Whole genome sequencing revealed microbiome in lung adenocarcinomas presented as ground-glass nodules

Microbiome-Mucosal Immunity Nexus: Driving Forces in Respiratory Disease Progression

The importance of the complex interplay between the microbiome and mucosal immunity, particularly within the respiratory tract, has gained significant attention due to its potential implications for the severity and progression of lung diseases. Therefore, this review summarizes the specific interactions through which the respiratory tract-specific microbiome influences mucosal immunity and ultimately impacts respiratory health. Furthermore, we discuss how the microbiome affects mucosal immunity, considering tissue-specific variations, and its capacity in respiratory diseases containing asthma, chronic obstructive pulmonary disease, and lung cancer. Additionally, we investigate the external factors which affect the relationship between respiratory microbiome and mucosal immune responses. By exploring these intricate interactions, this review provides valuable insights into the potential for microbiome-based interventions to modulate mucosal immunity and alleviate the severity of respiratory diseases.

Integrative metagenomic, transcriptomic, and proteomic analysis reveal the microbiota-host interplay in early-stage lung adenocarcinoma among non-smokers

BACKGROUND

The incidence of early-stage lung adenocarcinoma (ES-LUAD) is steadily increasing among non-smokers. Previous research has identified dysbiosis in the gut microbiota of patients with lung cancer. However, the local microbial profile of non-smokers with ES-LUAD remains largely unknown. In this study, we systematically characterized the local microbial community and its associated features to enable early intervention.

METHODS

A prospective collection of ES-LUAD samples (46 cases) and their corresponding normal tissues adjacent to the tumor (41 cases), along with normal lung tissue samples adjacent to pulmonary bullae in patients with spontaneous pneumothorax (42 cases), were subjected to ultra-deep metagenomic sequencing, host transcriptomic sequencing, and proteomic sequencing. The obtained omics data were subjected to both individual and integrated analysis using Spearman correlation coefficients.

RESULTS

We concurrently detected the presence of bacteria, fungi, and viruses in the lung tissues. The microbial profile of ES-LUAD exhibited similarities to NAT but demonstrated significant differences from the healthy controls (HCs), characterized by an overall reduction in species diversity. Patients with ES-LUAD exhibited local microbial dysbiosis, suggesting the potential pathogenicity of certain microbial species. Through multi-omics correlations, intricate local crosstalk between the host and local microbial communities was observed. Additionally, we identified a significant positive correlation (rho > 0.6) between Methyloversatilis discipulorum and GOLM1 at both the transcriptional and protein levels using multi-omics data. This correlated axis may be associated with prognosis. Finally, a diagnostic model composed of six bacterial markers successfully achieved precise differentiation between patients with ES-LUAD and HCs.

CONCLUSIONS

Our study depicts the microbial spectrum in patients with ES-LUAD and provides evidence of alterations in lung microbiota and their interplay with the host, enhancing comprehension of the pathogenic mechanisms that underlie ES-LUAD. The specific model incorporating lung microbiota can serve as a potential diagnostic tool for distinguishing between ES-LUAD and HCs.

The implication of microbiome in lungs cancer: mechanisms and strategies of cancer growth, diagnosis and therapy

Available evidence illustrates that microbiome is a promising target for the study of growth, diagnosis and therapy of various types of cancer. Lung cancer is a leading cause of cancer death worldwide. The relationship of microbiota and their products with diverse pathologic conditions has been getting large attention. The novel research suggests that the microbiome plays an important role in the growth and progression of lung cancer. The lung microbiome plays a crucial role in maintaining mucosal immunity and synchronizing the stability between tolerance and inflammation. Alteration in microbiome is identified as a critical player in the progression of lung cancer and negatively impacts the patient. Studies suggest that healthy microbiome is essential for effective therapy. Various clinical trials and research are focusing on enhancing the treatment efficacy by altering the microbiome. The regulation of microbiota will provide innovative and promising treatment strategies for the maintenance of host homeostasis and the prevention of lung cancer in lung cancer patients. In the current review article, we presented the latest progress about the involvement of microbiome in the growth and diagnosis of lung cancer. Furthermore, we also assessed the therapeutic status of the microbiome for the management and treatment of lung cancer.

