Identification of cardiomyopathy-related core genes through human metabolic networks and expression data

A circular network of purine metabolism as coregulators of dilated cardiomyopathy

BACKGROUND

The crosstalk of purine biosynthesis and metabolism exists to balance the cell energy production, proliferation, survival and cytoplasmic environment stability, but disorganized mechanics of with respect to developing heart failure (HF) is currently unknown.

METHODS

We conducted a multi-omics wide analysis, including microarray-based transcriptomes, and full spectrum metabolomics with respect to chronic HF. Based on expression profiling by array, we applied a bioinformatics platform of quantifiable metabolic pathway changes based on gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), Shapley Additive Explanations (SHAP), and Xtreme Gradient Boosting (XGBoost) algorithms to comprehensively analyze the dynamic changes of metabolic pathways and circular network in the HF development. Additionally, left ventricular tissue from patients undergoing myocardial biopsy and transplantation were collected to perform the protein and full spectrum metabolic mass spectrometry.

RESULTS

Systematic bioinformatics analysis showed the purine metabolism reprogramming was significantly detected in dilated cardiomyopathy. In addition, this result was also demonstrated in metabolomic mass spectrometry. And the differentially expressed metabolites analysis showing the guanine, urea, and xanthine were significantly detected. Hub markers, includes IMPDH1, ENTPD2, AK7, AK2, and CANT1, also significantly identified based on XGBoost, SHAP model and PPI network.

CONCLUSION

The crosstalk in the reactions involved in purine metabolism may involving in DCM metabolism reprogramming, and as coregulators of development of HF, which may identify as potential therapeutic targets. And the markers of IMPDH1, ENTPD2, AK7, AK2, and CANT1, and metabolites involved in purine metabolism shown an important role.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.