Classification of colorectal cancer into clinically relevant subtypes based on genes and mesenchymal cells.

BACKGROUND: Most studies on subtype identification of colorectal cancer (CRC) were based on expressions of either genes or immune cells. However, few studies have hitherto used the combination of genes with immune and stroma cells for subtype identification. METHODS: Dataset GSE17536 was obtained from the Gene Expression Omnibus (GEO) database. The xCell algorithm was used to estimate the composition and density of 64 cell types, including immune and stroma cell types. Clustering analysis was then conducted on the top 3000 most variable genes from a total of 20,174 genes for CRC subtype identification. We employed the ensemble method of Similarity network fusion and 112 Consensus Clustering (SNF-CC) for cancer subtype identification. Reactome pathway analysis was conducted to identify the impact of the representative genes on prognosis. The results were validated in independent gene expression data from dataset GSE17537. RESULTS: In this study, we identified 3 clinically relevant subtypes and their representative genes, immune and stroma cells. Moreover, we confirmed the correlation of these subtypes with their clinical characteristics. The representative genes of the subtype with poor prognosis correlated with extracellular matrix structural constituent, while the subtype with good prognosis correlated with Toll-like receptor signaling pathway or chemokine signaling pathway. However, different subtypes were associated with distinct cell subtypes; the subtype with poor prognosis had a high abundance of fibroblasts and endothelial cells; the subtype with median prognosis had a higher abundance of immune cells, such as CD4 + T-cell, Th2 cells and aDC; the subtype with good prognosis had a higher abundance of NKT. CONCLUSION: This study highlights the utility of immune and innate cells, especially during gene analysis, to provide the theoretical basis for personalized treatment in colorectal cancer patients.

The Immune Landscape of Hepatitis B Virus-Related Acute Liver Failure by Integration Analysis.

Hepatitis B virus-related acute liver failure (HBV-ALF) is a common type of liver failure, associated with high short-term mortality and morbidity rates. However, the immune landscape of HBV-ALF and its correlation with cell death are currently unknown. Based on 3 Gene Expression Omnibus data sets, infiltrated immune cells were quantified by single-sample gene set enrichment analysis method. The expression levels of immune genes and the abundance of immune cells in liver failure were compared with those in normal liver. The enrichment scores of cell death gene sets from Kyoto Encyclopedia of Genes and Genomes (KEGG) were calculated by gene set variation analysis method, and a protein-protein interaction (PPI) network was constructed using Cytoscape. Besides 21 differentially expressed immune genes, we identified 11 types of differentially infiltrated immune cells in HBV-ALF compared with normal liver. Enriched pathways of these immune genes mainly consisted of chemokine receptors, chemokine binding, interleukin-10 signaling, and TNFs bind their physiological receptors by Reactome pathway analysis. In addition, the enrichment scores of apoptosis and necroptosis pathway instead of autophagy and ferroptosis were increased in liver failure compared with normal liver. PPI network and gene cluster analysis of immune genes and apoptosis and necroptosis genes suggested that hub genes were mainly related to immune response and apoptosis. In summary, our study offers a conceptual framework to understand the immune landscape of HBV-ALF, which might help to improve prognosis.

Activated Hepatic Stellate Cells Induce Infiltration and Formation of CD163+ Macrophages via CCL2/CCR2 Pathway.

Background: Activated hepatic stellate cells (aHSCs) regulate the function of immune cells during liver fibrosis. As major innate cells in the liver, macrophages have inducible plasticity. Nevertheless, the mechanisms through which aHSCs regulate macrophages' phenotype and function during liver fibrosis and cirrhosis remain unclear. In this study, we examined the immunoregulatory function of aHSCs during liver fibrosis and explored their role in regulating macrophage phenotype and function. Methods: A total of 96 patients with different stages of chronic hepatitis B-related liver fibrosis were recruited in the study. Metavir score system was used to evaluate the degree of fibrosis. The expression of hepatic CCL2 and M2 phenotype macrophage marker CD163 were detected by immunohistochemistry, and the relationship among hepatic CD163, CCL2, and fibrosis scores were also explored. In the in vitro model, the aHSCs isolated from human liver tissues and THP-1-derived M0-type macrophages (M0MΦ) were co-cultured to observe whether and how aHSCs regulate the phenotype and function of macrophages. To explore whether CCL2/CCR2 axis has a crucial role in macrophage phenotypic changes during liver fibrosis, we treated the M0MΦ with recombinant human CCL2 or its specific receptor antagonist INCB-3284. Furthermore, we used LX2 and TGF-ß-activated LX2 to mimic the different activation statuses of aHSCs to further confirm our results. Results: In patients, the infiltration of M2 macrophages increased during the progression of liver fibrosis. Intriguingly, as a key molecule for aHSC chemotactic macrophage aggregation, CCL2 markedly up-regulated the expression of CD163 and CD206 on the macrophages, which was further confirmed by adding the CCR2 antagonist (INCB 3284) into the cell culture system. In addition, the TGF-ß stimulated LX2 further confirmed that aHSCs up-regulate the expression of CD163 and CD206 on macrophages. LX2 stimulated with TGF-ß could produce more CCL2 and up-regulate other M2 phenotype macrophage-specific markers, including IL-10, ARG-1, and CCR2 besides CD163 and CD206 at the gene level, indicating that the different activation status of aHSCs might affect the final phenotype and function of macrophages. Conclusions: The expression of the M2 macrophage marker increases during liver fibrosis progression and is associated with fibrosis severity. AHSCs can recruit macrophages through the CCL2/CCR2 pathway and induce M2 phenotypic transformation.

Bayesian network to predict hepatitis B surface antigen seroclearance in chronic hepatitis B patients.

There is a need for an interpretable, accurate and interactions-considered model for predicting hepatitis B surface antigen (HBsAg) seroclearance. We aimed to construct a Bayesian network (BN) model using available medical records to predict HBsAg seroclearance in chronic hepatitis B (CHB) patients, and to evaluate the model's performance. This was a case-control study. A total of 1966 consecutive CHB patients (mean age 39.04 ± 11.23 years) between January 2006 and June 2015 were included. The demographic and clinical characteristics, laboratory data and imaging parameters were obtained and used to build a BN model to estimate the probability of HBsAg seroclearance. Baseline serum HBsAg and hepatitis Be antigen (HBeAg) levels, virological response and HBeAg seroclearance were the most significant predictors of HBsAg seroclearance. The post-test probability table showed that patients with baseline HBsAg concentrations ≤2000 IU/mL, negative baseline HBeAg, an initial virological response and without HBeAg seroclearance (i.e. no recurrence of HBeAg positivity during follow-up) were most likely to have HBsAg seroclearance. The constructed BN model had an area under the receiver operating characteristic curves of 0.896 (95% confidence interval [CI]: 0.892, 0.899), a sensitivity of 0.840 (95% CI: 0.833, 0.846), a specificity of 0.880 (95% CI: 0.876, 0.884) and an accuracy of 0.878 (95% CI: 0.874, 0.882) for predicting HBsAg seroclearance. The established BN model accurately estimated the probability of HBsAg seroclearance and is a promising tool to assist clinical decision-making.