Screening differentially expressed genes of pancreatic cancer between Mongolian and Han people using bioinformatics technology

PCSK9 inhibitor effectively alleviated cognitive dysfunction in a type 2 diabetes mellitus rat model

Background

The incidence of diabetes-associated cognitive dysfunction (DACD) is increasing; however, few clinical intervention measures are available for the prevention and treatment of this disease. Research has shown that proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, particularly SBC-115076, have a protective effect against various neurodegenerative diseases. However, their role in DACD remains unknown. In this study, we aimed to explore the impact of PCSK9 inhibitors on DACD.

Methods

Male Sprague-Dawley (SD) rats were used to establish an animal model of type 2 diabetes mellitus (T2DM). The rats were randomly divided into three groups: the Control group (Control, healthy rats, n = 8), the Model group (Model, rats with T2DM, n = 8), and the PCSK9 inhibitor-treated group (Treat, T2DM rats treated with PCSK9 inhibitors, n = 8). To assess the spatial learning and memory of the rats in each group, the Morris water maze (MWM) test was conducted. Hematoxylin-eosin staining and Nissl staining procedures were performed to assess the structural characteristics and functional status of the neurons of rats from each group. Transmission electron microscopy was used to examine the morphology and structure of the hippocampal neurons. Determine serum PCSK9 and lipid metabolism indicators in each group of rats. Use qRT-PCR to detect the expression levels of interleukin (IL)-1β, IL-6, and tumor necrosis factor-alpha (TNF-α) in the hippocampal tissues of each group of rats. Western blot was used to detect the expression of PCSK9 and low-density lipoprotein receptor (LDLR) in the hippocampal tissues of rats. In addition, a 4D label-free quantitative proteomics approach was used to analyse protein expression in rat hippocampal tissues. The expression of selected proteins in hippocampal tissues was verified by parallel reaction monitoring (PRM) and immunohistochemistry (IHC).

Results

The results showed that the PCSK9 inhibitor alleviated cognitive dysfunction in T2DM rats. PCSK9 inhibitors can reduce PCSK9, total cholesterol (TC), and low-density lipoprotein (LDL) levels in the serum of T2DM rats. Meanwhile, it was found that PCSK9 inhibitors can reduce the expression of PCSK9, IL-1β, IL-6, and TNF-α in the hippocampal tissues of T2DM rats, while increasing the expression of LDLR. Thirteen potential target proteins for the action of PCSK9 inhibitors on DACD rats were identified. PRM and IHC revealed that PCSK9 inhibitors effectively counteracted the downregulation of transthyretin in DACD rats.

Conclusion

This study uncovered the target proteins and specific mechanisms of PCSK9 inhibitors in DACD, providing an experimental basis for the clinical application of PCSK9 inhibitors for the potential treatment of DACD.

Effects of electroacupuncture on rats with cognitive impairment: An iTRAQ-based proteomics analysis

OBJECTIVE

The study explores the effects of electroacupuncture (EA) at the governing vessel (GV) on proteomic changes in the hippocampus of rats with cognitive impairment.

METHODS

Healthy male rats were randomly divided into 3 groups: sham, model and EA. Cognitive impairment was induced by left middle cerebral artery occlusion in the model and EA groups. Rats in the EA group were treated with EA at Shenting (GV24) and Baihui (GV20) for 7 d. Neurological deficit was scored using the Longa scale, the learning and memory ability was detected using the Morris water maze (MWM) test, and the proteomic profiling in the hippocampus was analyzed using protein-labeling technology based on the isobaric tag for relative and absolute quantitation (iTRAQ). The Western blot (WB) analysis was used to detect the proteins and validate the results of iTRAQ.

RESULTS

Compared with the model group, the neurological deficit score was significantly reduced, and the escape latency in the MWM test was significantly shortened, while the number of platform crossings increased in the EA group. A total of 2872 proteins were identified by iTRAQ. Differentially expressed proteins (DEPs) were identified between different groups: 92 proteins were upregulated and 103 were downregulated in the model group compared with the sham group, while 142 proteins were upregulated and 126 were downregulated in the EA group compared with the model group. Most of the DEPs were involved in oxidative phosphorylation, glycolipid metabolism and synaptic transmission. Furthermore, we also verified 4 DEPs using WB technology. Although the WB results were not exactly the same as the iTRAQ results, the expression trends of the DEPs were consistent. The upregulation of heat-shock protein β1 (Hspb1) was the highest in the EA group compared to the model group.

CONCLUSION

EA can effect proteomic changes in the hippocampus of rats with cognitive impairment. Hspb1 may be involved in the molecular mechanism by which acupuncture improves cognitive impairment.

Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network

Background: Breast cancer is the second largest cancer in the world, the incidence of breast cancer continues to rise worldwide, and women's health is seriously threatened. Therefore, it is very important to explore the characteristic changes of breast cancer from the gene level, including the screening of differentially expressed genes and the identification of diagnostic markers. Methods: The gene expression profiles of breast cancer were obtained from the TCGA database. The edgeR R software package was used to screen the differentially expressed genes between breast cancer patients and normal samples. The function and pathway enrichment analysis of these genes revealed significant enrichment of functions and pathways. Next, download these pathways from KEGG website, extract the gene interaction relations, construct the KEGG pathway gene interaction network. The potential diagnostic markers of breast cancer were obtained by combining the differentially expressed genes with the key genes in the network. Finally, these markers were used to construct the diagnostic prediction model of breast cancer, and the predictive ability of the model and the diagnostic ability of the markers were verified by internal and external data. Results: 1060 differentially expressed genes were identified between breast cancer patients and normal controls. Enrichment analysis revealed 28 significantly enriched pathways (p < 0.05). They were downloaded from KEGG website, and the gene interaction relations were extracted to construct the gene interaction network of KEGG pathway, which contained 1277 nodes and 7345 edges. The key nodes with a degree greater than 30 were extracted from the network, containing 154 genes. These 154 key genes shared 23 genes with differentially expressed genes, which serve as potential diagnostic markers for breast cancer. The 23 genes were used as features to construct the SVM classification model, and the model had good predictive ability in both the training dataset and the validation dataset (AUC = 0.960 and 0.907, respectively). Conclusion: This study showed that the difference of gene expression level is important for the diagnosis of breast cancer, and identified 23 breast cancer diagnostic markers, which provides valuable information for clinical diagnosis and basic treatment experiments.

A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes

Objective

To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model.

Methods

The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model.

Results

339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage.

Conclusion

The prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.