Identification of core genes and clinical roles in pregnancy-associated breast cancer based on integrated analysis of different microarray profile datasets

Abstract More women are delaying child-birth. Thus, the diagnosis of pregnancy-associated breast cancer (PABC) will continue to increase. The aim of this study was to identify core candidate genes of PABC, and the relevance of the genes on the prognosis of PABC. GSE31192 and GSE53031 microarray profile datasets were downloaded from the Gene Expression Omnibus database and differentially expressed genes were analyzed using the R package and GEO2R tool. Then, Gene Ontology and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Moreover, the Search Tool for the Retrieval of Interacting Genes and the Molecular Complex Detection Cytoscape software plug-in were utilized to visualize protein–protein interactions and to screen candidate genes. A total of 239 DEGs were identified in PABC, including 101 up-regulated genes mainly enriched in fatty acid activation and the fibroblast growth factor signaling pathway, while 138 down-regulated genes particularly involved in activation of DNA fragmentation factor and apoptosis-induced DNA fragmentation. Fourteen hub genes with a high degree of connectivity were selected, including CREB1, ARF3, UBA5, SIAH1, KLHL3, HECTD1, MMP9, TRIM69, MEX3C, ASB6, UBE2Q2, FBXO22, EIF4A3, and PXN. Overall survival (OS) analysis of core candidate genes was performed using the Gene Expression Profiling Interactive Analysis and UALCAN websites. High ASB6 expression was associated with worse OS of PABC patients. Molecular subtypes and menopause status were also associated with worse OS for PABC patients. In conclusion, ASB6 could be a potential predictor and therapeutic target in patient with PABC.

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Integrated Analysis of the Association Between Alzheimer`s Disease (AD) and Cardiovascular Disease (CVD)

10.1101/2021.06.15.21258992 ◽

2021 ◽

Author(s):

Katherine Liu Wei

Keyword(s):

Gene Expression ◽

Cardiovascular Disease ◽

Cause Of Death ◽

Protein Interactions ◽

Brain Regions ◽

Gene Expression Omnibus ◽

Integrated Analysis ◽

Biological Processes ◽

Protein Protein Interactions ◽

The Us

Alzheimer`s Disease (AD), the sixth leading cause of death in the US, and cardiovascular disease (CVD), the first leading cause of death in the US, are frequently associated. Past studies hypothesize that amyloid deposits damage organs, causing this association. Examining how vascular factors can influence AD pathogenesis can help in understanding the link between the blood to the brain, which can provide alternative paths of exploration for disease treatment. This study analyzes gene expression and shared biological processes between AD and CVD, specifically myocardial infarction and heart failure, via bioinformatic approaches and published datasets from the Gene Expression Omnibus (GEO). First, 73 differentially expressed genes (DEGs) were identified among four datasets using blood samples from AD and CVD patients. Panther`s Gene Ontology Analysis validated several biological processes such as xylulose biosynthetic process and toll-like receptor TLR1:TLR2 signaling pathway along with molecular functions, cellular components, and pathways to be significantly enriched in the list of 73 DEGs. Analysis of protein-protein interactions and the associated gene network indicated that from the list of 73 DEGs, only six (MAPK14, TLR2, HCK, GRB2, PRKCD, PTPN6) had eight or more degrees. Next, those six genes were identified in a normalized dataset containing different brain regions of AD and non-AD patients. Two-sample t-tests for differences in mean showed statistically significant differences in GRB2 and PRKCD, supporting a blood-brain relationship in the association between AD and CVD. This study can help in developing new medications to target AD and CVD susceptible genes.

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Integrated analysis of gene expression and methylation profiles of 48 candidate genes in breast cancer patients

Breast Cancer Research and Treatment ◽

10.1007/s10549-016-4004-8 ◽

2016 ◽

Vol 160 (2) ◽

pp. 371-383 ◽

Cited By ~ 20

Author(s):

Zibo Li ◽

Jianfu Heng ◽

Jinhua Yan ◽

Xinwu Guo ◽

Lili Tang ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Patients ◽

Candidate Genes ◽

Integrated Analysis ◽

Breast Cancer Patients

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Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis

Breast Cancer Research and Treatment ◽

10.1007/s10549-008-0038-x ◽

2008 ◽

Vol 113 (2) ◽

pp. 251-252 ◽

Cited By ~ 3

Author(s):

Mads Thomassen ◽

Qihua Tan ◽

Torben A. Kruse

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Candidate Genes ◽

Cancer Metastasis ◽

Meta Analysis ◽

Breast Cancer Metastasis

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Decision theory for precision therapy of breast cancer