Exploring Co-occurrence patterns and microbial diversity in the lung microbiome of patients with non-small cell lung cancer

BACKGROUND

It has been demonstrated in the literature that a dysbiotic microbiome could have a negative impact on the host immune system and promote disease onset or exacerbation. Co-occurrence networks have been widely adopted to identify biomarkers and keystone taxa in the pathogenesis of microbiome-related diseases. Despite the promising results that network-driven approaches have led to in various human diseases, there is a dearth of research pertaining to key taxa that contribute to the pathogenesis of lung cancer. Therefore, our primary goal in this study is to explore co-existing relationships among members of the lung microbial community and any potential gained or lost interactions in lung cancer.

RESULTS

Using integrative and network-based approaches, we integrated four studies assessing the microbiome of lung biopsies of cancer patients. Differential abundance analyses showed that several bacterial taxa are different between tumor and tumor-adjacent normal tissues (FDR adjusted p-value < 0.05). Four, fifteen, and twelve significantly different associations were found at phylum, family, and genus levels. Diversity analyses suggested reduced alpha diversity in the tumor microbiome. However, beta diversity analysis did not show any discernible pattern between groups. In addition, four distinct modules of bacterial families were detected by the DBSCAN clustering method. Finally, in the co-occurrence network context, Actinobacteria, Firmicutes, Bacteroidetes, and Chloroflexi at the phylum level and Bifidobacterium, Massilia, Sphingobacterium, and Ochrobactrum at the genus level showed the highest degree of rewiring.

CONCLUSIONS

Despite the absence of statistically significant differences in the relative abundance of certain taxa between groups, it is imperative not to overlook them for further exploration. This is because they may hold pivotal central roles in the broader network of bacterial taxa (e.g., Bifidobacterium and Massilia). These findings emphasize the importance of a network analysis approach for studying the lung microbiome since it could facilitate identifying key microbial taxa in lung cancer pathogenesis. Relying exclusively on differentially abundant taxa may not be enough to fully grasp the complex interplay between lung cancer and the microbiome. Therefore, a network-based approach can offer deeper insights and a more comprehensive understanding of the underlying mechanisms.

A Pilot Cross-Sectional Study of Immunological and Microbiome Profiling Reveals Distinct Inflammatory Profiles for Smokers, Electronic Cigarette Users, and Never-Smokers

Smokers (SM) have increased lung immune cell counts and inflammatory gene expression compared to electronic cigarette (EC) users and never-smokers (NS). The objective of this study is to further assess associations for SM and EC lung microbiomes with immune cell subtypes and inflammatory gene expression in samples obtained by bronchoscopy and bronchoalveolar lavage (n = 28). RNASeq with the CIBERSORT computational algorithm were used to determine immune cell subtypes, along with inflammatory gene expression and microbiome metatranscriptomics. Macrophage subtypes revealed a two-fold increase in M0 (undifferentiated) macrophages for SM and EC users relative to NS, with a concordant decrease in M2 (anti-inflammatory) macrophages. There were 68, 19, and 1 significantly differentially expressed inflammatory genes (DEG) between SM/NS, SM/EC users, and EC users/NS, respectively. CSF-1 and GATA3 expression correlated positively and inversely with M0 and M2 macrophages, respectively. Correlation profiling for DEG showed distinct lung profiles for each participant group. There were three bacteria genera-DEG correlations and three bacteria genera-macrophage subtype correlations. In this pilot study, SM and EC use were associated with an increase in undifferentiated M0 macrophages, but SM differed from EC users and NS for inflammatory gene expression. The data support the hypothesis that SM and EC have toxic lung effects influencing inflammatory responses, but this may not be via changes in the microbiome.

