Peiminine ameliorates Crohn's disease-like colitis by enhancing the function of the intestinal epithelial barrier through Nrf2/HO1 signal.

BACKGROUND AND AIMS: Impaired intestinal barrier function is key in maintaining intestinal inflammation in Crohn's disease (CD). However, no targeted treatment in clinical practice has been developed. Peiminine (Pm) strongly protects the epithelial barrier, the purpose of this study is to investigate whether Pm affects CD-like colitis and potential mechanisms for its action. METHODS: Trinitro-benzene-sulfonic acid (TNBS)-induced mice and Il-10-/- mice were used as CD animal models. Colitis symptoms, histological analysis, and intestinal barrier permeability were used to assess the Pm's therapeutic effect on CD-like colitis. The colon organoids were induced by TNF-α to evaluate the direct role of Pm in inhibiting apoptosis of the intestinal epithelial cells. Western blotting and small molecule inhibitors were used to investigate further the potential mechanism of Pm in inhibiting apoptosis of intestinal epithelial cells. RESULTS: Pm treatment reduced body weight loss, disease activity index (DAI) score, and inflammatory score, demonstrating that colonic inflammation in mice were alleviated. Pm decreased the intestinal epithelial apoptosis, improved the intestinal barrier function, and prevented the loss of tight junction proteins (ZO1 and claudin-1) in the colon of CD mice and TNF-α-induced colonic organoids. Pm activated Nrf2/HO1 signaling, which may protect intestinal barrier function. CONCLUSIONS: Pm inhibits intestinal epithelial apoptosis in CD mice by activating Nrf2/HO1 pathway. This partially explains the potential mechanism of Pm in ameliorating intestinal barrier function in mice and provides a new approach to treating CD.

Colorectal cancer tumor cell-derived exosomal miR-203a-3p promotes CRC metastasis by targeting PTEN-induced macrophage polarization.

(1) Background: Tumor-associated macrophages (TAMs) are important immunocytes associated with liver metastasis of colorectal cancer (CRLM). However, the molecular processes underpinning the interaction between the TME and the tumour-derived exosomal miRNAs in CRLM are not being fully understood; (2) Methods: Transmission electron microscopy was utilized to confirm the existence of exosomes after differential ultracentrifugation. To determine the roles of exosomal miR-203a-3p, an in vivo and in vitro investigation was conducted. The mechanism by which exosomal miR-203a-3p governs the interaction between CRC cells and M2 macrophages was investigated using a dual-luciferase reporter assay, western blot, and other techniques; (3) Results: Overexpression of miR-203a-3p was associated with poor prognosis and liver metastasis in CRC patients. Exosomal miR-203a-3p was upregulated in the plasma of CRC patients and highly metastatic CRC cells HCT116, and it could be transported to macrophages via exosomes. Exosomal miR-203a-3p induced M2 polarization of macrophages by controlling PTEN and activating the PI3K/Akt signaling pathway. M2-polarized macrophages secreted the CXCL12, which increased cancer metastasis and resulted in pre-metastatic niches in CRLM by CXCL12/CXCR4/NF-κB signaling pathway. Co-culture of macrophages with miR-203a-3p-transfected or exosome-treated cells increased the ability of HCT116 cells to metastasize both in vitro and in vivo; (4) Conclusions: Exosomes produced by highly metastatic CRC cells and rich in miR-203a-3p may target PTEN and alter the TME, promoting liver metastasis in CRC patients. These findings offer fresh understanding of the liver metastatic process in CRC.

Construction of a fecal immune-related protein-based biomarker panel for colorectal cancer diagnosis: a multicenter study.

