Myeloperoxidase -463 G/A polymorphism is associated with lung cancer risk: A meta-analysis with 7420 cases and 9132 controls

Myeloperoxidase: Growing importance in cancer pathogenesis and potential drug target

Myeloperoxidase is a heme-peroxidase which makes up approximately 5% of the total dry cell weight of neutrophils where it is predominantly found in the primary (azurophilic) granules. Other cell types, such as monocytes and certain macrophage subpopulations also contain myeloperoxidase, but to a much lesser extent. Initially, the function of myeloperoxidase had been mainly associated with its ability as a catalyzer of reactive oxidants that help to clear pathogens. However, over the past years non-canonical functions of myeloperoxidase have been described both in health and disease. Attention has been specially focused on inflammatory diseases, in which an exacerbate infiltration of leukocytes can favor a poorly-controlled production and release of myeloperoxidase and its oxidants. There is compelling evidence that myeloperoxidase derived oxidants contribute to tissue damage and the development and propagation of acute and chronic vascular inflammation. Recently, neutrophils have attracted much attention within the large diversity of innate immune cells that are part of the tumor microenvironment. In particular, neutrophil-derived myeloperoxidase may play an important role in cancer development and progression. This review article aims to provide a comprehensive overview of the roles of myeloperoxidase in the development and progression of cancer. We propose future research approaches and explore prospects of inhibiting myeloperoxidase as a strategy to fight against cancer.

The Enzymatic and Non-Enzymatic Function of Myeloperoxidase (MPO) in Inflammatory Communication

Myeloperoxidase is a signature enzyme of polymorphonuclear neutrophils in mice and humans. Being a component of circulating white blood cells, myeloperoxidase plays multiple roles in various organs and tissues and facilitates their crosstalk. Here, we describe the current knowledge on the tissue- and lineage-specific expression of myeloperoxidase, its well-studied enzymatic activity and incoherently understood non-enzymatic role in various cell types and tissues. Further, we elaborate on Myeloperoxidase (MPO) in the complex context of cardiovascular disease, innate and autoimmune response, development and progression of cancer and neurodegenerative diseases.

Proteomic analysis of human cervical adenocarcinoma mucus to identify potential protein biomarkers

Background

Cervical cancer is the most common gynecological cancer, encompassing cervical squamous cell carcinoma, adenocarcinoma, and other epithelial tumors. There are many diagnostic methods to detect cervical cancers but no precision screening tool for cervical adenocarcinoma at present.

Material and methods

The cervical mucus from three normal cervices (Ctrl), three endocervical adenocarcinoma (EA), and three cervical adenocarcinoma in situ (AIS) was collected for proteomic analysis. The proteins were screened using liquid chromatography-mass spectrometry analysis (LC-MS). The biological function of the differently expressed proteins were predicted by Gene Ontology (GO).

Results

A total of 711 proteins were identified, including 237 differently expressed proteins identified in EA/Ctrl comparison, 256 differently expressed proteins identified in AIS/Ctrl comparison, and 242 differently expressed proteins identified in AIS/EA comparison (up-regulate ≥ 1.5 or down-regulate ≤ 0.67). Functional annotation was performed using GO analysis on 1,056 differently expressed proteins to identify those that may impact cervical cancer, such as heme protein myeloperoxidase, which is involved in the immune process, and APOA1, which is associated with lipid metabolism.

Conclusion

We used proteomic analysis to screen out differently expressed proteins from normal cervical mucus and cervical adenocarcinoma mucus samples. These differently expressed proteins may be potential biomarkers for the diagnosis and treatment of cervical adenocarcinoma but require additional study.

Integrative Network Analysis of Multi-Omics Data in the Link between Plasma Carotenoid Concentrations and Lipid Profile

INTRODUCTION

Carotenoids, which are a reliable biomarker of fruit and vegetable consumption, are positively associated with the lipid profile. Circulating carotenoid concentrations may interact with several omics profiles including genome, transcriptome, and epigenome. Few studies have used multi-omics approaches, and they rarely include environmental factors, such as diet.

OBJECTIVE

The objective of this observational study was to examine the potential role of multi-omics data in the interconnection between diet, represented by total carotenoids, and lipid profile using weighted gene correlation network analysis (WGCNA).

METHODS

Blood leukocyte DNA methylation levels of 472,245 CpG sites and whole blood gene expression levels of 18,160 transcripts were tested for associations with total carotenoid concentrations using regressions in 48 healthy subjects. WGCNA was used to identify co-omics modules and hub genes related to the lipid profile.

RESULTS

Among genes associated with total carotenoid concentrations, a total of 236 genes were identified at both DNA methylation and gene expression levels. Using WGCNA, six modules, consisting of groups of highly correlated genes represented by colors, were identified and linked to the lipid profile. Probes clustered in the turquoise and green modules correlated with plasma lipid concentrations. A total of 28 hub genes were identified.

CONCLUSIONS

Genome-wide DNA methylation and gene expression levels were both associated with plasma total carotenoid concentrations. Several hub genes, mostly involved in lipid metabolism and inflammatory response with several genetic variants associated with plasma lipid concentrations, came out of the integrative analysis. This provides a comprehensive understanding of the interactive molecular system between carotenoids, omics, and plasma lipid profile.