Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

Adsorption Behavior and Mechanism of Palladium on Diethylaminoethyl-Modified Polyglycidyl Methacrylate Macroporous Spheres.

The recovery of precious metals, such as palladium (Pd), from wastewater, is an economically important field. The present study reports the application of polyglycidyl methacrylate (PGMA) macroporous spheres with diethylaminoethyl (DEAE) functional groups (PGMA-DEAE) for the adsorption of palladium ions [Pd(II)] from simulated wastewater solutions. The effects of pH, adsorption duration, and initial concentration of Pd(II) on the adsorption amount were evaluated systematically. The results revealed that within the experimental pH range, the adsorption efficiency of Pd(II) increased with increasing pH. In particular, between pH 4 and 6, the Pd(II) adsorption efficiencies were approximately 100%. At 298 K and pH ∼ 4, the adsorption capacity of PGMA-DEAE for Pd(II) was 1.22 mmol/g. The adsorption rates of PGMA-DEAE for Pd(II) were high, and the adsorption equilibrium was reached within 10 min. Ca(II), Mg(II), Co(II), Cu(II), Ni(II), and Fe(II) were selected as representative competitive adsorption metal ions. PGMA-DEAE had good separation selectivity for Pd(II) at pH 1-6 (all RPd/Me > 30), especially at pH ∼ 4 (all RPd/Me > 100). The SEM, TEM, EDS, TG, XRD, and XPS results indicated that in a high-acidity environment (CHCl ≥ 1 mol/L), Pd(II) was adsorbed on PGMA-DEAE through electrostatic attraction, while in a low-acidity environment (pH 1-6), Pd(II) was adsorbed on PGMA-DEAE through coordinated bonding between the Pd(II) ions and the N. PGMA-DEAE exhibited excellent stability and regeneration performance for five regeneration cycles.

Microbiota and metabolites alterations in proximal and distal gastric cancer patients.

BACKGROUND: Globally, gastric cancer is the third most common cancer and the third leading cause of cancer death. Proximal and distal gastric cancers have distinct clinical and biological behaviors. The microbial composition and metabolic differences in proximal and distal gastric cancers have not been fully studied and discussed. METHODS: In this study, the gastric microbiome of 13 proximal gastric cancer tissues, 16 distal gastric cancer tissues, and their matched non-tumor tissues were characterized using 16S rRNA amplicon sequencing. Additionally, 10 proximal gastric cancer tissues, 11 distal gastric cancer tissues, and their matched non-tumor tissues were assessed by untargeted metabolomics. RESULTS: There was no significant difference in microbial diversity and richness between the proximal and distal gastric cancer tissues. At the genus level, the abundance of Rikenellaceae_RC9_gut_group, Porphyromonas, Catonella, Proteus, Oribacterium, and Moraxella were significantly increased in Proximal T, whereas that of Methylobacterium_Methylorubrum was significantly increased in Distal T. The untargeted metabolomics analysis revealed 30 discriminative metabolites between Distal T and Distal N. In contrast, there were only 4 discriminative metabolites between Proximal T and Proximal N. In distal gastric cancer, different metabolites were scattered through multiple pathway, including the sphingolipid signaling pathway, arginine biosynthesis, protein digestion and absorption, alanine, aspartate and, glutamate metabolism, etc.In proximal gastric cancer, differential microbial metabolites were mainly related to hormone metabolism. CONCLUSION: Methylobacterium-Methylorubrum was significantly increased in Distal T, positively correlated with cancer-promoting metabolites, and negatively correlated with cancer-inhibiting metabolites. Rikenellaceae_RC_gut_group was significantly increased in Proximal T and positively correlated with cancer-promoting metabolites. Further studies regarding the functions of the above-mentioned microorganisms and metabolites were warranted as the results may reveal the different mechanisms underlying the occurrence and development of proximal and distal gastric cancers and provide a basis for future treatments. IMPORTANCE: First, the differences in microbial composition and metabolites between the proximal and distal gastric cancers were described; then, the correlation between microbiota and metabolites was preliminarily discussed. These microbes and metabolites deserve further investigations as they may reveal the different mechanisms involved in the occurrence and development of proximal and distal gastric cancers and provide a basis for future treatments.

