Gene Expression to Hub Genes: Biomarker Discovery Using R, STRING & Cytoscape

To provide practical skills for analyzing gene-expression datasets, constructing molecular networks, identifying hub genes, and integrating pathway evidence for biomarker and therapeutic-target prioritization.

• Understand the fundamental principles of computational systems biology and network biology.
• Retrieve and analyse publicly available gene-expression datasets from GEO.
• Perform gene-expression preprocessing, normalisation, and differential-expression analysis using R and Bioconductor.
• Identify significantly upregulated and downregulated genes.
• Generate and interpret volcano plots, heatmaps, and gene-expression visualisations.
• Construct protein–protein interaction networks using STRING and Cytoscape.
• Analyse network topology using degree, betweenness, closeness, and MCC centrality measures.
• Identify hub genes using cytoHubba and functional molecular modules using MCODE.
• Perform Gene Ontology, KEGG, and Reactome pathway-enrichment analysis.
• Interpret disease-associated pathways and biological functions of candidate genes.
• Integrate gene-expression, network-centrality, and pathway evidence for biomarker prioritisation.

📅 Day 1: Gene-Expression Analysis & DEG Identification

Core Objective: Identify significant disease-associated genes from gene-expression data.

Topics Covered

• Introduction to computational systems biology
• Gene-expression datasets and GEO
• Dataset retrieval and sample understanding
• Data preprocessing and normalization
• Differential-expression analysis
• Fold change, p-values, and adjusted p-values
• Identification of upregulated and downregulated genes
• Volcano plots and heatmaps
• Preparing gene lists for network analysis

🛠️ Hands-on Session

Participants will retrieve a public GEO dataset, perform differential-expression analysis in R, filter significant genes, and generate a volcano plot and heatmap.

Hands-on Workflow:
GEO Dataset → Preprocessing → Differential Expression → DEG Filtering → Volcano Plot → Heatmap

Tools: R, RStudio, GEOquery, Bioconductor, limma/DESeq2, ggplot2

📅 Day 2: PPI Networks & Hub-Gene Discovery

Core Objective: Construct biological networks and identify important hub genes and molecular modules.

Topics Covered

• Introduction to protein–protein interaction networks
• Nodes, edges, connectivity, and network topology
• Constructing PPI networks using STRING
• Importing networks into Cytoscape
• Network-topology analysis
• Degree, betweenness, closeness, and MCC centrality
• Hub-gene identification using cytoHubba
• Molecular-module detection using MCODE
• Biological interpretation of hub genes and clusters

🛠️ Hands-on Session

Participants will use the significant genes identified on Day 1 to construct a STRING PPI network, analyze it in Cytoscape, identify top hub genes, and detect significant molecular modules.

Hands-on Workflow:
DEGs → STRING → Cytoscape → Network Analysis → cytoHubba → MCODE → Hub Genes

Tools: STRING, Cytoscape, NetworkAnalyzer, cytoHubba, MCODE

📅 Day 3: Pathway Analysis & Biomarker Prioritization

Core Objective: Integrate hub genes and pathway evidence to prioritize candidate biomarkers and therapeutic targets.

Topics Covered

• Introduction to functional-enrichment analysis
• Gene Ontology enrichment
• KEGG and Reactome pathway analysis
• Functional interpretation of hub genes
• Disease-associated pathway identification
• Gene- pathway interaction networks
• Biomarker prioritization
• Therapeutic-target identification
• Integrating differential-expression, centrality, and pathway evidence
• Publication-quality network visualization

🛠️ Hands-on Session

Participants will perform enrichment analysis on hub genes, identify important disease-associated pathways, construct a gene–pathway network, and develop a candidate biomarker shortlist.

Hands-on Workflow:
Hub Genes → GO/KEGG/Reactome → Pathway Interpretation → Gene–Pathway Network → Biomarker Prioritization

Tools: Enrichr, g, KEGG, Reactome, ClueGO, Cytoscape

• Ph.D. scholars and researchers
• Biotechnology and bioinformatics students
• Molecular biology and genomics researchers
• Biomedical and pharmaceutical researchers
• Faculty and academicians
• Wet-lab researchers interested in computational biology
• Researchers working on transcriptomics, disease mechanisms, biomarkers, or therapeutic targets