Wei Qu

Capsaicin, a natural compound, has demonstrated antitumor efficacy in estrogen receptor-positive breast cancer (ER-positive BC). However, its downstream molecular targets and mechanisms remain poorly understood, particularly those linked to metabolic reprogramming and immune modulation. This study aimed to identify capsaicin-responsive genes and explore their roles in ER-positive BC progression and tumor microenvironment remodeling. Bioinformatic analysis was performed on the GSE64155 dataset from GEO to screen differentially expressed genes (DEGs) in capsaicin-treated ER-positive BC. Functional enrichment (GO/KEGG) and protein-protein interaction (PPI) network analyses were performed to prioritize key genes. Experimental validation included qPCR and Western blotting to assess gene and protein expression in capsaicin-treated cells. Clinical relevance was evaluated using TCGA expression data, survival analysis (overall survival [OS], relapse-free survival [RFS], and distant metastasis-free survival [DMFS]), and immunohistochemistry (IHC) in BC tissues. Immune cell infiltration was analyzed via the CIBERSORT algorithm. SHMT2 and GARS were identified as the most significant capsaicin-responsive genes. Both genes were overexpressed in BC tissues and may be associated with a poor prognosis in patients with ER-positive BC. Capsaicin significantly reduced SHMT2 and GARS expression at the mRNA and protein levels. Immune infiltration analysis indicated that high SHMT2 and GARS expression may be associated with altered immune cell infiltration patterns. This study identifies SHMT2 and GARS as novel downstream targets of capsaicin in ER-positive BC and links their known oncogenic functions to a potential new mechanism underlying the antitumor effects of capsaicin.

Hashimoto thyroiditis (HT) is one of the most common autoimmune diseases, and the incidence of HT continues to increase. Long-term, uncontrollable HT results in thyroid dysfunction and even increases carcinogenesis risks. Since the origin and development of HT involve many complex immune processes, there is no effective therapy for HT on a pathogenesis level. Although bioinformatics analysis has been utilized to seek key genes and pathways of thyroid cancer, only a few bioinformatics studies that focus on HT pathogenesis and mechanisms have been reported. In the present study, the Gene Expression Omnibus dataset (GSE29315) containing 6 HT and 8 thyroid physiological hyperplasia samples was downloaded, and differentially expressed gene (DEG) analysis, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, protein–protein interaction analysis, and gene set enrichment analysis were performed. In total, 85 DEGs, containing 76 up-regulated and 9 down-regulated DEGS, were identified. The DEGs were mainly enriched in immune and inflammatory response, and the signaling pathways were involved in cytokine interaction and cytotoxicity. Moreover, ten hub genes were identified, and IFN-γ, IFN-α, IL6/JAK/STAT3, and inflammatory pathways may promote the origin and progression of HT. The present study indicated that exploring DEGs and pathways by bioinformatics analysis has important significance in understanding the molecular mechanisms of HT and providing potential targets for the prevention and treatment of HT.

Enzalutamide (ENZ) has been approved for the treatment of advanced prostate cancer (PCa), but some patients develop ENZ resistance initially or after long‐term administration. Although a few key genes have been discovered by previous efforts, the complete mechanisms of ENZ resistance remain unsolved. To further identify more potential key genes and pathways in the development of ENZ resistance, we employed the GSE104935 dataset, including 5 ENZ‐resistant (ENZ‐R) and 5 ENZ‐sensitive (ENZ‐S) PCa cell lines, from the Gene Expression Omnibus (GEO) database. Integrated bioinformatics analyses were conducted, such as analysis of differentially expressed genes (DEGs), Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein‐protein interaction (PPI) analysis, gene set enrichment analysis (GSEA), and survival analysis. From these, we identified 201 DEGs (93 upregulated and 108 downregulated) and 12 hub genes (AR, ACKR3, GPER1, CCR7, NMU, NDRG1, FKBP5, NKX3‐1, GAL, LPAR3, F2RL1, and PTGFR) that are potentially associated with ENZ resistance. One upregulated pathway (hedgehog pathway) and seven downregulated pathways (pathways related to androgen response, p53, estrogen response, TNF‐α, TGF‐β, complement, and pancreas β cells) were identified as potential key pathways involved in the occurrence of ENZ resistance. Our findings may contribute to further understanding the molecular mechanisms of ENZ resistance and provide some clues for the prevention and treatment of ENZ resistance.