TCGA Data Mining Service

Introduction of TCGA Data Mining Service

Data mining in cancer research is pivotal for uncovering critical insights from the vast and complex datasets available today. The Cancer Genome Atlas (TCGA) provides a comprehensive resource that includes genomic, transcriptomic, and clinical data across numerous cancer types. Through meticulous data mining, TCGA enables the discovery of hidden patterns and associations that enhance our understanding of cancer biology and inform the development of novel therapeutic strategies. By integrating multi-dimensional data from TCGA, our data mining service improves predictive accuracy and facilitates the identification of new biomarkers and therapeutic targets.

Our TCGA Data Mining Service leverages advanced analytical techniques to extract meaningful insights from TCGA's rich datasets. This service is designed to uncover the molecular underpinnings of cancer, enabling researchers and clinicians to make data-driven decisions that advance cancer diagnosis, treatment, and prevention.

Applications of TCGA Data Mining Service

Cancer Molecular Profiling: Our services enable detailed molecular characterization of tumors by analyzing genetic mutations, gene expression patterns, and epigenetic modifications. This helps in identifying key biomarkers and pathways associated with different cancer types, facilitating personalized medicine and targeted therapies.

Prognostic Marker Identification: By mining TCGA data, we can uncover prognostic markers that predict patient outcomes and survival rates. This includes analyzing the association between gene expression profiles and clinical outcomes, which aids in stratifying patients based on their risk levels.

Drug Discovery and Development: Leveraging TCGA data, we assist in identifying potential drug targets by analyzing alterations in gene expression and mutation patterns. This approach supports the development of novel therapeutics and repurposing existing drugs for new indications.

Tumor Subtype Classification: Our services help in classifying tumors into distinct subtypes based on genetic and molecular profiles. This classification improves our understanding of cancer heterogeneity and helps in tailoring treatment strategies to individual tumor characteristics.

Pathway and Network Analysis: We perform in-depth analyses of signaling pathways and gene networks affected by genetic alterations. This includes identifying critical nodes and interactions within biological pathways that contribute to cancer progression, offering insights into potential intervention points.

Integrative Multi-Omics Analysis: Our approach integrates genomic, transcriptomic, and epigenomic data to provide a holistic view of cancer biology. This comprehensive analysis facilitates the identification of complex relationships between different omics layers and enhances the accuracy of predictive models.

Survival Analysis: We analyze the relationship between genetic alterations and patient survival using Kaplan-Meier curves and Cox proportional hazards models. This helps in understanding the impact of specific genetic features on patient prognosis.

Clinical Trial Support: Our data mining services support clinical trials by identifying patient populations with specific genetic profiles, optimizing trial design, and analyzing trial outcomes based on genetic data.

TCGA Data Download

To begin TCGA data mining, researchers must download relevant datasets from the Genomic Data Commons (GDC) portal, the primary repository for TCGA data. The portal provides various data types, including gene expression, somatic mutations, copy number variations, methylation, and clinical data. Data can be accessed via the GDC Data Transfer Tool or through the portal's web interface, which allows filtering by project, data type, sample type, and more. Both raw and processed data are available, offering flexibility based on analytical needs. For larger datasets, the Data Transfer Tool is recommended for efficient, resumable downloads. Access to controlled data requires dbGaP authorization. Once downloaded, these datasets can be analyzed to uncover novel insights into cancer biology and identify potential therapeutic targets.

Workflow for TCGA Data Mining

Sample Submission Guidelines

Bioinformatics Analysis Content

What Are the Advantages of Our Service?

Expertise in Cancer Genomics

Our bioinformatics team specializes in cancer genomics, with extensive experience analyzing TCGA datasets. We utilize advanced, cancer-specific methodologies to extract meaningful and actionable insights. Our expertise spans a variety of analyses, including somatic mutations, copy number variations, methylation profiles, and multi-omics integration.

Comprehensive Multi-Omics Integration

We specialize in the integration of diverse omics data from TCGA, including genomics, transcriptomics, methylomics, and proteomics. This multi-omics strategy offers a complete perspective on cancer biology, uncovering intricate molecular interactions and pathways involved in cancer progression. Our thorough analysis enhances the understanding of tumor heterogeneity and aids in identifying potential therapeutic targets.

