Associations of lung cancer risk with biomarkers of Helicobacter pylori infection

Effects and mechanisms of Helicobacter pylori infection on the occurrence of extra-gastric tumors

Helicobacter pylori (H. pylori) colonizes the human stomach and many studies have discussed the mechanisms of H. pylori infection leading to gastric diseases, including gastric cancer. Additionally, increasing data have shown that the infection of H. pylori may contribute to the development of extra-gastric diseases and tumors. Inflammation, systemic immune responses, microbiome disorders, and hypergastrinemia caused by H. pylori infection are associated with many extra-gastric malignancies. This review highlights recent discoveries; discusses the relationship between H. pylori and various extra-gastric tumors, such as colorectal cancer, lung cancer, cholangiocarcinoma, and gallbladder carcinoma; and explores the mechanisms of extra-gastric carcinogenesis by H. pylori. Overall, these findings refine our understanding of the pathogenic processes of H. pylori, provide guidance for the clinical treatment and management of H. pylori-related extra-gastric tumors, and help improve prognosis.

Effects and mechanisms of Helicobacter pylori on cancers development and immunotherapy

Tumor immunotherapy has been widely used in clinical treatment of various cancers. However, some patients of these cancers do not respond to immunotherapy effectively. And H. pylori infection has been considered to be related to the efficacy of immunotherapy. This review aims to summarize the different effects and mechanisms of H. pylori infection on immunotherapy in different kinds of cancers. We searched the relevant literature on H. pylori and tumor immunotherapy, and summarized to form a review. Generally, H. pylori infection plays a role in affecting kinds of cancers' development, besides gastric cancer. Current evidence suggests that H. pylori infection may reduce the efficacy of immunotherapy for colorectal cancer, non-small cell lung cancer and melanoma, but due to the lack of sufficient evidence, more data is needed to prove that. While for gastric cancer, the effects remain controversial. The H. pylori regulation effects and metabolisms involved in systematic related cancers should be paid attention to. Whether H. pylori should be eradicated when immunotherapy performed may be a critical consideration for some kinds of tumors.

A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients

Background

Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection.

Method

This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6.

Result

The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6.

Conclusion

Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

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.

Association of Helicobacter pylori Positivity With Risk of Disease and Mortality

INTRODUCTION

Helicobacter pylori colonizes the human stomach. Infection causes chronic gastritis and increases the risk of gastroduodenal ulcer and gastric cancer. Its chronic colonization in the stomach triggers aberrant epithelial and inflammatory signals that are also associated with systemic alterations.

METHODS

Using a PheWAS analysis in more than 8,000 participants in the community-based UK Biobank, we explored the association of H. pylori positivity with gastric and extragastric disease and mortality in a European country.

RESULTS

Along with well-established gastric diseases, we dominantly found overrepresented cardiovascular, respiratory, and metabolic disorders. Using multivariate analysis, the overall mortality of H. pylori -positive participants was not altered, while the respiratory and Coronovirus 2019-associated mortality increased. Lipidomic analysis for H. pylori -positive participants revealed a dyslipidemic profile with reduced high-density lipoprotein cholesterol and omega-3 fatty acids, which may represent a causative link between infection, systemic inflammation, and disease.

DISCUSSION

Our study of H. pylori positivity demonstrates that it plays an organ- and disease entity-specific role in the development of human disease and highlights the importance of further research into the systemic effects of H. pylori infection.