scholarly journals High expression of RRM2 promotes the pathogenesis of malignant ovarian endometriosis.

2021 ◽  
Author(s):  
Binkai Yang ◽  
Yuanjing Hu ◽  
Tian Wang ◽  
Na Li ◽  
Wenwen Zhang
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Malignant Transformation ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Gene Expression Profiles ◽  
High Expression ◽  
Ovarian Endometriosis ◽  
Ki 67 ◽  
Significant Difference

Abstract Objective: Our objective was to investigate the upregulated expression of ribonucleotide reductase M2 (RRM2) in the ectopic endometrium (EC) of ovarian endometriosis (OE) patients that may indicate malignant transformation. RRM2 may be used as a marker of OE, which contribute to the research of the mechanism of the malignant transformation of OE.Methods: The gene expression profiles of ovarian cancer and OE were downloaded from Gene Expression Omnibus (GEO), and a common hub gene, RRM2, was identified. The expression of RRM2 was low in OE and high in ovarian cancer. A total of 44 patients with endometriosis-associated ovarian cancers (EAOC) and 44 with OE were enrolled in this study. Immunohistochemistry (IHC) and real-time quantitative polymerase chain reaction (RT-qPCR) were used to detect the expression of RRM2, while the relationship between RRM2 and Ki-67 was analyzed by IHC co-localization. Results: There was no significant difference in the expression of RRM2 in the eutopic endometrium (EU), EC, and cancer tissues of EAOC patients. Compared with OE patients, the mRNA and protein expression levels of RRM2 were higher in the EC of EAOC patients (p＜0.01). Moreover, the high expression of RRM2 was consistent with the expression of Ki-67 in EC of EAOC patients.Conclusions: The upregulated expression of RRM2 in the EC of OE patients may indicate malignant transformation. RRM2 may be used as a marker of OE, which allows the investigation of the mechanism of the malignant transformation of OE.

scholarly journals Biological Insights into Chemotherapy Resistance in Ovarian Cancer

2019 ◽  
Vol 20 (9) ◽  
pp. 2131 ◽  
Author(s):  
Michelle A. Glasgow ◽  
Peter Argenta ◽  
Juan E. Abrahante ◽  
Mihir Shetty ◽  
Shobhana Talukdar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Expression Profiles ◽  
Rna Extraction ◽  
Chemotherapy Resistance ◽  
Gene Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Before And After ◽  
Tumor Gene Expression ◽  
Tumor Gene

The majority of patients with high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy; however, most will develop chemotherapy resistance. Gene signatures may change with the development of chemotherapy resistance in this population, which is important as it may lead to tailored therapies. The objective of this study was to compare tumor gene expression profiles in patients before and after treatment with neoadjuvant chemotherapy (NACT). Tumor samples were collected from six patients diagnosed with HGSOC before and after administration of NACT. RNA extraction and whole transcriptome sequencing was performed. Differential gene expression, hierarchical clustering, gene set enrichment analysis, and pathway analysis were examined in all of the samples. Tumor samples clustered based on exposure to chemotherapy as opposed to patient source. Pre-NACT samples were enriched for multiple pathways involving cell cycle growth. Post-NACT samples were enriched for drug transport and peroxisome pathways. Molecular subtypes based on the pre-NACT sample (differentiated, mesenchymal, proliferative and immunoreactive) changed in four patients after administration of NACT. Multiple changes in tumor gene expression profiles after exposure to NACT were identified from this pilot study and warrant further attention as they may indicate early changes in the development of chemotherapy resistance.

scholarly journals Independent Test Set Performance in the Prediction of Early Relapse in Ovarian Cancer with Gene Expression Profiles

2005 ◽  
Vol 11 (21) ◽  
pp. 7958-7959 ◽  
Author(s):  
Frank De Smet ◽  
Nathalie L.M.M. Pochet ◽  
Bart L.R. De Moor ◽  
Toon Van Gorp ◽  
Dirk Timmerman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Test Set ◽  
Early Relapse ◽  
Independent Test

Predicting the clinical behavior of ovarian cancer from gene expression profiles

2006 ◽  
Vol 16 (S1) ◽  
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 ◽  
Gene Expression Profiles ◽  
Clinical Behavior

scholarly journals Over-expression of the kayopherin 𝛂2 gene (KPNA2) in high-grade serous ovarian cancers.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
High Grade ◽  
Over Expression ◽  
Ovarian Cancers ◽  
Microarray Datasets

Ovarian cancer is the most lethal gynecologic malignancy and 70-80% of ovarian cancers are of the high-grade serous type (1-3). To identify the most significant changes in gene expression in high-grade serous ovarian cancer (HGSC), we compared global gene expression profiles of tumors from patients with HGSC to that of normal ovary using published microarray datasets (4, 5). We found that the nuclear import receptor karyopherin 𝛂2 (KPNA2) (6) was among the genes whose expression changed most significantly when comparing HSGC tumors to the ovary. Karyopherin 𝛂2 may be relevant to the biology of high-grade serous ovarian tumors.

A Gene Expression Classifier for Improved Risk Classification and Outcome Prediction in Pediatric Acute Lymphoblastic Leukemia (ALL).