Advanced Analytical Tools and Techniques

Our service utilizes the most advanced bioinformatics tools and platforms, including GATK, MuTect2, CNVkit, and Cytoscape, to perform a wide array of analyses. We also implement cutting-edge statistical techniques to ensure the robustness and reproducibility of our findings. By applying these tools in a highly customized manner, we maximize the extraction of meaningful data, leading to high-impact results.

Rigorous Quality Control and Validation

Quality is at the forefront of our TCGA data mining service. Every analysis undergoes stringent quality control checks, and our results are validated through multiple approaches, including cross-validation and comparison with independent datasets. This rigorous process ensures that the insights we provide are not only accurate but also highly reliable for downstream applications.

Personalized and Interactive Reporting

We provide comprehensive, interactive reports that go beyond static documentation. These reports feature dynamic visualizations, including interactive heatmaps, Kaplan-Meier survival plots, and network diagrams, allowing clients to explore the data from multiple perspectives. Each report undergoes internal peer review to ensure it meets the highest standards, making it suitable for both publication and strategic decision-making.

Dedicated Client Support and Consultation

We prioritize client satisfaction by offering personalized support, including one-on-one consultations with our lead bioinformaticians. These sessions allow for in-depth discussions of project findings, addressing specific questions, and customizing the analysis to align with the client's research goals. This personalized approach ensures clients fully grasp and can effectively apply the insights we deliver.

Robust and Secure Data Infrastructure

We operate on a robust computing infrastructure capable of handling the large and complex datasets typical of TCGA. Our data security protocols are industry-leading, ensuring that sensitive client data is protected at all stages of the project. This infrastructure not only supports high-throughput data processing but also guarantees the confidentiality and integrity of client information.

What Does TCGA Data Mining Reveal?

Data Statistics of TCGA data Quality Control

The quality control of single-cell RNA-seq data from TCGA was performed by assessing key metrics such as the number of detected genes, the total number of cells, and the percentage of mitochondrial gene expression. The table below summarizes these quality control results, highlighting the quality of each sample based on these metrics.

Table 1. Quality Control Metrics for Single-Cell RNA-seq Data in TCGA

Kaplan-Meier Survival Analysis of Immune and Stromal Scores in Glioblastoma

Kaplan-Meier survival analysis was conducted to explore the correlation between overall survival and immune/stromal scores in 417 Glioblastoma Multiforme (GBM) cases. Patients were stratified into high and low score groups. The low immune score group demonstrated a trend toward longer median survival compared to the high score group, with a p-value approaching significance. Similarly, the low stromal score group exhibited longer median survival, though not statistically significant. These findings suggest that immune and stromal scores may serve as potential prognostic indicators in GBM.

Figure 1. Kaplan-Meier survival curves for GBM patients stratified by immune and stromal scores. (A) The left panel shows that patients with low immune scores had a longer median survival (442 days) compared to the high score group (394 days), with a log-rank p-value of 0.0537. (B) The right panel shows that the median survival for the low stromal score group was 442 days, compared to 422 days in the high score group, with a p-value of 0.1262. (Jia, 2018)

Differential Gene Expression Analysis Using Immune and Stromal Scores in GBM

To investigate the association between gene expression profiles and immune/stromal scores in GBM, Affymetrix microarray data from 417 GBM cases in the TCGA database was analyzed. Differential gene expression analysis identified distinct profiles between high and low immune/stromal score groups. In the high immune score group, 480 genes were upregulated and 127 downregulated (fold change >1.5, p < 0.05). Similarly, in the high stromal score group, 380 genes were upregulated and 25 downregulated. Venn diagrams illustrated 374 genes commonly upregulated and 25 genes commonly downregulated across both high-score groups. Functional enrichment analysis, particularly for genes upregulated in the high immune score group, revealed significant gene ontology (GO) terms related to extracellular matrix organization, immune responses, chemokine activities, and integrin binding, offering insights into GBM's molecular landscape.