Scientific Reports ◽

10.1038/s41598-021-82418-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Michael Kenn ◽

Dan Cacsire Castillo-Tong ◽

Christian F. Singer ◽

Rudolf Karch ◽

Michael Cibena ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Decision Theory ◽

Data Science ◽

Gene Expression Omnibus ◽

Hormone Receptor Status ◽

Breast Cancer Patients ◽

Receptor Status ◽

Kaplan Meier ◽

Precision Therapy

AbstractCorrectly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

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Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

Journal of Diabetes Research ◽

10.1155/2021/5546199 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Bojun Xu ◽

Lei Wang ◽

Huakui Zhan ◽

Liangbin Zhao ◽

Yuehan Wang ◽

...

Keyword(s):

Gene Expression ◽

Diabetic Nephropathy ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Topological Analysis ◽

Gene Expression Omnibus ◽

Complement Cascade ◽

Hub Genes ◽

Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

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High Moesin Expression Is a Predictor of Poor Prognosis of Breast Cancer: Evidence From a Systematic Review With Meta-Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.650488 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiaoli Hu ◽

Yang Liu ◽

Zhitong Bing ◽

Qian Ye ◽

Chengcheng Li

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Systematic Review ◽

Cancer Patients ◽

Poor Prognosis ◽

Data Extraction ◽

Meta Analysis ◽

Gene Expression Omnibus ◽

Breast Cancer Patients ◽

Node Metastases

Owing to metastases and drug resistance, the prognosis of breast cancer is still dismal. Therefore, it is necessary to find new prognostic markers to improve the efficacy of breast cancer treatment. Literature shows a controversy between moesin (MSN) expression and prognosis in breast cancer. Here, we aimed to conduct a systematic review and meta-analysis to evaluate the prognostic relationship between MSN and breast cancer. Literature retrieval was conducted in the following databases: PubMed, Web of Science, Embase, and Cochrane. Two reviewers independently performed the screening of studies and data extraction. The Gene Expression Omnibus (GEO) database including both breast cancer gene expression and follow-up datasets was selected to verify literature results. The R software was employed for the meta-analysis. A total of 9 articles with 3,039 patients and 16 datasets with 2,916 patients were ultimately included. Results indicated that there was a significant relationship between MSN and lymph node metastases (P < 0.05), and high MSN expression was associated with poor outcome of breast cancer patients (HR = 1.99; 95% CI 1.73–2.24). In summary, there is available evidence to support that high MSN expression has valuable importance for the poor prognosis in breast cancer patients.Systematic Review Registrationhttps://inplasy.com/inplasy-2020-8-0039/.

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Integrated Analysis of Gene Expression, CpG Island Methylation, and Gene Copy Number in Breast Cancer Cells by Deep Sequencing

PLoS ONE ◽

10.1371/journal.pone.0017490 ◽

2011 ◽

Vol 6 (2) ◽

pp. e17490 ◽

Cited By ~ 97

Author(s):

Zhifu Sun ◽

Yan W. Asmann ◽

Krishna R. Kalari ◽

Brian Bot ◽

Jeanette E. Eckel-Passow ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Deep Sequencing ◽

Copy Number ◽

Breast Cancer Cells ◽

Cpg Island ◽

Gene Copy Number ◽

Gene Copy ◽

Integrated Analysis ◽

Cpg Island Methylation

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Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis

Breast Cancer Research and Treatment ◽

10.1007/s10549-008-9927-2 ◽

2008 ◽

Vol 113 (2) ◽

pp. 239-249 ◽

Cited By ~ 67

Author(s):

Mads Thomassen ◽

Qihua Tan ◽

Torben A. Kruse

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Candidate Genes ◽

Cancer Metastasis ◽

Meta Analysis ◽

Breast Cancer Metastasis

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A Novel Approach for Discovering Condition-Specific Correlations of Gene Expressions within Biological Pathways by Using Cloud Computing Technology

BioMed Research International ◽

10.1155/2014/763237 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8

Author(s):

Tzu-Hao Chang ◽

Shih-Lin Wu ◽

Wei-Jen Wang ◽

Jorng-Tzong Horng ◽

Cheng-Wei Chang

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cloud Computing ◽

Cancer Recurrence ◽

Computation Time ◽

Gene Expression Omnibus ◽

Biological Pathways ◽

Data Sets ◽

Gene Expressions ◽

Differential Gene

Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.

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