Background: Non-alcoholic fatty liver disease (NAFLD) is the global most common chronic liver disease. Non-alcoholic steatohepatitis (NASH), an inflammatory subtype of NAFLD, has been shown to significantly increase the risk of colorectal adenoma (CRA). Therefore, from the perspective of bioinformatics analysis, the potential mechanisms of NASH/NAFLD-CRA can be explored.

Methods: In this study, we screened the differentially expressed genes (DEGs) and core effect pathways between NASH and CRA by analyzing the single-cell data of CRA patients and the high-throughput sequencing data (GSE37364 and GSE89632) in the online database. We screened therapeutic targets and biomarkers through gene function classification, pathway enrichment analysis, and protein-protein interaction network analysis. In terms of single cell data, we screened the core effect pathway and specific signal pathway of cell communication through cell annotation and cell communication analyses. The purpose of the study was to find potential biomarkers, therapeutic targets, and related effect pathways of NASH-CRA.

Results: NASH-CRA comorbidities were concentrated in inflammatory regulation-related pathways, and the core genes of disease progression included , , , , , and . The results suggested the key pathway of NASH-CRA might be the pathway. The main cell signal communication pathways included - ( + ) and - ( + ). The send-receive process occurred in embryonic stem cells.

Conclusions: The core genes of NASH-CRA (, , , , , and ) may participate in inflammation and immune responses through up-regulation in the process of disease occurrence, interfering with the pathophysiological process of CRA and NASH. NASH-CRA produces cell signal communication in the pathway sent by and received by , , and in embryonic stem cells. These findings may help formulate early diagnosis and treatment strategies for CRA in NAFLD/NASH patients, and further explore corresponding prognostic markers and potential approaches. The significance of scRNA-seq in exploring tumor heterogeneity lies in promoting our understanding of the expression program of tumor related genes in tumor development patterns. However, the biggest challenge is that this analysis may miss out on some biologically significant gene expression programs.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502531PMC
http://dx.doi.org/10.21037/jgo-23-502DOI Listing

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