Purpose: To explore fecal immune-related proteins that can be used for colorectal cancer (CRC) diagnosis. Patients and methods: Three independent cohorts were used in present study. In the discovery cohort, which included 14 CRC patients and 6 healthy controls (HCs), label-free proteomics was applied to identify immune-related proteins in stool that could be used for CRC diagnosis. Exploring potential links between gut microbes and immune-related proteins by 16S rRNA sequencing. The abundance of fecal immune-associated proteins was verified by ELISA in two independent validation cohorts and a biomarker panel was constructed that could be used for CRC diagnosis. The validation cohort I included 192 CRC patients and 151 HCs from 6 different hospitals. The validation cohort II included 141 CRC patients, 82 colorectal adenoma (CRA) patients, and 87 HCs from another hospital. Finally, the expression of biomarkers in cancer tissues was verified by immunohistochemistry (IHC). Results: In the discovery study, 436 plausible fecal proteins were identified. And among 67 differential fecal proteins (|log2 fold change| > 1, P< 0.01) that could be used for CRC diagnosis, 16 immune-related proteins with diagnostic value were identified. The 16S rRNA sequencing results showed a positive correlation between immune-related proteins and the abundance of oncogenic bacteria. In the validation cohort I, a biomarker panel consisting of five fecal immune-related proteins (CAT, LTF, MMP9, RBP4, and SERPINA3) was constructed based on the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. The biomarker panel was found to be superior to hemoglobin in the diagnosis of CRC in both validation cohort I and validation cohort II. The IHC result showed that protein expression levels of these five immune-related proteins were significantly higher in CRC tissue than in normal colorectal tissue. Conclusion: A novel biomarker panel consisting of fecal immune-related proteins can be used for the diagnosis of CRC.

Models for Predicting Response to Immunotherapy and Prognosis in Patients with Gastric Cancer: DNA Damage Response Genes.

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.

m7G-Related DNA Damage Repair Genes are Potential Biomarkers for Predicting Prognosis and Immunotherapy Effectiveness in Colon Cancer Patients.

Objective: m7G is a post-transcriptional modification modality, however, limited research has been conducted on its role in colon cancer. DNA damage repair (DDR) is an important factor that contributes to colon cancer development, growth and chemoresistance. This study aimed to explore whether m7G-related DNA damage repair genes may be used as biomarkers to predict the prognosis of colon cancer patients. Methods: We use non-negative matrix factorization (NMF) to type CRC patients into. Risk models were constructed using different expression genes in two clusters. We assessed the reliability of risk models with DCA curves, and a Nomogram. Meanwhile, The receiver operating characteristic and C-index curves were used to compare the predictive significance of the constructed risk models with other studies. In additional, we examined the significance of risk models on patients' immunity microenvironment and response to immune therapy. Finally, we used a series of cellular experiments to validate the effect of model genes on the malignant progression of CRC cells. Results: Twenty-eight m7G genes were obtained from the GSEA database. Multivariate Cox and LASSO Cox regression analysis was performed and eleven m7G-related DDR genes were identified for constructing the risk model. Survival and stage of CRC patients were worser in the high-risk group than in the low-risk group for both the training and test sets. Additionally, the different immune microenvironment status of patients in the high- and low-risk groups, suggesting that patients in the low-risk group may be more sensitive to immunotherapy, particularly immune checkpoint inhibitors. Finally, we found that depletion of ATP2A1, one of the risk genes in our model, influence the biologic behaviour of CRC cells significantly. Conclusion: The m7G-related DDR genes can be used as important markers for predicting patient prognosis and immunotherapy response. Our data suggest that ATP2A1 may promote the proliferation of colon cancer cells. These findings may provide new therapeutic targets for the treatment of colon cancer.

Received Signal Strength Indicator-Based Indoor Localization Using Distributed Set-Membership Filtering.

Most of the existing localization schemes necessitate a priori statistical characteristic of measurement noise, which may be unrealistic in practical applications. This article addresses the problem of indoor localization by implementing distributed set-membership filtering based on a received signal strength indicator (RSSI) under unknown-but-bounded process and measurement noises. First, the transmit power and the path-loss exponent are estimated by a novel least-squares curve fitting (LSCF) method in RSSI-based localization. Since the localization process of trilateration is susceptible to inaccuracy caused by the noise-affected distance measurements, a convex optimization method is then developed to obtain the state ellipsoid estimation under the unknown-but-bounded noises. Third, a recursive algorithm is established to compute the global ellipsoid that guarantees to locate the true target at every time step. Finally, experimental validation is presented to demonstrate the accuracy and effectiveness of the proposed set-membership filtering method for indoor localization.