Mechanism of Xing 9 ling tablet candy for alcoholic liver disease based on network pharmacology

Xing 9 Ling tablet candy (X9LTC) effectively treats alcoholic liver disease (ALD), but its potential mechanism and molecular targets remain unstudied. We aimed to address this gap using network pharmacology. Furthermore, high-performance liquid chromatography (HPLC) and database analysis revealed a total of 35 active ingredients and 311 corresponding potential targets of X9LTC. Protein interaction analysis revealed PTGS2, JUN, and FOS as its core targets. Enrichment analysis indicated that chemical carcinogenesis-receptor activation, IL-17 and TNF signaling pathway were enriched by multiple core targets, which might be the main pathway of action. Further molecular docking validation showed that the core targets had good binding activities with the identified compounds. Animal experiments showed that X9LTC could reduce the high expression of ALT, AST and TG in the serum of ALD mice, alleviate the lesions in liver tissues, and reverse the high expression of PTGS2, JUN, and FOS proteins in the liver tissues. In this study, we established a method for the determination of X9LTC content for the first time, and predicted its active ingredient and mechanism of action in treating ALD, providing theoretical basis for further research.

Transcriptome and Animal Model Integration Reveals Inhibition of Calcium Homeostasis-Associated Gene ITPKB Alleviates Amyloid Plaque Deposition

Alzheimer's disease (AD) is a severe neurological illness that causes memory loss and is a global problem. The calcium hypothesis recently steadily evolved in AD. The prospective targets for calcium homeostasis therapy, however, are limited, and gene expression-level research connected to calcium homeostasis in AD remains hazy. In this study, we analyzed the microarray dataset (GSE132903) taken from the Gene Expression Omnibus (GEO) database to investigate calcium homeostasis-related genes for AD. Using immunoblot analysis, we examined the association of ITPKB with inflammation in AD. Additionally, the immunofluorescence technique was employed to assess the impact of pharmacological inhibition of ITPKB on the amyloid-β (Aβ) plaque deposition in APP/PS1 mice. This article's further exploration of calcium homeostasis-related genes has propelled the validation of the calcium homeostasis theory in AD.

Mechanism of Astragalus mongholicus Bunge ameliorating cerebral ischemia-reperfusion injury: Based on network pharmacology analysis and experimental verification

ETHNOPHARMACOLOGICAL RELEVANCE

Astragalus mongholicus Bunge (AMB) is a herb with wide application in traditional Chinese medicine, exerting a wealth of pharmacological effects. AMB has been proven to have an evident therapeutic effect on ischemic cerebrovascular diseases, including cerebral ischemia-reperfusion injury (CIRI). However, the specific mechanism underlying AMB in CIRI remains unclear.

AIM OF THE STUDY

This study aimed to investigate the potential role of AMB in CIRI through a comprehensive approach of network pharmacology and in vivo experimental research.

METHODS

The intersection genes of drugs and diseases were obtained through analysis of the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and Gene Expression Omnibus (GEO) database. The protein-protein interaction (PPI) network was created through the string website. Meanwhile, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was carried out using R studio, and thereafter the key genes were screened. Then, the molecular docking prediction was made between the main active ingredients and target genes, and hub genes with high binding energy were obtained. In addition, molecular dynamic (MD) simulation was used to validate the result of molecular docking. Based on the results of network pharmacology, we used animal experiments to verify the predicted hub genes. First, the rat middle cerebral artery occlusion and reperfusion (MACO/R) model was established and the effective dose of AMB in CIRI was determined by behavioral detection and 2,3,5-Triphenyltetrazolium chloride (TTC) staining. Then the target proteins corresponding to the hub genes were measured by Western blot. Moreover, the level of neuronal death was measured using hematoxylin and eosin (HE) and Nissl staining.

RESULTS

Based on the analysis of the TCMSP database and GEO database, a total of 62 intersection target genes of diseases and drugs were obtained. The KEGG enrichment analysis showed that the therapeutic effect of AMB on CIRI might be realized through the advanced glycation endproduct-the receptor of advanced glycation endproduct (AGE-RAGE) signaling pathway in diabetic complications, nuclear factor kappa-B (NF-κB) signaling pathway and other pathways. Molecular docking results showed that the active ingredients of AMB had good binding potential with hub genes that included Prkcb, Ikbkb, Gsk3b, Fos and Rela. Animal experiments showed that AWE (60 g/kg) could alleviate CIRI by regulating the phosphorylation of PKCβ, IKKβ, GSK3β, c-Fos and NF-κB p65 proteins.

CONCLUSION

AMB exerts multi-target and multi-pathway effects against CIRI, and the underlying mechanism may be related to anti-apoptosis, anti-inflammation, anti-oxidative stress and inhibiting calcium overload.

Necroptosis pathway emerged as potential diagnosis markers in spinal cord injury

The present research focused on identifying necroptosis-related differentially expressed genes (NRDEGs) in spinal cord injury (SCI) to highlight potential therapeutic and prognostic target genes in clinical SCI. Three SCI-related datasets were downloaded, including GSE151371, GSE5296 and GSE47681. MSigDB and KEGG datasets were searched for necroptosis-related genes (NRGs). Differentially expressed genes (DEGs) and NRGs were intersected to obtain NRDEGs. The MCC algorithm was employed to select the first 10 genes as hub genes. A protein-protein interaction (PPI) network related to NRDEGs was developed utilizing STRING. Several databases were searched to predict interactions between hub genes and miRNAs, transcription factors, potential drugs, and small molecules. Immunoassays were performed to identify DEGs using CIBERSORTx. Additionally, qRT-PCR was carried out to verify NRDEGs in an animal model of SCI. Combined analysis of all datasets identified 15 co-expressed DEGs and NRGs. GO and KEGG pathway analyses highlighted DEGs mostly belonged to pathways associated with necroptosis and apoptosis. Hub gene expression analysis showed high accuracy in SCI diagnosis was associated with the expression of CHMP7 and FADD. A total of two hub genes, i.e. CHMP7, FADD, were considered potential targets for SCI therapy.

Comprehensive analysis of DNA methylation gene expression profiles in GEO dataset reveals biomarkers related to malignant transformation of sinonasal inverted papilloma

BACKGROUND

DNA methylation may be involved in the regulation of malignant transformation from sinonasal inverted papilloma (SNIP) to squamous cell carcinoma (SCC). The study of gene methylation changes and screening of differentially methylated loci (DMLs) are helpful to predict the possible key genes in the malignant transformation of SNIP-SCC.

MATERIALS AND METHODS

Microarray dataset GSE125399 was downloaded from the Gene Expression Omnibus (GEO) database and differentially methylated loci (DMLs) were analyzed using R language (Limma package). ClusterProfiler R package was used to perform Gene Ontology (GO) analysis on up-methylated genes and draw bubble maps. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and its visualization analysis were analyzed to speculate the possible key Genes in SNIP-SCC malignant transformation. Subsequently, SNIP cases archived in our department were collected, tissue microarray was made, and immunohistochemical staining was performed to analyze the expression levels of UCKL1, GSTT1, HLA-G, MAML2 and NRGN in different grades of sinonasal papilloma tissues.

RESULTS

Analysis of dataset GSE125399 identified 56 DMLs, including 49 upregulated DMLs and 7 downregulated DMLs. Thirty-one genes containing upregulated DNA methylation loci and three genes containing downregulated DNA methylation loci were obtained by methylation microarray annotation analysis. In addition, KEGG pathway visualization analysis of 31 up-methylated genes showed that there were four significantly up-methylated genes including UCKL1, GSTT1, HLA-G and MAML2, and one significantly down-methylated gene NRGN. Subsequently, compared with non-neoplasia nasal epithelial tissues, the expression of HLA-G and NRGN was upregulated in grade I, II, III and IV tissues, while the expression of MAML2 was lost. The protein expression changes of MAML2 and NRGN were significantly negatively correlated with their gene methylation levels.

CONCLUSIONS

By analyzing the methylation dataset, we obtained four up-regulated methylation genes UCKL1, GSTT1, HLA-G and MAML2 and one down-regulated gene NRGN. MAML2, a tumor suppressor gene with high methylation modification but loss of protein expression, and NRGN, a tumor gene with low methylation modification but upregulated protein expression, can be used as biological indicators to judge the malignant transformation of SNIP-SCC.

Analysis of Key Genes and miRNA-mRNA Networks Associated with Glucocorticoids Treatment in Chronic Obstructive Pulmonary Disease

Background

Some patients with chronic obstructive pulmonary disease (COPD) benefit from glucocorticoid (GC) treatment, but its mechanism is unclear.

Objective

With the help of the Gene Expression Omnibus (GEO) database, the key genes and miRNA-mRNA related to the treatment of COPD by GCs were discussed, and the potential mechanism was explained.

Methods

The miRNA microarray dataset (GSE76774) and mRNA microarray dataset (GSE36221) were downloaded, and differential expression analysis were performed. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the differentially expressed genes (DEGs). The protein interaction network of the DEGs in the regulatory network was constructed with the STRING database, and the key genes were screened through Cytoscape. Potential downstream target genes regulated by differentially expressed miRNAs (DEMs) were predicted by the miRWalk3.0 database, and miRNA-mRNA regulatory networks were constructed. Finally, some research results were validated.

Results

① Four DEMs and 83 DEGs were screened; ② GO and KEGG enrichment analysis mainly focused on the PI3K/Akt signalling pathway, ECM receptor interaction, etc.; ③ CD2, SLAMF7, etc. may be the key targets of GC in the treatment of COPD; ④ 18 intersection genes were predicted by the mirwalk 3.0 database, and 9 pairs of miRNA-mRNA regulatory networks were identified; ⑤ The expression of miR-320d-2 and TFCP2L1 were upregulated by dexamethasone in the COPD cell model, while the expression of miR-181a-2-3p and SLAMF7 were downregulated.

Conclusion

In COPD, GC may mediate the expression of the PI3K/Akt signalling pathway through miR-181a-2-3p, miR-320d-2, miR-650, and miR-155-5p, targeting its downstream signal factors. The research results provide new ideas for RNA therapy strategies of COPD, and also lay a foundation for further research.

Diagnostic potential of NRG1 in benign nerve sheath tumors and its influence on the PI3K-Akt signaling and tumor immunity

OBJECTIVE

Benign nerve sheath tumors (BNSTs) present diagnostic challenges due to their heterogeneous nature. This study aimed to determine the significance of NRG1 as a novel diagnostic biomarker in BNST, emphasizing its involvement in the PI3K-Akt pathway and tumor immune regulation.

METHODS

Differential genes related to BNST were identified from the GEO database. Gene co-expression networks, protein-protein interaction networks, and LASSO regression were utilized to pinpoint key genes. The CIBERSORT algorithm assessed immune cell infiltration differences, and functional enrichment analyses explored BNST signaling pathways. Clinical samples helped establish PDX models, and in vitro cell lines to validate NRG1's role via the PI3K-Akt pathway.

