Poisson-based self-organizing feature maps and hierarchical clustering for serial analysis of gene expression data

Genome-Scale Expression Pattern of Long Non-Coding RNAs in Chinese Uyghur Patients with Parkinson's Disease

BACKGROUND Long non-coding RNAs (lncRNAs) are transcripts thought to regulate gene expression at the post-transcriptional level. Some lncRNAs are associated with Parkinson's disease (PD) and participate in pathological processes of PD. The incidence of PD is relatively high in members of the Uyghur minority living in Xingjiang province of China. This study measured the expression of lncRNAs in the peripheral blood cells of Chinese Uyghur individuals with and without PD and analyzed the possible function of these lncRNAs in the development of PD. MATERIAL AND METHODS Peripheral blood samples were collected from 55 Uyghur patients with PD and 55 healthy volunteers. Total RNA was extracted, and the levels of expression of whole-genome lncRNAs and mRNAs in 10 samples (5 PD and 5 controls) were determined by microarray method. The expression levels of lncRNAs in all 100 subjects were determined by qRT-PCR. The lncRNA expression profiles of PD patients were determined based on lncRNA microarray chip analysis, and differentially expressed lncRNAs were identified. The results of chip analysis were confirmed in a large clinical cohort. RESULTS Comparison of subjects with and without PD identified 32 significantly up-regulated and 18 significantly down-regulated lncRNAs in the PD group. GO analysis showed that mRNAs encoding proteins involved in the regulation of biological processes were differentially expressed, with the inflammatory immune response being the most significantly related pathway. CONCLUSIONS The expression of lncRNAs in peripheral blood differed significantly in PD patients and controls. These differentially expressed lncRNAs may play a role in the development of PD.

Expression pattern of genome-scale long noncoding RNA following acute myocardial infarction in Chinese Uyghur patients

In this study, we examined the long noncoding RNA (lncRNA) expression pattern in Uyghur patients (a minority of China) with acute myocardial infarction (AMI) on a genome-wide scale. Total RNAs were extracted from the peripheral blood of 55 Uyghur AMI patients and 55 healthy volunteers. The expression levels of genome-wide scale lncRNAs and mRNAs were determined by microarray in 10 samples (5 AMI and 5 controls). qRT-PCR was used to validate lncRNA expression levels in 100 samples (50 AMI and 50 controls). Data analyses were performed using R and Bioconductor. A total of 3624 up- and 1637 down-regulated lncRNAs were identified to be significantly and differentially expressed between these two groups. The annotation result of their co-expressed mRNAs showed that the most significantly related category of GO analysis was regulation of biological processes, and the most significantly related pathway was apoptosis and its corresponding p53. The microarray identified ENST00000416860.2, ENST00000421157.1 and TCONS_00025701 lncRNAs were confirmed by qRT-PCR. Our study indicated that clusters of lncRNAs were significantly and differentially expressed in the peripheral blood of AMI patients when compared with healthy controls within the Uyghur population. These newly identified lncRNAs may have a potential role in the development of AMI.

Gene expression profiling leads to discovery of correlation of matrix metalloproteinase 11 and heparanase 2 in breast cancer progression

Background

In order to identify biomarkers involved in breast cancer, gene expression profiling was conducted using human breast cancer tissues.

Methods

Total RNAs were extracted from 150 clinical patient tissues covering three breast cancer subtypes (Luminal A, Luminal B, and Triple negative) as well as normal tissues. The expression profiles of a total of 50,739 genes were established from a training set of 32 samples using the Agilent Sure Print G3 Human Gene Expression Microarray technology. Data were analyzed using Agilent Gene Spring GX 12.6 software. The expression of several genes was validated using real-time RT-qPCR.

Results

Data analysis with Agilent GeneSpring GX 12.6 software showed distinct expression patterns between cancer and normal tissue samples. A group of 28 promising genes were identified with ≥ 10-fold changes of expression level and p-values < 0.05. In particular, MMP11 and HPSE2 were closely examined due to the important roles they play in cancer cell growth and migration. Real-time RT-qPCR analyses of both training and testing sets validated the gene expression profiles of MMP11 and HPSE2.

Conclusions

Our findings identified these 2 genes as a novel breast cancer biomarker gene set, which may facilitate the diagnosis and treatment in breast cancer clinical therapies.

Molecular pattern discovery based on penalized matrix decomposition

A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes.

Developmental staging of male murine embryonic gonad by SAGE analysis

Despite the identification of key genes such as Sry integral to embryonic gonadal development, the genomic classification and identification of chromosomal activation of this process is still poorly understood. To better understand the genetic regulation of gonadal development, we performed Serial Analysis of Gene Expression (SAGE) to profile the genes and novel transcripts, and an average of 152,000 tags from male embryonic gonads at E10.5 (embryonic day 10.5), E11.5, E12.5, E13.5, E15.5 and E17.5 were analyzed. A total of 275,583 non-singleton tags that do not map to any annotated sequence were identified in the six gonad libraries, and 47,255 tags were mapped to 24,975 annotated sequences, among which 987 sequences were uncharacterized. Utilizing an unsupervised pattern identification technique, we established molecular staging of male gonadal development. Rather than providing a static descriptive analysis, we developed algorithms to cluster the SAGE data and assign SAGE tags to a corresponding chromosomal position; these data are displayed in chromosome graphic format. A prominent increase in global genomic activity from E10.5 to E17.5 was observed. Important chromosomal regions related to the developmental processes were identified and validated based on established mouse models with developmental disorders. These regions may represent markers for early diagnosis for disorders of male gonad development as well as potential treatment targets.

Molecular phenotypes distinguish patients with relatively stable from progressive idiopathic pulmonary fibrosis (IPF)

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a progressive, chronic interstitial lung disease that is unresponsive to current therapy and often leads to death. However, the rate of disease progression differs among patients. We hypothesized that comparing the gene expression profiles between patients with stable disease and those in which the disease progressed rapidly will lead to biomarker discovery and contribute to the understanding of disease pathogenesis.

METHODOLOGY AND PRINCIPAL FINDINGS

To begin to address this hypothesis, we applied Serial Analysis of Gene Expression (SAGE) to generate lung expression profiles from diagnostic surgical lung biopsies in 6 individuals with relatively stable (or slowly progressive) IPF and 6 individuals with progressive IPF (based on changes in DLCO and FVC over 12 months). Our results indicate that this comprehensive lung IPF SAGE transcriptome is distinct from normal lung tissue and other chronic lung diseases. To identify candidate markers of disease progression, we compared the IPF SAGE profiles in stable and progressive disease, and identified a set of 102 transcripts that were at least 5-fold up regulated and a set of 89 transcripts that were at least 5-fold down regulated in the progressive group (P-value</=0.05). The over expressed genes included surfactant protein A1, two members of the MAPK-EGR-1-HSP70 pathway that regulate cigarette-smoke induced inflammation, and Plunc (palate, lung and nasal epithelium associated), a gene not previously implicated in IPF. Interestingly, 26 of the up regulated genes are also increased in lung adenocarcinomas and have low or no expression in normal lung tissue. More importantly, we defined a SAGE molecular expression signature of 134 transcripts that sufficiently distinguished relatively stable from progressive IPF.

CONCLUSIONS

These findings indicate that molecular signatures from lung parenchyma at the time of diagnosis could prove helpful in predicting the likelihood of disease progression or possibly understanding the biological activity of IPF.

Clustering-based approaches to SAGE data mining

Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation.