Analyzing Microarray Data of Alzheimer's Using Cluster Analysis to Identify the Biomarker Genes

Guttula, Satya Vani; Apparao, A.; Gumpeny, Ramachandra Sridhar

doi:10.1155/2012/649456

Cited by 46 publications

(45 citation statements)

References 24 publications

(27 reference statements)

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“…Insulin growth factor receptor 2 is altered in AD brain 35 and is significantly up regulated in blood in this study. Insulin signaling is reported to be altered in AD brain in general, and global decreases of brain blood flow and glucose metabolism precede symptoms in AD 36 .…”

Section: Discussionsupporting

confidence: 54%

Distinctive RNA Expression Profiles in Blood Associated With Alzheimer Disease After Accounting for White Matter Hyperintensities

Bai

Stamova

et al. 2014

Alzheimer Disease &Amp; Associated Disorders

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Background Defining the RNA transcriptome in Alzheimer’s Disease (AD) will help understand disease mechanisms and provide biomarkers. Though the AD blood transcriptome has been studied, effects of white matter hyperintensities (WMH) were not considered. This study investigated the AD blood transcriptome and accounted for WMH. Methods RNA from whole blood was processed on whole-genome microarrays. Results 293 probe sets were differentially expressed in AD versus controls, 5 of which were significant for WMH status. The 288 AD-specific probe-sets classified subjects with 87.5% sensitivity and 90.5% specificity. They represented 188 genes of which 29 have been reported in prior AD blood and 89 in AD brain studies. Regulated blood genes included MMP9, MME (Neprilysin), TGFβ1, CA4, OCLN, ATM, TGM3, IGFR2, NOV, RNF213, BMX, LRRN1, CAMK2G, INSR, CTSD, SORCS1, SORL1 and TANC2. Conclusions RNA expression is altered in AD blood irrespective of WMH status. Some genes are shared with AD brain.

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Section: Discussionsupporting

confidence: 54%

Distinctive RNA Expression Profiles in Blood Associated With Alzheimer Disease After Accounting for White Matter Hyperintensities

Bai

Stamova

et al. 2014

Alzheimer Disease &Amp; Associated Disorders

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“…Since microarray can interrogate expression levels of thousands of genes in human genome simultaneously, it has been widely used in discovery of disease biomarkers [29], [30], [31]. In this work, we have analysed gene expression data with computational methods with the aim of uncovering genes that potentially dysregulate in PD.…”

Section: Discussionmentioning

confidence: 99%

Gene Expression Profiling Combined with Bioinformatics Analysis Identify Biomarkers for Parkinson Disease

Diao

et al. 2012

PLoS ONE

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Parkinson disease (PD) progresses relentlessly and affects approximately 4% of the population aged over 80 years old. It is difficult to diagnose in its early stages. The purpose of our study is to identify molecular biomarkers for PD initiation using a computational bioinformatics analysis of gene expression. We downloaded the gene expression profile of PD from Gene Expression Omnibus and identified differentially coexpressed genes (DCGs) and dysfunctional pathways in PD patients compared to controls. Besides, we built a regulatory network by mapping the DCGs to known regulatory data between transcription factors (TFs) and target genes and calculated the regulatory impact factor of each transcription factor. As the results, a total of 1004 genes associated with PD initiation were identified. Pathway enrichment of these genes suggests that biological processes of protein turnover were impaired in PD. In the regulatory network, HLF, E2F1 and STAT4 were found have altered expression levels in PD patients. The expression levels of other transcription factors, NKX3-1, TAL1, RFX1 and EGR3, were not found altered. However, they regulated differentially expressed genes. In conclusion, we suggest that HLF, E2F1 and STAT4 may be used as molecular biomarkers for PD; however, more work is needed to validate our result.

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“…In addition, the samples were subtyped based on clustering analysis. Heat maps between the gene expression values and samples were generated using TreeView (21).…”

Section: Datamentioning

confidence: 99%

Subpath analysis of each subtype of head and neck cancer based on the regulatory relationship between miRNAs and biological pathways

Zhang²,

Xia

et al. 2015

Oncology Reports

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Abstract. The aim of the present study was to explore the potential mechanisms involved in each subtype of head and neck squamous cell carcinoma (HNSCC) via subpath analysis and to investigate their relevance in the prevention of HNSCC. Gene expression profiles of GSE6631 and GSE39366 containing 44 and 168 HNSCC samples, respectively, were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) from samples in GSE6631 and GSE393666 were screened using the Detection of Imbalanced Differential Signal (DIDS) method respectively. DEGs in GSE39366 were matched with the DEGs in GSE6631 and were used to classify the subtypes of HNSCC based on hierarchical clustering analysis. Furthermore, DEGs were separated into different subtypes and then the pathway information was analyzed. The regulated miRNAs for the DEGs in each subtype were analyzed to select the significant subpaths. Totally, 1,095 DEGs from GSE6631 and 2,528 DEGs from GSE39366 were screened. Samples in GSE39366 were separated into four subtypes. Specific genes in each subtype and DEGs in the common gene set involved in a variety of pathways were identified. In addition, the significant miRNAtarget-pathway subpath of each subtype of HNSCC and the common gene set of HNSCC were also enriched. Our data suggest that human papillomavirus (HPV) is positively correlated with HNSCC in subtype 2. Several miRNAs (miRLet-7A, miR-1, miR-206, miR-153, miR-519A and miR-506) and their target genes (CYP46A1, BPNT1, MCM7 and COL5A1) are crucial for HNSCC prevention via different pathways and may provide further knowledge of the mechanisms involved in the progression of HNSCC.

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Analyzing Microarray Data of Alzheimer's Using Cluster Analysis to Identify the Biomarker Genes

Cited by 46 publications

References 24 publications

Distinctive RNA Expression Profiles in Blood Associated With Alzheimer Disease After Accounting for White Matter Hyperintensities

Distinctive RNA Expression Profiles in Blood Associated With Alzheimer Disease After Accounting for White Matter Hyperintensities

Gene Expression Profiling Combined with Bioinformatics Analysis Identify Biomarkers for Parkinson Disease

Subpath analysis of each subtype of head and neck cancer based on the regulatory relationship between miRNAs and biological pathways

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