The influence of Akkermansia muciniphila on intestinal barrier function.

Intestinal barriers play a crucial role in human physiology, both in homeostatic and pathological conditions. Disruption of the intestinal barrier is a significant factor in the pathogenesis of gastrointestinal inflammatory diseases, such as inflammatory bowel disease. The profound influence of the gut microbiota on intestinal diseases has sparked considerable interest in manipulating it through dietary interventions, probiotics, and fecal microbiota transplantation as potential approaches to enhance the integrity of the intestinal barrier. Numerous studies have underscored the protective effects of specific microbiota and their associated metabolites. In recent years, an increasing body of research has demonstrated that Akkermansia muciniphila (A. muciniphila, Am) plays a beneficial role in various diseases, including diabetes, obesity, aging, cancer, and metabolic syndrome. It is gaining popularity as a regulator that influences the intestinal flora and intestinal barrier and is recognized as a 'new generation of probiotics'. Consequently, it may represent a potential target and promising therapy option for intestinal diseases. This article systematically summarizes the role of Am in the gut. Specifically, we carefully discuss key scientific issues that need resolution in the future regarding beneficial bacteria represented by Am, which may provide insights for the application of drugs targeting Am in clinical treatment.

STARD12/14 are diagnostic and prognostic biomarkers of lung adenocarcinoma associated with epigenetic regulation, immune infiltration and ferroptosis.

Background: Metabolic reprogramming plays an important role in tumor progression and antitumor immunity. START domain-containing proteins (STARDs) are responsible for lipid metabolism. However, the underlying functions of STARDs in lung adenocarcinoma (LUAD) have not been clarified yet. Methods: Oncomine, UALCAN, TCGA and CPTAC were used to explore the expression landscape and clinicopathological characteristics of STARDs in LUAD. Diagnostic and prognostic values were assessed by Kaplan-Meier Plotter, Cox regression analysis, and ROC curve. GeneMANIA, GO, KEGG and GSEA were applied for exploring the potential biological functions. Epigenetic process, including mutation and m6A modification were analyzed by cBioPortal and TCGA. TIMER, TISIDB and TCGA cohort provided an immune signature. The correlation between STARDs expression and ferroptosis was analyzed by TCGA. Finally, the STARDs expression were confirmed by RT-qPCR and western blot. Results: STARD5/10/14 were overexpressed in LUAD compared with normal, while STARD4/7/8/11/12/13 were relatively low. STARD5/12/14 levels were positively related to clinical and lymph node stage. Survival analysis showed high STARD12 expression was associated with favorable overall survival, disease special survival as well as disease free survival, while STARD14 showed the opposite. GSEA analysis found STARD12 and STARD14 were associated with glycolysis, oxidative phosphorylation and tumor related signaling pathways. STARD12 co-expressed genes participated in cell cycle and DNA replication, and STARD14 were enriched in ECM-receptor interaction. Both STARD12 and STARD14 were corelated with epigenetic regulation, especially TP53 mutation and m6A modification. STARD12 expression was positively correlated with TMB level. The level of STARD12 was significantly associated with the abundance of infiltrating immune cells, including B cells, CD8+T cells, macrophages, dendritic cells, and chemokine, receptor, MHC, immunostimulatory related genes. STARD14 was negatively associated with the infiltration of CD8+T cells, while positively with CCL28 and immune checkpoints, including CTLA4 as well as PD-L2. In addition, STARD12/14 could regulate the ferroptosis related genes. Conclusion: STARD12 and STARD14 were expected to be potential biomarkers for LUAD, which were associated with epigenetic regulation, immune infiltration and ferroptosis.

Giant pulmonary sclerosing pneumocytoma with potentially malignant biological behavior: a case report and literature review.

Background: Pulmonary sclerosing pneumocytoma (PSP) is a rare benign lung tumor which generally presents as a solitary pulmonary nodule in middle-aged females. However, the PSP in some patients exhibits potentially malignant biological behavior, with recurrence and lymphatic or distant metastasis being observed. Case Description: We encountered a case of a 46-year-old female with an inordinately massive tumor 9.5 cm in diameter and a relatively high Ki-67 proliferation rate. Fine needle aspiration (FNA) played a significant but limited role in the preoperative diagnosis: the computed tomography (CT)-guided lung puncture biopsy was consistent with the typical pathology of PSP; however, endobronchial ultrasound-guided transbronchial lung biopsy (EBUS-TBLB) could not provide a definitive diagnosis. The patient ultimately underwent thoracoscopic resection and mediastinal lymph node dissection. Here, we provide a review of the literature on patients with PSP with malignant biological behavior to raise awareness of the malignant potential of PSP and describe our experience to inform future management. Conclusions: PSP lacks specificity in its clinical and radiological characteristics and has complex pathological manifestations. FNA is valuable in the diagnosis and differential diagnosis of PSP but involves the risk of misdiagnosis or missed diagnosis. Additionally, we believe that the accepted benign features of PSP need to be updated and that the potential malignant features of PSP should be carefully monitored. Surgical resection is curative but strict follow-up is crucial.

FOXP family DNA methylation correlates with immune infiltration and prognostic value in NSCLC.

