GEO and WES Integrated Data Mining Service

Introduction of GEO and WES Integrated Data Mining

Data mining in biomedical research is essential for extracting actionable insights from complex datasets. It reveals hidden patterns and correlations, which enhance our understanding of biological systems and inform therapeutic strategies. By integrating diverse data types, data mining improves predictive accuracy and facilitates the discovery of novel biomarkers and targets.

GEO and WES Integrated Data Mining is a data analysis approach that combines information from the Gene Expression Omnibus (GEO) and Whole Exome Sequencing (WES) to gain comprehensive insights into genetic and molecular factors influencing various biological processes or diseases. This method integrates gene expression data with genetic variant data to understand the relationship between gene activity and genetic variations.

Our GEO and WES Integrated Data Mining service utilizes sophisticated methodologies to integrate these diverse data sources. This approach enables a more profound exploration of disease mechanisms and biological processes, showcasing our capability to translate complex datasets into meaningful scientific insights.

• GEO: A public repository for gene expression data, including microarray and RNA sequencing studies. GEO provides valuable information about gene expression levels across different conditions or treatments.
• WES: A sequencing technique that focuses on capturing and analyzing the coding regions of the genome (exomes). WES identifies genetic variants in protein-coding genes that may be associated with diseases or traits.

Applications of GEO and WES Integrated Data Mining

• Disease Research: This approach identifies the influence of genetic variants on gene expression, shedding light on their roles in disease onset and progression.
• Biomarker Discovery:Integrated data mining aids in the identification of potential biomarkers, enhancing disease diagnosis, prognosis, and therapeutic response predictions.
• Drug Development: It evaluates the impact of genetic variations on drug response, thereby supporting the development of personalized medicine strategies.
• Functional Genomics: This method reveals the functional consequences of genetic variants on gene expression and cellular mechanisms.
• Precision Medicine: The integration of genetic and transcriptomic data supports research that advances the customization of medical treatments based on individual genetic profiles, contributing to the development of precision medicine.

Workflow for GEO and WES Integrated Data Mining

Sample Submission Guidelines

Bioinformatics Analysis Content

What Are the Advantages of Our Services?

Specialized Expertise and Advanced Methods:

Our team leverages advanced bioinformatics and statistical techniques specifically tailored for GEO and WES data. We use specialized tools such as GrapeTree, Circos, and Heatmap for visualizing gene expression data and genetic variations, ensuring precise and comprehensive analysis. Our expertise in handling and integrating large-scale datasets allows us to uncover critical insights into gene function and variation.

Integrated Data Insights:

Our approach combines GEO expression data with WES variant data to provide a holistic view of gene expression and genetic variations, leading to high-resolution, actionable insights.

Advanced Analytical and Visualization Tools:

We utilize an array of advanced tools to refine our analyses. GrapeTree is employed for population structure analysis, Circos creates circular genome plots, and Heatmaps are used for gene expression profiling. VENN Diagrams assist in examining data intersections. Each tool is customized to meet the unique requirements of individual projects, thereby enhancing the precision and relevance of our results. Furthermore, these sophisticated visualization techniques aid in illustrating complex data relationships and patterns, improving both the interpretation and communication of findings.

Interactive and Informative Reporting:

Our reports present detailed visualizations and analyses that are subjected to rigorous peer review, guaranteeing their suitability for publication or in-depth exploration.

Personalized Consultation:

Our team of expert bioinformaticians offers review calls to discuss results, provide deeper insights, and address any specific queries. This personalized consultation adds significant value to the findings and enhances their applicability.

Streamlined Project Management:

A dedicated project manager is assigned to facilitate seamless communication and oversee the entire process, from data transfer through to the delivery of the final report. This approach ensures the project is executed efficiently and on schedule.

Robust Infrastructure:

Our high-capacity computing resources support secure and accurate analysis of large GEO and WES datasets. This robust infrastructure guarantees reliable data processing and analysis.

What Does GEO and WES Integrated Data Mining Reveal?

Data Statistics of GEO and WES Quality Control

The quality of GEO and WES data was evaluated by assessing gene expression normalization metrics and the accuracy of variant calling. The table below summarizes the quality control results, including the number of genes expressed above a specific threshold in GEO data, the total number of variants called in WES data, and the percentage of high-confidence variants after filtering.

