Efficacy and safety of robotic vs. laparoscopic gastrectomy for patients with gastric cancer: systematic review and meta-analysis.

BACKGROUND: The emergence of robotic surgical systems compensated for the technological shortcomings of laparoscopic approaches. However, whether robotic gastrectomy (RG) has better perioperative outcomes and survival than laparoscopic gastrectomy (LG) for gastric cancer is still unclear but increasingly drawing attention. MATERIALS AND METHODS: In this systematic review and meta-analysis, we searched the PubMed, EMBASE, Web of Science, and Cochrane Library as of January 20, 2024 and referenced list of eligible articles for all published studies comparing RG and LG for patients with gastric cancer, Data on study characteristics, individual characteristics, and outcome parameters were extracted. The quality of studies was assessed using the Revised Cochrane risk-of-bias 2 tool and the risk of bias in non-randomized studies of interventions tool. The main outcome measures were overall survival (OS) and disease-free survival (DFS). RESULTS: We identified 3641 articles, of which 72 studies (30081 patients) were included in the meta-analysis. Compared with LG, RG was associated with higher OS [hazard ratio (HR)=0.89, 95% CI=0.83 to 0.96), lower rate of overall postoperative complications [odds ratio (OR)=0.77, 95% CI=0.71 to 0.84], longer operating time [mean difference (MD)=35.53, 95% CI=29.23 to 41.83], less estimated blood loss (MD=-37.45, 95% CI=-46.24 to -28.67), a higher number of retrieved lymph nodes (MD=1.88, 95% CI=0.77 to 3.00), faster postoperative recovery, and lower rate of conversion (OR=0.44, 95% CI=0.36 to 0.55). Mortality and DFS were not significantly different between the two groups. The subgroup of meta-analysis results also showed the advantages of robotic surgery over laparoscopic surgery in intracorporeal reconstruction, total gastrectomy, Ⅰ/Ⅱ stage, and BMI≥25, especially for patients with stage Ⅰ/Ⅱ, there is better overall survival and disease-free survival. CONCLUSION: Our findings point to robotic surgery having great benefits compared with laparoscopic surgery in gastric cancer. Our study may help inform decision-making in applying robotic surgical systems to clinical treatment.

Akkermansia muciniphila ameliorates colonic injury in mice with DSS-induced acute colitis by blocking macrophage pro-inflammatory phenotype switching via the HDAC5/DAB2 axis.

Akkermansia muciniphila (A. muciniphila), a probiotic, has been linked to macrophage phenotypic polarization in different diseases. However, the role and mechanisms of A. muciniphila in regulating macrophage during ulcerative colitis (UC) are not clear. This research aimed to examine the impact of A. muciniphila on dextran sulfate sodium (DSS)-induced acute colitis and elucidate the underlying mechanism related to macrophage phenotypic polarization. A. muciniphila inhibited weight loss, increased disease activity index, and ameliorated inflammatory injury in colonic tissues in mice induced with DSS. Furthermore, A. muciniphila reduced macrophage M1 polarization and ameliorated epithelial barrier damage in colonic tissues of DSS-induced mice through inhibition of histone deacetylase 5 (HDAC5). In contrast, the effect of A. muciniphila was compromised by HDAC5 overexpression. HDAC5 deacetylated H3K9ac modification of the disabled homolog 2 (DAB2) promoter, which led to repressed DAB2 expression. DAB2 overexpression blocked HDAC5-induced pro-inflammatory polarization of macrophages, whereas knockdown of DAB2 resulted in the loss of effects of A. muciniphila against colonic injury in DSS-induced mice. Taken together, A. muciniphila-induced loss of HDAC5 hampered the deacetylation of DAB2 and enhanced the expression of DAB2. Our findings propose that A. muciniphila may be a possible probiotic agent for alleviating DSS-induced acute colitis.

BTF3L4 Overexpression Mediates APAP-induced Liver Injury in Mouse and Cellular Models.

