The tumor immune microenvironment plays a decisive role in tumor initiation, progression and therapeutic response. Spatial transcriptomics (ST) enables comprehensive analysis of the whole transcriptome or specific gene sets while preserving the original spatial location information within tissues, offering a revolutionary approach to unraveling the complexity of the tumor immune microenvironment in the spatial dimension. The present review aims to systematically elaborate on the role and application of ST in deciphering the tumor immune microenvironment. Focusing on tumor microenvironment niches at different stages of tumor progression, from precancerous lesions and locally advanced tumors to distant metastasis, recent advances in ST for mapping the spatial distribution and functional states of key cell subpopulations are summarized. The present review further highlights its potential for clinical translation in identifying spatially defined biomarkers and elucidating the mechanisms underlying therapeutic responses and analyze the key technical challenges that constrain the application of ST in clinical practice. In recent years, the rapid development of machine learning algorithms has provided new opportunities to overcome the technical limitations of ST. The deep integration of machine learning with ST is laying a solid theoretical foundation and technical support for precision medicine and early intervention.
Jialin Jiang, Xinyu Tu, Yi Cao et al.· International Journal of Mol...· 0 citations
BACKGROUND
People with Acute Pancreatitis (AP) who also have Metabolic Dysfunctionassociated Fatty Liver Disease (MAFLD) are more likely to experience worse consequences. However, the mechanisms that link MAFLD and AP are not fully understood. Our study identified shared biomarkers utilizing bioinformatics and machine learning techniques.
METHODS
We selected datasets for AP and MAFLD from the GEO database. Differentially Expressed Genes (DEGs) were identified from the AP dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was conducted on the MAFLD dataset to identify the module highly correlated with the disease. Subsequently, we obtained shared genes by taking the intersection of the DEGs and the module genes. The shared genes were analyzed for GO and KEGG enrichment. A Protein-Protein Interaction (PPI) network and machine learning techniques were used to identify diagnostic genes, which were evaluated for diagnostic efficacy using ROC curves.
RESULTS
The AP dataset yielded 454 upregulated and 380 downregulated DEGs. WGCNA identified 715 module genes in MAFLD, producing 42 shared genes. Enrichment analyses implicated inflammatory responses, calcium signaling, and lipopolysaccharide-related immune pathways. FPR1 and S100A9 emerged as the final diagnostic candidates, with AUC values exceeding 0.7 across all datasets.
DISCUSSION
FPR1 and S100A9 are involved in immune activation, inflammatory signaling, and oxidative stress, suggesting roles in the pathogenesis of both AP and MAFLD. MAFLD may indirectly worsen AP severity through inflammatory and lipid pathways.
CONCLUSION
FPR1 and S100A9 are promising common biomarkers for AP and MAFLD, and may provide important insights into common mechanisms and therapeutic opportunities.
Kang Gong, Jialin Jiang, Yifan Zhang et al.· Endocrine, Metabolic & Immun...· 0 citations
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