Introducing Serine as Cardiovascular Disease Biomarker Candidate via Pathway Analysis

Gene Ontology Assessment of Indirect Cold Physical Plasma and UV-Radiation Molecular Mechanism at the Cellular Level

Introduction: The development of therapeutic methods implies an understanding of the molecular mechanism of the applied methods. Due to the widespread use of UV radiation and cold physical plasma in medicine, the molecular mechanism of these two methods is compared via gene ontology. Methods: Data were derived from Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) which discriminate the cells treated with UV radiation versus indirect cold physical plasma were analyzed via gen ontology enrichment. The related biochemical pathways were extracted from the "Kyoto Encyclopedia of Genes and Genomes" (KEGG). Results: Among the 152 queried DEGs, 18 critical genes including SOC1, LDLR, ALO5, PTGS2, TNF, JUNB, TNFRSF1A, CD40, SMAD7, ID1, SMAD6, SERPINE1, PMAIP1, MDM2, CREB5, GADD45A, E2F3, and ETV5 were highlighted as the genes that victimize the two methods. Conclusion: NOTCH1 and TNF as the main genes plus SEREPINE1, KLF, and BDNF were introduced as the significant genes that are involved in the processes which discriminate cold physical plasma administration and UV-radiation as the two evaluated therapeutic methods.

Integrated Metabolomics and Lipidomics Approach for the Study of Metabolic Network and Early Diagnosis in Cerebral Infarction

Cerebral infarction (CI) remains a major cause of high mortality and long-term disability worldwide. The exploration of biomarkers and pathogenesis is crucial for the early diagnosis of CI. Although the understanding of metabolic perturbations underlying CI has increased in recent years, the relationship between altered metabolites and disease pathogenesis has only been partially elucidated and requires further investigation. In this study, we performed an integrated metabolomics and lipidomics analysis on 59 healthy subjects and 47 CI patients. Ultimately, 49 metabolite and 68 lipid biomarkers were identified and enriched in 24 disturbed pathways. The metabolic network revealed a significant interaction between altered lipids and other metabolites. Using receiver operating characteristic curve (ROC) analysis, a panel of three polar metabolites and seven lipids was optimized in the training set, which included taurine, oleoylcarnitine, creatinine, PE(22:6/P-18:0), Cer 34:2, GlcCer(d18:0/18:0), DG 44:0, LysoPC(16:0), 22:6-OH/LysoPC, and TAG58:7-FA22:4. Subsequently, a support vector machine (SVM) model was constructed and validated, which showed excellent predictive ability in the validation set. Thereby, the integrated metabolomics and lipidomics approach could contribute to a comprehensive understanding of the metabolic dyshomeostasis associated with the pathogenesis of underlying CI. The present research may promote a deeper understanding and early diagnosis of CI in the clinic. All raw data were deposited in PRIDE (PXD036199).

Introducing physical exercise as a potential strategy in liver cancer prevention and development

AIM

This study aimed to investigate the anticancer properties of physical activity by network analysis in trained rats.

BACKGROUND

Much evidence supports the benefits of physical activity, most of which are related to metabolism regulation and body health. Deeper investigation deals with other features of physical activity, such as its anticancer properties.

METHODS

Protein-protein interaction network analysis was applied to investigate the proteome profile of livers of rats subjected to physical activity through bioinformatics. Twelve differentially expressed proteins were searched and analyzed by Cytoscape 3.7.2 and its plug-ins. The network was analyzed to identify hub-bottleneck nodes. An action map was constructed for the central proteins.

RESULTS

Among the queried proteins, Eno1 and Pgm1 were only assigned as hubs by Network Analzyer. Gpi, Pkm, Aldoa, and Aldoart2 were identified as central nodes among the first neighbors of network elements. Furthermore, the glycolytic, carbohydrate catabolic, and glucose metabolic processes are key elements that could be imperative in the mechanism of exercise in liver function. The anticancer properties of the central nodes were highlighted.

CONCLUSION

The network findings indicate the anticancer properties of physical activity, which has also been supported by previous investigations.

Assessment of the Microbiome Role in Skin Protection Against UV Irradiation Via Network Analysis

Introduction: Diverse microbiotas which have some contributions to gene expression reside in human skin. To identify the protective role of the skin microbiome against UV exposure, proteinprotein interaction (PPI) network analysis is used to assessment gene expression alteration. Methods: A microarray dataset, GEO accession number GSE117359, was considered in this respect. Differential expressed genes (DEGs) in the germ-free (GF) and specific pathogen-free (SPF) groups are analyzed by GEO2R. The top significant DEGs were assigned for network analysis via Cytoscape 3.7.2 and its applications. Results: A total of 28 genes were identified as significant DEGs and the centrality analysis of the network indicated that only one of the seven hub-bottlenecks was from queried genes. The gene ontology analysis of Il6, Cxcl2, Cxcl1, TNF, Il10, Cxcl10, and Mmp9 showed that the crucial genes were highly enriched in the immune system. Conclusion: The skin microbiome plays a significant role in the protection of skin against UV irradiation and the role of TNF and IL6 is prominent in this regard.