The Airway Microbiome-IL-17 Axis: a Critical Regulator of Chronic Inflammatory Disease

The respiratory tract is home to a diverse microbial community whose influence on local and systemic immune responses is only beginning to be appreciated. Increasing reports have linked changes in this microbiome to a range of pulmonary and extrapulmonary disorders, including asthma, chronic obstructive pulmonary disease and rheumatoid arthritis. Central to many of these findings is the role of IL-17-type immunity as an important driver of inflammation. Despite the crucial role played by IL-17-mediated immune responses in protection against infection, overt Th17 cell responses have been implicated in the pathogenesis of several chronic inflammatory diseases. However, our knowledge of the influence of bacteria that commonly colonise the respiratory tract on IL-17-driven inflammatory responses remains sparse. In this article, we review the current knowledge on the role of specific members of the airway microbiota in the modulation of IL-17-type immunity and discuss how this line of research may support the testing of susceptible individuals and targeting of inflammation at its earliest stages in the hope of preventing the development of chronic disease.

The lung, the niche, and the microbe: Exploring the lung microbiome in cancer and immunity

The lung is a complex and unique organ system whose biology is strongly influenced by environmental exposure, oxygen abundance, connection to extrapulmonary systems via a dense capillary network, and an array of immune cells that reside in the tissue at steady state. The lung also harbors a low biomass community of commensal microorganisms that are dynamic during both health and disease with the capacity to modulate regulatory immune responses during diseases such as cancer. Lung cancer is the third most common cancer worldwide with the highest mortality rate amongst cancers due to the difficulty of an early diagnosis. This review discusses the current body of work addressing the interactions between the lung microbiota and the immune system, and how these two components of the pulmonary system are linked to lung cancer development and outcomes. Bringing in lessons from broader studies examining the effects of the gut microbiota on cancer outcomes, we highlight many challenges and gaps in this nascent field.

Microbial Biomarkers for Lung Cancer: Current Understandings and Limitations

As our "hidden organ", microbes widely co-exist at various sites on the human body. These microbes are collectively referred to as the microbiome. A considerable number of studies have already proven that the microbiome has significant impacts on human health and disease progression, including cancers. The recent discovery of cancer-specific microbiomes renders these cancer-associated microbes as potential biomarkers and therapeutic targets. While at low biomass levels, the lung microbiome still dramatically influences the initiation, progression and treatment of lung cancers. However, research on lung cancer-associated microbiomes is emerging, and most profiling studies are performed within three years. Unfortunately, there are substantial inconsistencies across these studies. Variations in microbial diversity were observed, and different microbial biomarkers for lung cancer have been proposed. In this review, we summarized the current findings of lung cancer microbiome studies and attempt to explain the potential reasons for the dissimilarities. Other than lung microbiomes, oral and airway microbiomes are highly related to lung microbiomes and are therefore included as well. In addition, several lung cancer-associated bacterial genera have been detected by different independent studies. These bacterial genera may not be perfect biomarkers, but they still serve as promising risk factors for lung cancers and show great prognostic value.

Urine metabolomics and microbiome analyses reveal the mechanism of anti-tuberculosis drug-induced liver injury, as assessed for causality using the updated RUCAM: A prospective study

Background

Anti-tuberculosis drug-induced liver injury (ATB-DILI) is one of the most common adverse reactions that brings great difficulties to the treatment of tuberculosis. Thus, early identification of individuals at risk for ATB-DILI is urgent. We conducted a prospective cohort study to analyze the urinary metabolic and microbial profiles of patients with ATB-DILI before drug administration. And machine learning method was used to perform prediction model for ATB-DILI based on metabolomics, microbiome and clinical data.

Methods

A total of 74 new TB patients treated with standard first-line anti-TB treatment regimens were enrolled from West China Hospital of Sichuan University. Only patients with an updated RUCAM score of 6 or more were accepted in this study. Nontargeted metabolomics and microbiome analyses were performed on urine samples prior to anti-tuberculosis drug ingestion to screen the differential metabolites and microbes between the ATB-DILI group and the non-ATB-DILI group. Integrating electronic medical records, metabolomics, and microbiome data, four machine learning methods was used, including random forest algorithm, artificial neural network, support vector machine with the linear kernel and radial basis function kernel.