Biological Characteristics of the Mycelium and Optimization of the Culture Medium for Phallus dongsun.

This study aimed to elucidate the influence of various culture medium components, including carbon sources, nitrogen sources, inorganic salts, suspension agents, and temperature, on the mycelial growth characteristics of Phallus dongsun. Employing single-factor experiments and response surface methodology within glass Petri dishes, the research identified that carrot powder, soybean powder, and ZnSO4 notably enhanced the proliferation of aerial mycelium, significantly augmenting the growth rate of P. dongsun mycelium. The resultant mycelium was observed to be dense, robust, and fluffy in texture. In particular, ZnSO4 markedly accelerated the mycelium growth rate. Furthermore, xanthan gum was found to effectively modulate the medium's viscosity, ensuring a stable suspension and facilitating nutrient equilibrium. The optimal cultivation temperature was determined to be 25°C, with mycelial growth ceasing below 5°C and mycelium perishing at temperatures exceeding 35°C. The optimal medium composition was established as follows: wheat starch 5 g/l, carrot powder 5 g/l, soybean powder 7.50 g/l, glucose 10 g/l, ZnSO4 0.71 g/l, NH4Cl 0.68 g/l, xanthan gum 0.5 g/l, and agar 20 g/l. Under these optimized conditions, the mycelium of P. dongsun exhibited a rapid growth rate (1.04 ± 0.14 mm/day), characterized by a thick, dense, and well-developed structure. This investigation provides a theoretical foundation for the conservation, strain selection, and breeding of P. dongsun.

Sulfotransferase SULT2B1 facilitates colon cancer metastasis by promoting SCD1-mediated lipid metabolism.

Metastasis is responsible for at least 90% of colon cancer (CC)-related deaths. Lipid metabolism is a critical factor in cancer metastasis, yet the underlying mechanism requires further investigation. Herein, through the utilisation of single-cell sequencing and proteomics, we identified sulfotransferase SULT2B1 as a novel metastatic tumour marker of CC, which was associated with poor prognosis. CC orthotopic model and in vitro assays showed that SULT2B1 promoted lipid metabolism and metastasis. Moreover, SULT2B1 directly interacted with SCD1 to facilitate lipid metabolism and promoted metastasis of CC cells. And the combined application of SCD1 inhibitor CAY with SULT2B1- konockout (KO) demonstrated a more robust inhibitory effect on lipid metabolism and metastasis of CC cells in comparison to sole application of SULT2B1-KO. Notably, we revealed that lovastatin can block the SULT2B1-induced promotion of lipid metabolism and distant metastasis in vivo. Further evidence showed that SMC1A transcriptionally upregulated the expression of SULT2B1. Our findings unveiled the critical role of SULT2B1 in CC metastasis and provided a new perspective for the treatment of CC patients with distant metastasis.

The Establishment and Verification of a Nomogram Model for Predicting the Overall Survival of Medullary Thyroid Carcinoma: An Analysis Based on the SEER Database.

(1) Background: This study aimed to establish a nomogram model for predicting the overall survival (OS) of medullary thyroid carcinoma (MTC) patients based on the Surveillance, Epidemiology, and End Results (SEER) database. (2) Methods: Patients with MTC in the SEER database from 2004 to 2015 were included and divided into a modeling group and an internal validation group. We also selected MTC patients from our center from 2007 to 2019 to establish an external validation group. Univariate and multivariate Cox regression analyses were used to screen for significant independent variables and to establish a nomogram model. Kaplan-Meier (K-M) curves were plotted to evaluate the influence of the predictors. The C-indexes, areas under the curves (AUCs), and calibration curves were plotted to validate the predictive effect of the model. (3) Results: A total of 1981 MTC patients from the SEER database and 85 MTC patients from our center were included. The univariate and multivariate Cox regression analyses showed that age, tumor size, N stage, and M stage were significant factors, and a nomogram model was established. The C-index of the modeling group was 0.792, and the AUCs were 0.811, 0.825, and 0.824. The C-index of the internal validation group was 0.793, and the AUCs were 0.847, 0.846, and 0.796. The C-index of the external validation group was 0.871, and the AUCs were 0.911 and 0.827. The calibration curves indicated that the prediction ability was reliable. (4) Conclusions: A nomogram model based on age, tumor size, N stage, and M stage was able to predict the OS of MTC patients.

