Minimally invasive versus open McKeown esophagectomy for patients with esophageal squamous cell carcinoma after neoadjuvant PD-1 inhibitor plus chemotherapy

IntroductionThe purpose of this study was to compare short and mid-term outcomes in esophageal squamous cell carcinoma (ESCC) patients undergoing open or minimally invasive McKeown esophagectomy (MIE) after neoadjuvant PD-1 inhibitor plus chemotherapy.MethodsPatients with locally advanced ESCC underwent open or minimally invasive McKeown esophagectomy after neoadjuvant PD-1 inhibitor plus chemotherapy were retrospectively included from June 2019 to June 2021. The baseline characteristics, pathological data, short-and mid-term outcomes were collected and compared based on the surgical approach.ResultsA total of 35 patients were included in the study. An open procedure was performed for 13 patients (37.1%), and 22 (62.9%) patients underwent MIE after neoadjuvant therapy. Compared with open group, MIE group had shorter operative times (350.8± 117.8 vs. 277.9 ± 30.2 min, P = 0.009). The total number of resected lymph nodes was not significantly different, but more left recurrent laryngeal lymph nodes were harvested from the Open group (2.6 ± 3.2 vs. 0.9 ± 1.7, P = 0.047). The median follow-up time was 1.42 years (range, 0.35–2.59 years) from the first day of treatment. Three patients (8.6%) died during follow-up, one in the open surgery group and two in the MIE group. There were six (17.1%) patients developed recurrence, three in each group. The 2-year cumulative survival rates were 92.3 ± 7.4% and 89.5 ± 7.1% for the open and MIE groups, respectively. Overall survival was not different between the two surgical approaches.ConclusionsMIE might be safe and feasible for patients with locally advanced ESCC undergoing neoadjuvant PD-1 inhibitor plus chemotherapy

Cell metabolism-based optimization strategy of CAR-T cell function in cancer therapy

Adoptive cell therapy (ACT) using chimeric antigen receptor (CAR)-modified T cells has revolutionized the field of immune-oncology, showing remarkable efficacy against hematological malignancies. However, its success in solid tumors is limited by factors such as easy recurrence and poor efficacy. The effector function and persistence of CAR-T cells are critical to the success of therapy and are modulated by metabolic and nutrient-sensing mechanisms. Moreover, the immunosuppressive tumor microenvironment (TME), characterized by acidity, hypoxia, nutrient depletion, and metabolite accumulation caused by the high metabolic demands of tumor cells, can lead to T cell “exhaustion” and compromise the efficacy of CAR-T cells. In this review, we outline the metabolic characteristics of T cells at different stages of differentiation and summarize how these metabolic programs may be disrupted in the TME. We also discuss potential metabolic approaches to improve the efficacy and persistence of CAR-T cells, providing a new strategy for the clinical application of CAR-T cell therapy

A High-Throughput Sequencing Data-Based Classifier Reveals the Metabolic Heterogeneity of Hepatocellular Carcinoma

Metabolic heterogeneity plays a key role in poor outcomes in malignant tumors, but its role in hepatocellular carcinoma (HCC) remains largely unknown. In the present study, we aim to disentangle the metabolic heterogeneity features of HCC by developing a classification system based on metabolism pathway activities in high-throughput sequencing datasets. As a result, HCC samples were classified into two distinct clusters: cluster 1 showed high levels of glycolysis and pentose phosphate pathway activity, while cluster 2 exhibited high fatty acid oxidation and glutaminolysis status. This metabolic reprogramming-based classifier was found to be highly correlated with several clinical variables, including overall survival, prognosis, TNM stage, and -fetoprotein (AFP) expression. Of note, activated oncogenic pathways, a higher TP53 mutation rate, and increased stemness were also observed in cluster 1, indicating a causal relationship between metabolic reprogramming and carcinogenesis. Subsequently, distinct metabolism-targeted therapeutic strategies were proven in human HCC cell lines, which exhibit the same metabolic properties as corresponding patient samples based on this classification system. Furthermore, the metabolic patterns and effects of different types of cells in the tumor immune microenvironment were explored by referring to both bulk and single-cell data. It was found that malignant cells had the highest overall metabolic activities, which may impair the anti-tumor capacity of CD8+ T cells through metabolic competition, and this provided a potential explanation for why immunosuppressive cells had higher overall metabolic activities than those with anti-tumor functions. Collectively, this study established an HCC classification system based on the gene expression of energy metabolism pathways. Its prognostic and therapeutic value may provide novel insights into personalized clinical practice in patients with metabolic heterogeneity

Machine Learning Model in Predicting Sarcopenia in Crohn’s Disease Based on Simple Clinical and Anthropometric Measures