RESULTS

Nine hundred eighty-two genes were upregulated, and 375 downregulated in BNST samples. WGCNA revealed the brown module with the most significant difference. Top hub genes included NRG1, which was also determined as a pivotal gene in disease characterization. Immune infiltration showed significant variances in neutrophils and M2 macrophages, with NRG1 playing a central role. Functional analyses confirmed NRG1's involvement in key pathways. Validation experiments using PDX models and cell lines further solidified NRG1's role in BNST.

CONCLUSION

NRG1 emerges as a potential diagnostic biomarker for BNST, influencing the PI3K-Akt pathway, and shaping the tumor immune microenvironment.

Identification and Analysis of Gene Biomarkers for Ovarian Cancer

Objective: To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. Methods: As the study cohort, two gene expression datasets of human OC (GSE27651 and GSE26712, taken as the metadata) taken from the Gene Expression Omnibus (GEO) database were combined, comprising 228 OC and 16 control samples. Analysis was performed to identify the differentially expressed genes between the OC and control samples, while support vector machine analysis using the recursive feature elimination algorithm and least absolute shrinkage and selection operator regression were performed to identify candidate biomarkers that could discriminate OC. In addition, immunohistochemistry staining was performed to verify the diagnostic value and protein expression levels of the candidate biomarkers. The GSE146553 dataset (OC n = 40, control n = 3) was used to further validate the diagnostic values of those biomarkers. Further, the proportions of various immune cells infiltration in the OC and control samples were evaluated using the CIBERSORT algorithm. Results: CLEC4M, PFKP, and SCRIB were identified as potential diagnostic markers for OC in both the metadata (area under the receiver operating characteristic curve [AUC] = 0.996, AUC = 1.000, AUC = 1.000) and GSE146553 dataset (AUC = 0.983, AUC = 0.975, AUC = 0.892). Regarding immune cell infiltration, there was an increase in the infiltration of follicular helper dendritic cells, and a decrease in the infiltration of M2 macrophages and neutrophils, as well as activated natural killer (NK) cells and T cells in OC. CLEC4M showed a significantly positive correlation with neutrophils (r = 0.57, p < 0.001) and resting NK cells (r = 0.42, p = 0.0047), but a negative correlation with activated dendritic cells (r = -0.33, p = 0.032). PFKP displayed a significantly positive correlation with activated NK cells (r = 0.36, p = 0.016) and follicular helper T cells (r = 0.32, p = 0.035), but a negative correlation with the naive B cells (r = -0.3, p = 0.049) and resting NK cells (r = -0.41, p = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells (r = 0.39, p = 0.01), memory B cells (r = 0.34, p = 0.025), and follicular helper T cells (r = 0.31, p = 0.04), but a negative correlation with neutrophils (r = -0.46, p = 0.002) and naive B cells (r = -0.48, p = 0.0012). Conclusion: CLEC4M, PFKP, and SCRIB were identified and verified as potential diagnostic biomarkers for OC. This work and identification of the three biomarkers may provide guidance for future studies into the mechanism and treatment of OC.

Identification of key genes and regulatory mechanisms in adult degenerative scoliosis

BACKGROUND

Adult degenerative scoliosis (ADS) is a spinal disorder, but its pathogenesis remain unclear. Therefore, in this study, we utilized data from the GEO database and explored the key genes and regulatory mechanisms involved in ADS.

METHODS

We performed bioinformatics analysis on the GSE209825 dataset of GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify ADS-related gene modules, and we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. We constructed a protein-protein interaction (PPI) network using the STRING database. We validated the specificity of hub genes in ADS using the GSE34095 dataset and plotted ROC curves for the identification of different degenerative spinal diseases based on the hub genes expression RESULTS: We identified 113 differentially expressed lncRNAs. WGCNA identified the MEblack module had the strongest correlation to ADS. GO and KEGG analyses of target genes in lncRNAs revealed their involvement in immune responses, inflammation, cellular processes, and metabolic pathways. Through PPI and ROC analysis, 10 hub genes linked to ADS diseases with certain specificity were found: ELANE, LTF, DEFA1B, SLC2A4, DEFA1, FAXDC2, LCN2, CTSB, FDFT1, and AURKA.

CONCLUSIONS

We identified 10 potential hub genes associated with ADS and constructed a transcription factors (TFs)-lncRNAs-hub genes regulatory network. These findings provide a new direction and research basis for the targeted treatment and mechanism research of ADS.

Network Pharmacology Combined with GEO Analysis of the Mechanism of Qing-Jin-Hua-Tan Decoction in the Treatment of Non-small Cell Lung Cancer

BACKGROUND

Non-small-cell lung cancer (NSCLC) is one of the most prevalent malignancies and poses a significant threat to human health. Qing-Jin-Hua-Tan (QJHT) decoction is a classical herbal remedy that has demonstrated therapeutic effects in various diseases, including NSCLC, and can improve the quality of life of patients with respiratory conditions. However, the mechanism underlying the effect of the QJHT decoction on NSCLC remains unclear and requires further investigation.

METHODS

We collected NSCLC-related gene datasets from the GEO database and performed differential gene analysis, followed by using WGCNA to identify the core set of genes associated with NSCLC development. The TCMSP and HERB databases were searched to identify the active ingredients and drug targets, and the core gene target datasets related to NSCLC were merged to identify the intersecting targets of drugs and diseases for GO and KEGG pathway enrichment analysis. We then constructed a protein-protein interaction (PPI) network map of drug diseases using the MCODE algorithm and identified key genes using topology analysis. The disease-gene matrix underwent immunoinfiltration analysis, and we analyzed the association between intersecting targets and immunoinfiltration.

RESULTS

We obtained the GSE33532 dataset that met the screening criteria, and a total of 2211 differential genes were identified using differential gene analysis. We performed GSEA analysis and WGCNA analysis for a crossover with differential genes, resulting in 891 key targets for NSCLC. The drug database was screened to obtain 217 active ingredients and 339 drug targets of QJHT. By constructing a PPI network, the active ingredients of QJHT decoction were intersected with the targets of NSCLC, resulting in 31 intersected genes. Enrichment analysis of the intersection targets showed that 1112 biological processes, 18 molecular functions, and 77 cellular compositions were enriched in GO functions, and 36 signaling pathways were enriched in KEGG pathways. Based on immune-infiltrating cell analysis, we found that the intersection targets were significantly associated with multiple infiltrating immune cells.

CONCLUSION

Our analysis using network pharmacology and mining of the GEO database revealed that QJHT decoction can potentially treat NSCLC through multi-target and multi-signaling pathways, while also regulating multiple immune cells.

Integrating GEO, network pharmacology, and in vitro assays to explore the pharmacological mechanism of Bruceae Fructus against laryngeal cancer

The goal of this study is to look into the pharmacological mechanism of Bruceae Fructus in conjunction with GEO, network pharmacology, and in vitro assays for the treatment of laryngeal cancer to provide theoretical support for its therapeutic use. The active components and matching targets of Bruceae Fructus were retrieved from the TCMSP database, while genes linked with laryngeal cancer were obtained from the GEO, GeneCards, DisGeNET, and DrugBank databases. Besides, the components and targets were supplemented by literatures in PubMed database. Cytoscape software was used to create the active ingredients-target network diagram. The String database was used to build the PPI network. Following that, the core targets were subjected to GO enrichment and KEGG pathway analysis using the DAVID database. Finally, AutoDock was used to perform molecular docking between the core components and the core targets. To investigate the biological effects of beta-sitosterol, the viability of laryngeal cancer cells was assessed after beta-sitosterol therapy using the MTS technique. Following that, how beta-sitosterol affected colony formation after 14 days of culture of treated cells was researched. Flow cytometry was utilized to detect apoptosis to examine the influence of beta-sitosterol on laryngeal cancer cell apoptosis, and then detected mRNA and protein expression levels of 10 key genes by RT-qPCR and Western Blot assay. There were 1258 laryngeal cancer-related genes and 15 Bruceae Fructus components, with beta-sitosterol and luteolin serving as key components. Bruceae Fructus' primary targets against laryngeal cancer were IL6, JUN, TNF, IL2, IL4, IFNG, RELA, TP53, CDKN1A, and AKT1. GO enrichment yielded 41 CC, 78 MF, and 383 BP. Platinum drug resistance, the PI3K-Akt signaling pathway, the p53 signaling pathway, apoptosis, the HIF-1 signaling pathway, and 147 additional pathways have been added to KEGG. The results of molecular docking revealed that the core components had a high affinity for the core target. The results of the cell experiment indicate that beta-sitosterol suppressed Hep-2 cell activity in a concentration-dependent manner. Besides, beta-sitosterol has powerful antiproliferative properties in Hep-2 cells. Flow cytometry results showed that beta-sitosterol promoted laryngeal cancer cell apoptosis in a concentration-dependent manner. The results of RT-qPCR and Western Blot assay showed that the mRNA and protein expression levels of TP53, JUN, TNF-α, CDKN1A, and IL-2 were significantly up-regulated after beta-sitosterol treatment, while the mRNA and protein expression levels of RELA, AKT1, IL-6, IFNG, and IL-4 were significantly down-regulated. This study integrating GEO, network pharmacology, and in vitro assays investigated the probable mechanism of Bruceae Fructus' anti-laryngeal cancer activity, which can give a theoretical foundation for additional future animal experiments.

Ferroptosis-related genes as diagnostic markers for major depressive disorder and their correlations with immune infiltration

Background

Major depression disorder (MDD) is a devastating neuropsychiatric disease, and one of the leading causes of suicide. Ferroptosis, an iron-dependent form of regulated cell death, plays a pivotal role in numerous diseases. The study aimed to construct and validate a gene signature for diagnosing MDD based on ferroptosis-related genes (FRGs) and further explore the biological functions of these genes in MDD.

Methods

The datasets were downloaded from the Gene Expression Omnibus (GEO) database and FRGs were obtained from the FerrDb database and other literatures. Least absolute shrinkage and selection operator (LASSO) regression and stepwise logistic regression were performed to develop a gene signature. Receiver operating characteristic (ROC) curves were utilized to assess the diagnostic power of the signature. Gene ontology (GO) enrichment analysis was used to explore the biological roles of these diagnostic genes, and single sample gene set enrichment analysis (ssGSEA) algorithm was used to evaluate immune infiltration in MDD. Animal model of depression was constructed to validate the expression of the key genes.

Results

Eleven differentially expressed FRGs were identified in MDD patients compared with healthy controls. A signature of three FRGs (ALOX15B, RPLP0, and HP) was constructed for diagnosis of MDD. Afterwards, ROC analysis confirmed the signature's discriminative capacity (AUC = 0.783, 95% CI = 0.719-0.848). GO enrichment analysis revealed that the differentially expressed genes (DEGs) related to these three FRGs were mainly involved in immune response. Furthermore, spearman correlation analysis demonstrated that these three FRGs were associated with infiltrating immune cells. ALOX15B and HP were significantly upregulated and RPLP0 was significantly downregulated in peripheral blood of the lipopolysaccharide (LPS)-induced depressive model.

