Background. Aberrant DNA methylation patterns are of increasing interest in the study of psoriasis mechanisms. This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms. Methods. GSE13355, GSE14905, and GSE73894 were collected from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated region- (DMR-) genes between psoriasis and control samples were combined to obtain differentially expressed methylated genes. Subsequently, a protein-protein interaction (PPI) network was established to analyze the interaction between differentially expressed methylated genes. Moreover, the hub genes of psoriasis were screened by the least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM), which were further performed single-gene gene set enrichment analysis (GSEA) to clarify the pathogenesis of psoriasis. The druggable genes were predicted using DGIdb. Finally, the expressions of hub genes in psoriasis lesions and healthy controls were detected by immunohistochemistry (IHC) and quantitative real-time PCR (RT-qPCR). Results. In this study, a total of 767 DEGs and 896 DMR-genes were obtained. Functional enrichment showed that they were significantly associated with skin development, skin barrier function, immune/inflammatory response, and cell cycle. The combined transcriptomic and DNA methylation data resulted in 33 differentially expressed methylated genes, of which GJB2 was the final identified hub gene for psoriasis, with robust diagnostic power. IHC and RT-qPCR showed that GJB2 was significantly higher in psoriasis samples than those in healthy controls. Additionally, GJB2 may be involved in the development and progression of psoriasis by disrupting the body’s immune system, mediating the cell cycle, and destroying the skin barrier, in addition to possibly inducing diseases related to the skeletal aspects of psoriasis. Moreover, OCTANOL and CARBENOXOLONE were identified as promising compounds through the DGIdb database. Conclusion. The abnormal expression of GJB2 might play a critical role in psoriasis development and progression. The genes identified in our study might serve as a diagnostic indicator and therapeutic target in psoriasis.

Background The objective response rate of microsatellite instability-high (MSI-H) metastatic colorectal cancer (mCRC) patients with first-line anti-programmed cell death protein-1 (PD-1) monotherapy is only 40–45%. Single-cell RNA sequencing (scRNA-seq) enables unbiased analysis of the full variety of cells comprising the tumor microenvironment. Thus, we used scRNA-seq to assess differences among microenvironment components between therapy-resistant and therapy-sensitive groups in MSI-H/mismatch repair-deficient (dMMR) mCRC. Resistance-related cell types and genes identified by this analysis were subsequently verified in clinical samples and mouse models to further reveal the molecular mechanism of anti-PD-1 resistance in MSI-H or dMMR mCRC. Methods The response of primary and metastatic lesions to first-line anti-PD-1 monotherapy was evaluated by radiology. Cells from primary lesions of patients with MSI-H/dMMR mCRC were analyzed using scRNA-seq. To identify the marker genes in each cluster, distinct cell clusters were identified and subjected to subcluster analysis. Then, a protein‒protein interaction network was constructed to identify key genes. Immunohistochemistry and immunofluorescence were applied to verify key genes and cell marker molecules in clinical samples. Immunohistochemistry, quantitative real-time PCR, and western blotting were performed to examine the expression of IL-1β and MMP9. Moreover, quantitative analysis and sorting of myeloid-derived suppressor cells (MDSCs) and CD8+ T cells were performed using flow cytometry. Results Tumor responses in 23 patients with MSI-H/dMMR mCRC were evaluated by radiology. The objective response rate was 43.48%, and the disease control rate was 69.57%. ScRNA-seq analysis showed that, compared with the treatment-resistant group, the treatment-sensitive group accumulated more CD8+ T cells. Experiments with both clinical samples and mice indicated that infiltration of IL-1β-driven MDSCs and inactivation of CD8+ T cells contribute to anti-PD-1 resistance in MSI-H/dMMR CRC. Conclusions CD8+ T cells and IL-1β were identified as the cell type and gene, respectively, with the highest correlation with anti-PD-1 resistance. Infiltration of IL-1β-driven MDSCs was a significant factor in anti-PD-1 resistance in CRC. IL-1β antagonists are expected to be developed as a new treatment for anti-PD-1 inhibitor resistance.

The value of insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), an N6-methyladenosine (m6A) RNA methylation regulatory factor, in the prognosis of colon cancer was still unclear. High levels of IGF2BP3 were expressed in colon adenocarcinoma (COAD) samples and in human colon cancer tissues, which was associated with poorer overall survival (OS). We validated IGF2BP3 as an independent prognostic risk biomarker in COAD patients. Moreover, functional enrichment analysis suggested that differentially expressed genes (DEGs) of groups with high versus low IGF2BP3 expression were related to immune- and cancer-related pathways. Furthermore, the tumor microenvironments of high- versus low-IGF2BP3 expression groups showed significant differences and IGF2BP3 predicted the efficiency of immunotherapy. Finally, protein-protein interaction network analysis suggested that there was a direct or indirect interaction among IGF2BP3, WNT7B, VANGL2, NKD1, AXIN2, RNF43, and CDKN2A. In brief, IGF2BP3 was confirmed as an independent prognostic signature in COAD patients and might be a therapeutic target in this study. Moreover, IGF2BP3 could be used in personalized immunotherapy for COAD.

Background : Aberrant DNA methylation patterns are of increasing interest in the study of psoriasis mechanisms. This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms. Methods : GSE13355, GSE14905, and GSE73894 were collected from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated region (DMR)-genes between psoriasis and control samples were combined to obtain differentially expressed methylated genes. Subsequently, protein-protein interaction (PPI) network establishment, multiple machine learning algorithm analysis, including, the least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM), receiver operating characteristic (ROC) curve analysis, and single-gene gene set enrichment analysis (GSEA) were performed to analyze the interaction networks, to recognize hub genes, and to clarify the pathogenesis of psoriasis. The druggable genes were predicted using DGIdb. The expression of GJB2 in psoriasis lesions and healthy controls was detected by immunohistochemistry (IHC) and quantitative real-time PCR(RT-qPCR). Results: In this study, a total of 767 DEGs and 896 DMR-genes were obtained. Functional enrichment showed that they were significantly associated with skin development, skin barrier function, immune/inflammatory response, and cell cycle. The combined transcriptomic and DNA methylation data resulted in 33 differentially expressed methylated genes, of which The gap junction beta 2 (GJB2) was the final identified hub gene for psoriasis, with the robust diagnostic power. IHC and RT-qPCR showed that GJB2 was significant higher in psoriasis than that in healthy controls. Single gene GSEA suggested that GJB2 may be involved in the development and progression of psoriasis by disrupting the body's immune system, mediating the cell cycle, and destroying the skin barrier, in addition to possibly inducing diseases related to the skeletal aspects of psoriasis. Moreover, OCTANOL and CARBENOXOLONE were identified as promising compounds through the DGIdb database. Conclusion: Our findings suggest that the abnormal expression of GJB2 may play a critical role in psoriasis development and progression. The genes identified in our study may serve as a diagnostic indicator and therapeutic target in psoriasis.