Sarcopenia is associated with increased morbidity and mortality in Crohn’s disease. The present study is aimed at investigating the different diagnostic performance of different machine learning models in identifying sarcopenia in Crohn’s disease. Patients diagnosed with Crohn’s disease at our center provided clinical, anthropometric, and radiological data. The cross-sectional CT slice at L3 was used for segmentation and the calculation of body composition. The prevalence of sarcopenia was calculated, and the clinical parameters were compared. A total of 167 patients were included in the present study, of which 127 (76.0%) were male and 40 (24.0%) were female, with an average age of 36.1 ± 14.3 years old. Based on the previously defined cut-off value of sarcopenia, 118 (70.7%) patients had sarcopenia. Seven machine learning models were trained with the randomly allocated training cohort (80%) then evaluated on the validation cohort (20%). A comprehensive comparison showed that LightGBM was the most ideal diagnostic model, with an AUC of 0.933, AUCPR of 0.970, sensitivity of 72.7%, and specificity of 87.0%. The LightGBM model may facilitate a population management strategy with early identification of sarcopenia in Crohn’s disease, while providing guidance for nutritional support and an alternative surveillance modality for long-term patient follow-up

ITGB1 as a prognostic biomarker correlated with immune suppression in gastric cancer

Abstract Introduction Gastric cancer is one of the common malignant tumors with a high incidence and mortality in China. Prognostic biomarkers and potential predictors of the treatment efficacy of gastric cancer urgently need to be identified. Integrin‐β (ITGB) is a superfamily of integrins and is involved in cell adhesion, tissue repair, immune response, and tumor metastasis. Methods We analyzed ITGB1 expression in our hospital samples of the gastric cancer cohort. And the public data of The Cancer Genome Atlas stomach adenocarcinoma (TCGA‐STAD), The Asian Cancer Research Group (ACRG)/GSE62254, and GSE15459 data sets were analyzed by using the bioinformatic methods. The relationships between ITGB1 expression and clinicopathological features, patient prognosis, activation of the Wnt/β‐catenin signaling pathway, and tumor immunosuppressive factors were also explored. Results The positive rate of ITGB1 expression in the Fudan University Shanghai Cancer Center gastric cancer tumor tissues was 61.4% (258/420) and correlated with deep invasion (p = 0.017), an advanced clinical stage (p = 0.011), and a poor prognosis (p < 0.05). The TCGA‐STAD/ACRG/GSE15459 cohorts also showed similar results. ITGB1 is one of the upstream molecules of the Wnt/β‐catenin signaling pathway and is correlated with tumor immune suppression. In gastric cancer, we found a correlation between ITGB1 expression and Wnt/β‐catenin signaling pathway activity. In the TCGA‐STAD/ACRG/GSE15459 cohorts, ITGB1 expression was positively associated with immunosuppressive factors and negatively associated with immunoactive factors. Patients with low ITGB1 expression exhibited a significantly high immunotherapy response ratio according to an analysis of tumor immune dysfunction and exclusion (TIDE), which may indicate that ITGB1 is a potential predictor of immunotherapy efficacy. Conclusions ITGB1 affects the prognosis in gastric cancer patients and plays a core role in immune suppression in gastric cancer

Robust Validation and Comprehensive Analysis of a Novel Signature Derived from Crucial Metabolic Pathways of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a dismal prognosis. PDAC have extensively reprogrammed metabolic characteristics influenced by interactions with normal cells, the effects of the tumor microenvironment and oncogene-mediated cell-autonomous pathways. In this study, we found that among all cancer hallmarks, metabolism played an important role in PDAC. Subsequently, a 16-gene prognostic signature was established with genes derived from crucial metabolic pathways, including glycolysis, bile acid metabolism, cholesterol homeostasis and xenobiotic metabolism (gbcx). The signature was used to distinguish overall survival in multiple cohorts from public datasets as well as a validation cohort followed up by us at Shanghai Cancer Center. Notably, the gbcx-related risk score (gbcxMRS) also accurately predicted poor PDAC subtypes, such as pure-basal-like and squamous types. At the same time, it also predicted PDAC recurrence. The gbcxMRS was also associated with immune cells, especially CD8 T cells, Treg cells. Furthermore, a high gbcxMRS may indicate high drug sensitivity to irinotecan and docetaxel and CTLA4 inhibitor immunotherapy. Taken together, these results indicate a robust and reproducible metabolic-related signature based on analysis of the overall pathogenesis of pancreatic cancer, which may have excellent prognostic and therapeutic implications for PDAC

Metabolomics analyses of serum metabolites perturbations associated with Naja atra bite.