Conclusion

Our results suggest that the novel FRG signature had a good diagnostic performance for MDD, and these three FRGs correlated with immune infiltration in MDD.

Integrative bioinformatics analysis and experimental validation of key biomarkers for risk stratification in primary biliary cholangitis

BACKGROUND

Primary biliary cholangitis (PBC) is an autoimmune liver disease, whose etiology is yet to be fully elucidated. Currently, ursodeoxycholic acid (UDCA) is the only first-line drug. However, 40% of PBC patients respond poorly to it and carry a potential risk of disease progression. So, in this study, we aimed to explore new biomarkers for risk stratification in PBC patients to enhance treatment.

METHODS

We first downloaded the clinical characteristics and microarray datasets of PBC patients from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Hub genes were further validated in multiple public datasets and PBC mouse model. Furthermore, we also verified the expression of the hub genes and developed a predictive model in our clinical specimens.

RESULTS

A total of 166 DEGs were identified in the GSE79850 dataset, including 95 upregulated and 71 downregulated genes. Enrichment analysis indicated that DEGs were significantly enriched in inflammatory or immune-related process. Among these DEGs, 15 risk-related genes were recognized and further validated in the GSE119600 cohort. Then, TXNIP, CD44, ENTPD1, and PDGFRB were identified as candidate hub genes. Finally, we proceeded to the next screening with these four genes in our serum samples and developed a three-gene panel. The gene panel could effectively identify those patients at risk of disease progression, yielding an AUC of 0.777 (95% CI, 0.657-0.870).

CONCLUSIONS

In summary, combining bioinformatics analysis and experiment validation, we identified TXNIP, CD44, and ENTPD1 as promising biomarkers for risk stratification in PBC patients.

Identification of the shared mechanisms and common biomarkers between Sjögren's syndrome and atherosclerosis using integrated bioinformatics analysis

Background

Sjögren's syndrome (SS) is a chronic autoimmune disease characterized by exocrine and extra-glandular symptoms. The literature indicates that SS is an independent risk factor for atherosclerosis (AS); however, its pathophysiological mechanism remains undetermined. This investigation aimed to elucidate the crosstalk genes and pathways influencing the pathophysiology of SS and AS via bioinformatic analysis of microarray data.

Methods

Microarray datasets of SS (GSE40611) and AS (GSE28829) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were acquired using R software's "limma" packages, and the functions of common DEGs were determined using Gene Ontology and Kyoto Encyclopedia analyses. The protein-protein interaction (PPI) was established using the STRING database. The hub genes were assessed via cytoHubba plug-in and validated by external validation datasets (GSE84844 for SS; GSE43292 for AS). Gene set enrichment analysis (GSEA) and immune infiltration of hub genes were also conducted.

Results

Eight 8 hub genes were identified using the intersection of four topological algorithms in the PPI network. Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7. Furthermore, the immune infiltration analysis revealed that lymphocytes and macrophages are essentially linked with the pathogenesis of AS and SS. Moreover, the shared genes were enriched in multiple metabolisms and autoimmune disease-related pathways, as evidenced by GSEA analyses.

Conclusion

This is the first study to explore the common mechanism between SS and AS. Four key genes, including CTSS, CYBB, IRF8, and PTPRC, were associated with the pathogenesis of SS and AS. These hub genes and their correlation with immune cells could be a potential diagnostic and therapeutic target.

Integrated bioinformatics analysis elucidates granulosa cell whole-transcriptome landscape of PCOS in China

BACKGROUND

Polycystic ovary syndrome (PCOS) is a common reproductive, neuroendocrine, and metabolic disorder in women of reproductive age that affects up to 5-10% of women of reproductive age. The aetiology of follicle development arrest and critical issues regarding the abnormal follicular development in PCOS remain unclear. The present study aims to systematically evaluate granulosa cell whole-transcriptome sequencing data to gain more insights into the transcriptomic landscape and molecular mechanism of PCOS in China.

METHODS

In the present study, the microarray datasets GSE138518, GSE168404, GSE193123, GSE138572, GSE95728, and GSE145296 were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, differential expression analysis was performed on the PCOS and control groups, followed by functional interaction prediction analysis to investigate gene-regulatory circuits in PCOS. Finally, hub genes and their associated ncRNAs were validated by qPCR in human-luteinized granulosa (hGL) cells and were correlated with the clinical characteristics of the patients.

RESULTS

A total of 200 differentially expressed mRNAs, 3 differentially expressed miRNAs, 52 differentially expressed lncRNAs, and 66 differentially expressed circRNAs were found in PCOS samples compared with controls. GO and KEGG enrichment analyses indicated that the DEGs were mostly enriched in phospholipid metabolic processes, steroid biosynthesis and inflammation related pathways. In addition, the upregulated miRNA hsa-miR-205-5p was significantly enriched in the ceRNA network, and two hub genes, MVD and PNPLA3, were regulated by hsa-miR-205-5p, which means that hsa-miR-205-5p may play a fundamental role in the pathogenesis of PCOS. We also found that MVD and PNPLA3 were related to metabolic processes and ovarian steroidogenesis, which may be the cause of the follicle development arrest in PCOS patients.

CONCLUSIONS

In summary, we systematically constructed a ceRNA network depicting the interactions between the ncRNAs and the hub genes in PCOS and control subjects and correlated the hub genes with the clinical characteristics of the patients, which provides valuable insights into the granulosa cell whole-transcriptome landscape of PCOS in China.

Bioinformatic Analysis and Machine Learning Methods in Neonatal Sepsis: Identification of Biomarkers and Immune Infiltration

The disease neonatal sepsis (NS) poses a serious threat to life, and its pathogenesis remains unclear. Using the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified and functional enrichment analyses were conducted. Three machine learning algorithms containing the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) were applied to identify the optimal feature genes (OFGs). This study conducted CIBERSORT to present the abundance of immune infiltrates between septic and control neonates and assessed the relationship between OFGs and immune cells. In total, 44 DEGs were discovered between the septic and control newborns. Throughout the enrichment analysis, DEGs were primarily related to inflammatory signaling pathways and immune responses. The OFGs derived from machine learning algorithms were intersected to yield four biomarkers, namely Hexokinase 3 (HK3), Cystatin 7 (CST7), Resistin (RETN), and Glycogenin 1 (GYG1). The potential biomarkers were validated in other datasets and LPS-stimulated HEUVCs. Septic infants showed a higher proportion of neutrophils (p < 0.001), M0 macrophages (p < 0.001), and regulatory T cells (p = 0.004). HK3, CST7, RETN, and GYG1 showed significant correlations with immune cells. Overall, the biomarkers offered promising insights into the molecular mechanisms of immune regulation for the prediction and treatment of NS.

Expression of m6A RNA Methylation Regulators and Their Clinical Predictive Value in Intrahepatic Cholangiocarcinoma

BACKGROUND

N6-methyladenosine (m6A) RNA methylation regulators have been implicated in the carcinogenesis and progress of a variety of cancers. Until now, the effects of them on intrahepatic cholangiocarcinoma (ICC) have been poorly understood.

METHODS

We used the GEO databases to systematically evaluate the expression profiles of 36 m6A RNA methylation regulators in ICC patients and produced a signature to assess its prognostic values. In vitro experiments were implemented to confirm the expression level.

RESULTS

Compared to normal intrahepatic bile duct tissues, more than half of these 36 genes showed different levels of expression in ICC tissues. Two groups emerged from the consensus cluster analysis of these 36 genes. The two cluster of patients had significantly different clinical outcomes. In addition, we created a m6A-related prognostic signature that performed exceptionally well in the prognostic categorization of ICC patients, based on the ROC curves, Kaplan-Meier curves, and univariate and multivariate Cox regression analyses. Further research showed that there was a significant association between the m6A-related signature and the manifestations of tumor immune microenvironment in ICC. The expression level and biological effect of METTL16, one of the two m6A RNA methylation regulators incorporated in the signature, were confirmed and explored by using in vitro experiments.

CONCLUSIONS

This analysis revealed the predictive roles of m6A RNA methylation regulators in ICC.

Identification and validation of autophagy-related genes in Kawasaki disease

BACKGROUND

Kawasaki disease (KD) is a systemic vasculitis of unknown etiology affecting mainly children. Studies have shown that the pathogenesis of KD may be related to autophagy. Using bioinformatics analysis, we assessed the significance of autophagy-related genes (ARGs) in KD.

METHODS

Common ARGs were identified from the GeneCards Database, the Molecular Signatures Database (MSigDB), and the Gene Expression Omnibus (GEO) database. ARGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network analysis. Furthermore, related microRNAs (miRNAs), transcription factors (TFs), and drug interaction network were predicted. The immune cell infiltration of ARGs in tissues was explored. Finally, we used receiver operating characteristic (ROC) curves and quantitative real-time PCR (qRT-PCR) to validate the diagnostic value and expression levels of ARGs in KD.

RESULTS

There were 20 ARGs in total. GO analysis showed that ARGs were mainly rich in autophagy, macro-autophagy, and GTPase activity. KEGG analysis showed that ARGs were mainly rich in autophagy-animal and the collecting duct acid secretion pathway. The expression of WIPI1, WDFY3, ATP6V0E2, RALB, ATP6V1C1, GBA, C9orf72, LRRK2, GNAI3, and PIK3CB is the focus of PPI network. A total of 72 related miRNAs and 130 related TFs were predicted by miRNA and TF targeting network analyses. Ten pairs of gene-drug interaction networks were also predicted; immune infiltration analysis showed that SH3GLB1, ATP6V0E2, PLEKHF1, RALB, KLHL3, and TSPO were closely related to CD8 + T cells and neutrophils. The ROC curve showed that ARGs had good diagnostic value in KD. qRT-PCR showed that WIPI1 and GBA were significantly upregulated.

CONCLUSION

Twenty potential ARGs were identified by bioinformatics analysis, and WIPI1 and GBA may be used as potential drug targets and biomarkers.

Microanatomy of the metabolic associated fatty liver disease (MAFLD) by single-cell transcriptomics

BACKGROUND

Metabolic-associated fatty liver disease (MAFLD) is a major cause of liver disease worldwide and comprises non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH). Due to the high prevalence and poor prognosis of NASH, it is critical to identify and treat patients at risk. However, the aetiology and mechanisms remain largely unknown, warranting further analysis.

METHODS

We first identified differential genes in NASH by single-cell analysis of the GSE129516 dataset and conducted expression profiling data analysis of the GSE184019 dataset from the Gene Expression Omnibus (GEO) database. Then single-cell trajectory reconstruction and analysis, immune gene score, cellular communication, key gene screening, functional enrichment analysis, and immune microenvironment analysis were carried out. Finally, cell experiments were performed to verify the role of key genes in NASH.

RESULTS

We conducted transcriptome profiling of 30,038 single cells, including hepatocytes and non-hepatocytes from normal and steatosis adult mouse livers. Comparative analysis of hepatocytes and non-hepatocytes revealed pronounced heterogeneity as non-hepatocytes acted as major cell-communication hubs. The results showed that Hspa1b, Tfrc, Hmox1 and Map4k4 could effectively distinguish NASH tissues from normal samples. The results of scRNA-seq and qPCR indicated that the expression levels of hub genes in NASH were significantly higher than in normal cells or tissues. Further immune infiltration analysis showed significant differences in M2 macrophage distribution between healthy and metabolic-associated fatty liver samples.