Results

Of all included patients, 69 patients completed follow-up, with 16 (23.19%) patients developing ATB-DILI after antituberculosis treatment. Finally, 14 ATB-DILI patients and 30 age- and sex-matched non-ATB-DILI patients were subjected to urinary metabolomic and microbiome analysis. A total of 28 major differential metabolites were screened out, involving bile secretion, nicotinate and nicotinamide metabolism, tryptophan metabolism, ABC transporters, etc. Negativicoccus and Actinotignum were upregulated in the ATB-DILI group. Multivariate analysis also showed significant metabolic and microbial differences between the non-ATB-DILI and severe ATB-DILI groups. Finally, the four models showed high accuracy in predicting ATB-DILI, with the area under the curve of more than 0.85 for the training set and 1 for the validation set.

Conclusion

This study characterized the metabolic and microbial profile of ATB-DILI risk individuals before drug ingestion for the first time. Metabolomic and microbiome characteristics in patient urine before anti-tuberculosis drug ingestion may predict the risk of liver injury after ingesting anti-tuberculosis drugs. Machine learning algorithms provides a new way to predict the occurrence of ATB-DILI among tuberculosis patients.

The Lung Microbiota and Lung Cancer: A Growing Relationship

Simple Summary

In the past few years, the microbiota has emerged as a major player in cancer management. The efficacy of chemotherapy or immunotherapy may be influenced by the concomitant use of antibiotics before, during, or shortly after treatment with immune checkpoint inhibitors. Despite this, the mechanism linking the microbiota, host immunity, and malignancies are not clear, and the role of microbiota manipulation and analyses in cancer management is underway. In this manuscript, we discuss the role of the microbiota in the initiation, progression, and treatment outcomes of lung cancer.

Abstract

The lung is home to a dynamic microbial population crucial to modulating immune balance. Interest in the role of the lung microbiota in disease pathogenesis and treatment has exponentially increased. In lung cancer, early studies suggested an important role of dysbiosis in tumor initiation and progression. These results have helped accelerate research into the lung microbiota as a potential diagnostic marker and therapeutic target. Microbiota signatures could represent diagnostic biomarkers of early-stage disease. Lung microbiota research is in its infancy with a limited number of studies and only single-center studies with a significant methodological variation. Large, multicenter longitudinal studies are needed to establish the clinical potential of this exciting field.

The microbiome of lower respiratory tract and tumor tissue in lung cancer manifested as radiological ground-glass opacity

Recent studies have confirmed the existence of microbiota in the lungs. The relationship between lung ground-glass opacity (GGO) and microbiota in the lung microenvironment is not clear. In this study, we investigated the microbial composition and diversity in bronchoalveolar lavage fluid (BALF) of diseased lung segments and paired contralateral healthy lung segments from 11 GGO patients. Furthermore, lung GGO and paired normal tissues of 26 GGO patients were explored whether there are microbial characteristics related to GGO. Compared with the control group, the community richness of GGO tissue and BALF of GGO lung segment (α-diversity) and overall microbiome difference (β-diversity) had no significant difference. The microbiome composition of BALF of GGO segments is distinct from that of paired healthy lung segments [genus (Rothia), order (Lachnospiraceae), family (Lachnospiraceae), genus (Lachnospiraceae_NK4A136_group, Faecalibacterium), and species (Faecalibacterium prausnitzii, Bacteroides uniforms)]. GGO tissue and adjacent lung tissue had more significant differences at the levels of class, order, family, genus, and species level, and most of them are enriched in normal lung tissue. The area under the curve (AUC) using 10 genera-based biomarkers to predict GGO was 91.05% (95% CI: 81.93–100%). In conclusion, this study demonstrates there are significant differences in the lower respiratory tract and lung microbiome between GGO and the non-malignant control group through the BALF and lung tissues. Furthermore, some potential bacterial biomarkers showed good performance to predict GGO.