Background: Forkhead box P (FOXP) family was introduced as a double-edged sword in tumorigenesis and influenced immunotherapy response by modulating host immunity. This study aimed to summarize the involvement of the FOXP family in non-small cell lung cancer (NSCLC). Methods: The UALCAN, Gene Expression Profiling Interactive Analysis (GEPIA), and Reverse transcription-quantitative polymerase chain reaction (RT‒qPCR) were used to analyse the expression levels of the FOXP family in NSCLC. The prognostic impact was evaluated using Kaplan-Meier Plotter. MethSurv, UALCAN, and cBioPortal were applied to analyse the DNA methylation and mutation status of the FOXP family respectively. COEXPEDIA, STRING, and GeneMANIA were used to explore the interaction mechanism. Finally, TISIDB was used to investigate all of the immune-related characteristics regulated by the FOXP family. Results: The expression levels of FOXP1/3/4 were dysregulated in NSCLC tissues than that in normal tissues. Groups with low expression levels of FOXP1/4 and high expression levels of FOXP2/3 were associated with poor prognosis in NSCLC. The transcriptional levels of FOXP2/3/4 were correlated with DNA methylation in NSCLC. FOXP1/3/4 DNA methylation were correlated with prognosis. Pathway enrichment analysis indicated the FOXP family was mainly related to immune-related pathways. After DNA methylation, the correlations between FOXP family and immune factors were opposite to that before alteration in NSCLC. Conclusion: This study elucidated FOXP family could serve as vital diagnostic and prognostic biomarkers in NSCLC. Our study highlighted novel potential functions of FOXP family DNA methylation in regulation of immune-related signatures in NSCLC.

Autophagy Induced by BCL2-Related ceRNA Network Participates in the Occurrence of COPD.

Purpose: Chronic obstructive pulmonary disease (COPD) is a predominant cause of mortality worldwide. Autophagy, which depends on a lysosomal degradation pathway, plays an essential role in the occurrence of COPD. The aim of our study was to identify the potential function of autophagy and construct a BCL2-related competing endogenous RNA (ceRNA) network that induces autophagy in COPD. Methods: Blood sample data from GSE31568, GSE24709, and GSE61741 were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs in COPD and controls were identified via GEO2R. Transcription factors were obtained from FunRich. DIANA, miRDB, miRTarBase, and TargetScan were used to predict target genes of miRNAs. Autophagy genes were collected from the Human Autophagy Database (HADb). The GSE151052 dataset was used to identify autophagy-related differentially expressed genes in tissues. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted via Metascape and the STRING network. Spearman correlation analysis was used to analyze the relationship between autophagy-related differentially expressed genes and lung function. The BCL2-related ceRNA network was modeled by Cytoscape. Results: We obtained 41 differentially expressed miRNAs and 10 significantly different transcription factors. We identified 19 autophagy-related differentially expressed genes that were significantly different (P<0.05) in tissue samples. The most significant enrichment in Metascape was an autophagy item, which further confirmed autophagy participation in the occurrence of COPD. PPI network analysis found four genes (BCL2, BECN1, MAPK8, and ITPR1), among which BCL2 was correlated with both FEV1/FVC and FEV1 prediction. Finally, the BCL2-related ceRNA network was constructed to clarify the interaction of RNAs and occurrence of autophagy, including 18 miRNAs and 65 lncRNAs. Conclusion: We identified 19 autophagy-related differentially expressed genes that participated in COPD; among them, BCL2 was correlated with lung function, and a BCL2-related ceRNA network was constructed, which further revealed the potential mechanism of autophagy involvement in COPD.

Identification of a Five Autophagy Subtype-Related Gene Expression Pattern for Improving the Prognosis of Lung Adenocarcinoma.

Background: Autophagy plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the autophagy-related gene (ARG) expression pattern and to identify promising autophagy-related biomarkers to improve the prognosis of LUAD. Methods: The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. The Human Autophagy Database (HADb) was used to extract ARGs. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the differentially expressed ARGs (DEARGs). Then, consensus clustering revealed autophagy-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, the univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic ARGs. After overlapping DEGs and the independent prognostic ARGs, the predictive risk model was established and validated. Correlation analyses between ARGs and clinicopathological variables were also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels and the risk score as well as clinicopathological variables in the predictive risk model. Results: A total of 222 genes from the HADb were identified as ARGs, and 28 of the 222 genes were pooled as DEARGs. The most significant GO term was autophagy (p = 3.05E-07), and KEGG analysis results indicated that 28 DEARGs were significantly enriched in the ErbB signaling pathway (p < 0.001). Then, consensus clustering analysis divided the LUAD into two clusters, and a total of 168 DEGs were identified according to cluster subtypes. Then univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators. After overlapping 168 DEGs and 12 genes, 10 genes (ATG4A, BAK1, CAPNS1, CCR2, CTSD, EIF2AK3, ITGB1, MBTPS2, SPHK1, ST13) were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model identified the prognosis (p = 4.379E-10). Combined with the correlation analysis results between ARGs and clinicopathological variables, five ARGs were screened as prognostic genes. Among them, SPHK1 expression levels were positively correlated with CD4+ T cells and dendritic cell infiltration levels. Conclusions: In this study, we constructed a predictive risk model and identified a five autophagy subtype-related gene expression pattern to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful to accurately characterize the LUAD and develop personalized treatment.