CONCLUSIONS

Our results suggest that Hspa1b, Tfrc, Hmox1 and Map4k4 have huge prospects as diagnostic and prognostic biomarkers for NASH and may be potential therapeutic targets for NASH.

Metformin attenuates multiple myeloma cell proliferation and encourages apoptosis by suppressing METTL3-mediated m6A methylation of THRAP3, RBM25, and USP4

Based on the results of epidemiological and preclinical studies, metformin can improve the prognosis of patients with malignant tumors. Studies have confirmed that metformin inhibits multiple myeloma (MM) cell proliferation and promotes apoptosis. Nevertheless, the specific mechanism remains to be elucidated. MM cells were intervened with different doses of metformin to detect cell proliferation and apoptosis. Western blotting and RT-qPCR were employed to assess the expression of METTL3, METTL14, WTAP, FTO, and ALKBH5 after metformin intervention. The microarray dataset GSE29023 was retrieved from the Gene Expression Omnibus (GEO) database and calculated using the R language (limma package) to authenticate differentially expressed genes (DEGs). The database for annotation, visualization, and integrated discovery (David) was applied for GO annotation analysis of DEGs. Subsequently, the string database and Cytoscape software were applied to construct protein-protein interaction (PPI) and DEM hub gene networks. Bioinformatics analysis and MeRIP were applied to predict and test METTL3-mediated m6A levels on mRNA of THRAP3, RBM25, and USP4 in METTL3 knocked-down cells. Then rescue experiments were performed to explore effects of METTL3 and THRAP3, RBM25, or USP4 on cell proliferation and apoptosis. The effect on MM cell xenograft tumor growth was observed by injection of metformin or/and overexpression of METTL3 in in vivo experiments. Metformin decreased cell proliferation and encouraged cell apoptosis in a dose-dependent manner. Global m6A modification was elevated in MM cells compared to normal cells, which was counteracted by metformin treatment. Furthermore, THRAP3, RBM25, and USP4 were identified as possible candidate genes for metformin treatment by GSE29023 data mining. METTL3 interference impaired m6A modification on mRNA of THRAP3, RBM25, and USP4 as well as expression levels. The mRNA stability and expression of THRAP3, RBM25, and USP4 was decreased after metformin treatment, which was reversed by METTL3 overexpression. THRAP3, RBM25 or USP4 knockdown reversed the assistance of METTL3 overexpression on the malignant behavior of MM cells. Finally, upregulation of METTL3 was shown to exert facilitative effects on xenograft tumor growth by blocking metformin injection. The present study demonstrates that metformin can repress the expression of THRAP3, RBM25, and USP4 by inhibiting METTL3-mediated m6A modification, which in turn hamper cell proliferation and promotes cell apoptosis.Abbreviations: multiple myeloma (MM), Gene Expression Omnibus (GEO), differentially expressed genes (DEGs), database for annotation, visualization and integrated discovery (David), protein-protein interaction (PPI), epithelial‑mesenchymal transition (EMT), methyltransferase like 3 (METTL3), methyltransferase like 14 (METTL14), wilms tumor 1-associated protein (WTAP), methyltransferase like 16 (METTL16), acute myeloid leukemia (AML), non-small lung cancer (NSCLC), glioma stem cells (GSCs), normal bone marrow-derived plasma cells (nPCs), false discovery rate (FDR), biological process (BP), optical density (OD), horseradish peroxidase (HRP), M6A RNA immunoprecipitation assay (MeRIP).

Exploration and Clinical Verification of the Blood Co-Expression Genes of Type 2 Diabetes Mellitus and Mild Cognitive Dysfunction in the Elderly

With the development of society, the incidence of dementia and type 2 diabetes (T2DM) in the elderly has been increasing. Although the correlation between T2DM and mild cognitive impairment (MCI) has been confirmed in the previous literature, the interaction mechanism remains to be clarified. To explore the co-pathogenic genes in the blood of MCI and T2DM patients, clarify the correlation between T2DM and MCI, achieve the purpose of early disease prediction, and provide new ideas for the prevention and treatment of dementia. We downloaded T2DM and MCI microarray data from GEO databases and identified the differentially expressed genes associated with MCI and T2DM. We obtained co-expressed genes by intersecting differentially expressed genes. Then, we performed GO and KEGG enrichment analysis of co-DEGs. Next, we constructed the PPI network and found the hub genes in the network. By constructing the ROC curve of hub genes, the most valuable genes for diagnosis were obtained. Finally, the correlation between MCI and T2DM was clinically verified by means of a current situation investigation, and the hub gene was verified by qRT-PCR. A total of 214 co-DEGs were selected, 28 co-DEGs were up-regulated, and 90 co-DEGs were down-regulated. Functional enrichment analysis showed that co-DEGs were mainly enriched in metabolic diseases and some signaling pathways. The construction of the PPI network identified the hub genes in MCI and T2DM co-expression genes. We identified nine hub genes of co-DEGs, namely LNX2, BIRC6, ANKRD46, IRS1, TGFB1, APOA1, PSEN1, NPY, and ALDH2. Logistic regression analysis and person correlation analysis showed that T2DM was correlated with MCI, and T2DM increased the risk of cognitive impairment. The qRT-PCR results showed that the expressions of LNX2, BIRC6, ANKRD46, TGFB1, PSEN1, and ALDH2 were consistent with the results of bioinformatic analysis. This study screened the co-expressed genes of MCI and T2DM, which may provide new therapeutic targets for the diagnosis and treatment of diseases.

Tumor-suppressive miR-29c binds to MAPK1 inhibiting the ERK/MAPK pathway in pancreatic cancer

INTRODUCTION

GEO- and TCGA-based data analysis suggested the differential expression of miR-29c in pancreatic cancer. However, limited data are available on the downstream mechanistic actions of miR-29c, which may fuel the in vitro and in vivo studies of pancreatic cancer.

METHODS

The downstream target gene of miR-29c and the downstream ERK/MAPK pathway involved in pancreatic cancer were predicted by bioinformatics tools. Next, the expression of miR-29c and MAPK1 was determined in pancreatic cancer tissues and cells. After ectopic expression and depletion experiments in pancreatic cancer cells, oncogenic phenotypes of pancreatic cancer cells were tested by MTS assay, Transwell assay, and flow cytometry. Effects of miR-29c/MAPK1 on tumorigenic ability in vivo were evaluated in pancreatic cancer xenografts in nude mice.

RESULTS

Through differential analysis, five pancreatic cancer-related miRNAs (hsa-miR-29c, hsa-miR-107, hsa-miR-324-3p, hsa-miR-375, and hsa-miR-210) were screened out, among which miR-29c was selected as the key miRNA related to prognosis of pancreatic cancer patients. miR-29c could target and inhibit MAPK1 to suppress the activation of ERK/MAPK pathway. miR-29c was downregulated in pancreatic cancer, and its high expression was related to the good prognosis of pancreatic cancer patients. Both in vitro and in vivo experiments demonstrated that restoration of miR-29c inhibited oncogenic phenotypes of pancreatic cancer cells, as well as repressed tumorigenic ability of pancreatic cancer cells in nude mice.

CONCLUSIONS

Taken together, we unveil a novel miR-29c/MAPK1/ERK/MAPK axis that suppresses pancreatic cancer both in vitro and in vivo.

Feasibility of olfactomedin 4 as a molecular biomarker for early diagnosis of gastric neoplasia after intestinal metaplasia

OBJECTIVES

This study discusses whether olfactomedin 4 (OLFM4) could be used as a sensitive and specific biomarker in the early diagnosis of gastric cancer (GC) after gastric intestinal metaplasia (GIM).

METHODS

An integrative analysis combining data derived from the Gene Expression Omnibus (GEO) and cBioPortal databases was performed to investigate the potential molecular biomarker. Immunohistochemistry and quantitative real-time polymerase chain reactions were used to measure the expression of messenger ribonucleic acid (mRNA) and protein by OLFM4. In combination with the gastroscopic findings and the OLFM4 expression in GIM-GC, a predictive model was established. The receiver operator characteristic curve (ROC) was applied to assess the diagnostic value of the model for GIM-GC.

RESULTS

According to the GEO and cBioPortal databases, OLFM4 was identified as a key gene in the diagnosis of GIM-GC. Higher protein expression of OLFM4 was found in GIM and GIM-GC compared with chronic superficial gastritis (GS) (p < 0.05). The positive expression rate of OLFM4 in paracancerous tissue (GCP) was higher than in GIM (p > 0.05). There was no significant difference between GIM-GC and GCP (p > 0.05). The mRNA expression of OLFM4 was similar to the protein expression, and the positive expression rate was higher in early GIM-GC than in GIM (p < 0.05).

CONCLUSION

Olfactomedin 4 could be used as a biomarker for the early diagnosis of GIM-GC, and the logistic predictive model could be an effective tool for increasing the early diagnostic rate.

FAS-mediated circRNA-miRNA-mRNA Crosstalk Network Regulates Immune Cell Infiltration in Cerebral Infarction

New data are accumulating on the involvement of interaction among circular RNAs (circRNAs), microRNAs (miRNAs/miRs), and messenger RNAs (mRNAs) in cerebral infarction (CI). This study aims to illustrate the GEO database-based identification of a circRNA-miRNA-mRNA crosstalk network underlying immune cell infiltration in CI. The differential analysis suggested that 1696 circRNAs, 1989 miRNAs, and 5550 mRNAs that were differentially expressed in CI samples were retrieved from GEO database. GO and KEGG functional enrichment analyses showed that the differentially expressed mRNAs were mainly associated with common risk factors of CI, such as immune and inflammatory response. Next, the circRNA-miRNA pairs and miRNA-mRNA pairs were predicted, and the circRNA-miRNA-mRNA network was constructed by Cytoscape software. Totally, 436 circRNA-miRNA pairs were obtained through the online database, and 2033 miRNA-mRNA pairs were used to construct the circRNA-miRNA-mRNA crosstalk network. A protein-protein interaction (PPI) network was constructed on the basis of the ceRNA network, followed by key gene identification in the GSE9877 dataset. FAS was identified as the key gene in CI. The constructed FAS-mediated circRNA-miRNA-mRNA crosstalk network included five upregulated circRNAs (hsa_circ_0075341, hsa_circ_0049637, hsa_circ_0001085, hsa_circ_0004808 and hsa_circ_0092337) and five downregulated miRNAs (hsa-miR-92a-2-5p, hsa-miR-1245b-3p, hsa-miR-592, hsa-miR-224-5p, and hsa-miR-30e-3p). Furthermore, the CIBERSORT algorithm indicated that FAS was associated with immune cell infiltration in CI. In conclusion, this study revealed a role for FAS-centered circRNA-miRNA-mRNA crosstalk network in regulating immune cell infiltration of CI, which may be a viable target for CI prevention.