Table 1. Quality Control Metrics for GEO and WES Data

Mutational Landscape of Significantly Mutated Genes (SMGs)

The genomic landscape of SMGs in colorectal cancer, specifically focusing on the KRAS-mutant (KRAS-Mut) and wild-type (KRAS-WT) subgroups, provides critical insights into the molecular heterogeneity of these tumors. This analysis identifies the differences in mutation profiles between KRAS-Mut subtypes (KM1 and KM2) and KRAS-WT tumors, highlighting key genes involved in oncogenic pathways. Notably, the study demonstrates how these mutational patterns vary across different genetic backgrounds, offering potential targets for personalized therapy.

Figure 1. Mutational landscape of SMGs. The upper barplot shows overall mutational loads across KRAS-Mut (KM1, KM2) and KRAS-WT subgroups. The right barplot illustrates mutation frequencies of specific genes, with ARID1A and BRAF mutations higher in KRAS-WT tumors, and APC and PCBP1 mutations more frequent in KRAS-Mut tumors. (Chong, 2022)

Nucleotide Mutation Patterns

Analyzing nucleotide mutation patterns in colorectal cancer offers critical insights into the distinct mutational processes associated with KRAS-mutant (KRAS-Mut) subtypes. By classifying single-nucleotide substitutions according to their surrounding bases, this study reveals variations in mutation frequencies that contribute to the molecular heterogeneity observed in these tumors. The heightened prevalence of C>A transitions in KM2 tumors highlights the influence of specific mutational processes in shaping the diversity of KRAS mutations.

Figure 2. Lego plot of nucleotide mutation patterns in colorectal cancer. The 96 possible nucleotide mutation patterns are categorized into six major types, with a pie chart displaying their proportions. The increased frequency of C>A transitions, particularly in KM2 tumors, is noteworthy. (Chong, 2022)

Mutational Signatures

The exploration of mutational signatures in colorectal cancer reveals distinct mutational processes across KRAS-mutant (KRAS-Mut) subtypes and wild-type (KRAS-WT) tumors. By analyzing the activities of specific signatures from the COSMIC-V3 database, this study highlights the molecular differences that influence tumorigenesis. Notably, the increased SBS44 signature in KRAS-WT tumors and the decreased SBS15 signature in KM1 tumors emphasize the impact of DNA repair mechanisms on the mutational landscape of these cancer subtypes.

Figure 3. Mutational signatures in colorectal cancer. The activities of five extracted mutational signatures from the COSMIC-V3 database are depicted. Significant increases in the SBS44 signature are observed in KRAS-WT tumors, while a marked decrease in the SBS15 signature is noted in KM1 tumors. (Chong, 2022)

Somatic Copy-Number Alterations (SCNAs)

The study of SCNAs in KRAS-mutant colorectal cancer provides insight into the genomic changes driving tumorigenesis. By examining focal SCNAs, distinct patterns of amplifications and deletions are identified across KRAS-Mut subtypes. These genomic alterations highlight the heterogeneity within KRAS-Mut tumors and underscore the complexity of their molecular landscape.

Figure 4. Focal SCNA analysis in KRAS-Mut colorectal cancer. Significant amplifications (red) and deletions (blue) are shown across different cytobands in KRAS-Mut subtypes, with KM1 tumors exhibiting notable changes in regions such as 2q31.2 and 5p15.33, and KM2 tumors showing alterations in 12p13.33 and 8p11.22. (Chong, 2022)

Title: Meta-data analysis of kidney stone disease highlights ATP1A1 involvement in renal crystal formation

Publication: Redox Biology

Main Methods: Whole Exome Data Analysis, DNA methylation, WGCNA

Abstract: This study investigates nephrolithiasis, a complex disease influenced by environmental and genetic factors, with crystal-cell adhesion being a key process in kidney stone formation. The research focuses on the role of Na/K-ATPase (NKA), particularly the ATP1A1 gene, in this process. By integrating gene expression profiles from calcium stone formers and whole-exome sequencing (WES) data from patients, ATP1A1 was identified as a key susceptibility gene. The study found that the T-allele of rs11540947 in ATP1A1 is associated with an increased risk of nephrolithiasis and reduced promoter activity. The research further explores how calcium oxalate crystals decrease ATP1A1 expression, triggering the ATP1A1/Src/ROS signaling pathway, leading to oxidative stress, inflammation, and stone formation. The inhibition of this pathway, either through ATP1A1 overexpression or specific inhibitors, was shown to alleviate these effects, suggesting ATP1A1 as a potential therapeutic target for calcium stones.