Pragmatic Expectancy on Microbiota and Non-Small Cell Lung Cancer: A Narrative Review

It is well known that lung cancer relies on a number of genes aberrantly expressed because of somatic lesions. Indeed, the lungs, based on their anatomical features, are organs at a high risk of development of extremely heterogeneous tumors due to the exposure to several environmental toxic agents. In this context, the microbiome identifies the whole assemblage of microorganisms present in the lungs, as well as in distant organs, together with their structural elements and metabolites, which actively interact with normal and transformed cells. A relevant amount of data suggest that the microbiota plays a role not only in cancer disease predisposition and risk but also in its initiation and progression, with an impact on patients’ prognosis. Here, we discuss the mechanistic insights of the complex interaction between lung cancer and microbiota as a relevant component of the microenvironment, mainly focusing on novel diagnostic and therapeutic objectives.

Biomarkers and the microbiome in the detection and treatment of early-stage non-small cell lung cancer

Lung cancer is one of the most common and deadly cancers in the world. However, over the last several years, research into lung cancer screening and novel therapeutic approaches have provided promise that earlier detection combined with new treatment strategies may result in significantly improved outcomes. Biomarkers will most certainly play a major role in identifying those who may benefit from, and how to apply, these new treatment strategies. Here we discuss potential biomarkers, including the microbiome, in both detection and treatment strategies for early stage lung cancer.

Airway Microbiota in Patients With Synchronous Multiple Primary Lung Cancer: The Bacterial Topography of the Respiratory Tract

Microbes and microbiota dysbiosis are correlated with the development of lung cancer; however, the airway taxa characteristics and bacterial topography in synchronous multiple primary lung cancer (sMPLC) are not fully understood. The present study aimed to investigate the microbiota taxa distribution and characteristics in the airways of patients with sMPLC and clarify specimen acquisition modalities in these patients. Using the precise positioning of electromagnetic navigation bronchoscopy (ENB), we analyzed the characteristics of the respiratory microbiome, which were collected from different sites and using different sampling methods. Microbiome predictor variables were bacterial DNA burden and bacterial community composition based on 16sRNA. Eight non-smoking patients with sMPLC in the same pulmonary lobe were included in this study. Compared with other sampling methods, bacterial burden and diversity were higher in surface areas sampled by bronchoalveolar lavage (BAL). Bacterial topography data revealed that the segment with sMPLC lesions provided evidence of specific colonizing bacteria in segments with lesions. After taxonomic annotation, we identified 4863 phylotypes belonging to 185 genera and 10 different phyla. The four most abundant specific bacterial community members detected in the airway containing sMPLC lesions were Clostridium, Actinobacteria, Fusobacterium, and Rothia, which all peaked at the segments with sMPLC lesions. This study begins to define the bacterial topography of the respiratory tract in patients with sMPLC and provides an approach to specimen acquisition for sMPLC, namely BAL fluid obtained from segments where lesions are located.

Association between Human Genetic Variants and the Vaginal Bacteriome of Pregnant Women

The influence of human genetic variants on the vaginal bacterial traits (VBTs) of pregnant women is still unknown. Using a genome-wide association approach based on the 16S rRNA bacteriome analysis, a total of 72 host genetic variant (single nucleotide polymorphisms [SNPs], indels, or copy number variations [CNVs])-VBT associations were found that reached the genome-wide significance level (P < 5 × 10^-8) with an acceptable genomic inflation factor λ of <1.1. The majority of these SNPs that reached the genome-wide significance level had a relatively low minor allele frequency (MAF), and only seven of them had MAFs greater than 0.05. rs303212, located at the IFIT1 gene on chromosome 10, was the most eye-catching variant, which had a genome-wide association with the relative abundance (RAB) of Actinobacteria and Bifidobacteriaceae and also had a suggestive association with the RAB of a few common vaginal bacteria including Actinobacteriota, Firmicutes, Lactobacillus, and Gardnerella vaginalis and the beta diversity weighted UniFrac (P < 1 × 10^-5). The findings of the study suggest that the vaginal bacteriome may be influenced by a number of genetic variants across the human genome and that interferon signaling may have an important influence on vaginal bacterial communities during pregnancy. IMPORTANCE Knowledge about the influence of host genetics on the vaginal bacteriome in pregnancy is still limited. Although a number of environmental and behavioral factors may exert influences on the structure of vaginal bacterial communities, the vaginal bacteriome often undergoes a relatively fixed transition to a more stable and less diverse state as the menstrual cycle stops, which raises questions on the effects of human genetics. We utilized a genome-wide approach to identify the associations between genetic variants and multiple VBTs and performed enrichment analyses. The human genetics during pregnancy may be involved in multiple pathways. The results may disclose innate functional factors involved in shaping the vaginal bacteriome during pregnancy and provide insight into the establishment of specific strategies for prevention and clinical treatment of pregnancy complications.