Western blot normalization: Time to choose a proper loading control seriously

Recent research has questioned the validity of housekeeping proteins in Western blot. Our present study proposed new ideas for Western blot normalization that improved the reproducibility of scientific research. We used the Gene Expression Omnibus (GEO) database and the web tool GEO2R to exclude unstable housekeeping genes quickly. In ischemic heart tissues, actin and tubulin changed significantly, whereas no statistically significant changes were observed in the expression of genes relative to glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Besides, the reliability of GAPDH was further examined by Western blot. Additionally, unstable housekeeping genes were found in other animal models of cardiovascular medicine. We also found that sodium dodecyl sulfate and temperature significantly impacted the results of Ponceau S staining. Membranes stained with Ponceau S after immunodetection could avoid this interference, and the coefficients of variation for post-immunodetection staining are lower than those produced by GAPDH immunodetection. Overall, we described a new use of differential gene expression analysis and proposed a modified Ponceau S staining method, which provided researchers with a proper loading control for Western blot and hence could improve reproducibility in research.

The Potential of ANK1 to Predict Parkinson's Disease

The main cause of Parkinson's disease (PD) remains unknown and the pathologic changes in the brain limit rapid diagnosis. Herein, differentially expressed genes (DEGs) in the Gene Expression Omnibus (GEO) database (GSE8397 and GSE22491) were assessed using linear models for microarray analysis (limma). Ankyrin 1 (ANK1) was the only common gene differentially down-regulated in lateral substantia nigra (LSN), medial substantia nigra (MSN) and blood. Additionally, DEGs between high ANK1 and low ANK1 in GSE99039 were picked out and then uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID) for gene ontology (GO) functional annotation analysis. GO analysis displayed that these DEGs were mainly enriched in oxygen transport, myeloid cell development and gas transport (biological process (BP)); hemoglobin complex, haptoglobin-hemoglobin complex and cortical cytoskeleton (cellular component (CC)); and oxygen transporter activity, haptoglobin binding and oxygen binding (molecular function (MF)). Receiver operating characteristic (ROC) curve analysis showed ANK1 had good diagnostic accuracy and increased the area under the curve (AUC) value when combined with other biomarkers. Consistently, intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropy-ridi-ne (MPTP) in C57BL/6J mice reduced ANK1 mRNA expression in both substantia nigra and blood compared to the control group. Thus, ANK1 may serve as a candidate biomarker for PD diagnosis.

Synapsin 1 Ameliorates Cognitive Impairment and Neuroinflammation in Rats with Alzheimer's Disease: An Experimental and Bioinformatics Study

BACKGROUND

Alzheimer's disease (AD) is a persistent neuropathological injury that manifests via neuronal/synaptic death, age spot development, tau hyperphosphorylation, neuroinflammation, and apoptosis. Synapsin 1 (SYN1), a neuronal phosphoprotein, is believed to be responsible for the pathology of AD.

OBJECTIVE

This study aimed to elucidate the exact role of SYN1 in ameliorating AD and its potential regulatory mechanisms.

METHODS

The AD dataset GSE48350 was downloaded from the GEO database, and SYN1 was focused on differential expression analysis and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. After establishing an AD rat model, they were treated with RNAi lentivirus to trigger SYN1 overexpression. The amelioration of SYN1 in AD-associated behavior was validated using multiple experiments (water maze test and object recognition test). SYN1's repairing effect on the important factors in AD was confirmed by detecting the concentration of inflammatory factors (interleukin (IL)-6, IL-1β, tumor necrosis factor (TNF)-α), neurotransmitters (acetylcholine (ACh), dopamine (DA), and 5-hydroxytryptophan (5-HT)) and markers of oxidative stress (glutathione (GSH), malondialdehyde (MDA), reactive oxygen species (ROS)). Molecular biology experiments (qRT-PCR and western blot) were performed to examine AD-related signaling pathways after SYN1 overexpression.

RESULTS

Differential expression analysis yielded a total of 545 differentially expressed genes, of which four were upregulated and 541 were downregulated. The enriched pathways were basically focused on synaptic functions, and the analysis of the protein- protein interaction network focused on the key genes in SYN1. SYN1 significantly improved the spatial learning and memory abilities of AD rats. This enhancement was reflected in the reduced escape latency of the rats in the water maze, the significantly extended dwell time in the third quadrant, and the increased number of crossings. Furthermore, the results of the object recognition test revealed reduced time for rats to explore familiar and new objects. After SYN1 overexpression, the cAMP signaling pathway was activated, the phosphorylation levels of the CREB and PKA proteins were elevated, and the secretion of neurotransmitters such as ACh, DA, and 5-HT was promoted. Furthermore, oxidative stress was suppressed, as supported by decreased levels of MDA and ROS. Regarding inflammatory factors, the levels of IL-6, IL-1β, and TNF-α were significantly reduced in AD rats with SYN1 overexpression.

CONCLUSION

SYN1 overexpression improves cognitive function and promotes the release of various neurotransmitters in AD rats by inhibiting oxidative stress and inflammatory responses through cAMP signaling pathway activation. These findings may provide a theoretical basis for the targeted diagnosis and treatment of AD.

Multi-omics analysis reveals focal adhesion characteristic associated tumor immune microenvironment in colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most frequent malignant lesions of the digestive system in humans, with an insidious onset. At the time of diagnosis, most of them have developed to the middle and late stages, and cancer cells have metastasized, and the prognosis is poor. Treatment options for progressive COAD are limited, and despite the promise of immunotherapy, immunotherapy response rates are low. The assembly and disaggregation of focal adhesion are critical for the directional migration of tumor cells to different sites, and it is unclear whether focal adhesion-related genes are involved in the development and prognosis of colon adenocarcinoma. This study aimed to investigate the role of focal adhesion genes in the occurrence and prognosis of COAD. We obtained datasets of COAD patients, including RNA-sequencing data and clinical information, from the TCGA and GEO databases (GSE17538 and GSE39582). Through CNMF clustering, two molecular subtypes with different expression patterns of focal adhesion genes were identified, and it was found that the molecular subtype with low expression of focal adhesion genes had better prognosis. Then the prediction signature was constructed by LASSO-Cox regression model, and the receiver operating characteristic (ROC) curve showed that the 4-gene signature had a good prediction effect on COAD 1-, 2-, and 3-year OS. Gene function enrichment analysis showed that the high-risk group was mainly enriched in immune and adhesion-related signaling pathways, suggesting that focal adhesion genes may affect the development and prognosis of COAD by regulating the immune microenvironment and tumor metastasis. The interaction between focal adhesion genes and immunity during the occurrence of COAD may help improve the response rate of immunotherapy, which also provides new ideas for the molecular mechanism and targeted therapy in COAD.

Integrated bioinformatics-based identification of potential diagnostic biomarkers associated with atopic dermatitis

Introduction

In-depth analysis of the rambling genes of atopic dermatitis may help to identify the pathologic mechanism of this disease. However, this has seldom been performed.

Aim

Using bioinformatics approaches, we analysed 3 gene expression profiles in the gene expression omnibus (GEO) database, identified the differentially expressed genes (DEGs), and found out the overlapping DEGs (common DEGs, cDEGs) in the above 3 profiles.

Material and methods

We identified 91 upregulated cDEGs, which were then arranged into a protein-protein interaction (PPI) network, and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) term enrichment analyses were performed to explore the functional roles of these genes.

Results

GO analyses revealed these DEGs to be significantly enriched in biological processes including immune system process, immune response, defence response, leukocyte activation, and response to the biotic stimulus. These DEGs were also enriched in the KEGG pathway, including influenza A, amoebiasis, primary immunodeficiency, cytokine-cytokine receptor interaction, and IL-17 signalling pathway. PPI analysis showed that 9 genes (PTPRC-CTLA4-CD274-CD1C-IL7R-GZMB-CCL5-CD83, and CCL22) were probably the novel hub genes of atopic dermatitis.

Conclusions

Together, the findings of these bioinformatics analyses thus identified key hub genes associated with AD development.

Upregulation of key genes Eln and Tgfb3 were associated with the severity of cardiac hypertrophy

Background

Hypertension-induced cardiac hypertrophy is one of the most common pre-conditions that accompanies heart failure. This study aimed to identify the key pathogenic genes in the disease process.

Methods

GSE18224 was re-analyzed and differentially expressed genes (DEGs) were obtained. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out. Networks of transcription factor (TF)-mRNA, microRNA (miRNA)-mRNA and Protein-Protein interaction (PPI) were constructed, and a key module was further screened out from PPI network. GSE36074 dataset and our transverse aortic constriction (TAC) mouse model were used to validate gene expression in the module. Finally, the correlation between the genes and biomarkers of cardiac hypertrophy were evaluated.

Results

Totally, there were 348 DEGs in GSE18224, which were mainly enriched in biological processes including collagen fibril organization, cellular response to transforming growth factor-beta stimulus and were involved in ECM-receptor interaction and Oxytocin signaling pathway. There were 387 miRNAs targeted by 257 DEGs, while 177 TFs targeted 71 DEGs. The PPI network contained 222 nodes and 770 edges, with 18 genes screened out into the module. After validation, 8 genes, which were also significantly upregulated in the GSE36074 dataset, were selected from the 18 DEGs. 2 of the 8 DEGs, including Eln and Tgfb3 were significantly upregulated in our mouse model of myocardial hypertrophy. Finally, the expression of Eln and Tgfb3 were found to be positively correlated with the level of the disease biomarkers.

Conclusions

Upregulated key genes Eln and Tgfb3 were positively correlated with the severity of cardiac hypertrophy, which may provide potential therapeutic targets for the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08778-0.

