Digestive and Liver Disease最新文献

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the predominant liver disease and is becoming the paramount contributor to end-stage liver disease and liver-related deaths. Liver sinusoidal endothelial cells (LSECs) located between the hepatic parenchyma and blood from viscera and gastrointestinal tract are the gatekeepers for the hepatic microenvironment and normal function. In normal physiological conditions, LSECs govern the substance exchange between hepatic parenchyma and blood through dynamic regulation of fenestration and maintain the quiescent state of Kupffer cells (KCs) and hepatic stellate cells. In MASLD, lipotoxicity, insulin resistance, gastrointestinal microbiota dysbiosis, and mechanical compression caused by fat-laden hepatocytes result in LSECs capillarization and dysfunction. The altered LSECs progressively shift from healer to injurer, exacerbating liver inflammation and advancing liver fibrosis. This review focuses on the deteriorative roles of LSECs and related molecular mechanisms involved in MASLD and their contribution to metabolic dysfunction-associated steatohepatitis (MASH) and liver fibrosis development and progression. Furthermore, in this review, we propose that targeting LSECs dysfunction is a prospective therapeutic strategy to restore the physiological function of LSECs and mitigate MASLD progression.

Background: Hepatocellular carcinoma (HCC) is a substantial global health challenge owing to its high mortality rate and limited therapeutic options. We aimed to develop an efferocytosis-related gene signature (ER.Sig) and conduct a transcriptomic analysis to predict the prognosis and immunotherapeutic responses of patients with HCC.

Methods: Single-cell RNA sequencing data and bulk RNA sequencing data were obtained from public databases. Based on single-sample gene set enrichment analysis and Weighted Gene Co-expression Network analyses, efferocytosis-related genes (ERGs) were selected at both the single-cell and bulk transcriptome levels. A machine-learning framework employing ten different algorithms was used to develop the ER.Sig. Subsequently, a multi-omics approach (encompassing genomic analysis, single-cell transcriptomics, and bulk transcriptomics) was employed to thoroughly elucidate the prognostic signatures.

Results: Analysis of the HCC single-cell transcriptomes revealed significant efferocytotic activity in macrophages, endothelial cells, and fibroblasts within the HCC microenvironment. We then constructed a weighted co-expression network and identified six modules, among which the brown module (168 genes) was most highly correlated with the efferocytosis score (cor = 0.84). Using the univariate Cox regression analysis, 33 prognostic ERGs were identified. Subsequently, a predictive model was constructed using 10 machine-learning algorithms, with the random survival forest model showing the highest predictive performance. The final model, ER.Sig, comprised nine genes and demonstrated robust prognostic capabilities across multiple datasets. High-risk patients exhibited greater intratumoral heterogeneity and higher TP53 mutation frequencies than did low-risk patients. Immune landscape analysis revealed that compared with high-risk patients, low-risk patients exhibited a more favorable immune environment, characterized by higher proportions of CD8+ T and B cells, tumor microenvironment score, immunophenoscore, and lower Tumor Immune Dysfunction and Exclusion scores, indicating better responses to immunotherapy. Additionally, an examination of an independent immunotherapy cohort (IMvigor210) demonstrated that low-risk patients exhibited more favorable responses to immunotherapy and improved prognoses than did their high-risk counterparts.

Conclusions: The developed ER.Sig effectively predicted the prognosis of patients with HCC and revealed significant differences in tumor biology and treatment responses between the risk groups.