Naja atra bite is one of the most common severe snakebites in emergency departments. Unfortunately, the pathophysiological changes caused by Naja atra bite are unclear due to the lack of good animal models. In this study, an animal model of Naja atra bite in Guangxi Bama miniature pigs was established by intramuscular injection at 2 mg/kg of Naja atra venom, and serum metabolites were systematically analyzed using untargeted metabolomic and targeted metabolomic approaches. Untargeted metabolomic analysis revealed that 5045 chromatographic peaks were obtained in ESI+ and 3871 chromatographic peaks were obtained in ESI-. Screening in ESI+ modes and ESI- modes identified 22 and 36 differential metabolites compared to controls. The presence of 8 core metabolites of glutamine, arginine, proline, leucine, phenylalanine, inosine, thymidine and hippuric acid in the process of Naja atra bite was verified by targeted metabolomics significant difference (P<0.05). At the same time, during the verification process of the serum clinical samples with Naja atra bite, we found that the contents of three metabolites of proline, phenylalanine and inosine in the serum of the patients were significantly different from those of the normal human serum (P<0.05). By conducting functional analysis of core and metabolic pathway analysis, we revealed a potential correlation between changes in key metabolites after the Naja atra bite and the resulting pathophysiological alterations, and our research aims to establish a theoretical foundation for the prompt diagnosis and treatment of Naja atra bite

DataSheet_1_Single-nucleus RNA sequencing reveals the shared mechanisms inducing cognitive impairment between COVID-19 and Alzheimer’s disease.zip

Alzheimer’s disease (AD)-like cognitive impairment, a kind of Neuro-COVID syndrome, is a reported complication of SARS-CoV-2 infection. However, the specific mechanisms remain largely unknown. Here, we integrated single-nucleus RNA-sequencing data to explore the potential shared genes and pathways that may lead to cognitive dysfunction in AD and COVID-19. We also constructed ingenuity AD-high-risk scores based on AD-high-risk genes from transcriptomic, proteomic, and Genome-Wide Association Studies (GWAS) data to identify disease-associated cell subtypes and potential targets in COVID-19 patients. We demonstrated that the primary disturbed cell populations were astrocytes and neurons between the above two dis-eases that exhibit cognitive impairment. We identified significant relationships between COVID-19 and AD involving synaptic dysfunction, neuronal damage, and neuroinflammation. Our findings may provide new insight for future studies to identify novel targets for preventive and therapeutic interventions in COVID-19 patients.</p

Image_1_Single-nucleus RNA sequencing reveals the shared mechanisms inducing cognitive impairment between COVID-19 and Alzheimer’s disease.tif

Alzheimer’s disease (AD)-like cognitive impairment, a kind of Neuro-COVID syndrome, is a reported complication of SARS-CoV-2 infection. However, the specific mechanisms remain largely unknown. Here, we integrated single-nucleus RNA-sequencing data to explore the potential shared genes and pathways that may lead to cognitive dysfunction in AD and COVID-19. We also constructed ingenuity AD-high-risk scores based on AD-high-risk genes from transcriptomic, proteomic, and Genome-Wide Association Studies (GWAS) data to identify disease-associated cell subtypes and potential targets in COVID-19 patients. We demonstrated that the primary disturbed cell populations were astrocytes and neurons between the above two dis-eases that exhibit cognitive impairment. We identified significant relationships between COVID-19 and AD involving synaptic dysfunction, neuronal damage, and neuroinflammation. Our findings may provide new insight for future studies to identify novel targets for preventive and therapeutic interventions in COVID-19 patients.</p

Image_2_Single-nucleus RNA sequencing reveals the shared mechanisms inducing cognitive impairment between COVID-19 and Alzheimer’s disease.tif

Alzheimer’s disease (AD)-like cognitive impairment, a kind of Neuro-COVID syndrome, is a reported complication of SARS-CoV-2 infection. However, the specific mechanisms remain largely unknown. Here, we integrated single-nucleus RNA-sequencing data to explore the potential shared genes and pathways that may lead to cognitive dysfunction in AD and COVID-19. We also constructed ingenuity AD-high-risk scores based on AD-high-risk genes from transcriptomic, proteomic, and Genome-Wide Association Studies (GWAS) data to identify disease-associated cell subtypes and potential targets in COVID-19 patients. We demonstrated that the primary disturbed cell populations were astrocytes and neurons between the above two dis-eases that exhibit cognitive impairment. We identified significant relationships between COVID-19 and AD involving synaptic dysfunction, neuronal damage, and neuroinflammation. Our findings may provide new insight for future studies to identify novel targets for preventive and therapeutic interventions in COVID-19 patients.</p