[Mining of Differentially Expressed Genes in Diabetic Cardiomyopathy Based on GEO Database]

Objective To screen out the key genes leading to diabetic cardiomyopathy by analyzing the mRNA array associated with diabetic cardiomyopathy in the GEO database. Methods The online tool GEO2R of GEO was used to mine the differentially expressed genes (DEG) in the datasets GSE4745 and GSE5606.R was used to draw the volcano map of the DEG,and the Venn diagram was established online to identify the common DEG shared by the two datasets.The clusterProfile package in R was used for gene ontology annotation and Kyoto encyclopedia of genes and genomes pathway enrichment of the DEG.GSEA was used for gene set enrichment analysis,and STRING for the construction of a protein-protein interaction network.The maximal clique centrality algorithm in the plug-in Cytohubba of Cytoscape was used to determine the top 10 key genes. The expression of key genes was studied in the primary cardiomyocytes of rats and compared between the normal control group and high glucose group. Results The expression of Pdk4,Ucp3,Hmgcs2,Asl6,and Slc2a4 was consistent with the array analysis results.The expression of Pdk4,Ucp3,and Hmgcs2 was up-regulated while that of Acsl6 and Slc2a4 was down-regulated in the cardiomyocytes stimulated by high glucose (25 mmol/L) for 72 h. Conclusion Pdk4,Ucp3,Hmgcs2,Asl6,and Slc2a4 may be associated with the occurrence and development of diabetic cardiomyopathy,and may serve as the potential biomarkers of diabetic cardiomyopathy.

m6A echoes with DNA methylation: Coordinated DNA methylation and gene expression data analysis identified critical m6A genes associated with asthma

Asthma is a chronic inflammatory disease that involves complex gene-environment interactions. Methylation of nucleotides, such as 5-methylcytosine (5mC) in DNA and N6-methyladenosine (m6A) in mRNA, carries important information for gene regulation. Our study screened m6A genes and genes associated with asthma from the Gene Expression Omnibus (GEO) databases GSE63383, GSE119580, GSE38003, GSE34313, GSE13168, and GSE35643. GSE52778, GSE35643, GSE40996, and GSE64744), and DNA methylation data from GSE85568 and GSE146377. We screened out 6 m6A related genes (FTO, IGF2BP2, RBM15, RBMX, WTAP, and YTHDC1) that were significantly dysregulated in asthma or proinflammatory conditions. A correlation study showed a high correlation between m6A genes and gene pairs such as WTAP, IL7R, and TLR2; RBMX, SLC22A4, IL33, TNC, FLG, and IL6R (|r| ≥ 0.8). Following DNA methylation dataset analysis, we proposed several DNA methylation-m6A modification asthma-related gene axes such as cg19032951/cg15153914-IGF2BP2-SMAD3. Interestingly, several target genes, such as SMAD3, possess the ability to participate in DNA methylation processes, which may reciprocally regulate the expression of m6A genes and form a closed-loop regulation axis. Some classic DNA methylation-related genes, such as TET1, UHRF1, and ZBTB4, were also involved. We identified an integrated profile of m6A gene expression in asthma and proposed a novel potential interplay between DNA methylation and m6A modification in asthma pathogenesis. Using the CMAP database, we found that resveratrol may target these dysregulated m6A genes, and therefore may serve as a potential therapeutic agent for asthma.

Construction of lncRNA-miRNA-mRNA network based on ceRNA mechanism reveals the function of lncRNA in the pathogenesis of gout

Objective

To identify differentially expressed lncRNA, miRNA, and mRNA during the pathogenesis of gout, explore the ceRNA network regulatory mechanism of gout, and seek potential therapeutic targets.

Method

First, gout‐related chips were retrieved by GEO database. Then, the analysis of differentially expressed lncRNAs and mRNAs was conducted by R language and other software. Besides, miRNA and its regulated mRNA were predicted based on public databases, the intersection of differentially expressed mRNA and predicated mRNA was taken, and the lncRNA‐miRNA‐mRNA regulatory relationships were obtained to construct the ceRNA regulatory network. Subsequently, hub genes were screened by the STRING database and Cytoscape software. Then the DAVID database was used to illustrate the gene functions and related pathways of hub genes and to mine key ceRNA networks.

Results

Three hundred and eighty‐eight lncRNAs and 758 mRNAs were identified with significant differential expression in gout patient, which regulates hub genes in the ceRNA network, such as JUN, FOS, PTGS2, NR4A2, and TNFAIP3. In the ceRNA network, lncRNA competes with mRNA for miRNA, thus affecting the IL‐17 signaling pathway, TNF signaling pathway, Oxytocin signaling pathway, and NF‐κB signaling pathway through regulating the cell's response to chemical stress. The research indicates that five miRNAs (miR‐429, miR‐137, miR‐139‐5p, miR‐217, miR‐23b‐3p) and five lncRNAs (SNHG1, FAM182A, SPAG5‐AS1, HNF1A‐AS1, UCA1) play an important role in the formation and development of gout.

Conclusion

The interaction in the ceRNA network can affect the formation and development of gout by regulating the body's inflammatory response as well as proliferation, differentiation, and apoptosis of chondrocytes and osteoclasts. The identification of potential therapeutic targets and signaling pathways through ceRNA network can provide a reference for further research on the pathogenesis of gout.

Identification of a circRNA-mediated comprehensive ceRNA network in spinal cord injury pathogenesis

RNAs are closely associated with human diseases; however, immune-related genes (IRGs) and their potential regulatory networks in relation to spinal cord injury (SCI) are still poorly understood. Here, we investigated the key IRGs as well as the competing endogenous RNA (ceRNA) mechanisms that are associated with SCI pathogenesis based on microarray datasets and the use of a rat SCI model. Specifically, four independent SCI microarray datasets from Gene Expression Omnibus (GEO) database were analyzed and, thereafter, differentially expressed IRGs were annotated via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Furthermore, based on the GEO datasets, differentially expressed RNAs (DERNAs), including DEcircRNAs, DEmiRNAs, and DEmRNAs were identified and interactions between them were also predicted using online databases, and to construct a circular RNA (circRNA) mediated ceRNA network, candidate RNAs were also identified. Furthermore, the support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) methods were used for the identification of critical DERNAs, while differential gene expression was validated using the GSE20907 dataset. Our results were as follows. In the SCI microarray datasets, 32, 58, and 74 DEIRGs, DEcircRNAs, and DEmiRNAs were identified, respectively. In addition, GO and KEGG analyses showed that the DEIRGs were primarily enriched in neutrophil-mediated immunity and nuclear factor-kappa B (NF-κB) and hypoxia-inducible factor-1 (HIF-1) signaling pathways, and based on LASSO and SVM screening, PLXNB2 was identified as a DEIRG, while hsa_circ_0026646 was identified as the key circRNA, showing a higher SCI expression. Furthermore, our results proved that PLXNB2 and hsa_circ_0026646 were upregulated in SCI, whereas miR-331-3p was downregulated, and, interestingly, similar expression profiles were confirmed using the rat SCI model. Furthermore, fluorescent reporter assay indicated that both hsa_circ_0026646 and PLXNB2 have miR-331-3p target sites, and the ceRNA hypothesis suggested the dysregulation of hsa_circ_0026646, miR-331-3p, and PLXNB2 in SCI. Thus, our results suggested that in SCI pathogenesis, hsa_circ_0026646 correlates with PLXNB2 by targeting miR-331-3p.

Exploring the Potential Toxicological Mechanisms of Vine Tea on the Liver Based on Network Toxicology and Transcriptomics

Objective: This study focuses on whether vine tea contains potentially toxic components that trigger hepatotoxicity as a mechanism of action, which further provides some reference for the consumption and guides future product development of vine tea.

Methods: The chemical components of vine tea were collected from the reported literature and the toxicological information matched with the CTD database was collected, and the dataset of potential toxic components was established. The toxic components were submitted to the PharmMapper server to obtain potential targets. At the same time, the relevant targets were searched in the CTD database and GeneCards database with keywords such as “Hepatic Toxicity,” “Liver Damage,” and “Drug-induced liver injury.” After intersection, the potential hepatotoxic targets of vine tea were obtained. The protein interactions of potential hepatotoxic targets of vine tea were analyzed by the STRING database. Protein–protein interaction (PPI) networks were constructed by Cytoscape3.6.1 software. The GO molecular function and KEGG pathway of hepatotoxic targets were enriched by the R package to screen the key targets. The role of the components and key targets was analyzed by the LEDOCK program. The data from GEO database were mined for the functional correlation characterized by cell transcriptional expression caused by vine tea as a disturbance factor.

Results: This study has searched 34 potential toxic components and 57 potential hepatotoxic targets of vine tea, and the result showed that these targets were mainly involved in oxidative stress, cell metabolism, and apoptosis to affect the liver.

Conclusion: Vine tea has the interrelationship of multi-components, multi-targets, and multi-pathways. At the cellular level, the toxic components of vine tea, mainly flavonoids, may promote oxidative stress, promote oxidation to produce free radicals, guide apoptosis, and affect cell metabolism and other cytotoxic mechanisms. However, this hepatotoxicity is related to the dose, duration of vine tea, and individual differences. This study revealed the potential hepatotoxic components of vine tea and provides a reference for further research and development of related functional products.

GC-MS Profile of Hua-Feng-Dan and RNA-Seq Analysis of Induced Adaptive Responses in the Liver

Background: Hua-Feng-Dan is a patent Chinese medicine for stroke recovery and various diseases. This study used GC-MS to profile its ingredients and RNA-Seq to analyze the induced adaptive response in the liver. Methods: Hua-Feng-Dan was subjected to steam distillation and solvent extraction, followed by GC-MS analysis. Mice were orally administered Hua-Feng-Dan and its "Guide drug" Yaomu for 7 days. Liver pathology was examined, and total RNA isolated for RNA-Seq, followed by bioinformatic analysis and quantitative real-time PCR (qPCR). Results: Forty-four volatile and fifty liposoluble components in Hua-Feng-Dan were profiled and analyzed by the NIST library and their concentrations quantified. The major components (>1%) in volatile (5) and liposoluble (10) were highlighted. Hua-Feng-Dan and Yaomu at hepatoprotective doses did not produce liver toxicity as evidenced by histopathology and serum enzyme activities. GO Enrichment revealed that Hua-Feng-Dan affected lipid homeostasis, protein folding, and cell adhesion. KEGG showed activated cholesterol metabolism, bile secretion, and PPAR signaling pathways. Differentially expressed genes (DEGs) were identified by DESeq2 with p < 0.05 compared to controls. Hua-Feng-Dan produced more DEGs than Yaomu. qPCR on selected genes largely verified RNA-Seq results. Ingenuity Pathways Analysis of the upstream regulator revealed activation of MAPK and adaptive responses by Hua-Feng-Dan, and Yaomu was less effective. Hua-Feng-Dan-induced DEGs were highly correlated with the Gene Expression Omnibus database of chemical-induced adaptive transcriptome changes in the liver. Conclusion: GC-MS primarily profiled volatile and liposoluble components in Hua-Feng-Dan. Hua-Feng-Dan at the hepatoprotective dose did not produce liver pathological changes but induced metabolic and signaling pathway activations. The effects of Hua-Feng-Dan on liver transcriptome changes point toward induced adaptive responses to program the liver to produce hepatoprotective effects.

Long Non-Coding RNAs MALAT1 and NEAT1 in Non-syndromic Orofacial Clefts

Long non-coding RNAs (lncRNAs) are thought to play important roles in non-syndromic orofacial clefts (NSOFC). Clinical diagnosis was categorized as either non-syndromic cleft lip with or without cleft palate (NSCL/P), or non-syndromic cleft palate-only (NSCPO). Tissues excised from the trimmed wound edge were reserved as experimental samples; adjacent normal control was used as a positive control, and tissue from healthy individuals was used as a blank control. Target lncRNAs in the collected tissues were identified using microarrays and quantitative reverse transcription PCR (RT-qPCR). Immunohistochemical (IHC) staining and RT-qPCR were used to verify the target mRNAs. Pathway, gene ontology (GO) enrichment, and TargetScan predictions were employed to construct competing endogenous RNA networks (ceRNA networks) and explore their potential functions. RNA-Seq revealed 24 upregulated and 43 downregulated lncRNAs; MALAT1 and NEAT1 were screened and validated using RT-qPCR. Common NSOFC risk factors were positively correlated with MALAT1 and NEAT1 expression. Bioinformatics predicted four ceRNA networks; GO enrichment focused on their potential functions. RT-qPCR and IHC data were consistent with respect to expression levels of proteins and the mRNAs that encode them. As MALAT1 and NEAT1 are associated with the severity of NSOFC, they represent potential therapeutic targets and prognostic biomarkers.

