Biomarkers of early chronic obstructive pulmonary disease (COPD) in smokers and former smokers. Protocol of a longitudinal study

Apelin promotes blood and lymph vessel formation and the growth of melanoma lung metastasis

Apelin, a ligand of the APJ receptor, is overexpressed in several human cancers and plays an important role in tumor angiogenesis and growth in various experimental systems. We investigated the role of apelin signaling in the malignant behavior of cutaneous melanoma. Murine B16 and human A375 melanoma cell lines were stably transfected with apelin encoding or control vectors. Apelin overexpression significantly increased melanoma cell migration and invasion in vitro, but it had no impact on its proliferation. In our in vivo experiments, apelin significantly increased the number and size of lung metastases of murine melanoma cells. Melanoma cell proliferation rates and lymph and blood microvessel densities were significantly higher in the apelin-overexpressing pulmonary metastases. APJ inhibition by the competitive APJ antagonist MM54 significantly attenuated the in vivo pro-tumorigenic effects of apelin. Additionally, we detected significantly elevated circulating apelin and VEGF levels in patients with melanoma compared to healthy controls. Our results show that apelin promotes blood and lymphatic vascularization and the growth of pulmonary metastases of skin melanoma. Further studies are warranted to validate apelin signaling as a new potential therapeutic target in this malignancy.

Do sputum or circulating blood samples reflect the pulmonary transcriptomic differences of COPD patients? A multi-tissue transcriptomic network META-analysis

BACKGROUND

Previous studies have identified lung, sputum or blood transcriptomic biomarkers associated with the severity of airflow limitation in COPD. Yet, it is not clear whether the lung pathobiology is mirrored by these surrogate tissues. The aim of this study was to explore this question.

METHODS

We used Weighted Gene Co-expression Network Analysis (WGCNA) to identify shared pathological mechanisms across four COPD gene-expression datasets: two sets of lung tissues (L1 n = 70; L2 n = 124), and one each of induced sputum (S; n = 121) and peripheral blood (B; n = 121).

RESULTS

WGCNA analysis identified twenty-one gene co-expression modules in L1. A robust module preservation between the two L datasets was observed (86%), with less preservation in S (33%) and even less in B (23%). Three modules preserved across lung tissues and sputum (not blood) were associated with the severity of airflow limitation. Ontology enrichment analysis showed that these modules included genes related to mitochondrial function, ion-homeostasis, T cells and RNA processing. These findings were largely reproduced using the consensus WGCNA network approach.

CONCLUSIONS

These observations indicate that major differences in lung tissue transcriptomics in patients with COPD are poorly mirrored in sputum and are unrelated to those determined in blood, suggesting that the systemic component in COPD is independently regulated. Finally, the fact that one of the preserved modules associated with FEV1 was enriched in mitochondria-related genes supports a role for mitochondrial dysfunction in the pathobiology of COPD.

Recent insights into human bronchial proteomics - how are we progressing and what is next?

The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries. Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed. Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.

Can glypican-3 be a disease-specific biomarker?

Background

Glypican-3 (GPC3) is a cell surface-bound proteoglycan which has been identified as a potential biomarker candidate in hepatocellular carcinoma, lung carcinoma, severe pneumonia, and acute respiratory distress syndrome (ARDS). The aim of our review is to evaluate whether GPC3 has utility as a disease-specific biomarker, to discuss the potential involvement of GPC3 in cell biology, and to consider the changes of GPC3 gene and protein expression and regulation in hepatocellular carcinoma, lung cancer, severe pneumonia, and ARDS.

Results

Immunohistochemical studies have suggested that over-expression of GPC3 is associated with a poorer prognosis for hepatocellular carcinoma patients. Expression of GPC3 leads to an increased apoptosis response in human lung carcinoma tumor cells, and is considered to be a candidate lung tumor suppressor gene. Increased serum levels of GPC3 have been demonstrated in ARDS patients with severe pneumonia.

Conclusions

Glypican-3 could be considered as a clinically useful biomarker in hepatocellular carcinoma, lung carcinoma, and ARDS, but further research is needed to confirm and expand on these findings.

Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies

Background

Data from biological samples and medical evaluations plays an essential part in clinical decision making. This data is equally important in clinical studies and it is critical to have an infrastructure that ensures that its quality is preserved throughout its entire lifetime. We are running a 5-year longitudinal clinical study, KOL-Örestad, with the objective to identify new COPD (Chronic Obstructive Pulmonary Disease) biomarkers in blood. In the study, clinical data and blood samples are collected from both private and public health-care institutions and stored at our research center in databases and biobanks, respectively. The blood is analyzed by Mass Spectrometry and the results from this analysis then linked to the clinical data.

Method

We built an infrastructure that allows us to efficiently collect and analyze the data. We chose to use REDCap as the EDC (Electronic Data Capture) tool for the study due to its short setup-time, ease of use, and flexibility. REDCap allows users to easily design data collection modules based on existing templates. In addition, it provides two functions that allow users to import batches of data; through a web API (Application Programming Interface) as well as by uploading CSV-files (Comma Separated Values).

Results

We created a software, DART (Data Rapid Translation), that translates our biomarker data into a format that fits REDCap's CSV-templates. In addition, DART is configurable to work with many other data formats as well. We use DART to import our clinical chemistry data to the REDCap database.

Conclusion

We have shown that a powerful and internationally adopted EDC tool such as REDCap can be extended so that it can be used efficiently in proteomic studies. In our study, we accomplish this by using DART to translate our clinical chemistry data to a format that fits the templates of REDCap.

Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma

INTRODUCTION

Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges. Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma. Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.