--- title: Pathway Analysis description: >- Map biological pathways, analyze gene signatures, and connect drug mechanisms to transcriptional effects. tags: - workflows - pathway-analysis --- ## Pathway Analysis Understanding how a drug candidate affects biological pathways is essential for mechanism validation, off-target prediction, and therapeutic hypothesis refinement. This workflow combines literature mining, gene expression analysis, and pathway enrichment to build a comprehensive picture of drug-pathway interactions from molecular to systems-level resolution. ## Overview Pathway analysis bridges the gap between single-target pharmacology and systems-level biology. A drug that inhibits a kinase does not just affect that kinase -- it perturbs downstream signaling cascades, transcription factor networks, and feedback loops. BioBERT extracts gene-disease-drug relationships from biomedical literature. PubMed search retrieves primary research articles with pathway-relevant data. Genomics tools perform enrichment analysis on gene sets derived from transcriptomic experiments or literature mining, mapping individual gene hits to curated pathway databases (KEGG, Reactome, Gene Ontology). ## Tool Chain 1. [BioBERT](/docs/biobert) -- Biomedical literature mining and entity extraction 2. [PubMed](/docs/pubmed) -- Literature search across MEDLINE for pathway evidence 3. [Genomics](/docs/genomics) -- Gene expression and pathway enrichment analysis ## Prerequisites - A target, compound, or disease of interest - Gene lists from differential expression experiments (optional but valuable) - Understanding of pathway concepts (signaling cascades, gene ontology, enrichment) ## Step-by-Step Guide ### Step 1: Mine the literature with BioBERT Submit a research question to BioBERT (for example, "What pathways are affected by EGFR inhibitors in non-small cell lung cancer?"). BioBERT uses biomedical NLP to extract entities (genes, proteins, drugs, diseases, pathways) from semantically relevant papers and generates summaries. Review the extracted gene lists, pathway mentions, and drug-gene associations. Export the entity lists for downstream enrichment. ### Step 2: Expand the evidence base with PubMed Use PubMed search to retrieve primary research articles that report pathway-level data for your target or compound. Search with MeSH terms combining your drug/target name with "signaling pathway", "gene expression", "transcriptome", or specific pathway names (e.g., "MAPK", "PI3K/AKT", "Wnt"). Review abstracts for reported gene lists, fold-change data, and pathway enrichment results from published experimental studies. ### Step 3: Compile the gene set Assemble a comprehensive gene set from multiple sources: genes extracted by BioBERT from literature, genes reported in PubMed publications, genes from your own differential expression experiments (if available), and known direct targets and downstream effectors from pathway databases. Remove duplicates and standardize gene nomenclature to HGNC symbols for consistent analysis. ### Step 4: Run pathway enrichment Submit the compiled gene set to the genomics enrichment analysis tool. Run enrichment against multiple pathway databases: KEGG for metabolic and signaling pathways, Reactome for detailed reaction-level annotations, Gene Ontology Biological Process for functional categories, and WikiPathways for community-curated disease-specific pathways. Apply multiple testing correction (Benjamini-Hochberg FDR below 0.05) to identify statistically significant pathway enrichments. ### Step 5: Interpret pathway results Review the enriched pathways ranked by statistical significance and gene overlap. Distinguish between expected enrichments (pathways directly involving your target, confirming on-target activity) and unexpected enrichments (pathways not previously associated with your target, suggesting off-target effects or novel biology). For the top enriched pathways, examine which specific genes from your list are pathway members and whether they represent upstream regulators, core components, or downstream effectors. ### Step 6: Validate with compound-pathway connectivity Cross-reference the enriched pathways with known drug-pathway interactions from the Connectivity Map or published pharmacogenomics studies. If your compound produces a gene signature that reverses a disease signature in specific pathways, this provides functional evidence for therapeutic relevance. Conversely, if the compound activates pathways associated with adverse effects (e.g., pro-apoptotic pathways in cardiac tissue), this flags safety concerns requiring investigation. ### Step 7: Document and visualize Create pathway maps highlighting the genes and interactions relevant to your compound. Export enrichment tables, pathway diagrams, and entity relationship networks for inclusion in reports, presentations, and publications. Document the evidence chain from literature mining through enrichment to mechanism hypothesis for reproducibility. ## Expected Outputs - BioBERT entity extraction results (genes, drugs, diseases, pathways) - PubMed literature summaries with pathway-relevant findings - Compiled gene sets with provenance annotations - Pathway enrichment tables with p-values, FDR, and gene overlap - Pathway visualization maps with compound-relevant annotations - Mechanism-of-action hypothesis document with supporting evidence ## Tips and Best Practices - Use multiple pathway databases for enrichment. KEGG, Reactome, and GO have different strengths and coverage. Pathways significant across multiple databases carry higher confidence. - Distinguish between statistical significance and biological relevance. A pathway with FDR 1e-10 but only 2 overlapping genes may be less informative than a pathway with FDR 0.01 and 15 overlapping genes. - For drug mechanism studies, compare the gene signature of your compound against signatures of known drugs with established mechanisms. Similar signatures suggest shared mechanisms. - Include both up-regulated and down-regulated genes in the analysis separately, then compare enrichments. Up-regulated and down-regulated pathways provide complementary mechanistic information. - When using gene lists from published studies, verify that the original analysis used appropriate controls and statistical thresholds. Literature-mined gene lists sometimes include genes from underpowered studies. - Revisit pathway analysis after each round of experimental validation. As you confirm or reject individual gene-pathway connections, the mechanism model becomes progressively more refined. ## Run it from a prompt The same goal as a copy-and-adapt recipe you can paste into a chat: - [Discover targets from omics](/docs/workflows/omics-to-target) — start from expression data instead of the literature. - [Analyze single-cell data](/docs/workflows/single-cell-de-enrichment) — differential expression and enrichment on single-cell data.