Research Results:

Integrated analysis of the gene expression profiles and WES data

An integrated analysis combining gene expression profiles and Whole Exome Sequencing data has identified ATP1A1 as a gene of significant relevance to nephrolithiasis. This study utilized 52 samples from the GSE73680 dataset, employing Weighted Gene Co-Expression Network Analysis (WGCNA) to identify 13 gene modules. Among these, the "salmon" module, which consists of 434 genes, demonstrated a robust association with calcium stone formation. Functional annotation of these genes revealed their involvement in critical biological processes such as transmembrane transport and sodium reabsorption, with five genes, including ATP1A1, showing strong associations with nephrolithiasis. To further refine the analysis, these genes were integrated with 67 candidate genes identified from WES data of 28 patients with calcium oxalate (CaOx) stones. ATP1A1 emerged as the sole gene common to both datasets, underscoring its potential pivotal role in the etiology of calcium stone formation. Subsequent investigations focused on the single nucleotide polymorphism (SNP) rs11540947 located within the 5′ untranslated region (UTR) of ATP1A1. The presence of the T-allele of this SNP was significantly correlated with an elevated risk of calcium stone formation, particularly in male patients. Functional assays revealed that the T-allele is associated with decreased promoter activity of ATP1A1, potentially leading to reduced gene expression and contributing to the pathogenesis of calcium stones.

Figure 5. ATP1A1 Genetic Variation and CaOx Stone Association Analysis. (a) WGCNA analysis of 52 samples from the GSE73680 dataset identified the "salmon" module (434 genes) as significantly associated with calcium stone formation (p = 0.01). (b) Venn diagram analysis revealed ATP1A1 as the only gene common to both the "salmon" module and the 67 candidate genes identified from WES data. (c) Genotyping of SNP rs11540947 (NM_000701: c.-78C > T) using HRM identified three genotypes (CC, CT, TT). (d) Sanger sequencing confirmed rs11540947 in ATP1A1. (e-f) Dual-luciferase assays demonstrated that the T-allele significantly reduced ATP1A1 promoter activity in HEK293 and HK2 cells (p < 0.001, p < 0.0001), indicating its potential impact on gene expression. (Li, 2023)

COM-Induced Signaling Pathway Activation

The study investigated the effects of COM exposure on HK2 cells, concentrating on NKA activity and ATP1A1 expression. Results showed a marked reduction in both NKA activity and ATP1A1 mRNA levels three hours after COM treatment. Initially, ATP1A1 protein levels increased at six hours but later declined. This alteration in ATP1A1 protein levels was associated with elevated phosphorylation of Src, activation of oxidative stress-related signaling pathways including p38 and JNK, and upregulation of the inflammation regulator NF-κB. Additionally, there was a notable rise in intracellular ROS levels, which may be related to the activation of the ATP1A1/Src signaling pathway.

Figure 6. COM Exposure Activates the ATP1A1/Src/ROS/MAPKs/NF-κB Pathway in HK2 Cells. (a) NKA activity in HK2 cells decreased significantly after COM exposure at various time points (*p < 0.01, n = 5). (b) ATP1A1 mRNA levels were also significantly reduced following COM exposure (*p < 0.01, **p < 0.001, ***p < 0.0001, n = 3). (c) Western blot analysis revealed increased activation of ATP1A1, p65, p50, Nrf2, and phosphorylation of Src, p38, and JNK (*p < 0.05, **p < 0.01, ***p < 0.0001, n = 3). (d-e) DCFHDA staining showed significant ROS accumulation after COM treatment (**p < 0.001, n = 3). (Li, 2023)

To verify whether the activation of the Src/ROS/MAPKs/NF-κB signaling pathway by COM was due to a decline in ATP1A1 protein, a recombinant adenovirus (Ad-hATP1A1) was constructed to overexpress ATP1A1, with HBAD-mCherry as a negative control. Successful ATP1A1 overexpression was confirmed through red fluorescence in cells and western blot analysis. Compared to the HBAD-mCherry group, ATP1A1 overexpression suppressed the COM-induced activation of Src, p38, JNK, p65, and p50, and prevented the decrease in Nrf2. Additionally, the COM-induced increase in ROS levels was inhibited by Ad-ATP1A1 infection, indicating that COM exposure activates the ATP1A1/Src/ROS/MAPKs/NF-κB signaling pathway by reducing ATP1A1 expression.