Background and Aims: Acetaminophen (APAP)-induced liver injury (AILI) has an increasing incidence worldwide. However, the mechanisms contributing to such liver injury are largely unknown and no targeted therapy is currently available. The study aimed to investigate the effect of BTF3L4 overexpression on apoptosis and inflammation regulation in vitro and in vivo. Methods: We performed a proteomic analysis of the AILI model and found basic transcription factor 3 like 4 (BTF3L4) was the only outlier transcription factor overexpressed in the AILI model in mice. BTF3L4 overexpression increased the degree of liver injury in the AILI model. Results: BTF3L4 exerts its pathogenic effect by inducing an inflammatory response and damaging mitochondrial function. Increased BTF3L4 expression increases the degree of apoptosis, reactive oxygen species generation, and oxidative stress, which induces cell death and liver injury. The damage of mitochondrial function by BTF3L4 triggers a cascade of events, including reactive oxygen species accumulation and oxidative stress. According to the available AILI data, BTF3L4 expression is positively associated with inflammation and may be a potential biomarker of AILI. Conclusions: Our results suggest that BTF3L4 is a pathogenic factor in AILI and may be a potential diagnostic maker for AILI.

Small extracellular vesicles-mediated cellular interactions between tumor cells and tumor-associated macrophages: Implication for immunotherapy.

The tumor microenvironment consists of cancer cells and various stromal cells, including macrophages, which exhibit diverse phenotypes with either pro-inflammatory (M1) or anti-inflammatory (M2) effects. The interaction between cancer cells and macrophages plays a crucial role in tumor progression. Small extracellular vesicles (sEVs), which facilitate intercellular communication, are known to play a vital role in this process. This review provides a comprehensive summary of how sEVs derived from cancer cells, containing miRNAs, lncRNAs, proteins, and lipids, can influence macrophage polarization. Additionally, we discuss the impact of macrophage-secreted sEVs on tumor malignant transformation, including effects on proliferation, metastasis, angiogenesis, chemoresistance, and immune escape. Furthermore, we address the therapeutic advancements and current challenges associated with macrophage-associated sEVs, along with potential solutions.

Identifying immune infiltration by deep learning to assess the prognosis of patients with hepatocellular carcinoma.

BACKGROUND: The treatment situation for hepatocellular carcinoma remains critical. The use of deep learning algorithms to assess immune infiltration is a promising new diagnostic tool. METHODS: Patient data and whole slide images (WSIs) were obtained for the Xijing Hospital (XJH) cohort and TCGA cohort. We wrote programs using Visual studio 2022 with C# language to segment the WSI into tiles. Pathologists classified the tiles and later trained deep learning models using the ResNet 101V2 network via ML.NET with the TensorFlow framework. Model performance was evaluated using AccuracyMicro versus AccuracyMacro. Model performance was examined using ROC curves versus PR curves. The percentage of immune infiltration was calculated using the R package survminer to calculate the intergroup cutoff, and the Kaplan‒Meier method was used to plot the overall survival curve of patients. Cox regression was used to determine whether the percentage of immune infiltration was an independent risk factor for prognosis. A nomogram was constructed, and its accuracy was verified using time-dependent ROC curves with calibration curves. The CIBERSORT algorithm was used to assess immune infiltration between groups. Gene Ontology was used to explore the pathways of differentially expressed genes. RESULTS: There were 100 WSIs and 165,293 tiles in the training set. The final deep learning models had an AccuracyMicro of 97.46% and an AccuracyMacro of 82.28%. The AUCs of the ROC curves on both the training and validation sets exceeded 0.95. The areas under the classification PR curves exceeded 0.85, except that of the TLS on the validation set, which might have had poor results (0.713) due to too few samples. There was a significant difference in OS between the TIL classification groups (p < 0.001), while there was no significant difference in OS between the TLS groups (p = 0.294). Cox regression showed that TIL percentage was an independent risk factor for prognosis in HCC patients (p = 0.015). The AUCs according to the nomogram were 0.714, 0.690, and 0.676 for the 1-year, 2-year, and 5-year AUCs in the TCGA cohort and 0.756, 0.797, and 0.883 in the XJH cohort, respectively. There were significant differences in the levels of infiltration of seven immune cell types between the two groups of samples, and gene ontology showed that the differentially expressed genes between the groups were immune related. Their expression levels of PD-1 and CTLA4 were also significantly different. CONCLUSION: We constructed and tested a deep learning model that evaluates the immune infiltration of liver cancer tissue in HCC patients. Our findings demonstrate the value of the model in assessing patient prognosis, immune infiltration and immune checkpoint expression levels.

Development and validation of a ligand-receptor pairs signature to predict outcome and provide a therapeutic strategy in gastric cancer.

