Gene expression profiling using Nanostring technology to predict surgical outcome in advanced primary high grade serous ovarian cancer (HGSOC) patients (pts). Study of the Tumor Bank Ovarian Cancer (TOC).

e17091 Background: Previously four molecular subtypes of high grade serous ovarian cancer (HGSOC) with distinct biological features and clinical outcome have been described: C1 (mesenchymal), C2 (immunoreactive), C4 (differentiated), and C5 (proliferative). Using Nanostring technique and a minimal signature of 39 classifier genes could reproduce the subtypes identified by microarray gene expression profiling (Leong HS et al. Australian Ovarian Cancer Study. J Pathol. 2015). Methods: We characterized paraffin embedded tissue samples from 279 HGSOC patients for molecular subtypes, utilizing the 39 classifier signature and 9 control genes by Nanostring nCounter Analysis System. From 16 patients paired primary and relapsed samples were available. Only chemonaive primary HGSOC patients were included in the study. FFPEs and clinical data were provided by TOC ( www.toc-network.de ). For each sample, probability scores for the four molecular subtypes (C1, C2, C4, and C5) were calculated. The highest calculated score determined the most likely subtype of the tumor. Results: Of all analyzed primary tumor samples, 88 (31.5%) were classified as C1, 83 (29.8%), 53 (19.0%) and 55 (19.7%) as subtypes C2, C4 and C5, respectively. Our results confirmed data by the AOCS study, which described the distribution of HGSOC with 40.2% (C1), 22.5% (C2), 20.1% (C4) and 17.2% (C5), respectively. Within the paired samples, for 12 of the 16 patients dynamic changes in the molecular subtypes between primary and relapse occurred, while in the remaining 4 patients the phenotype was stable. Conclusions: Molecular subtypes of HGSOC using Nanostring technology with a small panel of classifier genes can be confirmed. Furthermore, the data showed that a change of the established molecular subtype might occur during the evolution of the disease, and therefore translate in a different clinical outcome.

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Gene expression profiling of epithelial ovarian cancer reveals key genes and pathways associated with chemotherapy resistance

Genetics and Molecular Research ◽

10.4238/gmr.15017496 ◽

2016 ◽

Vol 15 (1) ◽

Cited By ~ 5

Author(s):

M. Zhang ◽

S.C. Luo

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Chemotherapy Resistance ◽

Key Genes

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Gene expression profiling of CD8+ T cells induced by ovarian cancer cells suggests a possible mechanism for CD8+ Treg cell production

Cell Proliferation ◽

10.1111/cpr.12294 ◽

2016 ◽

Vol 49 (6) ◽

pp. 669-677 ◽

Cited By ~ 4

Author(s):

Meng Wu ◽

Jianfang Lou ◽

Shuping Zhang ◽

Xian Chen ◽

Lei Huang ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

T Cells ◽

Gene Expression Profiling ◽

Cancer Cells ◽

Treg Cell ◽

Cd8 T Cells ◽

Expression Profiling ◽

Ovarian Cancer Cells ◽

Cell Production

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Gene expression profiling of single circulating tumor cells in ovarian cancer - Establishment of a multi-marker gene panel

Molecular Oncology ◽

10.1016/j.molonc.2016.04.002 ◽

2016 ◽

Vol 10 (7) ◽

pp. 1030-1042 ◽

Cited By ~ 46

Author(s):

Christina Blassl ◽

Jan Dominik Kuhlmann ◽

Alessandra Webers ◽

Pauline Wimberger ◽

Tanja Fehm ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Gene Expression Profiling ◽

Tumor Cells ◽

Circulating Tumor Cells ◽

Expression Profiling ◽

Marker Gene ◽

Gene Panel

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Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes

10.1101/030239 ◽

2016 ◽

Cited By ~ 2

Author(s):

Gregory P. Way ◽

James Rudd ◽

Chen Wang ◽

Habib Hamidi ◽

Brooke L. Fridley ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Conflicts Of Interest ◽

Population Analysis ◽

Expression Patterns ◽

Rocky Mountain ◽

High Grade ◽

Serous Ovarian Cancer ◽

Bioinformatics Pipeline ◽

Early Results

AbstractFour gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and non-negative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by including serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.CONFLICTS OF INTERESTThe authors do not declare any conflicts of interest.OTHER PRESENTATIONSAspects of this study were presented at the 2015 AACR Conference and the 2015 Rocky Mountain Bioinformatics Conference.

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Comprehensive analyses reveal three-gene signatures in ovarian cancer

10.21203/rs.3.rs-28741/v1 ◽

2020 ◽

Author(s):

Chuang Li ◽

Yuan Lyu ◽

Caixia Liu

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Hub Genes ◽

Clinical Prognosis ◽

Gene Signatures ◽

Cancer Tissues ◽

Differently Expressed Genes

Abstract Background: Ovarian cancer is a common cancer that affects the quality of women’s life. With the limitation of the early diagnosis of the disease, ovarian cancer has a high mortality rate worldwide. However, the molecular mechanisms underlying tumor invasion, proliferation, and metastasis in ovarian cancer remain unclear. We aimed to identify, using bioinformatics, important genes and pathways that may serve crucial roles in the prevention, diagnosis, and treatment of ovarian cancer. Methods: Three microarray datasets (GSE14407, GSE36668, and GSE26712) were selected for whole-genome gene expression profiling , and differentially expressed genes were identified between normal and ovarian cancer tissues. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using DAVID. Additionally, a protein-protein interaction network was constructed to reveal possible interactions among the differently expressed genes. The prognostic values of the hub genes were investigated using Gene Expression Profiling Interactive Analysis (GEPIA) and the KM plotter database. Meanwhile, the mRNA expression analysis of the hub genes was performed using the GEPIA database. Results: We obtained 247 upregulated and 530 downregulated differently expressed genes, and 52 hub genes in the significant gene modules. Enrichment analysis revealed that the hub genes were significantly ( P < 0.05) associated with proliferation. Additionally, BIRC5, CXCL13, and PBK were revealed to be significantly associated with the clinical prognosis of patients with ovarian cancer. Immunohistochemical staining results obtained from the Human Protein Atlas revealed that BIRC5, PBK, and CXCL13 were highly expressed in ovaria cancer tissues. Conclusion Three-gene signatures ( BIRC5, CXCL13 , and PBK ) are associated with the occurrence, development, and prognosis of OC, and may therefore serve as biological markers of the disease.

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