Figure 2. Differential Gene Expression Analysis in GBM. (A) Heatmap of DEGs between high and low immune score groups (p<0.05, fold change >1.5). (B) Heatmap of DEGs between high and low stromal score groups (p<0.05, fold change >1.5). (C, D) Venn diagrams showing overlapping upregulated (C) and downregulated (D) DEGs in immune and stromal score groups. (E-G) Top 10 GO terms from DAVID analysis highlighting key biological processes involved (p < 0.05). (Jia, 2018).

Protein-Protein Interaction Analysis of Prognostically Relevant Genes Using STRING

To explore the functional relationships of differentially expressed genes (DEGs) linked to prognosis in GBM, protein-protein interaction (PPI) networks were generated using the STRING database. The PPI network comprised 224 nodes and 1,282 edges, forming 8 distinct modules. The most significant modules—IL6, TIMP1, and TLR2—were further examined. The IL6 module involved 83 edges among 26 nodes, with IL6, IL8, ITGB2, ICAM1, CSF1R, IL1B, and CD163 as key hubs. The TIMP1 module centered on TIMP1, CCR5, CXCL12, SERPINE1, SERPING1, C3AR1, SRGN, and SERPINA3. The TLR2 module featured immune-related genes like TLR2, CCL2, CCL5, IGSF6, and CD14. These PPI modules highlight complex molecular interactions that could be targeted for therapeutic intervention.

Figure 3. Protein-Protein Interaction Analysis Using STRING. Nodes represent proteins, colored by log (FC) expression values, and sized by the number of interacting partners. (A) IL6 module: 83 edges among 26 nodes, highlighting IL6 and related hubs. (B) TIMP1 module: Central nodes include TIMP1 and related proteins. (C) TLR2 module: Key nodes include TLR2 and immune-related genes. (Jia, 2018).

Functional Enrichment Clustering of Prognostically Significant Genes

Functional enrichment clustering of genes associated with prognosis revealed a significant correlation with immune response pathways, consistent with the PPI network findings. This analysis identified 30 notable GO terms related to biological processes, 12 related to cellular components, and 5 associated with molecular functions, all with a false discovery rate (FDR) below 0.05 and -log FDR values exceeding 1.301. Key GO terms included extracellular exosome and extracellular matrix (ECM) components, immune and inflammatory responses, and chemotaxis. Additionally, molecular functions involved integrin and proteoglycan binding. KEGG pathway analysis further highlighted pathways related to immune responses, emphasizing the role of these genes in the immune landscape of GBM.

Figure 4. Functional Enrichment Clustering of Prognostically Significant Genes. Top pathways with FDR < 0.05 and -log FDR > 1.301 are presented: (A) Biological processes such as extracellular exosome and ECM organization. (B) Cellular components including immune and inflammatory responses, chemotaxis. (C) Molecular functions involving integrin and proteoglycan binding. (D) KEGG pathways related to immune responses. (Jia, 2018).

Title: GGT5: a potential immunotherapy response inhibitor in gastric cancer by modulating GSH metabolism and sustaining memory CD8+ T cell infiltration

Publication: Cancer Immunology, Immunotherapy

Main Methods: Single-Cell RNA Sequencing Analysis, Immune Cell Infiltration Estimation

Abstract: This study identifies GGT5 as a crucial gene in glutathione metabolism, linking its expression to the immune response in gastric cancer (GC). By analyzing pan-cancer datasets and performing single-cell RNA sequencing, the research reveals a strong association between GGT5 and memory CD8+ T cells, which correlates with poor responses to immunotherapy in GC patients. The findings suggest that targeting GGT5 could enhance the efficacy of immune checkpoint inhibitors, offering new therapeutic strategies for GC.

Research Results:

Workflow of the Study

This study systematically investigated signature genes involved in glutathione (GSH) metabolism across various cancer types. The initial step involved searching the MSigDB database to identify relevant GSH metabolism genes. By intersecting gene sets from KEGG, WK, and GOBP databases, 16 key genes were selected. These genes were then analyzed across 33 types of pan-cancer datasets from TCGA. After excluding cancer types without matched normal samples, 17 cancer types were included for further analysis. Differential expression analysis of GSH metabolic genes was conducted between these 17 cancer types and normal tissues.

