Microarray Expression Data Mining Service

Why Perform Microarray Expression Data Mining?

Microarray expression data mining has become a cornerstone in genomics, offering a powerful tool for investigating gene expression patterns across thousands of genes simultaneously. The importance of this data lies in its ability to decode the complex regulatory mechanisms within cells, enabling researchers to uncover gene functions, identify biomarkers, and analyze genetic variations. From cancer research to drug development, microarray data supports a wide array of applications, including disease diagnosis, therapeutic target identification, and personalized medicine strategies. This data-driven approach enhances understanding of the molecular underpinnings of various conditions, facilitating the development of more targeted interventions.

What Can We Offer?

At CD Genomics, we provide a comprehensive Microarray Expression Data Mining Service that encompasses the entire workflow, from data collection to in-depth analysis. Our service begins with assisting clients in gathering high-quality microarray datasets from various sources, followed by efficient downloading from reputable public repositories such as GEO and ArrayExpress. Once the data is collected, we perform detailed bioinformatics analysis, including normalization, differential gene expression analysis, and functional annotation, tailored to meet specific research goals. By offering customized solutions and expert guidance throughout the process, we ensure that researchers can extract meaningful insights from their microarray data efficiently and effectively.

Workflow for Microarray Expression Data Mining

Microarray Expression Data Download

We specializes in efficiently sourcing and downloading expression data from prominent public repositories such as GEO and ArrayExpress. Our approach involves identifying relevant studies based on specific research questions, using accession numbers to retrieve datasets, and utilizing batch download tools to efficiently manage large datasets. Once downloaded, the raw data is processed and organized for subsequent bioinformatics analysis, ensuring that researchers have access to high-quality data for their studies.Using the GEO database as an example, the data page provides access to the raw CEL files for each sample, as well as the option to download the processed expression matrix (by clicking on Series Matrix File(s) in the Download family section to access the download page).

Bioinformatics Analysis Content

What Are the Advantages of Our Service?

Expertise in Data Handling

With many years of experience in genomics and bioinformatics, our team is well-versed in the complexities of microarray technologies. This expertise allows us to navigate the intricacies of data processing, ensuring accurate and reliable results that you can trust.

End-to-End Service

Our comprehensive service covers the entire workflow, from data collection and downloading to advanced bioinformatics analysis and reporting. By providing an integrated solution, we eliminate the need for multiple vendors or outsourcing, streamlining the research process and saving you valuable time.

Customizable Solutions

We recognize that every research project is unique, which is why we offer tailored services designed to meet the specific needs of your study. Whether you require exploratory data analysis, pathway enrichment, or complex machine learning models, we adapt our approach to align with your research objectives, ensuring relevance and impact.

High-Quality Results

Our bioinformatics pipeline is designed to deliver accuracy and reproducibility in data analysis. We employ cutting-edge algorithms and rigorous statistical methods to ensure that the insights generated from your microarray data are both meaningful and reliable, providing a solid foundation for your research conclusions.

Integration of Multi-Omics Data

Our service facilitates the integration of microarray expression data with other omics data types, such as genomics, proteomics, and metabolomics. This multi-omics approach provides a comprehensive understanding of biological systems, enabling researchers to identify complex interactions and regulatory networks that may not be apparent when analyzing single omics datasets alone.

Confidentiality and Data Security

At CD Genomics, we prioritize the confidentiality and security of your research data. We implement robust data protection measures to ensure that your information is handled with the utmost care and remains secure throughout the data mining process.

Support and Collaboration

Our team is dedicated to providing ongoing support and collaboration throughout your project. We work closely with clients to understand their specific research needs and goals, offering expert guidance and feedback at every stage of the analysis.

Scalability and Flexibility

Our services are designed to be scalable, accommodating projects of any size, from small pilot studies to large-scale research initiatives. This flexibility allows us to adapt our resources and strategies to suit your project's evolving requirements.

Comprehensive Reporting

After data analysis, we provide detailed reports that summarize our findings, methodologies, and insights. These reports are designed to be clear and accessible, enabling researchers to easily interpret the results and apply them to their ongoing work.

By choosing CD Genomics for your microarray expression data mining needs, you gain a partner dedicated to advancing your research through expert guidance, cutting-edge technology, and a comprehensive service model that supports every aspect of your study.

What Does Microarray Expression Data Mining Reveal?

Microarray Expression Data Download and Normalization Process

This process begins by identifying relevant studies based on specific research objectives and using accession numbers to access the desired datasets. Once the data is downloaded, it undergoes normalization to correct for technical variations that may affect the accuracy of gene expression measurements. Normalization techniques, such as total intensity normalization, regression-based normalization, and ratio-based normalization, are applied to ensure that the data is comparable across different samples and experimental conditions.After organizing the expression matrix, we need to convert the probe IDs into gene symbols. Different sequencing platforms have varying correspondences for these mappings.

