Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma

BackgroundIt is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC.MethodsTwo expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes.Results1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups.ConclusionsIn summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.     

Identification of key genes and expression profiles in osteoarthritis by co-expressed network analysis

BackgroundThe underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA).Material and methodsWe obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the “limma” package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples.ResultsThe preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples.ConclusionsWe identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.     

Network pharmacology analysis of pharmacological mechanisms underlying the anti-type 2 diabetes mellitus effect of guava leaf

The current study aimed to explore the anti-type 2 diabetes mellitus (T2DM) mechanism of guava leaf based on network pharmacology. The compounds contained in guava leaf was summarized from the literature, and a series of databases was used to identify the active components and corresponding potential targets. The intersection between diabetes-associated genes searched in the GeneCard database and the predicted targets of guava leaf active components was defined as target genes, which were then used to construct a “compound-active components-target genes” pharmacological network. The biological functions and pathway enrichment analyses of target genes were performed in KOBAS 3.0. The differential expression analysis of GSE76894 was performed to obtain the differential expressed genes (DEGs) in T2DM patients by comparing non-diabetic controls. Finally, the intersection between DEGs and target genes were named key genes, and the representative pathways in which these genes were involved were drawn through KEGG Mapper. We found that the active components of guava leaf may regulate the PI3K-AKT signaling pathway, T2DM regulation process, and insulin resistance pathway, which was evidenced by KEGG pathway analysis of key genes. These results implied that guava leaf has a potential anti-T2DM property and its mode of action may be exerted via regulating insulin secretion and reducing blood sugar level.     

The protective effect of icariin on glucocorticoid-damaged BMECs explored by microfluidic organ chip

Osteonecrosis of the femoral head(ONFH) is a devastating musculoskeletal disease characterized by the impaired circulation of bone. The purpose of this study was to explore the underlying mechanisms of the protective effect of icariin on the glucocorticoid-induced injury of bone microvascular endothelial cells(BMECs). Normal BMECs were extracted from the femoral heads by enzymatic isolation and magneticactivated cell sorting methods. Dexamethasone and icariin were used to intervene BMECs in micr...     

Insights regarding novel biomarkers and the pathogenesis of primary colorectal carcinoma based on bioinformatic analysis

BackgroundBiomarkers are important in the study of tumor processes for early detection and precise treatment. The biomarkers that have been previously detected are not useful for clinical application for primary colorectal carcinoma (PCRC). The aim of this study was to explore clinically valuable biomarkers of PCRC based on integrated bioinformatic analysis.Material and methodsGene expression data were acquired from the GSE41258 dataset, and the differentially expressed genes were determined between PCRC and normal colorectal samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were implemented via Gene Set Enrichment Analysis. A protein-protein interaction (PPI) network was constructed. The significant modules and hub genes were screened and identified in the PPI network.ResultsA total of 202 DEGs were identified, including 58 upregulated and 144 downregulated genes in PCRC samples compared to those in normal colorectal samples. Enrichment analysis demonstrated that the gene sets enriched in PCRC were significantly related to bicarbonate transport, regulation of sodium ion transport, potassium ion homeostasis, regulation of telomere maintenance, and other processes. A total of 10 hub genes was identified by cytoHubba: PYY, CXCL3, CXCL11, CXCL8, CXCL12, CCL20, MMP3, P2RY14, NPY1R, and CXCL1.ConclusionThe hub genes, such as NPY1R, P2RY14, and CXCL12, and the electrolyte disequilibrium resulting from the differential expression of genes, especially bicarbonate imbalance, may provide novel insights and evidence for the future diagnosis and targeted therapy of PCRC.     

