Critical Roles of ELVOL4 and IL-33 in the Progression of Obesity-Related Cardiomyopathy via Integrated Bioinformatics Analysis

Metabolic status indicators and influencing factors in non-obese, non-centrally obese nonalcoholic fatty liver disease

Non-obese nonalcoholic fatty liver disease (NAFLD) is characterized by metabolic disorders and related complications. This study aimed to provide an integrated description of clinical, metabolic, and influencing factors for a specific category of patients with non-obese NAFLD. A total of 36 participants with body mass index (BMI) < 28 kg/m2 and visceral adipose tissue < 100 cm2 were classified into 2 groups: the non-obese, non-centrally obese control group (n = 17) and non-obese, non-centrally obese NAFLD group (n = 19). Hypertriglyceridemia, impaired fasting glucose, low high-density lipoprotein cholesterol levels, and hypertension were used to determine whether participants were metabolically abnormal. Based on a logistic regression model, odds ratios for the factors influencing NAFLD with 95% confidence intervals were calculated. Insulin resistance (IR) and fasting plasma glucose (FPG) levels were higher in the NAFLD group than in the control group (P < .05). The NAFLD group had a higher metabolic abnormality rate than the healthy control group (36.84% vs 5.88%, P = .044). Correlation analysis showed that IR was positively correlated with FPG and triglyceride (P < .05). BMI was the main influencing factor of NAFLD (regression coefficient β = 0.631; odds ratio = 1.879; 95% confidence interval, 1.233-2.863). NAFLD patients with a BMI < 28 kg/m2 and visceral adipose tissue < 100 cm2 had more apparent IR, higher FPG, and a higher metabolic abnormality rate. IR may be affected by FPG and triglyceride. Even in non-obese and non-centrally obese individuals, BMI should be controlled to avoid NAFLD.

Identification of hub genes and candidate herbal treatment in obesity through integrated bioinformatic analysis and reverse network pharmacology

Obesity is a global epidemic elevating the risk of various metabolic disorders. As there is a lack of effective drugs to treat obesity, we combined bioinformatics and reverse network pharmacology in this study to identify effective herbs to treat obesity. We identified 1011 differentially expressed genes (DEGs) of adipose tissue after weight loss by analyzing five expression profiles (GSE103766, GSE35411, GSE112307, GSE43471, and GSE35710) from the Gene Expression Omnibus (GEO) database. We identified 27 hub genes from the protein-protein interaction (PPI) network by performing MCODE using the Search Tool for the Retrieval of Interacting Genes (STRING) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these hub genes have roles in the extracellular matrix-receptor interaction, cholesterol metabolism, PI3K-Akt signaling pathway, etc. Ten herbs (Aloe, Portulacae Herba, Mori Follum, Silybum Marianum, Phyllanthi Fructus, Pollen Typhae, Ginkgo Semen, Leonuri Herba, Eriobotryae Folium, and Litseae Fructus) targeting the nine hub genes (COL1A1, MMP2, MMP9, SPP1, DNMT3B, MMP7, CETP, COL1A2, and MUC1) using six ingredients were identified as the key herbs. Quercetin and (-)-epigallocatechin-3-gallate were determined to be the key ingredients. Lastly, Ingredients-Targets, Herbs-Ingredients-Targets, and Herbs-Taste-Meridian Tropism networks were constructed using Cytoscape to elucidate this complex relationship. This study could help identify promising therapeutic targets and drugs to treat obesity.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Identification and validation of potential novel biomarkers to predict distant metastasis in differentiated thyroid cancer

Background

Distant metastasis (DM) is not common in differentiated thyroid cancer (DTC). However, it is associated with a significantly poor prognosis. Early detection of high-risk DTC patients is difficult, and the molecular mechanism is still unclear. Therefore, the present study aims to establish a novel predictive model based on clinicopathological parameters and DM-related gene signatures to provide guidelines for clinicians in decision making.

Methods

Weighted gene co-expression network analysis (WGCNA) was performed to discover co-expressed gene modules and hub genes associated with DM. Univariate and multivariate analyses were carried out to identify independent clinicopathological risk factors based on The Cancer Genome Atlas (TCGA) database. An integrated nomogram prediction model was established. Finally, real hub genes were validated using the GSE60542 database and various thyroid cell lines.