Table 1.Microarray Expression Data(CEL Format Representation)

Hierarchical Clustering of Differentially-Expressed Genes (DEGs)

Following normalization, DEGs between tumor and normal tissues from the 52 samples were identified using the cut-off criteria of |log2FC| > 1 and FDR < 0.05. A total of 1,765 DEGs were detected between pancreatic cancer (PC) and normal tissues, with 1,312 genes being upregulated and 453 genes downregulated. The hierarchical clustering of these 1,765 DEGs . The LogFC values for the DEGs varied from 6-fold downregulation to 6-fold upregulation. Notably, most of the DEGs showed increased expression in PC tumors compared to normal tissues, allowing for clear differentiation between tumor samples and normal controls based on the characteristics of the DEGs.

Figure 1.The heatmap illustrates the clustering of differentially expressed genes between the two sample types. The x-axis corresponds to normal and tumor samples, while the y-axis lists the genes. In the heat map, blue represents downregulated gene expression (<0), while orange signifies upregulated gene expression (>0) in pancreatic versus normal tissues.(Long,2016)

Protein-Protein Interactions(PPIs) Analysis of DEGs

To identify the PPIs and predict protein functions, PPI network analysis was conducted utilizing the STRING database with a threshold greater than 0.9. The analysis revealed that the PPI network of upregulated differentially expressed genes (DEGs) was connected to 92 nodes (proteins) through 171 PPIs. In contrast, the PPI network of downregulated DEGs was linked to 82 nodes through 83 PPIs.

Figure 2. The protein-protein interaction networks . (A) upregulated DEGs are depicted in orange.(B) downregulated DEGs are shown in blue. In these networks, the nodes represent proteins, and the lines connecting the nodes indicate the interactions between them. DEGs refers to differentially expressed genes.(Long,2016)

Pathway Analysis of DEGs

To obtain the enriched GO biological processes for the DEGs in the PPI network, GO functional enrichment analysis was conducted separately for the upregulated and downregulated DEGs (FDR < 0.05). Pathway analysis of the DEGs indicated that only the pancreatic cancer pathway was associated with the DEGs in the PPI network, including the upregulated TGFB1 and TGFBR1, as well as the downregulated EGF.

Figure 3. Molecular Pathways in Pancreatic Cancer Associated with DEGs in Protein-Protein Interaction Networks. The red boxes denote upregulated DEGs, while the green box indicates a downregulated DEG. Red letters correspond to tumor suppressors or oncogenes that have been validated in earlier studies. (Long,2016)

Title:Identification of a TLR-Induced Four-lncRNA Signature as a Novel Prognostic Biomarker in Esophageal Carcinoma

Publication:Front Cell Dev Biol

Main Methods: microarray data mining

Abstract:The study explores the role of long non-coding RNAs (lncRNAs) in regulating Toll-like receptor (TLR) signaling and their implications for prognosis in esophageal carcinoma (ESCA). Utilizing clinical and lncRNA expression data from 179 ESCA patients profiled by the Agilent lncRNA + mRNA microarray and additional RNA-seq data from The Cancer Genome Atlas (TCGA), the researchers constructed a lncRNA-TLR co-expression network and identified 357 TLR-related lncRNAs. Among these, four lncRNAs (AP000696.1, LINC00689, LINC00900, and AP000487.1) were significantly associated with overall survival (OS) and were capable of stratifying patients into high-risk and low-risk groups, confirming their prognostic significance through multivariate analysis. This TLR-induced four-lncRNA signature represents a promising biomarker for ESCA and may offer new therapeutic targets for intervention.

Research Results:

Identification of TLR-Related lncRNAs in ESCA

The researchers aimed to identify lncRNAs associated with ESCA by comparing expression profiles between 179 paired ESCA patients and normal tissues. They identified 587 differentially expressed lncRNAs (|log2(fold change)| > 1 and FDR adjusted p-value < 0.05), including 258 upregulated and 329 downregulated lncRNAs. Hierarchical clustering analysis demonstrated that these lncRNAs could effectively distinguish ESCA patients from normal tissues (chi-square test p < 2.2e-16). Furthermore, they calculated Pearson correlation coefficients to assess the relationship between 104 TLR-related genes and the lncRNAs, identifying 357 lncRNAs as TLR-related (Pearson correlation coefficient > 0.6 and p < 0.05). A TLR-related lncRNAs-mRNA network was constructed, consisting of 1,404 edges involving 51 TLR genes and 357 lncRNAs.

Figure 4.Identification of Toll-like receptors-induced lncRNAs in ESCA.(A) Volcano plots illustrating differentially expressed lncRNAs. (B) Heatmap displaying hierarchical clustering analysis of differentially expressed lncRNAs. (C) Overview of the TLR-related lncRNAs-mRNA network.(Liu,2020)

Identification of a TLR-Induced Four-lncRNA Signature in the Discovery Set

The researchers identified four TLR-related lncRNAs (AP000696.1, LINC00689, LINC00900, and AP000487.1) as biomarkers for predicting overall survival (OS) in esophageal carcinoma (ESCA) by analyzing 357 lncRNAs in the TLR-related lncRNAs-mRNA network. These lncRNAs formed a four-TLR-lncRNA signature, which stratified 120 patients into high-risk (n = 88) and low-risk (n = 32) groups, with significant differences in OS (Log-rank test p < 0.001). Low-risk patients had a median OS of 4.93 years compared to 1.56 years for high-risk patients, with five-year survival rates of 49.5% and 12.5%, respectively. The model exhibited AUC values of 0.77 at five years and 0.67 at three years. Three lncRNAs were upregulated in high-risk patients, while one lncRNA served as a protective factor in the low-risk group.