Figure 5. Workflow chart of the study, detailing the identification and analysis process of GSH metabolism signature genes across pan-cancer datasets from TCGA. (Zhao, 2024)

Differential Expression, Prognostic, and Enrichment Analysis of GSH Metabolism Genes

This study identified six GSH metabolism genes—GGT1, GGT5, GPX1, GPX4, GSS, and GSTA1—as significantly differentially expressed in GC (p < 0.05). These genes were also confirmed as potential prognostic factors through LASSO regression analysis, establishing them as GSH metabolic signatures. To further elucidate the connection between these GSH metabolic signatures and GC, GO and KEGG enrichment analyses were conducted using the ‘clusterProfiler' package. The analysis validated the strong association of these genes with GSH metabolism pathways. KEGG analysis revealed significant enrichment in arachidonic acid metabolism, taurine and hypotaurine metabolism, and ferroptosis. GO analysis identified the top biological processes as cellular modified amino acid metabolism and sulfur compound metabolism, with peroxidase activity and oxidoreductase activity as the most prominent molecular functions.

Figure 6. (a) Bubble plot showing differential expression of GSH metabolism genes between tumor and normal tissues across various cancer types, with six genes significantly expressed in GC. Dot size indicates the FDR, and color represents fold-change. (b, c) LASSO regression analysis identifying prognostic-related genes. (d) Circle plot of KEGG enrichment analysis, highlighting terms with p < 0.05. Top 10 GO BP (e) and MF (f) terms are also shown, all with p < 0.05. (Zhao, 2024)

Identification and Clinical Relevance of GSH Metabolism Key Gene in GC

In this study, the role of GSH metabolism in GC was explored by comparing gene expression profiles between GC tissues and adjacent normal tissues. Significant overexpression was observed for GGT5, GPX1, and GSS in GC samples (p < 0.05), while other genes like GGT1, GPX4, and GSTA1 did not show notable differences. Among these, GGT5 emerged as a key prognostic marker. Kaplan-Meier survival analysis demonstrated that high GGT5 expression correlates with poorer outcomes in overall survival, progression-free interval, disease-free interval, and disease-specific survival (p-values ranging from 0.00029 to 0.023). These findings, confirmed in an independent cohort, highlight GGT5's potential as a significant prognostic indicator in GC.

Figure 7. Key GSH Metabolism Genes in GC. (a) Differential expression of GSH metabolism genes, with GGT5, GPX1, and GSS showing higher expression in GC tissues. (b) Kaplan-Meier survival curves illustrating the association between high GGT5 expression and poorer survival outcomes. (Zhao, 2024).

Immune Infiltration and GGT5 Expression in GC

Analysis of immune cell infiltration revealed that high GGT5 expression is associated with increased levels of certain immune cells, including naïve B cells, Tregs, monocytes, and resting Mast cells, while lower GGT5 expression corresponds with higher levels of resting and activated CD4 memory T cells, follicular helper T cells, and M0 macrophages. Notably, CD8+ T cells were consistently elevated in the high GGT5 group across multiple validation methods. Correlation analysis further confirmed GGT5's positive relationship with Tregs, CD8 T cells, naïve B cells, monocytes, and resting Mast cells, and a negative association with resting CD4 memory T cells. These findings imply that GGT5 may influence immune cell dynamics within the tumor microenvironment in GC.

Figure 8. GGT5 Expression and Immune Cell Infiltration in GC. (a) Differential infiltration of immune cells in high vs. low GGT5 expression groups as shown by CIBERSORT analysis (p < 0.05). (b) Correlation analysis indicating significant associations between GGT5 expression and various immune cell types. (Zhao, 2024).

GGT5-Related Immune Cell Infiltration in GC

In this study, 10 samples from GSE167297 were analyzed to explore GGT5-related immune cell infiltration. After correcting for batch effects, the top 2000 highly variable genes (HVGs) were selected for principal component analysis (PCA), retaining 20 dimensions. t-SNE clustering of 15,729 cells revealed 14 distinct clusters, identifying eight key cell types: T cells, B cells, dendritic cells endothelial cells, epithelial cells, monocytes, NK cells, and smooth muscle cells. Among these, T cells were predominant, highlighting their potential role in GC. Further analysis showed that GGT5 was significantly overexpressed in T cells, suggesting this cell type as a focal point for further investigation.

