Breast cancer (BC) is a prevalent cancer of the female reproductive system and a major contributor to cancer-related mortality. The activation of NLRP3, a key inflammasome, has been extensively associated with tumor-related molecular and cellular processes; however, the regulatory mechanisms and specific role of NLRP3 in breast cancer remain incompletely elucidated. This study aimed to evaluate the molecular mechanisms of NLRP3-related genes in BC. Utilizing bioinformatics methods, the present research analyzed the TCGA-BRCA dataset, which included four groups of transcriptome sequencing data as follows, normal (WT), NLRP3 knockout (KO), non-knockout-BRCA (BC-WT), and NLRP3-knockout-BRCA (BC-KO). Results indicated that NLRP3 was significantly down-regulated in TCGA-BRCA. Key module genes were mainly enriched in leukocyte cell-cell adhesion and cytokine-cytokine receptor interaction. Moreover, correlation analysis showed that NLRP3 was positively associated with cancer-associated fibroblasts and negatively associated with CD4 + Th1 T-cells. In addition, the DEGs1 and DEGs2 overlapping indicated 505 feature genes, with Chac1 (negative) and Ugt8a (positive) had the strongest correlation with differential immune cells (class-switched memory B cells). Pathway intersection revealed 13 co-KEGG pathways. The BC-KO group indicated markedly reduced levels of four genes (Ccl19, Ccl20, Ccl21a, and H2-Oa) and increased levels of two genes (Il2ra and H2-Ob). This study delved into the role of NLRP3 in BC, exploring its regulatory mechanisms and the impact gene knockout. Bioinformatics approaches identified NLRP3-associated genes, their enriched pathways, and interactions within the tumor microenvironment (TME), providing novel insights into NLRP3 function, TME dynamics, and potential targets for BC prevention and treatment.
Keyphrases
- genome wide
- nlrp inflammasome
- single cell
- genome wide identification
- transcription factor
- cell adhesion
- dna methylation
- rna seq
- stem cells
- rheumatoid arthritis
- breast cancer risk
- bioinformatics analysis
- coronary artery disease
- machine learning
- multidrug resistant
- risk factors
- squamous cell carcinoma
- mesenchymal stem cells
- young adults
- ankylosing spondylitis
- lymph node metastasis
- neural network