Exploring the role of respiratory microbiome in lung cancer: A systematic review

Giving the potential contribute in cancer initiation and progression, lung microbiota represents a promising topic in cancer research, although still unexplored. We performed a systematic literature search to identify clinical studies evaluating lung microbiota composition, its correlation with lung cancer patients' clinico-pathological features and prognosis. Of the identified 370 studies, 21 were eligible and included. Although studies were heterogeneous, lung cancer resulted to be enriched in peculiar microbial communities, with differences in composition and diversity according to clinico-pathological parameters. Few studies explored how lung microbiota influences cancer outcome. In light of these findings and borrowing the suggestions coming from gut microbiota, we speculate that respiratory microbiome may influence pathogenesis, progression and outcome of lung cancer. Taking advantage of the experience of chronical lung diseases, prospective studies should be designed to evaluate lung microbiota changes throughout any phase of lung cancer course, particularly with the advent of immunotherapy as pivotal treatment.

Distinct tumor bacterial microbiome in lung adenocarcinomas manifested as radiological subsolid nodules

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Subsolid nodules have higher microbiome diversity compared with solid nodules

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Microbiome composition of Subsolid nodules is distinct from that of solid nodules

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Microbial signatures show robust performance to predict lung adenocarcinoma or subsolid nodules

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Some lung microbial species are associated with clinical characteristics

Objectives

Increasing evidence indicates that microbiota dysbiosis in the human body may play vital roles in carcinogenesis. However, the relationship between microbiome and lung cancer remains unclear. In this study, we aimed to characterize the microbiome in early stage of lung adenocarcinoma (LUAD), which presented as subsolid nodules (SSN) or solid nodules (SN).

Materials and Methods

We performed 16S rRNA sequencing of 35 pairs (10 SSN and 25 SN) of LUAD tumor tissues and paired adjacent normal tissues. Machine learning was used to identify microbial signatures and construct predictive models.

Results

SSN has higher microbiome richness and diversity compared with SN (richness p = 0.017, Shannon index p = 0.17), and the microbiome composition of SSN is distinct from that of SN (Bray-Curtis p = 0.013, unweighted unifrac p = 0.001). Phylum Chloroflexi (p = 0.009), Gemmatimonadetes (p = 0.018) and genus including Cloacibacterium (p = 0.003), Subdoligranulum (p = 0.002), and Mycobacterium (p = 0.034) were significantly increased in SSN. Tumor and normal tissues had similar richness and diversity, as well as overall microbiome composition. Probiotics with anti-cancer potential, like Lactobacillus, showed elevated levels in normal tissues (p = 0.018). A random forest model with 20 genera-based biomarkers achieved high accuracy for LUAD prediction (area under curve, AUC = 0.879). Meanwhile, a five genera-based signature can accurately discriminate SSN between SN (AUC = 0.950). Cross-validation of these two models also showed high predictive performance (LUAD AUC = 0.813, SSN AUC = 0.933).

Conclusions

This study demonstrates, for the first time, the tumor bacterial microbiome composition of LUAD manifested as SSN is distinct from that presented as SN, which adds new knowledge to SSN in the perspective of microbiome. Furthermore, microbiome signatures showed good performance to predict LUAD or SSN.

