A Bioinformatics Investigation of Hub Genes Involved in Treg Migration and Its Synergistic Effects, Using Immune Checkpoint Inhibitors for Immunotherapies.
Nari KimSeoungwon NaJunhee PyoJi-Sung JangSoo-Min LeeKyoung Won KimPublished in: International journal of molecular sciences (2024)
This study aimed to identify hub genes involved in regulatory T cell (Treg) function and migration, offering insights into potential therapeutic targets for cancer immunotherapy. We performed a comprehensive bioinformatics analysis using three gene expression microarray datasets from the GEO database. Differentially expressed genes (DEGs) were identified to pathway enrichment analysis to explore their functional roles and potential pathways. A protein-protein interaction network was constructed to identify hub genes critical for Treg activity. We further evaluated the co-expression of these hub genes with immune checkpoint proteins (PD-1, PD-L1, CTLA4) and assessed their prognostic significance. Through this comprehensive analysis, we identified CCR8 as a key player in Treg migration and explored its potential synergistic effects with ICIs. Our findings suggest that CCR8-targeted therapies could enhance cancer immunotherapy outcomes, with breast invasive carcinoma (BRCA) emerging as a promising indication for combination therapy. This study highlights the potential of CCR8 as a biomarker and therapeutic target, contributing to the development of targeted cancer treatment strategies.
Keyphrases
- bioinformatics analysis
- gene expression
- combination therapy
- network analysis
- protein protein
- genome wide
- dendritic cells
- cancer therapy
- regulatory t cells
- dna methylation
- small molecule
- papillary thyroid
- emergency department
- human health
- squamous cell carcinoma
- drug delivery
- wastewater treatment
- skeletal muscle
- genome wide identification
- insulin resistance
- risk assessment
- metabolic syndrome
- immune response
- genome wide analysis
- electronic health record
- adverse drug
- weight loss
- lymph node metastasis
- data analysis