Figure 7. ATP1A1 Overexpression Reduces COM-Induced Activation of the ATP1A1/Src/ROS/MAPKs/NF-κB Pathway. (a) Western blot analysis demonstrated that ATP1A1 overexpression in HK2 cells significantly decreased Src, p38, JNK, p65, p50, and Nrf2 activation post-COM exposure, with substantial reductions compared to the HBAD-mCherry group (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, n = 3). (b-c) Lower intracellular ROS levels were observed in ATP1A1-overexpressing cells, indicated by reduced DCFHDA fluorescence intensity relative to the HBAD-mCherry group (***p < 0.001, ****p < 0.0001, ###p < 0.001, n = 3). (Li, 2023)

pNaKtide's Effect on Oxidative Stress and Inflammation

pNaKtide, a specific antagonist of the ATP1A1/Src signaling complex, alleviated COM-induced oxidative stress and inflammation. pNaKtide inhibited the activation of Src, p38, JNK, p65, and p50, and prevented the decrease in Nrf2, similar to ATP1A1 overexpression results. Additionally, pNaKtide reduced ROS accumulation induced by COM, improving the intracellular oxidative environment.

Figure 8. pNaKtide Modulates COM-Induced ROS and Inflammation Through the ATP1A1/Src Pathway. (a) Western blotting revealed that pNaKtide inhibits Src, p38, JNK, p65, and p50 activation and promotes Nrf2 expression. (b-c) pNaKtide reduced intracellular ROS levels after 24 hours of COM exposure. (Li, 2023)

Conclusion

The integrated analysis of gene expression and WES data highlights ATP1A1's critical role in nephrolithiasis, with COM-induced activation of the ATP1A1/Src/ROS/MAPKs/NF-κB pathway being mitigated by ATP1A1 overexpression and pNaKtide treatment, suggesting potential therapeutic targets for calcium stone formation.

1. What are the key objectives of GEO and WES integrated data mining?

The primary objectives of GEO and WES integrated data mining are to identify the relationships between genetic variants (identified from WES data) and gene expression patterns (from GEO data). This integration helps in understanding the molecular mechanisms underlying diseases, identifying potential biomarkers, and discovering new therapeutic targets.

2. How does integrating GEO and WES data improve genetic research?

Integrating GEO and WES data enhances genetic research by providing a more comprehensive view of gene regulation and the effects of genetic variants. While WES identifies the genetic variants, GEO data helps link these variants to changes in gene expression, thus providing insights into the functional consequences of genetic alterations.

3. What are the common challenges in GEO and WES integrated data analysis?

Common challenges include managing and integrating large datasets from different platforms, dealing with the heterogeneity of data, normalizing gene expression data, accurately mapping genetic variants to genes, and addressing statistical issues like multiple testing corrections in integrated analyses.

4. What tools are commonly used for GEO data acquisition and processing?

Commonly used tools for GEO data acquisition and processing include GEOquery (for data retrieval), DESeq2, EdgeR, and Limma (for normalization and differential expression analysis). These tools help ensure that the data is of high quality and ready for integration with WES data.

5. How are genetic variants from WES data integrated with GEO expression data?

Genetic variants identified from WES data are mapped to corresponding genes, and their effects on gene expression are analyzed. Tools like BEDTools and various Bioconductor packages are used for this mapping and correlation analysis, facilitating the integrated analysis of these datasets.

6. What is the role of pathway and network analysis in GEO and WES integrated data mining?

Pathway and network analysis play a crucial role by revealing the biological pathways and gene networks that are affected by genetic variants and associated gene expression changes. Tools like KEGG, Reactome, and Cytoscape are used to explore and visualize these pathways and networks.

7. Why is validation important in GEO and WES integrated data mining?

Validation is important to confirm the findings from integrated data analysis. This step ensures that the identified relationships between genetic variants and gene expression are robust and reproducible. Validation can be performed using additional datasets or experimental approaches.

8. How can insights from GEO and WES integrated data mining be applied in clinical practice?

Insights from GEO and WES integrated data mining can be applied in clinical practice for disease research, biomarker discovery, and drug development. By understanding the genetic and expression profiles associated with specific diseases, researchers can develop targeted therapies and personalized medicine strategies.

References

1. Chong, W.; et al. Integrated multi-omics characterization of KRAS mutant colorectal cancer. Theranostics. 2022, 12(11), 5138–5154.
2. Li, Y.; et al. Meta-data analysis of kidney stone disease highlights ATP1A1 involvement in renal crystal formation. Redox Biology. 2023, 61, 102648.