BACKGROUND: An important factor in tumor development and progression is the tumor microenvironment (TME), which is heterogeneous. Previous studies have mainly investigated the expression profile and prognostic values of genes in gastric cancer (GC) at the cell population level but neglected the interactions and heterogeneity between cells. METHODS: The pattern of ligand-receptor (LR) interactions was delineated on a scRNA-seq dataset containing 44,953 cells from nine GC patients and a fourth bulk RNA-seq dataset including data from 1159 GC patients. We then constructed an LR.Score scoring model to comprehensively evaluate the influence of LR-pairs on the TME, overall survival, and immunotherapy response in GC patients from several cohorts. RESULTS: Cell communication network among 13 cell types was constructed based on the LR-pairs. We proposed a new molecular subtyping model for GC based on the LR-pairs and revealed the differences in prognosis, pathophysiologic features, mutation characteristics, function enrichment, and immunological characteristics among the three subtypes. Finally, an LR.Score model based on LR-pairs was developed and validated on several datasets. CONCLUSIONS: Based on the selected LR-pairs, we successfully constructed a novel prediction model and observed its well performance on molecular subtyping, target and pathway screening, prognosis judging, and immunotherapy response predicting.

Prognostic role and immune infiltration characteristics of EI24 in multiple cancer types.

BACKGROUND: The autophagy-associated transmembrane protein EI24 is associated with cancer growth and patient survival. We aimed to explore the prognostic role and immune infiltration characteristics of EI24 at a pan-cancer level. METHODS: We collected data from multiple databases to explore the expression and prognostic role of EI24 in various cancers. Correlations between EI24 expression and DNA methylation, RNA modification, tumor mutation burden (TMB), microsatellite instability (MSI), immune moderator, immune checkpoint-related genes, the tumor immune microenvironment, and clinicopathological characteristics were analyzed. Finally, immunohistochemistry and western blotting were performed to validate the protein levels of EI24 in different tumors. RESULTS: Differential expression of EI24 was observed in most cancer types compared to non-cancerous tissues. EI24 showed a significant association with prognosis and may represent a new indicator of prognosis in patients with cancer. In most cancers, EI24 is closely associated with tumor immunity and interacts with various immune cells. Moreover, significant correlations were observed between EI24 expression and RNA modification, TMB, MSI, immune moderators, and immune checkpoint-related genes. CONCLUSION: This study provides new insights into the functions and clinical value of EI24 in different tumors and suggests that EI24 may serve as a promising biomarker or therapeutic target for cancer management.

Thyroid cancer risk prediction model using m6A RNA methylation regulators: integrated bioinformatics analysis and histological validation.

BACKGROUND: Epigenetic reprogramming has been reported to play a critical role in the progression of thyroid cancer. RNA methylation accounts for more than 60% of all RNA modifications, and N6-methyladenosine (m6A) is the most common modification of RNAs in higher organisms. The purpose of this study was to explore the related modification mode of m6A regulators construction and its evaluation on the clinical prognosis and therapeutic effect of thyroid cancer. METHODS: The levels of 23 m6A regulators in The Cancer Genome Atlas (TCGA) were analyzed. Differentially expressed genes (DEGs) and survival analysis were performed based on TCGA-THCA clinicopathological and follow-up information, and the mRNA levels of representative genes were verified using clinical thyroid cancer data. In order to detect the effects of m6A regulators and their DEGs, consensus cluster analysis was carried out, and the expression of different m6A scores in Tumor Mutation Burden (TMB) and immune double antibodies (PD-1 antibody and CTLA4 antibody) were evaluated to predict the correlation between m6A score and thyroid cancer tumor immunotherapy response. RESULTS: Different expression patterns of m6A regulatory factors were detected in thyroid cancer tumors and normal tissues, and several prognoses related m6A genes were obtained. Two different m6A modification patterns were determined by consensus cluster analysis. Two different subgroups were established by screening overlapping DEGs between two m6A clusters, with cluster A having the best prognosis. According to the m6A score extracted from DEGs, thyroid cancer patients can be divided into high and low score subgroups. Patients with lower m6A score have longer survival time and better clinical features. The relationship between m6A score and Tumor Mutation Burden (TMB) and its correlation with the expression of PD-1 antibody and CTLA4 antibody proved that m6A score could be used as a potential predictor of the efficacy of immunotherapy in thyroid cancer patients. CONCLUSIONS: We screened DEGs from cluster m6A and constructed a highly predictive model with prognostic value by dividing TCGA-THCA into two different clusters and performing m6A score analysis. This study will help clarify the overall impact of m6A modification patterns on thyroid cancer progression and formulate more effective immunotherapy strategies.