression analysis of multiple stages of breast cancer. Conclusion: Our analysis identified 13 stage-I specific genes, 12 stage-II specific genes, and 42 stage-III specific genes that are significantly regulated and could be promising targets of breast cancer therapy. That apart, we could identify 29, 18 and 26 lncRNAs specific to stage I, stage II and stage III respectively.

scholarly journals Comprehensive Comparison of Amnion Stromal Cells and Chorion Stromal Cells by RNA-Seq

2021 ◽  
Vol 22 (4) ◽  
pp. 1901
Author(s):  
Brielle Jones ◽  
Chaoyang Li ◽  
Min Sung Park ◽  
Anne Lerch ◽  
Vimal Jacob ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stromal Cells ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Principal Component ◽  
Differentially Expressed ◽  
Inflammatory Factor ◽  
Next Generation Sequencing Technology ◽  
Angiogenic Potential ◽  
Anti Inflammatory

Mesenchymal stromal cells derived from the fetal placenta, composed of an amnion membrane, chorion membrane, and umbilical cord, have emerged as promising sources for regenerative medicine. Here, we used next-generation sequencing technology to comprehensively compare amniotic stromal cells (ASCs) with chorionic stromal cells (CSCs) at the molecular and signaling levels. Principal component analysis showed a clear dichotomy of gene expression profiles between ASCs and CSCs. Unsupervised hierarchical clustering confirmed that the biological repeats of ASCs and CSCs were able to respectively group together. Supervised analysis identified differentially expressed genes, such as LMO3, HOXA11, and HOXA13, and differentially expressed isoforms, such as CXCL6 and HGF. Gene Ontology (GO) analysis showed that the GO terms of the extracellular matrix, angiogenesis, and cell adhesion were significantly enriched in CSCs. We further explored the factors associated with inflammation and angiogenesis using a multiplex assay. In comparison with ASCs, CSCs secreted higher levels of angiogenic factors, including angiogenin, VEGFA, HGF, and bFGF. The results of a tube formation assay proved that CSCs exhibited a strong angiogenic function. However, ASCs secreted two-fold more of an anti-inflammatory factor, TSG-6, than CSCs. In conclusion, our study demonstrated the differential gene expression patterns between ASCs and CSCs. CSCs have superior angiogenic potential, whereas ASCs exhibit increased anti-inflammatory properties.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

scholarly journals Comparison of gene expression in fresh and frozen–thawed human preimplantation embryos

Reproduction ◽  
2012 ◽  
Vol 144 (5) ◽  
pp. 569-582 ◽  
Author(s):  
Lisa Shaw ◽  
Sharon F Sneddon ◽  
Daniel R Brison ◽  
Susan J Kimber
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Developmental Expression ◽  
Preimplantation Embryos ◽  
Human Embryos ◽  
Molecular Events ◽  
Reliable Source ◽  
Human Preimplantation Embryos ◽  
Early Human

Identification and characterisation of differentially regulated genes in preimplantation human embryonic development are required to improve embryo quality and pregnancy rates in IVF. In this study, we examined expression of a number of genes known to be critical for early development and compared expression profiles in individual preimplantation human embryos to establish any differences in gene expression in fresh compared to frozen–thawed embryos used routinely in IVF. We analysed expression of 19 genes by cDNA amplification followed by quantitative real-time PCR in a panel of 44 fresh and frozen–thawed human preimplantation embryos. Fresh embryos were obtained from surplus early cleavage stage embryos and frozen–thawed embryos from cryopreserved 2PN embryos. Our aim was to determine differences in gene expression between fresh and frozen–thawed human embryos, but we also identified differences in developmental expression patterns for particular genes. We show that overall gene expression among embryos of the same stage is highly variable and our results indicate that expression levels between groups did differ and differences in expression of individual genes was detected. Our results show that gene expression from frozen–thawed embryos is more consistent when compared with fresh, suggesting that cryopreserved embryos may represent a reliable source for studying the molecular events underpinning early human embryo development.

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