Hexin Wen

To investigate the effect of niranthin (Nir) in mice with Crohn's disease (CD)‑like colitis and its therapeutic mechanism. In a mouse model of CD-like colitis induced using 2,4,6-trinitrobenzene sulfonic acid, the effects of niranthin on colitis symptoms were evaluated by measuring changes in disease activity index (DAI) score, body weight, colon length, and colonic pathologies. Intestinal barrier function and cell apoptosis were evaluated using AB-PAS staining, immunofluorescence staining, Western blotting, and TUNEL staining. The changes in Th1 and Th2 cells in the mesenteric lymph nodes and colonic TNF-α and IL-10 expression levels were determined with flow cytometry and ELISA. In lipopolysaccharide (LPS)-induced mouse colon organoids, the effects of niranthin on organoid budding number and barrier protein expressions were observed. Network pharmacology and in vivo experiments were employed to explore and verify the therapeutic mechanism of niranthin on colitis. In the mouse models of CD-like colitis, niranthin treatment obviously improved weight loss, DAI scores, and colorectal shortening and significantly reduced tissue inflammation scores, goblet cell loss, and intestinal epithelial cell apoptosis. Niranthin significantly increased the budding number in LPS-induced mouse colon organoids. In both mouse colon tissues and LPS-induced mouse colon organoids, niranthin obviously increased the expressions of ZO-1 and claudin-1, downregulated the expressions of cleaved caspase-3 and BAX, and upregulated Bcl‑2 expression. The niranthin-treated mouse models showed significantly ameliorated Th1/Th2 imbalance in the mesenteric lymph nodes, downregulated TNF‑α and upregulated IL‑10 levels in the colon tissues. Network pharmacology predicted that the therapeutic mechanism of niranthin for CD-like colitis involved the PI3K/AKT pathway, which was validated in the mouse models treated with niranthin and the PI3K/AKT pathway activator Recilisib. Niranthin ameliorates colitis in mice by antagonizing epithelial apoptosis and regulating Th1/Th2 balance via inhibiting the PI3K/AKT pathway.

Objective. DNA damage response (DDR) is a complex system that maintains genetic integrity and the stable replication and transmission of genetic material. m6A modifies DDR‐related gene expression and affects the balance of DNA damage response in tumor cells. In this study, a risk model based on m6A‐modified DDR‐related gene was established to evaluate its role in patients with gastric cancer. Methods. We downloaded 639 DNA damage response genes from the Gene Set Enrichment Analysis (GSEA) database and constructed risk score models using typed differential genes. We used Kaplan‐Meier curves and risk curves to verify the clinical relevance of the model, which was then validated with the univariate and multifactorial Cox analysis, ROC, C‐index, and nomogram, and finally this model was used to evaluate the correlation of the risk score model with immune microenvironment, microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints. Results. In this study, 337 samples in The Cancer Genome Atlas (TCGA) database were used as training set to construct a DDR‐related gene model, and GSE84437 was used as external data set for verification. We found that the prognosis and immunotherapy effect of gastric cancer patients in the low‐risk group were significantly better than those in the high‐risk group. Conclusion. We screened eight DDR‐related genes (ZBTB7A, POLQ, CHEK1, NPDC1, RAMP1, AXIN2, SFRP2, and APOD) to establish a risk model, which can predict the prognosis of gastric cancer patients and guide the clinical implementation of immunotherapy.

The recent advances in gene chip technology have led to the identification of multiple metabolism-related genes that are closely associated with colorectal cancer (CRC). Nevertheless, none of these genes could accurately diagnose or predict CRC. The prognosis of CRC has been made by previous prognostic models constructed by using multiple genes, however, the predictive function of multi-gene prognostic models using metabolic genes for the CRC prognosis remains unexplored. In this study, we used the TCGA-CRC cohort as the test dataset and the GSE39582 cohort as the experimental dataset. Firstly, we constructed a prognostic model using metabolic genes from the TCGA-CRC cohort, which were also associated with CRC prognosis. We analyzed the advantages of the prognostic model in the prognosis of CRC and its regulatory mechanism of the genes associated with the model. Secondly, the outcome of the TCGA-CRC cohort analysis was validated using the GSE39582 cohort. We found that the prognostic model can be employed as an independent prognostic risk factor for estimating the CRC survival rate. Besides, compared with traditional clinical pathology, it can precisely predict CRC prognosis as well. The high-risk group of the prognostic model showed a substantially lower survival rate as compared to the low-risk group. In addition, gene enrichment analysis of metabolic genes showed that genes in the prognostic model are enriched in metabolism and cancer-related pathways, which may explain its underlying mechanism. Our study identified a novel metabolic profile containing 11 genes for prognostic prediction of CRC. The prognostic model may unravel the imbalanced metabolic microenvironment, and it might promote the development of biomarkers for predicting treatment response and streamlining metabolic therapy in CRC.

通过生物信息学方法，分析多种蛋白质联合预测结直肠癌(CRC)预后的作用及潜在的分子机制。 从癌症蛋白质组图集(TCPA)数据库下载CRC蛋白质表达数据及临床数据，应用Perl及R软件对数据进行整理后筛选出预后相关的蛋白质; 进一步通过多因素Cox分析筛选出可作为CRC预后独立风险因子的蛋白质并据此构建预测模型。对模型中每一个蛋白质及模型风险评分进行生存分析，并对风险评分与患者生存状态绘制风险曲线验证预测模型对预后的预测作用。独立预后分析及ROC分析可反映预测模型在预后预测中的价值及优势。对模型蛋白质与CRC所有的相关蛋白质进行相互作用分析并在mRNA水平分析关键蛋白相关基因的差异表达。 通过单因素及多因素Cox分析筛选出了6个蛋白质用于预测模型的构建; 生存分析发现与单个基因相比，预测模型表现出更强大的预后价值。单因素及多因素独立预后分析均提示预测模型风险评分与预后显著相关(P < 0.001)，预测模型可作为评估患者预后的独立风险因子; ROC分析显示预测模型在预后预测中表现出更加稳定的特异性和灵敏度(AUC=0.734)。蛋白质相互作用关系显示，蛋白质BID、SLC1A5及SRC_pY527与其他蛋白质表现出较明显的相关性(P < 0.001)，蛋白质SLC1A5及SRC_pY527与其他CRC相关的蛋白质间的相互作用最显著(SLC1A5与11种蛋白质间存在显著相关性; SRC_pY527与12种蛋白质间存在显著相关性，P < 0.001);除INPP4B外，各蛋白相关基因在mRNA水平均呈差异表达(P < 0.05)。 6种蛋白质构建的预测模型对CRC 具有较好的预后预测作用，同时蛋白质SLC1A5及SRC_pY527在CRC的预后中起关键的作用，尤其是蛋白质SRC_pY527可能通过SRC/ AKT/MAPK信号轴调节CRC的发生发展并有望为CRC的治疗提供新的靶标。