Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and Mortality

Ma, Hossain; Islam, Shahidul; Quinn, Julian M.W.; F, Huq; Ma, Moni

doi:10.1101/473165

Cited by 11 publications

(9 citation statements)

References 45 publications

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“…Mascot (Matrix Science Ltd, London, UK) database search program was used to analysis the peptides masses data. SwissPort database was used to search the peak lists against Homo sapiens entries [16].…”

Section: Maldi-ms (Matrix-assisted Laser Desorption Ionisation Mass Smentioning

confidence: 99%

Protein Expression Patterns in ovarian cancer cells Associated with Monofunctional Platinums Treatment

Arzuman

Moni

Beale

et al. 2019

Preprint

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@s y d ne y .ed u .au @ Funding information 1 | BACKGROUND Ovarian cancer (OC) is the leading cause of death from gynaecological cancers in women [15, 36, 29] and the fifth most common cause of cancer deaths in women overall, with an estimated 239,000 new cases and over 152,000 deaths worldwide annually [10, 27]. It has a five year mortality rate in the United States of approximately 35% [12, 35] [3, 22].Current standard treatment mainly comprises platinum-based chemotherapeutics, with platinum drugs cisplatin and carboplatin given in combination with paclitaxel used to treat nearly all women with OC. However, the most prevalent OC type, high grade serous carcinoma (HGSC), often develops significant chemotherapy resistance alongside greatly altered genomes and transcriptome. Thus, most patients initially respond to the treatment, but over the longer term

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Section: Maldi-ms (Matrix-assisted Laser Desorption Ionisation Mass Smentioning

confidence: 99%

Protein Expression Patterns in ovarian cancer cells Associated with Monofunctional Platinums Treatment

Arzuman

Moni

Beale

et al. 2019

Preprint

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“…The top five significant pathways associated with AS are shown in figure 4(b). Among these pathways, 'Non-small cell lung cancer' is linked to development of adeno-squamous cell and large-cell carcinoma types that are responsible for approximately 75% of all lung cancer [34,35]. 'IL-1 Signaling Pathway' is responsible to control pro-inflammatory responses, including inflammasome activity tissues [36].…”

Section: Pathway and Functional Association Analysismentioning

confidence: 99%

Genetic Effects of Welding Fumes on the Development of Respiratory System Diseases

Rana

Akhtar

Islam

et al. 2018

Preprint

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Background: The welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WFs may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood. Methods: We developed a quantitative framework to identify gene expression effects of WFs that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (diseasome) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology. Results: We observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEGs analysis, with disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC. Conclusions: Our network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure. † These authors hold joint first authorship of this work.

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“…For such pathway studies, our starting point is the differential expression of genes (DEGs) between these lesions. We can then use analytic tools to study these DEGs and discover pathways, and pathway hub genes that may affect cell functions and thereby underlie tumour morbidity, growth, and invasion [9][10][11][12]. The repertoire of candidate pathway factors can be extended using approaches such as gene ontology analysis and protein-protein interaction (PPI) studies.…”

Section: Introductionmentioning

confidence: 99%

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

Hossain

Akter

Rahman

et al. 2020

IJERPH

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Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

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Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and Mortality

Cited by 11 publications

References 45 publications

Protein Expression Patterns in ovarian cancer cells Associated with Monofunctional Platinums Treatment

Protein Expression Patterns in ovarian cancer cells Associated with Monofunctional Platinums Treatment

Genetic Effects of Welding Fumes on the Development of Respiratory System Diseases

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

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