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 762-762 ◽  
Author(s):  
Cheryl L. Willman ◽  
Huining Kang ◽  
Jeffrey W. Potter ◽  
Richard C. Harvey ◽  
Susan R. Atlas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Outcome Prediction ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Risk Classification ◽  
High Expression ◽  
Molecular Heterogeneity ◽  
Prognostic Variables ◽  
Classification Schemes ◽  
Gene Expression Classifier

Abstract Significant advances in the treatment of pediatric ALL have been achieved through the use of risk classification schemes that target children to increasing therapeutic intensities based on their relapse risk. However, current classification schemes do not fully reflect the molecular heterogeneity of the disease and do not precisely identify those children more prone to relapse or those who could be cured with less intensive regimens. To improve risk classification and outcome prediction in ALL, gene expression profiles were obtained using oligonucleotide arrays in a retrospective case control study of 220 children with B precursor ALL, balanced for outcome (continuous complete remission (CCR) vs. failure at 4 years) across several established prognostic variables (age, sex, WBC, karyotype). Using multiple statistical methods and computational tools, these comprehensive gene expression profiles were reduced to a 26 gene expression classifier that was highly predictive of overall outcome (two tailed p values ranging from 0.00001–0.001). Each of these 26 genes was shown to provide additional prognostic information relative to established prognostic variables (p<0.01). The 26 genes include signaling, adhesion, and growth regulatory proteins (RhoGEF4, FYB, HNK-1 sulfotransferase, SMAD1, HABP4, PHYN, IFI44L, JAG1, EFN-B2, type 3 inositol-1,4,5 triphosphate receptor, MONDOA, DOK1, CDK8, CD44, CCL5/RANTES, galectin, SPARC) and novel genes not previously known to play a role in hematopoiesis or leukemogenesis (DREBIN, MIDKINE, and the hypothetical protein FLJ20154 or OPAL1 which have cloned and characterized). High expression of 18 of the 26 genes was predictive of CCR while high expression of the remaining 8 genes (LGALS1/galectin, DOK1, GST𝛉1, CCL5/RANTES, PRG1, CD44, ATP2C1, SPARC) was predictive of treatment failure. Interestingly, 8 of the 26 genes are linked in a cell death regulatory network; 7 genes are components of a chemokine/CD44 signaling pathway; and 2 genes are critical regulators of intracellular calcium ion transport and apoptosis. Using stepwise logistic regression on the expression values of the 26 genes and 4 established prognostic variables (sex, age, WBC, t(12;21)), the best predictive outcome model was built using 9 genes alone (MIDKINE, CHST10, PHYH, IFI44L, OPAL1, CDK8, DOK1, ATP2C1, SPARC). This 9 gene predictive model was then tested for its ability to predict outcome in two independent B precursor ALL cohorts: 1) a series of 198 B precursor ALL cases previously published by Yeoh et al. (Cancer Cell 2002 1:133) where our 9 gene model was found to predict outcome with high statistical significance (p < 1.0−8); and, 2) a series of 59 B precursor ALL patients treated with a distinct modified BFM regimen CCG-1961 (p=.002; W.L. Carroll et al, in preparation). These results demonstrate that gene expression profiling can yield unique genes and classifiers that can improve outcome prediction and risk classification in ALL. Further studies may provide new insights into how these genes and pathways promote leukemogenesis and effect therapeutic responsiveness.

scholarly journals Explaining Ovarian Cancer Gene Expression Profiles with Fuzzy Rules and Genetic Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 375
Author(s):  
Arianna Consiglio ◽  
Gabriella Casalino ◽  
Giovanna Castellano ◽  
Giorgio Grillo ◽  
Elda Perlino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Genetic Algorithms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Fuzzy Rule ◽  
Expression Data ◽  
Final Model ◽  
Ovarian Diseases ◽  
Rule Systems

The analysis of gene expression data is a complex task, and many tools and pipelines are available to handle big sequencing datasets for case-control (bivariate) studies. In some cases, such as pilot or exploratory studies, the researcher needs to compare more than two groups of samples consisting of a few replicates. Both standard statistical bioinformatic pipelines and innovative deep learning models are unsuitable for extracting interpretable patterns and information from such datasets. In this work, we apply a combination of fuzzy rule systems and genetic algorithms to analyze a dataset composed of 21 samples and 6 classes, useful for approaching the study of expression profiles in ovarian cancer, compared to other ovarian diseases. The proposed method is capable of performing a feature selection among genes that is guided by the genetic algorithm, and of building a set of if-then rules that explain how classes can be distinguished by observing changes in the expression of selected genes. After testing several parameters, the final model consists of 10 genes involved in the molecular pathways of cancer and 10 rules that correctly classify all samples.

scholarly journals Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

2020 ◽  
Vol 21 (S9) ◽  
Author(s):  
Mona Maharjan ◽  
Raihanul Bari Tanvir ◽  
Kamal Chowdhury ◽  
Wenrui Duan ◽  
Ananda Mohan Mondal
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Lines ◽  
High Expression ◽  
Different Types ◽  
Low Expression

Abstract Background Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. Results The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. Conclusion A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

Sign in / Sign up

Export Citation Format

Share Document