scholarly journals Integrated Analysis of Gene Expression Profiles Associated with Response of Platinum/Paclitaxel-Based Treatment in Epithelial Ovarian Cancer

PLoS ONE ◽  
2012 ◽  
Vol 7 (12) ◽  
pp. e52745 ◽  
Author(s):  
Yong Han ◽  
Hao Huang ◽  
Zhen Xiao ◽  
Wei Zhang ◽  
Yanfei Cao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Integrated Analysis

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.

scholarly journals A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching

2021 ◽  
Author(s):  
Taguchi Y-h. ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Learning Strategies ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Prior Learning ◽  
Multiple Gene ◽  
Machine Learning Applications

Abstract The integrated analysis of multiple gene expression profiles measured in distinct studies is always problematic. Especially, missing sample matching and missing common labeling between distinct studies prevent the integration of multiple studies in fully data-driven and unsupervised manner. In this study, we propose a strategy enabling the integration of multiple gene expression profiles among multiple independent studies without either labeling or sample matching, using tensor decomposition-based unsupervised feature extraction. As an example, we applied this strategy to Alzheimer’s disease (AD)-related gene expression profiles that lack exact correspondence among samples as well as AD single-cell RNA-seq (scRNA-seq) data. We found that we could select biologically reasonable genes with integrated analysis. Overall, integrated gene expression profiles can function analogously to prior learning and/or transfer learning strategies in other machine learning applications. For scRNA-seq, the proposed approach was able to drastically reduce the required computational memory.

scholarly journals Differential expression of sarcospan in human epithelial ovarian cancer.

2021 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Fallopian Tube ◽  
Expression Profiles ◽  
Gynecologic Cancer ◽  
Gene Expression Profiles ◽  
Progression Free Survival ◽  
High Grade ◽  
Primary Tumors ◽  
Ovarian Cancers

Epithelial ovarian cancer (EOC) is the most lethal gynecologic cancer (1). We performed discovery of genes associated with epithelial ovarian cancer and of the high-grade serous ovarian cancer (HGSC) subtype, using published microarray data (2, 3) to compare global gene expression profiles of normal ovary or fallopian tube with that of primary tumors from women diagnosed with epithelial ovarian cancer or HGSC. We identified the gene encoding sarcospan, SSPN, as among the genes whose expression was most different in epithelial ovarian cancer as compared to the normal fallopian tube. SSPN expression was significantly lower in high-grade serous ovarian tumors relative to normal fallopian tube. SSPN expression correlated with progression-free survival in patients with ovarian cancer. These data indicate that expression of SSPN is perturbed in epithelial ovarian cancers broadly and in ovarian cancers of the HGSC subtype. SSPN may be relevant to pathways underlying ovarian cancer initiation (transformation) or progression.

scholarly journals Differential expression of phosphodiesterase 5A in human epithelial ovarian cancer.

2021 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Fallopian Tube ◽  
Expression Profiles ◽  
Gynecologic Cancer ◽  
Gene Expression Profiles ◽  
Progression Free Survival ◽  
High Grade ◽  
Primary Tumors ◽  
Ovarian Cancers

Epithelial ovarian cancer (EOC) is the most lethal gynecologic cancer (1). We performed discovery of genes associated with epithelial ovarian cancer and of the high-grade serous ovarian cancer (HGSC) subtype, using published microarray data (2, 3) to compare global gene expression profiles of normal ovary or fallopian tube with that of primary tumors from women diagnosed with epithelial ovarian cancer or HGSC. We identified the gene encoding phosphodiesterase 5A, PDE5A, as among the genes whose expression was most different in epithelial ovarian cancer as compared to the normal fallopian tube. PDE5A expression was significantly lower in high-grade serous ovarian tumors relative to normal fallopian tube. PDE5A expression correlated with progression-free survival in patients with p53 mutant ovarian cancer. These data indicate that expression of PDE5A is perturbed in epithelial ovarian cancers broadly and in ovarian cancers of the HGSC subtype. PDE5A may be relevant to pathways underlying ovarian cancer initiation (transformation) or progression.

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

scholarly journals Integrated analysis of the aging brain transcriptome and proteome in tauopathy

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Carl Grant Mangleburg ◽  
Timothy Wu ◽  
Hari K. Yalamanchili ◽  
Caiwei Guo ◽  
Yi-Chen Hsieh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Mutant Form ◽  
Brain Gene Expression ◽  
Brain Transcriptome ◽  
Age Dependent

Abstract Background Tau neurofibrillary tangle pathology characterizes Alzheimer’s disease and other neurodegenerative tauopathies. Brain gene expression profiles can reveal mechanisms; however, few studies have systematically examined both the transcriptome and proteome or differentiated Tau- versus age-dependent changes. Methods Paired, longitudinal RNA-sequencing and mass-spectrometry were performed in a Drosophila model of tauopathy, based on pan-neuronal expression of human wildtype Tau (TauWT) or a mutant form causing frontotemporal dementia (TauR406W). Tau-induced, differentially expressed transcripts and proteins were examined cross-sectionally or using linear regression and adjusting for age. Hierarchical clustering was performed to highlight network perturbations, and we examined overlaps with human brain gene expression profiles in tauopathy. Results TauWT induced 1514 and 213 differentially expressed transcripts and proteins, respectively. TauR406W had a substantially greater impact, causing changes in 5494 transcripts and 697 proteins. There was a ~ 70% overlap between age- and Tau-induced changes and our analyses reveal pervasive bi-directional interactions. Strikingly, 42% of Tau-induced transcripts were discordant in the proteome, showing opposite direction of change. Tau-responsive gene expression networks strongly implicate innate immune activation. Cross-species analyses pinpoint human brain gene perturbations specifically triggered by Tau pathology and/or aging, and further differentiate between disease amplifying and protective changes. Conclusions Our results comprise a powerful, cross-species functional genomics resource for tauopathy, revealing Tau-mediated disruption of gene expression, including dynamic, age-dependent interactions between the brain transcriptome and proteome.

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