Identification of a novel signature based on macrophage-related marker genes to predict prognosis and immunotherapeutic effects in hepatocellular carcinoma.

Background: Tumor-related macrophages (TAMs) have emerged as an essential part of the immune regulatory network in hepatocellular carcinoma (HCC). Constructing a TAM-related signature is significant for evaluating prognosis and immunotherapeutic response of HCC patients. Methods: Informative single-cell RNA sequencing (scRNA-seq) dataset was obtained from the Gene Expression Omnibus (GEO) database, and diverse cell subpopulations were identified by clustering dimension reduction. Moreover, we determined molecular subtypes with the best clustering efficacy by calculating the cumulative distribution function (CDF). The ESTIMATE method, CIBERSORT (cell-type identification by estimating relative subsets of RNA transcripts) algorithm and publicly available tumor immune dysfunction and exclusion (TIDE) tools were used to characterize the immune landscape and tumor immune escape status. A TAM-related gene risk model was constructed through Cox regression and verified in multiple datasets and dimensions. We also performed functional enrichment analysis to detect potential signaling pathways related to TAM marker genes. Results: In total, 10 subpopulations and 165 TAM-related marker genes were obtained from the scRNA-seq dataset (GSE149614). After clustering 3 molecular subtypes based on TAM-related marker genes, we found significantly different prognostic survival and immune signatures among the three subtypes. Subsequently, a 9-gene predictive signature (TPP1, FTL, CXCL8, CD68, ATP6V1F, CSTB, YBX1, LGALS3, and APLP2) was identified as an independent prognostic factor for HCC patients. Those patients with high RiskScore had a lower survival rate and benefited less from immunotherapy than those with low RiskScore. Moreover, more samples of the Cluster C subtype were enriched in the high-risk group, with higher tumor immune escape incidence. Conclusions: We constructed a TAM-related signature with excellent efficacy for predicting prognostic survival and immunotherapeutic responses in HCC patients.

Investigating the Prognostic Significance of Pyroptosis-Related Genes in Gastric Cancer and Their Impact on Cells' Biological Functions.

Objective: To probe into the role of pyroptosis-related genes in gastric cancer. Methods: To establish pyroptosis-related genes, observe their expression in gastric cancer, and analyze the prognosis of pyroptosis-related genes in gastric cancer by single-factor COX, which showed that only GSDME had prognostic significance in gastric cancer. The mRNA expression profiles and lncRNA expression profiles of gastric cancer downloaded from the Cancer Genome Atlas were combined for weighted gene regulatory network analysis, after which the lncRNA nodes of the module to which GSDME belongs were extracted to obtain the lncRNAs-GSDME interactions, which were visualized with Cytoscape network plots. Finally, the effects of GSDME on the proliferation, migration, and apoptosis of gastric cancer cells were observed with CCK8, and flow cytometry. Results: Our results show that only GSDME has prognostic significance in gastric cancer, and show that it has an important role in a variety of tumors. In addition, our results show that 16 lncRNAs have a significant interaction with GSDME. Finally, the experimental analysis showed that knocking down the expression level of GSDME could affect the growth as well as apoptosis of gastric cancer cells. Conclusion: The significant prognostic significance of GSDME in gastric cancer and the fact that affecting GSDME expression inhibits gastric cancer cell growth suggest that GSDME can be used as a predictive biomarker.