Results

The midnightblue module was most significantly positively correlated with DM (R=0.56, P=9e-06) by as per WGCNA. DLX5 (AUC: 0.769), COX6B2 (AUC: 0.764), and LYPD1 (AUC: 0.760) were determined to be the real hub genes that play a crucial role in predicting DM. Meanwhile, univariate and multivariate analyses demonstrated that T-stage (OR, 15.03; 95% CI, 1.75-319.40; and P=0.024), histologic subtype (OR, 0.17; 95% CI, 0.03-0.92; and P=0.042) were the independent predictors of DM. Subsequently, a nomogram model was constructed based on gene signatures and independent clinical risk factors exhibited good performance. Additionally, the mRNA expressions of real hub genes in the GSE60542 dataset were consistent with TCGA.

Conclusions

The present study has provided a reliable model to predict DM in patients with DTC. This model is likely to serve as an individual risk assessment tool in therapeutic decision-making.

Decreased Spp1 Expression in Acute Myocardial Infarction after Ischemia and Reperfusion Injury

BACKGROUND

With the progress of shock therapy and the establishment and promotion of methods such as thrombolytic therapy and percutaneous coronary intervention (PCI), many tissues and organs have been reperfused after ischemia which may cause even worse disorder called ischemia-reperfusion injury (IRI). mRNAs have been found to have significant impacts on ischemia-reperfusion through various mechanisms. In view of the accessibility of mRNAs from blood, we aimed to find the association between mRNA and ischemia-reperfusion.

METHODS

We used the GSE83472 dataset from the Gene Expression Omnibus (GEO) database to find differential RNA expression between ischemia-reperfusion tissue and control samples. In addition, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to find the biological property of 449 RNAs from GSE83472 via the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Besides, Gene Set Enrichment Analysis (GSEA), a tool to find the pathway orientation of a gene set, was used for further study in the four most significant KEGG pathways. Furthermore, we constructed a protein-protein interaction (PPI) network. In the end, we used quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blotting to measure and compare the expression of Spp1 in patients who accepted percutaneous coronary intervention.

RESULTS

The bioinformatics analyses suggested that Spp1 was a hub gene in reperfusion after ischemia. The qRT-PCR result showed that the Spp1 expression was significantly downregulated in ischemia-reperfusion cells after PCI compared with normal samples and so as the western blotting.

CONCLUSION

Spp1 might play an essential role in acute myocardial infarction after ischemia and reperfusion injury.

High-Density Lipoprotein Cholesterol in Young Nondiabetic Coronary Heart Disease Patients

OBJECTIVE

To investigate the association between the lipid profiles and coronary heart disease (CHD) in nondiabetic patients younger than 65 years of age.

METHOD

424 patients were enrolled in this study from January 2019 to December 2020. All the patients were screened for clinically indicated coronary angiography. They were divided into two groups according to the coronary angiography results: 340 patients with the presence of CHD (at least one coronary artery stenosis ≥50%) were classified as the CHD group, and the rest with the absence of CHD comprised the normal group. The demographic data and lipid profiles were compared.

RESULT

CHD was higher in males than females (84.5% vs. 62.2%, P < 0.001). In the CHD group, the level of high-density lipoprotein cholesterol (HDL-C) was lower (P < 0.001), while the triglyceride (TG)/HDL-C ratio was higher (P=0.022). No significant differences were shown between the two groups in terms of age, family history of CHD, hypertension, and the levels of TC, TG, and LDL-C. Gender differences were further explored. In men, except for the level of HDL-C which was significantly lower in the CHD group than that in the normal group (P=0.017), parameters were comparable. A binary logistic regression model further indicated that HDL-C was associated with CHD (OR = 0.137, 95%CI: 0.031-0.594, P=0.008). Also, with the increase of the number of coronary artery with lesions, the levels of HDL-C decreased significantly in men. In women, no differences were observed between the CHD group and normal group.

CONCLUSION

HDL-C may be inversely associated with the risk of CHD in young nondiabetes patients, especially in men. More research is needed to confirm it.