Exploring the relationship between systemic lupus erythematosus and osteoporosis based on bioinformatics

OBJECTIVE

This study aimed to explore the relationship between systemic lupus erythematosus (SLE) and osteoporosis (OP) based on bioinformatics.

METHODS

The expression profiles of SLE and OP gene chips were searched through the GEO database, and the differentially expressed genes (DEGs) were screened out to obtain the intersection. Then, the Funrich software was used to predict the upstream miRNAs of the intersection genes, and the miRNA-mRNA relationship network was constructed. Afterward, the String database and Cytoscape software were used to construct the protein interaction network of the intersection genes to screen out the key genes. Finally, the functions and related pathways of key genes were analyzed by using the DAVID database.

RESULTS

①A total of 140 intersection genes of SLE and OP were obtained; ②There were 217 miRNAs regulating the intersection genes; ③IL-4, FOS, TLR1, TLR6, CD40LG, CCR1 were the key genes in the protein interaction network; ④The DAVID enrichment analysis mainly covered the positive regulation of cytokine production, the regulation of osteoclast differentiation, macrophage activation and other biological processes, involving Toll-like receptor signaling pathway, T cell receptor signaling pathway, Th1, Th2, and Th17 cells Differentiation, IL-17 signaling pathway.

CONCLUSIONS

SLE and OP still have some highly overlapping differential gene expressions under the background of complex gene networks. The gene functions and signaling pathways involved can simultaneously regulate the two diseases, suggesting that there is a close relationship between the molecular mechanisms of the two diseases, and that it may be a target of drugs that interfere with two diseases at the same time.

Dissecting lncRNA-mRNA competitive regulatory network in human islet tissue exosomes of a type 1 diabetes model reveals exosome miRNA markers

BACKGROUND

Emerging evidence shows that exosomes play a crucial role in the occurrence and development of diabetes and its complications. The molecules in exosomes can be regarded as important markers for the diagnosis of diseases. However, it is presently unclear the pathological association mechanism between exosomes and diabetes.

RESULTS

In this study, transcriptome data and lncRNA regulatory association data of human pancreatic islet-derived exosome were integrated to construct the ceRNA network. Network analysis revealed that lncRNA with differential expression were primarily involved in islet insulin secretion signaling pathways, including Hippo, TGF-beta, Wnt, FOXO, Neurotrophin and ErbB signaling pathway. Further, combined with miRNA mediated competitive regulation and differential expression analysis results, potential markers of diabetes were revealed and validated in independent datasets. Finally, we analyzed the mechanisms of diabetes based on the competitive regulatory association and function of lncRNA.

CONCLUSION

Our results suggest that lncRNA such as lncRNA PVT1, LINC00960 and hsa-miR-107 might be involved in inflammation response in T1DM, and the former lncRNA chose in the present study may serve as novel biomarkers and potential targets for the diagnosis and treatment of T1DM.

Weighted Correlation Network Analysis of Cancer Stem Cell-Related Prognostic Biomarkers in Esophageal Squamous Cell Carcinoma

Background: The role of cancer stem cells in esophageal squamous cell carcinoma (ESCC) remains unclear. Methods: The mRNA stemness index (mRNAsi) of 179 ESCC patients (GSE53625) was calculated using a machine learning algorithm based on their mRNA expression. Stemness-related genes were identified by weighted correlation network analysis (WGCNA) and LASSO regression, whose associations with mutation status, immune cell infiltrations, and potential compounds were also analyzed. The role of these genes in proliferation and their expressions was assessed in ESCC cell lines and 112 samples from our center. Results: The ESCC samples had significantly higher mRNAsi than the normal tissues. Patients with high mRNAsi exhibited higher worse OS. Seven stemness-related genes were identified by WGCNA and LASSO regression, based on which a risk-predicted score model was constructed. Among them, CST1, CILP, PITX2, F2RL2, and RIOX1 were favorable for OS, which were adverse for DPP4 and ZFHX4 in the GSE53625 dataset. However, RIOX1 was unfavorable for OS in patients from our center. In vitro assays showed that CST1, CILP, PITX2, F2RL2, and RIOX1 were pro-proliferated, which were opposite for DDP4 and ZFHX4. In addition, SMARCA4, NOTCH3, DNAH5, and KALRN were more mutated in the low-score group. The low-score group had significantly more memory B cells, monocytes, activated NK cells, and Tregs and less macrophages M2, resting mast cells, and resting dendritic cells. Conclusions: Seven stemness-related genes are significantly related to the prognosis, gene mutations, and immune cell infiltration of ESCC. Some potential anticancer compounds may be favorable for OS.

Growth differentiation factor 10 induces angiogenesis to promote wound healing in rats with diabetic foot ulcers by activating TGF-β1/Smad3 signaling pathway

BACKGROUND

Diabetic foot ulcer (DFU) represents a highly-prevalent complication of diabetes mellitus (DM). Herein, the current study sought to identify the role of growth differentiation factor 10 (GDF-10) in wound healing in DFU via regulation of the transforming growth factor-beta 1 (TGF-β1)/Smad3 pathway.

METHODS

DM- and DFU-related microarray datasets GSE29221 and GSE134431 were firstly retrieved, and weighted gene co-expression network analysis (WGCNA) was carried out to construct a co-expression network affecting wound healing in DFU, followed by differential analysis. A protein-protein interaction (PPI) network of the DFU-related genes was subsequently constructed, and the core genes and signaling pathways in DFU were screened with the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional analyses. A DFU rat model was constructed for mechanism verification of the effect of GDF-10 on wound healing in DFU.

RESULTS

WGCNA screened five co-expression modules, and the brown module was most closely-related to DM. Clustering analysis screened 4417 candidate genes, of which 175 differential genes were associated with wound healing, further involved in TGF-β1/Smad3 signaling pathway regulation of wound healing in DFU. The PPI network analysis predicted that GDF-10 might regulate the TGF-β1/Smad3 signaling pathway to participate in DFU development. Results of animal experimentation showed that the wound healing rates of NFU, DFU, DFU + GDF and GDF + SIS3 groups on the 22nd day were (87.66 ± 6.80)%, (56.31 ± 7.29)%, (71.64 ± 9.43)% and (55.09 ± 7.13)%, respectively. Besides, the expression of TGF-β1 in NFU, DFU, DFU + GDF and GDF + SIS3 groups was 0.988 ± 0.086, 0.297 ± 0.036, 0.447 ± 0.044, and 0.240 ± 0.050, respectively, and that of Smad3 was 1.009 ± 0.137, 0.145 ± 0.017, 0.368 ± 0.048, and 0.200 ± 0.028, respectively. Specifically, GDF-10 exerted a significant diminishing effect on fasting blood glucose level, and promoted wound healing in DFU rats, in addition to up-regulation of VEGF, FGF, Ang-1, TGF-β1, Smad3 and enhancement of IL-1b, IL-6, TNF-a and MMP-9, thereby promoting fibroblast proliferation, collagen deposition and angiogenesis.

CONCLUSIONS

Our findings highlight that GDF-10 may promote angiogenesis by activating TGF-β1/Smad3 signaling, thereby promoting wound healing in DFU rats.

Induction of PI3K/Akt-Mediated Apoptosis in Osteoclasts Is a Key Approach for Buxue Tongluo Pills to Treat Osteonecrosis of the Femoral Head

The Buxue Tongluo pill (BTP) is a self-made pill with the functions of nourishing blood, promoting blood circulation, dredging collaterals, and relieving pain. It consists of Angelica sinensis (Oliv.) Diels, Pheretima aspergillum (E.Perrier), Panax notoginseng (Burk.) F. H. Chen, Astragalus membranaceus (Fisch.) Bge, and Glycyrrhiza uralensis Fisch. Various clinical practices have confirmed the therapeutic effect of BTP on osteonecrosis of the femoral head (ONFH), but little attention has been paid to the study of its bioactive ingredients and related mechanisms of action. In this study, UPLC/MS-MS combined with GEO data mining was used to construct a bioactive ingredient library of BTP and a differentially expressed gene (DEG) library for ONFH. Subsequently, Cytoscape (3.7.2) software was used to analyze the protein-protein interaction between BTP and DEGs of ONFH to screen the key targets, and functional annotation analysis and pathway enrichment analysis were carried out. Finally, 34 bioactive compounds were screened, which acted on 1,232 targets. A total of 178 DEGs were collected, and 17 key genes were obtained after two screenings. By bioinformatics annotation on these key genes, a total of 354 gene ontology (GO) functional annotation analyses and 42 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained. The present study found that GO and KEGG enrichment were mainly related to apoptosis, suggesting that BTP may exert an anti-ONFH effect by promoting osteoclast apoptosis. Experiments in vitro demonstrated that BTP could increase the mitochondrial membrane potential (MMP) and induce remarkable apoptosis in osteoclasts. Furthermore, we determined the apoptosis marker of cleaved(C)-caspase-3, bcl-2, and bax and found that BTP could upregulate the C-caspase-3 and bax expression in osteoclasts and decrease the expression of bcl-2, p-Akt, and p-PI3K in a dose-dependent manner, indicating that BTP could induce PI3K/Akt-mediated apoptosis in osteoclasts to treat ONFH. This study explored the pharmacodynamic basis and mechanism of BTP against ONFH from the perspective of systemic pharmacology, laying a foundation for further elucidating the therapeutic effects of BTP against ONFH.

IMUP and GPRC5A: two newly identified risk score indicators in pancreatic ductal adenocarcinoma

Background

Pancreatic cancer has been a threateningly lethal malignant tumor worldwide. Despite the promising survival improvement in other cancer types attributing to the fast development of molecular precise medicine, the current treatment situation of pancreatic cancer is still woefully challenging since its limited response to neither traditional radiotherapy and chemotherapy nor emerging immunotherapy. The study is to explore potential responsible genes during the development of pancreatic cancer, thus identifying promising gene indicators and probable drug targets.

Methods

Different bioinformatic analysis were used to interpret the genetic events in pancreatic cancer development. Firstly, based on multiple cDNA microarray profiles from Gene Expression Omnibus (GEO) database, the genes with differently mRNA expression in cancer comparing to normal pancreatic tissues were identified, followed by being grouped based on the difference level. Then, GO and KEGG were performed to separately interpret the multiple groups of genes, and further Kaplan–Meier survival and Cox Regression analysis assisted us to scale down the candidate genes and select the potential key genes. Further, the basic physicochemical properties, the association with immune cells infiltration, mutation or other types variations besides expression gap in pancreatic cancer comparing to normal tissues of the selected key genes were analyzed. Moreover, the aberrant changed expression of key genes was validated by immunohistochemistry (IHC) experiment using local hospital tissue microarray samples and the clinical significance was explored based on TCGA clinical data.