Figure 5.Creation of a four-lncRNA signature triggered by Toll-like receptors in the discovery set. (A) Kaplan–Meier survival curves comparing overall survival between the high-risk and low-risk groups stratified by the four-lncRNA signature. (B) Time-dependent ROC analysis for 3-year and 5-year survival. (C) Distribution of risk scores, patient survival status, and expression patterns of lncRNAs.(Liu,2020)

Validation of the Four-TLR-lncRNA Signature in the Internal Testing Set

The same scoring formula and risk cutoff value derived from the discovery set were applied to patients in the internal testing set to calculate each patient's risk score. As seen in the discovery set, the expression patterns of the four TLR-related lncRNA biomarkers were consistent in this cohort. Specifically, three lncRNAs (AP000696.1, LINC00900, and AP000487.1) served as risk factors, while LINC00689 acted as a protective factor.

Figure 6.Creation of a four-lncRNA signature triggered by Toll-like receptors in the testing set. (Liu,2020)

Independent Validation of the Four-TLR-lncRNA Signature in the TCGA Set With Cross-Platform

To further evaluate the robustness of the four-TLR-lncRNA signature in predicting OS, the prognostic value of this signature was tested in an entirely independent TCGA dataset utilizing an RNA-seq platform. Consistent with the findings from the discovery and internal testing sets, the expression patterns of the four TLR-related lncRNA biomarkers were similar in this independent TCGA dataset.

Functional Analysis of the Four-TLR-lncRNA Signature

The researchers assessed the correlation between the expression levels of four TLR-related lncRNA biomarkers and mRNAs using the Pearson correlation coefficient, identifying 3,313 mRNAs associated with the lncRNA biomarkers, including 22 well-known TLR genes. Hypergeometric test results showed a marginally significant enrichment of TLR genes among the co-expressed mRNAs (p = 0.076). Further functional enrichment analyses revealed that the co-expressed mRNAs were enriched in TLR-related and cancer-related GO terms and KEGG pathways, such as ECM-receptor interaction, focal adhesion, and the PI3K-Akt signaling pathway.

Figure 7.Functional enrichment analysis.(A) Venn diagram illustrating the overlap of co-expressed genes with lncRNAs and known TLR genes. (B) Enriched GO terms and KEGG pathways.(Liu,2020)

Conclusion

This study investigates the roles of lncRNAs in TLR signaling and their prognostic significance in ESCA. The researchers identified 587 differentially expressed lncRNAs, with 357 correlated to known TLR genes. They established a four-lncRNA signature (AP000696.1, LINC00689, LINC00900, and AP000487.1) that stratified patients into high-risk and low-risk groups, showing significant differences in overall survival. The signature demonstrated robustness across independent datasets and was enriched in TLR-related pathways. Further validation is necessary to fully elucidate the regulatory mechanisms and enhance clinical application in ESCA.

1.What is microarray expression data mining?

Microarray expression data mining is the process of analyzing data generated from microarray experiments to identify patterns, correlations, and insights related to gene expression. This includes detecting differentially expressed genes (DEGs), understanding biological pathways, and uncovering potential biomarkers for diseases.

2.What types of data can be analyzed using microarray expression data mining?

Microarray expression data mining can analyze various types of data, including raw intensity values from microarrays, processed expression matrices, and associated clinical data from samples. It can also integrate data from other omics technologies, such as RNA-seq and proteomics.

3.What bioinformatics methods are commonly used in microarray data mining?

Common bioinformatics methods include normalization of expression data, differential expression analysis (using tools like limma), hierarchical clustering, pathway enrichment analysis (such as Gene Ontology and KEGG), and constructing gene co-expression networks.

4.How can microarray expression data mining help in disease research?

It helps identify genes and pathways involved in disease processes, uncover potential biomarkers for diagnosis and prognosis, and provides insights into the mechanisms of action for therapeutic targets. This can lead to improved treatment strategies and personalized medicine approaches.

5.What are the limitations of microarray expression data?

Limitations include the potential for cross-hybridization leading to non-specific binding, a limited dynamic range for detecting gene expression levels, and the fact that microarrays are designed to detect known sequences, which may overlook novel genes or variants.

6.What are some key considerations when interpreting microarray data?

Key considerations include the quality of the data (e.g., normalization and batch effects), statistical significance of the results, biological relevance of identified genes, and the need for validation of findings through additional experiments or independent datasets.

References

1. Long, J.,  et al. Gene expression profile analysis of pancreatic cancer based on microarray data. Molecular medicine reports.2016, 13(5), 3913–3919.
2. Liu, J.,  et al. Identification of a TLR-Induced Four-lncRNA Signature as a Novel Prognostic Biomarker in Esophageal Carcinoma. Frontiers in cell and developmental biology.2020, 8, 649.