Homotherapy for heteropathy active components and mechanisms of Qiang-Huo-Sheng-Shi decoction for treatment of rheumatoid arthritis and osteoarthritis

Qiang-Huo-Sheng-Shi decoction (QHSSD), a classic traditional Chinese herbal formula, which has been reported to be effective in rheumatoid arthritis (RA) and osteoarthritis (OA). However, the concurrent targeting mechanism of how the aforementioned formula is valid in the two distinct diseases OA and RA, which represents the homotherapy-for-heteropathy principle in traditional Chinese medicine (TCM), have not yet been clarified. In the present study, network pharmacology was adopted to analyze the potential molecular mechanism, and therapeutic effective components of QHSSD on both OA and RA. A total of 153 active ingredients in QHSSD were identified, 142 of which associated with 59 potential targets for the two diseases were identified. By constructing the protein-protein interaction network and the compound-target-disease network, 72 compounds and 10 proteins were obtained as the hub targets of QHSSD against OA and RA. The hub genes of ESR1, PTGS2, PPARG, IL1B, TNF, MMP2, IL6, CYP3A4, MAPK8, and ALB were mainly involved in osteoclast differentiation, the NF-κB and TNF signaling pathways. Moreover, molecular docking results showed that the screened active compounds had a high affinity for the hub genes. This study provides new insight into the molecular mechanisms behind how QHSSD presents homotherapy-for-heteropathy therapeutic efficacy in both OA and RA. For the first time, a two-disease model was linked with a TCM formula using network pharmacology to identify the key active components and understand the common mechanisms of its multi-pathway regulation. This study will inspire more innovative and important studies on the modern research of TCM formulas.     

Down regulation of lactotransferrin enhanced radio-sensitivity of nasopharyngeal carcinoma

IntroductionIt is reported that LTF had a radiation resistance effect, and its expression in nasopharyngeal carcinoma (NPC) was significantly down-regulated. However, the mechanism of down-regulated LTF affecting the sensitivity of radiotherapy has remained elusive.MethodsWe re-analyzed the microarray data GSE36972 and GSE48503 to find differentially expressed genes (DEGs) in NPC cell line 5−8 F transfected with LTF or vector control, and the DEGs between radio-resistant and radio-sensitive NPC cell lines. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and protein-protein interaction network (PPI) analysis of DEGs were performed to obtain the node genes. The target genes of miR-214 were also predicted to complement the mechanism associated with radiotherapy resistance because it could directly target LTF.ResultsThis study identified 1190 and 1279 DEGs, respectively. GO and KEGG analysis showed that apoptotic process and proliferation, PI3K-Akt signaling pathway were significantly enriched pathways. Four nodes (DUSP1, PPARGC1A, FOS and SMARCA1) associated with LTF were screened. And 42 target genes of miR-214 were cross-linked to radiotherapy sensitivity.ConclusionsThe present study demonstrates the possible molecular mechanism that the down-regulated LTF enhances the radiosensitivity of NPC cells through interaction with DUSP1, PPARGC1A, FOS and SMARCA1, and miR-214 as its superior negative regulator may play a role in regulating the radiotherapy effect.     

Role of an unclassified Lachnospiraceae in the pathogenesis of type 2 diabetes: a longitudinal study of the urine microbiome and metabolites

Recent investigations have revealed that the human microbiome plays an essential role in the occurrence of type 2 diabetes (T2D). However, despite the importance of understanding the involvement of the microbiota throughout the body in T2D, most studies have focused specifically on the intestinal microbiota. Extracellular vesicles (EVs) have been recently found to provide important evidence regarding the mechanisms of T2D pathogenesis, as they act as key messengers between intestinal microorganisms and the host. Herein, we explored microorganisms potentially associated with T2D by tracking changes in microbiota-derived EVs from patient urine samples collected three times over four years. Mendelian randomization analysis was conducted to evaluate the causal relationships among microbial organisms, metabolites, and clinical measurements to provide a comprehensive view of how microbiota can influence T2D. We also analyzed EV-derived metagenomic (N = 393), clinical (N = 5032), genomic (N = 8842), and metabolite (N = 574) data from a prospective longitudinal Korean community-based cohort. Our data revealed that GU174097_g, an unclassified Lachnospiraceae, was associated with T2D (β = −189.13; p = 0.00006), and it was associated with the ketone bodies acetoacetate and 3-hydroxybutyrate (r = −0.0938 and −0.0829, respectively; p = 0.0022 and 0.0069, respectively). Furthermore, a causal relationship was identified between acetoacetate and HbA1c levels (β = 0.0002; p = 0.0154). GU174097_g reduced ketone body levels, thus decreasing HbA1c levels and the risk of T2D. Taken together, our findings indicate that GU174097_g may lower the risk of T2D by reducing ketone body levels.Subject terms: Bacterial genetics, Calcium and phosphate metabolic disorders     