Protein tyrosine phosphatase receptor type delta (PTPRD) suppresses the expression of PD-L1 in human hepatocellular carcinoma by down-regulating STAT3

BACKGROUND

Protein tyrosine phosphatase receptor type delta (PTPRD) is a tumor suppressor that is often inactivated in hepatocellular carcinoma (HCC). However, the mechanisms of how PTPRD inhibits HCC are not well understood. Programmed cell death ligand 1 (PD-L1), an immune checkpoint, plays a seminal role in the regulation of carcinogenesis of HCC. The sustained activation of STAT3 is closely related to PTPRD deletion and PD-L1 overexpression; however, whether there is a relationship between PTPRD and PD-L1 expression in HCC has not been investigated. This study aims to investigate the relationship between PTPRD and PD-L1 in HCC samples and illuminate potential new molecular mechanisms of PTPRD effects on PD-L1 in HepG2 cells.

METHODS

We collected 16 pairs of tumorous tissues and adjacent normal tissues from HCC patients. The mRNA and protein expression levels of PTPRD and PD-L1 in the HCC tissues were detected by RT-PCR and Western blot analysis. Next, Spearman's correlation analysis was performed to evaluate the relationship between PTPRD and PD-L1. Then, we transfected the overexpressed or knocked-down PTPRD genes into the HepG2 cell line, and the effects of PTPRD on PD-L1 in HCC cells were evaluated. The activity from the STAT3 and p-STAT3 in the HepG2 cells transfected with PTPRD gene overexpression and knockdown was determined by Western blotting tests.

RESULTS

The expression of PTPRD was significantly down-regulated in the HCC tissues compared with the adjacent control tissues; however, PD-L1 was significantly higher in the HCC tissues. There was a negative correlation between PTPRD and PD-L1 expression in the HCC tissues. PTPRD over-expression significantly inhibited PD-L1 expression; meanwhile, PTPRD depletion promoted PD-L1 expression in the HepG2 cells. Furthermore, PTPRD over-expression significantly inhibited the expression of STAT3 and p-STAT3, while PTPRD depletion promoted these cytokines. Our studies revealed that PTPRD repressed PD-L1 expression in the HepG2 cells, which might occur via the STAT3 pathway.

CONCLUSIONS

The results from our study show that PTPRD and PD-L1 are negatively correlated in HCC tissues. PTPRD suppresses PD-L1 expression in HepG2 cells by down-regulating STAT3. These findings are expected to become a new target for the immunotherapy of HCC.

The importance of airway and lung microbiome in the critically ill

During critical illness, there are a multitude of forces such as antibiotic use, mechanical ventilation, diet changes and inflammatory responses that could bring the microbiome out of balance. This so-called dysbiosis of the microbiome seems to be involved in immunological responses and may influence outcomes even in individuals who are not as vulnerable as a critically ill ICU population. It is therefore probable that dysbiosis of the microbiome is a consequence of critical illness and may, subsequently, shape an inadequate response to these circumstances.Bronchoscopic studies have revealed that the carina represents the densest site of bacterial DNA along healthy airways, with a tapering density with further bifurcations. This likely reflects the influence of micro-aspiration as the primary route of microbial immigration in healthy adults. Though bacterial DNA density grows extremely sparse at smaller airways, bacterial signal is still consistently detectable in bronchoalveolar lavage fluid, likely reflecting the fact that lavage via a wedged bronchoscope samples an enormous surface area of small airways and alveoli. The dogma of lung sterility also violated numerous observations that long predated culture-independent microbiology.The body's resident microbial consortia (gut and/or respiratory microbiota) affect normal host inflammatory and immune response mechanisms. Disruptions in these host-pathogen interactions have been associated with infection and altered innate immunity.In this narrative review, we will focus on the rationale and current evidence for a pathogenic role of the lung microbiome in the exacerbation of complications of critical illness, such as acute respiratory distress syndrome and ventilator-associated pneumonia.