Chronic Stress influence on Cancer
Pathway: Education
I would like to do a research on how Chronic Stress influence on Cancer Genetically. Chronic Stress Activates Hormonal Responses • Chronic stress triggers the hypothalamic-pituitary-adrenal (HPA) axis. • This leads to increased cortisol (the “stress hormone”) and other stress hormones like adrenaline. • Impact: High cortisol over time suppresses immune surveillance — your body’s ability to detect and destroy abnormal cells decreases. Stress Leads to Chronic Inflammation • Stress hormones can increase pro-inflammatory molecules (like cytokines). • Chronic inflammation creates an environment that promotes DNA damage and cell proliferation. • Impact: Mutated cells are more likely to survive and multiply instead of being eliminated. Epigenetic Modifications • Chronic stress can change how genes are expressed without altering the DNA sequence. • Mechanisms include: o DNA methylation → silences tumor suppressor genes (e.g., TP53, BRCA1) o Histone modification → changes how DNA is packaged, affecting gene activity • Impact: Genes that normally prevent cancer may be “turned off,” increasing vulnerability. DNA Damage and Impaired Repair • Stress can interfere with DNA repair mechanisms, meaning mutations accumulate faster. • Cells with damaged DNA that aren’t repaired can transform into cancerous cells. • Impact: Genetic instability in breast tissue increases cancer risk. Interaction with Genetic Susceptibility • People with BRCA1/2 mutations or other cancer-related genes are more sensitive to these mechanisms.
#Chronic_Stress Activates Hormonal Responses#Stress_Leads to Chronic Inflammation#Epigenetic_Modifications#DNA_Damage and Impaired Repair#Interaction_with Genetic Susceptibility
1. Analyze and model endocrine–immune signaling networks by which chronic stress promotes oncogenesis, integrating HPA-axis glucocorticoid and β-adrenergic signaling with inflammatory transcriptional programs using pathway databases, transcription factor activity inference, and dynamic network modeling.
Learning Targets:
1. Map HPA and sympathetic cascades to tumor and microenvironment targets using KEGG, Reactome, and OmniPath; construct a directed, signed multi-layer interaction graph in Cytoscape including receptor–effector mappings.
2. Infer GR, NF-κB, STAT3, and AP-1 activities from bulk or single-cell transcriptomes using PROGENy/DoRothEA/VIPER and benchmark against perturbational controls (e.g., LINCS L1000).
3. Quantify chronic stress biomarker profiles by fitting mixed-effects models to diurnal cortisol (AUCg, CAR, slope) and HRV metrics; measure catecholamines via LC–MS/MS and derive a latent stress score using factor analysis.
4. Conduct a systematic review and random-effects meta-analysis of GR/β-AR activation effects on inflammatory signaling, estimating pooled effect sizes, heterogeneity (I2), and small-study bias.
5. Construct a causal DAG linking stress mediators to oncogenic outcomes, specify testable conditional independencies, and identify minimally sufficient adjustment sets for downstream analyses.
Modules
2. Execute and interpret multi-omic epigenomic profiling of stress-induced gene regulation using DNA methylation assays (WGBS/RRBS or EPIC), chromatin accessibility (ATAC-seq), and GR/histone mark ChIP-seq or CUT&Tag, integrated with RNA-seq data.
Learning Targets:
1. Generate and QC methylation, ATAC-seq, and GR/CUT&Tag or ChIP-seq datasets meeting thresholds (bisulfite conversion >99%, duplication <15%, ATAC TSS enrichment >7, FRiP >0.2, IDR <0.05) and document batch structure.
2. Perform differential methylation (Bismark/DSS or DMRcate), accessibility (MACS2/ArchR), and peak calling for ChIP/CUT&Tag; control FDR <0.05 with covariate adjustment (batch, cell cycle).