Transcriptome Profiling of HCT-116 Colorectal Cancer Cells with RNA Sequencing Reveals Novel Targets for Polyphenol Nano Curcumin

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide. The gemini nanoparticle formulation of polyphenolic curcumin significantly inhibits the viability of cancer cells. However, the molecular mechanisms and pathways underlying its toxicity in colon cancer are unclear. Here, we aimed to uncover the possible novel targets of gemini curcumin (Gemini-Cur) on colorectal cancer and related cellular pathways. After confirming the cytotoxic effect of Gemini-Cur by MTT and apoptotic assays, RNA sequencing was employed to identify differentially expressed genes (DEGs) in HCT-116 cells. On a total of 3892 DEGs (padj < 0.01), 442 genes showed a log2 FC >|2| (including 244 upregulated and 198 downregulated). Gene ontology (GO) enrichment analysis was performed. Protein–protein interaction (PPI) and gene-pathway networks were constructed by using STRING and Cytoscape. The pathway analysis showed that Gemini-Cur predominantly modulates pathways related to the cell cycle. The gene network analysis revealed five central genes, namely GADD45G, ATF3, BUB1B, CCNA2 and CDK1. Real-time PCR and Western blotting analysis confirmed the significant modulation of these genes in Gemini-Cur-treated compared to non-treated cells. In conclusion, RNA sequencing revealed novel potential targets of curcumin on cancer cells. Further studies are required to elucidate the molecular mechanism of action of Gemini-Cur regarding the modulation of the expression of hub genes.     

Exploring the mechanism of action Xianlingubao Prescription in the treatment of osteoporosis by network pharmacology

In this study, the network pharmacology analysis method was used to explore the bioactive components and targets of Xianlinggubao (XLGB) and further elucidate its potential biological mechanisms of action in the treatment of osteoporosis (OP). The bioactive compounds and predictive targets of XLGB were collected from the traditional Chinese medicine systems pharmacology databases and analysis platform(TCMSP), the Encyclopeida of traditional Chinese medicine (ETCM), traditional Chinese medicine Databse@Taiwan, ChEMBL, STITCH, and SymMap database. The targets corresponding to OP were obtained by using Online Mendelian Inheritance in Man® (OMIM), GeneCards, the National Center for Biotechnology Information-Gene database. The XLGB-OP targets were obtained by intersecting with the targets of XLGB and OP. Protien-Protien interaciton (PPI) network was constructed using STRING online database and analyzed using Cytoscape 3.7.0 software to screen out hub genes. Gene ontology (GO) and KEGG enrichment analysis of the target in the PPI network was conducted using the ClusterProfiler package in R with adjusted p-value<0.05. A total of 65 XLGB bioactive compounds were screened corresponding to 776 XLGB targets and 2556 OP targets. The GO analysis and KEGG enrichment analyses suggested XLGB played a therapeutic roles in OP treatment via the interleukin-17 signaling pathway, hypoxia-inducible factor-1 signaling pathway, insulin resistance, Th-17 signaling pathway, etc. Five hub genes (AKT1, MAPK1, MAPK8, TP53, and STAT3) were screened using the degree algorithm, and molecular docking stimulation results showed that most bioactive compounds of XLGB had strong binding efficiency with hub genes. Overall, this study laid the foundation for further in vivo and in vitro experimental research and expanded the clinical applications of XLGB.     

Perturbations of pathway co-expression network identify a core network in metastatic breast cancer

Metastases are the main cause of death in advanced breast cancer (BC) patients. Although chemotherapy and hormone therapy are current treatment strategies, drug resistance is frequent and still not completely understood.In this study, a bioinformatics analysis was performed on BC patients to explore the molecular mechanisms associated with BC metastasis. Microarray gene expression profiles of metastatic and non metastatic BC patients were downloaded from Gene Expression Omnibus (GEO) dataset. Raw data were normalized and merged using the Combat tool. Pathways enriched with differently expressed genes were identified and a pathway co-expression network was generated using Pearson’s correlation. We identified from this network, which includes 17 pathways and 128 interactions, the pathways that most influence the network efficiency. Moreover, protein interaction network was investigated to identify hub genes of the pathway network. The prognostic role of the network was evaluated with a survival analysis using an independent dataset.In conclusion, the pathway co-expression network could contribute to understanding the mechanism and development of BC metastases.