Figure 9. Single-Cell Analysis of GGT5 Expression in GC. (a) t-SNE clustering identified 14 cell clusters. (b) Marker genes used to classify eight cell types. (c) Predominance of T cells across clusters. (d) Heatmap of the top 10 marker genes per cluster. (e) Violin plot illustrating higher GGT5 expression in T cells. (Zhao, 2024).

Conclusion

The integrated analysis revealed that GGT5 plays a pivotal role in glutathione metabolism, influencing immune cell infiltration in GC. The study demonstrated that high GGT5 expression correlates with a poor prognosis and altered immune landscape, suggesting GGT5 as a potential target for optimizing immunotherapy in GC.

1. What is TCGA data, and how is it used in cancer research?

TCGA data encompasses a wide array of genomic, transcriptomic, and clinical data from various cancer types. It is used in cancer research to identify genetic mutations, analyze gene expression patterns, and uncover potential therapeutic targets. This comprehensive dataset aids in understanding cancer biology, discovering biomarkers for diagnosis and prognosis, and developing targeted treatments.

2. How can I access and download TCGA data?

Accessing and downloading data from TCGA can be done through platforms like the GDC portal or cBioPortal. To obtain the data, one must first register for an account. Once logged in, users can locate the required datasets or projects and follow the provided instructions to download the data. The GDC portal offers multiple tools for querying and obtaining data, including both a web-based interface and programmatic access via APIs.

3. What tools are available for analyzing TCGA data?

Tools for analyzing TCGA data include R/Bioconductor packages like TCGAbiolinks for data retrieval and analysis, cBioPortal for interactive exploration, UCSC Xena for data visualization, and Firehose for integrated analysis. These tools facilitate various types of analyses, including differential expression, mutation analysis, and survival studies.

4. What types of analyses can be performed on TCGA data?

Common analyses performed on TCGA data include differential gene expression analysis, mutation profiling, Copy Number Variation Analysis, pathway enrichment analysis, and survival analysis. Researchers use these analyses to identify key genetic alterations, understand their impact on cancer progression, and discover potential therapeutic targets.

5. How can TCGA data be integrated with clinical data?

TCGA data often includes clinical information such as cancer stage, treatment outcomes, and patient demographics. Integrating this clinical data with genomic data allows researchers to perform survival analyses, correlate genetic mutations with clinical outcomes, and identify prognostic biomarkers. This integration helps in understanding how genetic alterations impact patient prognosis and treatment responses.

6. What are the challenges of using TCGA data?

Challenges include dealing with the large volume of data, managing data heterogeneity, and ensuring data quality. Researchers must also handle missing data, batch effects, and complex data formats. Effective analysis requires significant computational resources and expertise in bioinformatics to address these challenges and derive meaningful insights.

7. How do I perform survival analysis using TCGA data?

Survival analysis with TCGA data involves correlating genomic features (such as gene expression or mutation status) with patient survival outcomes. Tools like survminer in R, cBioPortal, and Kaplan-Meier plots are used to visualize and analyze survival data. Statistical methods, such as Cox proportional hazards models, are applied to assess the impact of genetic factors on patient survival.

8. Why is validation important in TCGA data analysis?

Validation is crucial to confirm that findings from TCGA data analysis are accurate and reproducible. It helps ensure that identified relationships between genetic variants and clinical outcomes are reliable. Validation can be performed using external datasets, experimental approaches, or cross-validation within the TCGA dataset to reinforce the robustness of the results.

References

1. Jia, D.; et al. Mining TCGA database for genes of prognostic value in glioblastoma microenvironment. Aging (Albany NY). 2018, 10(4), 592–605.
2. Zhao, W.; et al. GGT5: A potential immunotherapy response inhibitor in gastric cancer by modulating GSH metabolism and sustaining memory CD8+ T cell infiltration. Cancer Immunology, Immunotherapy. 2024, 73, 131.