Results

Firstly, a total of 22,491 genes were identified to express differently in cancer comparing to normal pancreatic tissues based on 5 cDNA expression profiles, and the difference of 487/22491 genes was over eightfold, and 55/487 genes were shared in multi profiles. Moreover, after genes interpretation which showed the > eightfold genes were mainly related to extracellular matrix structural constituent regulation, Kaplan–Meier survival and Cox-regression analysis were performed continually, and the result indicated that of the 55 extracellular locating genes, GPRC5A and IMUP were the only two independent prognostic indicators of pancreatic cancer. Further, detailed information of IMUP and GPRC5A were analyzed including their physicochemical properties, their expression and variation ratio and their association with immune cells infiltration in cancer, as well as the probable signaling pathways of genes regulation on pancreatic cancer development. Lastly, local IHC experiment performed on PAAD tissue array which was produced with 62 local hospital patients samples confirmed that GPRC5A and IMUP were abnormally up-regulated in pancreatic cancer, which directly associated with worse patients both overall (OS) and recurrence free survival (RFS).

Conclusions

Using multiple bioinformatic analysis as well as local hospital samples validation, we revealed that GPRC5A and IMUP expression were abnormally up-regulated in pancreatic cancer which associated statistical significantly with patients survival, and the genes’ biological features and clinical significance were also explored. However, more detailed experiments and clinical trials are obligatory to support their further potential drug-target role in clinical medical treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02324-w.

Up-regulation of TGFBI and TGFB2 in the plasma of gestational diabetes mellitus patients and its clinical significance

BACKGROUND

Gestational diabetes mellitus (GDM) reflects a deficiency in the relative need for insulin during pregnancy, as well as temporary metabolic stress in the placenta and fetus. Our study aimed to research the potential diagnostic value of transforming growth factor-beta-induced protein ig-h3 (TGFBI) and transforming growth factor beta-2 proprotein (TGFB2) for GDM patients.

METHODS

Online database Gene Expression Omnibus (GEO) was used to screen for different expressed genes (DEGs) associated with GDM. Meanwhile, KEGG and GO were used to analyze the molecular functions as well as pathways of enriched DEGs. One hundred ten pregnant women diagnosed with GDM and 110 healthy controls were enrolled, of whose placenta and fasting venous blood samples were collected. mRNA expression levels were determined by real-time quantitative polymerase chain reaction (RT-qPCR), and fasting blood glucose (FBG) was measured by the clinical lab of hospital. Furthermore, receiver operating characteristics curve (ROC) analysis was performed to evaluate the sensitivity and specificity of detection indexed in the placenta and plasma of GDM patients. Finally, Pearson and Spearman analysis was used for the correlation analysis.

RESULTS

After GEO data analysis, TGFBI and TGFB2 were identified as the most significantly up-regulated genes of GDM. TGFBI and TGFB2 expressions in placenta and plasma samples of GDM patients were in line with bioinformatic analysis. Meanwhile, the area under the curve (AUC) of TGFBI in the placenta and plasma for the diagnosis of GDM were 0.8783 (95% CI, 0.8281 to 0.9284) and 0.7832 (95% CI, 0.7215 to 0.8449) while for TGFB2 were 0.9225 (95% CI, 0.8829 to 0.9621) and 0.8961 (95% CI, 0.8526 to 0.9396). Besides, levels of TGFBI along with TGFB2 in the placenta were positively correlated with that in the plasma of GDM patients. Furthermore, both TGFBI and TGFB2 expressions in the plasma were positively correlated with FBG levels of the GDM patients.

CONCLUSIONS

TGFBI and TGFB2 were up-regulated in the placenta and plasma of GDM patients, and TGFBI and TGFB2 in the plasma are potent to be diagnostic markers for the GDM.

Verification of the role of spiperone in the treatment of COPD through bioinformatics analysis

BACKGROUND

Aim of this study is investigates the influence of spiperone on hydrolase activity pathway in chronic obstructive pulmonary disease (COPD).

PATIENTS AND METHODS

Differentially expressed genes (DEGs) were calculated by the limma package from microarray data GSE20257, and analysed via gene set enrichment analysis (GSEA) for identifying COPD related pathways. The regulation of hydrolase activity pathway related drugs was predicted by connectivity Map analysis (CMap). Western blotting and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were used to investigate the effect of spiperone on regulation of hydrolase activity pathway in vitro experiment.

RESULTS

A total of 378 DEGs were identified by the limma package. GSEA suggested that the regulation of hydrolase activity pathway was involved in the development of COPD. CMap of hub genes of regulation of hydrolase activity pathwayshown the most significant compound was spiperone. Results of vitro experiment verify that cigarette smoke extract (CSE) can increase the expression of fibronectin 1 (FN1) and epidermal growth factor (EGF), coinsided with decrease the expression of chemokine (C-X3-C motif) ligand 1 (CX3CL1), chemokoine (C-C motif) ligand 20 (CCL20), complement component 3 (C3) and slithomolog 2 (SLIT2) in BESA-2B cells and U937 cells. Spiperone can reverse the effect of CSE in BESA-2B cells and U937 cells.

CONCLUSION

Regulation of hydrolase activity pathway was involved in the occurrence of COPD, spiperone was a potential drug for the treatment of COPD by affecting the regulation of hydrolase activity pathway. This study had provided new insights into the potential pathogenesis and treatment of COPD.

Identification of key genes for HNSCC from public databases using bioinformatics analysis

Background

The cause and underlying molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) are unclear. Our study aims to identify the key genes associated with HNSCC and reveal potential biomarkers.

Methods

In this study, the expression profile dataset GSE83519 of the Gene Expression Omnibus database and the RNA sequencing dataset of HNSCC of The Cancer Genome Atlas were included for analysis. Sixteen differentially expressed genes were screened from these two datasets using R software. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) was then adopted for survival analysis, and finally, three key genes related to the overall survival of HNSCC patients were identified. Furthermore, we verified these three genes using the Oncomine database and from real-time PCR and immunohistochemistry results from HNSCC tissues.

Results

The expression data of 44 samples from GSE83519 and 545 samples from TCGA-HNSC were collected. Using bioinformatics, the two databases were integrated, and 16 DEGs were screened out. Gene Ontology (GO) enrichment analysis showed that the biological functions of DEGs focused primarily on the apical plasma membrane and regulation of anoikis. Kyoto Encyclopedia of Genes and Genomes (KEGG) signalling pathway analysis showed that these DEGs were mainly involved in drug metabolism-cytochrome P450 and serotonergic synapses. Survival analysis identified three key genes, CEACAM5, CEACAM6 and CLCA4, that were closely related to HNSCC prognosis. The Oncomine database, qRT–PCR and IHC verified that all 3 key genes were downregulated in most HNSCC tissues compared to adjacent normal tissues.

Conclusions

This study indicates that integrated bioinformatics analyses play an important role in screening for differentially expressed genes and pathways in HNSCC, helping us better understand the biomarkers and molecular mechanism of HNSCC.

Elevated Stratifin promotes cisplatin-based chemotherapy failure and poor prognosis in non-small cell lung cancer

Drug resistance is a key factor in the treatment failure of clinical non-small cell lung cancer (NSCLC) patients after adjuvant chemotherapy. Here, our results provide the first evidence that eukaryotic translation initiation factor 2b subunit delta (EIF2B4)-Stratifin (SFN) fusion and increased SFN expression are associated with chemotherapy tolerance and activation of the phosphatidylinositol 3 kinase/v-akt murine thymoma viral oncogene (PI3K/Akt) signaling pathway in NSCLC patients, suggesting that SFN might have potential prognostic value as a tumor biomarker for the prognosis of patients with NSCLC.

Construction of Adipogenic ceRNA Network Based on lncRNA Expression Profile of Adipogenic Differentiation of Human MSC Cells

The Long non-coding RNA (lncRNA) expression profile data of ten samples including human Mesenchymal Stem Cell (MSC) adipogenic differentiation 0, 3, and 6 days from the GEO database, and then perform gene ID conversion, BLAST comparison, and annotation marking. Finally, group A (treatment group on day 3 of differentiation and control group on day 0 of differentiation) obtained a total of 1180 mRNA and 185 lncRNA; group B (treatment group on day 6 of differentiation and control group on day 0 of differentiation). A total of 1376 mRNA and 206 lncRNA were obtained. Finally, we processed the differential lncRNAs and mRNAs obtained in the two groups, and obtained 113 shared differential lncRNAs to further predict the targeted miRNA, a total of 815 lncRNA-miRNA pairs. The targeted mRNA was further predicted, and the grouped differential mRNAs were combined to obtain 64 differential mRNAs. In the end, we obtained 216 ceRNAs containing 26 lncRNAs, 27 miRNAs and 64 mRNAs. We found that the mRNAs in the ceRNA network were mainly enriched with 45 Gene Ontology (GO) terms, mainly including glucose homeostasis mechanism and insulin stimulation response. 69 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were mainly enriched. It mainly includes many pathways related to lipid metabolism such as Adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK), Rap1, cAMP, mitogen-activated protein kinase (MAPK), Ras, hypoxia inducible factor-1 (HIF-1), PI3K-Akt, insulin signaling and so on. In the end, we identified 216 ceRNA regulatory relationships related to obesity research. Our research provides a clearer direction for understanding the molecular mechanism of obesity, the screening and determination of drug targets biomarkers in the future.

Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Ulcerative Colitis

Background: Ulcerative colitis (UC) is a chronic, complicated, inflammatory disease with an increasing incidence and prevalence worldwide. However, the intrinsic molecular mechanisms underlying the pathogenesis of UC have not yet been fully elucidated.

Methods: All UC datasets published in the GEO database were analyzed and summarized. Subsequently, the robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between UC patients and controls. Gene functional annotation and PPI network analysis were performed to illustrate the potential functions of the DEGs. Some important functional modules from the protein-protein interaction (PPI) network were identified by molecular complex detection (MCODE), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), and analyses were performed. The results of CytoHubba, a plug for integrated algorithm for biomolecular interaction networks combined with RRA analysis, were used to identify the hub genes. Finally, a mouse model of UC was established by dextran sulfate sodium salt (DSS) solution to verify the expression of hub genes.

Results: A total of 6 datasets met the inclusion criteria (GSE38713, GSE59071, GSE73661, GSE75214, GSE87466, GSE92415). The RRA integrated analysis revealed 208 significant DEGs (132 upregulated genes and 76 downregulated genes). After constructing the PPI network by MCODE plug, modules with the top three scores were listed. The CytoHubba app and RRA identified six hub genes: LCN2, CXCL1, MMP3, IDO1, MMP1, and S100A8. We found through enrichment analysis that these functional modules and hub genes were mainly related to cytokine secretion, immune response, and cancer progression. With the mouse model, we found that the expression of all six hub genes in the UC group was higher than that in the control group (P < 0.05).

Conclusion: The hub genes analyzed by the RRA method are highly reliable. These findings improve the understanding of the molecular mechanisms in UC pathogenesis.