A dynamic, reproducible R-based pipeline to query, download, and analyze TCGA RNA-seq datasets to identify biomarkers and pathway activities (e.g., RAS signaling).
This pipeline has been configured to study The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) cohort.
The Cancer Genome Atlas (TCGA) is the gold standard for multi-omic cancer data. Colon Adenocarcinoma (COAD) was selected because colorectal cancers have extremely well-characterized progression models (adenoma-carcinoma sequence) heavily driven by specific signaling cascades like RAS, PI3K/Akt, and Wnt/β-catenin. By comparing Primary Solid Tumors against Solid Tissue Normal samples, this pipeline allows us to effectively isolate the exact transcriptomic hallmarks driving this cancer.
By applying this DESeq2 and GSEA pipeline to the TCGA-COAD cohort, we uncovered some striking preliminary results:
The analysis identified massive upregulation of specific genes in tumor tissues. The top 5 most statistically significant upregulated genes (with massive fold changes) include:
CDH3(Cadherin 3, P-Cadherin): Log2 Fold Change = ~5.96 (padj = 0.00)KRT80(Keratin 80): Log2 Fold Change = ~6.76 (padj = 3.99e-287)FOXQ1(Forkhead Box Q1): Log2 Fold Change = ~6.21 (padj = 9.50e-205)ESM1(Endothelial Cell Specific Molecule 1): Log2 Fold Change = ~5.74 (padj = 5.16e-217)ETV4(ETS Variant Transcription Factor 4): Log2 Fold Change = ~5.29 (padj = 9.47e-260)
These candidates serve as exceptional high-confidence predictive biomarkers for colon adenocarcinoma development, heavily out-expressing corresponding healthy tissue.
We pre-filtered pathway enrichment results for hallmark cancer cascades. The GSEA revealed significant alterations (in this run, widespread down-regulation/suppression of systemic baseline signaling components in the tumor microenvironment) across the following critical pathways:
- PI3K-Akt signaling pathway (KEGG): Normalized Enrichment Score (NES) = -1.44 (p.adj = 0.0003)
- Ras signaling pathway (KEGG): NES = -1.28 (p.adj = 0.033)
- Ras protein signal transduction (Gene Ontology): NES = -1.93 (p.adj = 1.33e-05)
- Cell surface receptor signaling pathway via STAT (Gene Ontology): NES = -1.63 (p.adj = 0.0002)
- Notch signaling pathway (Gene Ontology): NES = -1.46 (p.adj = 0.002)
This workflow uses gold-standard tools to simplify the process of discovering significant genes and pathways involved in various cancers:
TCGAbiolinks: For secure data retrieval from the NCI Genomic Data Commons (GDC).DESeq2: For differential expression statistical modeling.clusterProfiler&org.Hs.eg.db: For Gene Set Enrichment Analysis (GSEA).EnhancedVolcano&ggplot2: For generating high-quality visualizations.
- Automated Pipeline: Installs missing dependencies, pulls data, performs differential tests, and writes outputs automatically.
- Customizable Targets: Easily modify variables inside
tcga_biomarker_discovery.Rto study new diseases (e.g.TCGA-BRCA), different clinical outcomes (e.g. Tumor vs Normal, Stage variants), or target pathways (e.g. Wnt, PI3K, specific GO/KEGG references). - Comprehensive Outputs: Extracts full DEG data alongside matched symbols, visualizes them with volcano and GSEA dot plots, and saves outputs cleanly to
/resultsand/plotsdirectories.
tcga_biomarker_discovery.R: The core R script containing the entire automated pipeline.tcga_biomarker_guide.md: Detailed documentation and a step-by-step interpretation guide.
- R (version 4.0 or greater).
- Disk space and memory adequate for downloading bulk RNA-seq data matrixes and generating models.
Clone the repository and run the main analysis script:
git clone https://github.com/Waqaskhan600/tcga-biomarker-discovery.git
cd tcga-biomarker-discovery
Rscript tcga_biomarker_discovery.RFor more advanced instructions and parameters customization, please consult the tcga_biomarker_guide.md file.
If you found this useful and informative, or if you are looking for collaboration and research assistance, please feel free to reach out to me on LinkedIn: Waqas Khan - LinkedIn Waqas Khan Upwork