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.

scholarly journals Comparative Gene Expression Analysis Reveals Similarities and Differences of Chronic Myeloid Leukemia Phases

Cancers ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 256
Author(s):  
Annemarie Schwarz ◽  
Ingo Roeder ◽  
Michael Seifert
Keyword(s):  
Gene Expression ◽  
Chronic Myeloid Leukemia ◽  
Signaling Pathway ◽  
Myeloid Leukemia ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Chronic Phase ◽  
Receptor Interaction ◽  
Similarities And Differences

Chronic myeloid leukemia (CML) is a slowly progressing blood cancer that primarily affects elderly people. Without successful treatment, CML progressively develops from the chronic phase through the accelerated phase to the blast crisis, and ultimately to death. Nowadays, the availability of targeted tyrosine kinase inhibitor (TKI) therapies has led to long-term disease control for the vast majority of patients. Nevertheless, there are still patients that do not respond well enough to TKI therapies and available targeted therapies are also less efficient for patients in accelerated phase or blast crises. Thus, a more detailed characterization of molecular alterations that distinguish the different CML phases is still very important. We performed an in-depth bioinformatics analysis of publicly available gene expression profiles of the three CML phases. Pairwise comparisons revealed many differentially expressed genes that formed a characteristic gene expression signature, which clearly distinguished the three CML phases. Signaling pathway expression patterns were very similar between the three phases but differed strongly in the number of affected genes, which increased with the phase. Still, significant alterations of MAPK, VEGF, PI3K-Akt, adherens junction and cytokine receptor interaction signaling distinguished specific phases. Our study also suggests that one can consider the phase-wise CML development as a three rather than a two-step process. This is in accordance with the phase-specific expression behavior of 24 potential major regulators that we predicted by a network-based approach. Several of these genes are known to be involved in the accumulation of additional mutations, alterations of immune responses, deregulation of signaling pathways or may have an impact on treatment response and survival. Importantly, some of these genes have already been reported in relation to CML (e.g., AURKB, AZU1, HLA-B, HLA-DMB, PF4) and others have been found to play important roles in different leukemias (e.g., CDCA3, RPL18A, PRG3, TLX3). In addition, increased expression of BCL2 in the accelerated and blast phase indicates that venetoclax could be a potential treatment option. Moreover, a characteristic signaling pathway signature with increased expression of cytokine and ECM receptor interaction pathway genes distinguished imatinib-resistant patients from each individual CML phase. Overall, our comparative analysis contributes to an in-depth molecular characterization of similarities and differences of the CML phases and provides hints for the identification of patients that may not profit from an imatinib therapy, which could support the development of additional treatment strategies.

scholarly journals Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian Cancer

Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 713 ◽  
Author(s):  
Kulbe ◽  
Otto ◽  
Darb-Esfahani ◽  
Lammert ◽  
Abobaker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Pelvic Mass ◽  
Aurora Kinase ◽  
Malignant Tumours ◽  
Novel Biomarkers

Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial and specifically stromal compartments, which have been neglected in search for novel candidates. We queried gene expression profiles of EOC including microdissected epithelium and adjacent stroma from benign and malignant tumours. Genes significantly differentially expressed within either the epithelial or the stromal compartments were retrieved. The expression of genes whose products are secreted yet absent in the blood of healthy donors were validated in tissue and blood from patients with pelvic mass by NanoString analysis. Results were confirmed by the comprehensive gene expression database, CSIOVDB (Ovarian cancer database of Cancer Science Institute Singapore). The top 25% of candidate genes were explored for their biomarker potential, and twelve were able to discriminate between benign and malignant tumours on transcript levels (p < 0.05). Among them T-cell differentiation protein myelin and lymphocyte (MAL), aurora kinase A (AURKA), stroma-derived candidates versican (VCAN), and syndecan-3 (SDC), which performed significantly better than the recently reported biomarker fibroblast growth factor 18 (FGF18) to discern malignant from benign conditions. Furthermore, elevated MAL and AURKA expression levels correlated significantly with a poor prognosis. We identified promising novel candidates and found the stroma of EOC to be a suitable compartment for biomarker discovery.

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.

Gene expression in epithelial ovarian cancer: a study of intratumor heterogeneity

2007 ◽  
Vol 17 (5) ◽  
pp. 979-985 ◽  
Author(s):  
K. M. Jochumsen ◽  
Q. Tan ◽  
B. HØLUND ◽  
T. A. Kruse ◽  
O. Mogensen
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Tissue Sample ◽  
Expression Profiles ◽  
Intraclass Correlation ◽  
University Hospital ◽  
Intratumor Heterogeneity ◽  
Mutual Distance ◽  
Tissue Samples

The aim of this study was to investigate the intratumor heterogeneity of gene expression profiles in epithelial ovarian cancer (EOC). This was done to evaluate whether sampling of a single macrodissected tissue sample from each EOC case would bias the data and result in, eg, prognostic studies based on gene expression microarray experiments. From nine EOCs removed at Odense University Hospital, Denmark, three tumor samples of 200–300 mg each were taken with greatest possible mutual distance. The samples were immediately flash frozen. A parallel section was taken for histopathologic comparison. RNA was extracted from the tissue samples. Five micrograms of each RNA sample was used for labeling. The fragmented biotin-labeled complementary RNA was hybridized to Affymetrix GeneChip Human Genome U133 plus 2.0 arrays, and scanning was performed on the GeneArray scanner 3000 (Affymetrix, Santa Clara, CA). Data were evaluated using hierarchical clustering and intraclass correlation coefficient (ICC) from reliability analysis. All evaluation methods revealed low intratumor heterogeneity. Intratumor ICCs ranged from 0.888 to 0.978. In contrast, “between-tumor” ICC was 0.549 indicating much lower intra- than intertumor heterogeneity. Due to a low degree of intratumor variation, we conclude that it is sufficiently accurate in a clinical setup to use single, macrodissected tumor samples in the evaluation of gene expression in EOCs.

scholarly journals Modeling the Diversity of Epithelial Ovarian Cancer through Ten Novel Well Characterized Cell Lines Covering Multiple Subtypes of the Disease

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2222 ◽  
Author(s):  
Alexandre Sauriol ◽  
Kayla Simeone ◽  
Lise Portelance ◽  
Liliane Meunier ◽  
Kim Leclerc-Desaulniers ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Complex Disease ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Cancer Cell Lines ◽  
Future Research

Cancer cell lines are amongst the most important pre-clinical models. In the context of epithelial ovarian cancer, a highly heterogeneous disease with diverse subtypes, it is paramount to study a wide panel of models in order to draw a representative picture of the disease. As this lethal gynaecological malignancy has seen little improvement in overall survival in the last decade, it is all the more pressing to support future research with robust and diverse study models. Here, we describe ten novel spontaneously immortalized patient-derived ovarian cancer cell lines, detailing their respective mutational profiles and gene/biomarker expression patterns, as well as their in vitro and in vivo growth characteristics. Eight of the cell lines were classified as high-grade serous, while two were determined to be of the rarer mucinous and clear cell subtypes, respectively. Each of the ten cell lines presents a panel of characteristics reflective of diverse clinically relevant phenomena, including chemotherapeutic resistance, metastatic potential, and subtype-associated mutations and gene/protein expression profiles. Importantly, four cell lines formed subcutaneous tumors in mice, a key characteristic for pre-clinical drug testing. Our work thus contributes significantly to the available models for the study of ovarian cancer, supplying additional tools to better understand this complex disease.

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