Study on differentially expressed genes and participating pathways of ectopic endometrium in adenomyosis patients with different data sets.

BACKGROUND: Adenomyosis is a benign uterine disease and affected patients present with symptoms such as menorrhagia, chronic pelvic pain, abnormal uterine bleeding, and infertility. However, the specific mechanisms by which adenomyosis occurs need to be further studied. OBJECTIVE: Dataset of adenomyosis from our hospital and a public database were analyzed using bioinformatics. Corresponding differentially expressed genes (DEGs) and gene enrichment were detected to explore potential genetic adenomyosis targets. METHODS: Clinical data on adenomyosis were accessed based on the pathological specimens of patients with adenomyosis obtained from the Shengjing Hospital. R software was used to screen for DEGs, and volcano and cluster maps were drawn. Adenomyosis datasets (GSE74373) were downloaded from the GEO database. GEO2R online tool was used to screen for DEGs between adenomyosis and normal controls. Genes with P < 0.01 and |logFC| >1 were selected as DEGs. DAVID software was used for functional and pathway enrichment analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on common DEGs to obtain descriptions of the genes. The online database STRING was used for interaction gene retrieval. Moreover, Cytoscape software was used to construct a protein-protein interaction (PPI) network map for common DEGs to visualize potential gene interactions and screen the hub genes. RESULTS: A total of 845 DEGs were identified in the dataset obtained from Shengjing Hospital. A total of 175 genes were downregulated, and 670 genes were upregulated. In the GSE74373 database, 1679 genes were differentially expressed, 916 genes were downregulated, and 763 genes were upregulated. A total of 40 downregulated and 148 upregulated common DEGs showed potential gene interactions. The top ten upregulated hub genes were CDH1, EPCAM, CLDN7, ESRP1, RAB25, SPINT1, PKP3, TJP3, GRHL2, and CDKN2A. CONCLUSION: Genes involved in tight junction may be key in the development of adenomyosis and may provide a potential treatment strategy for adenomyosis.

Effects of pesticide exposure on the expression of selected genes in normal and cancer samples: Identification of predictive biomarkers for risk assessment.

Pesticides pivotal in controlling pests, can represent a threat for human health. Regulatory agencies constantly monitor their harmful effects, regulating their use. Several studies support a positive association between long-term exposure to pesticides and chronic pathologies, such as cancer. Geno-toxicological biomonitoring has proven to be valuable to assess genetic risks associated with exposure to pesticides, representing a promising tool to improve preventive measures and identify workers at higher risk. In this study, a differential gene expression analysis of 70 candidate genes deregulated upon pesticide exposure, was performed in 10 GEO human gene expression DataSets. It was found that six genes (PMAIP1, GCLM, CD36, SQSTM1, ABCC3, NR4A2) had significant AUC predictive values. Also, CD36 was upregulated in non-transformed cell samples and healthy workers, but downregulated in cancer cells. Further validation in larger groups of workers will corroborate the importance of the identified candidates as biomarkers of exposure/effect.

Analyzing Pooled Microarray Gene Expression Data to Uncover Common Pathways in Periodontitis and Oral Squamous Cell Carcinoma from the Gene Expression Omnibus.

Periodontitis and oral squamous cell carcinoma (OSCC) are prevalent oral diseases with distinct etiologies, yet they share certain molecular and biological characteristics. Gene expression datasets from the gene expression omnibus (GEO) repository (GSE30784 for OSCC and GSE10334 for periodontitis) were analyzed. Data preprocessing and differential gene expression analysis using GEO2R identified common differentially expressed genes (DEGs), and FunRich software facilitated the construction of a protein-protein interaction (PPI) network on the STRING database. Cytoscape, coupled with the CytoHubba plugin, identified Cluster of Differentiation 19 (CD19) and Von Willebrand Factor (VWF) as the top hub genes, with Complement C3 (C3) also highly ranked. Functional enrichment analysis highlighted pathways such as the B-cell receptor signaling pathway, complement and coagulation cascades, and hematopoietic cell lineage. Additionally, miRNet analysis identified key miRNAs, including hsa-mir-26a-5p, hsa-mir-129-2-3p, and hsa-mir-27a-3p, associated with these pathways. These findings suggested an association of molecular mechanisms between periodontitis and OSCC, with identified hub genes and miRNAs potentially serving as therapeutic targets.

Mechanisms Underlying the Therapeutic Effects of Nicotinamide Mononucleotide in Treating High-Fat Diet-induced Hypertrophic Cardiomyopathy based on GEO Datasets, Network Pharmacology, and Molecular Docking.

BACKGROUND: The beneficial effects of nicotinamide mononucleotide (NMN) on heart disease have been reported, but the effects of NMN on high-fat diet-induced hypertrophic cardiomyopathy (HCM) and its mechanisms of action are unclear. In this study, we systematically explored the effects and mechanism of action of NMN in HCM using network pharmacology and molecular docking. METHODS: Active targets of NMN were obtained from SWISS, CNKI, PubMed, DrugBank, BingingDB, and ZINC databases. HCM-related targets were retrieved from GEO datasets combined with GeneCards, OMIM, PharmGKB, and DisGeNET databases. A Protein-Protein Interaction (PPI) network was built to screen the core targets. DAVID was used for GO and KEGG pathway enrichment analyses. The tissue and organ distribution of targets was evaluated. Interactions between potential targets and active compounds were assessed by molecular docking. A molecular dynamics simulation was conducted for the optimal core protein-compound complexes obtained by molecular docking. RESULTS: In total, 265 active targets of NMN and 3918 potential targets of HCM were identified. A topological analysis of the PPI network revealed 10 core targets. GO and KEGG pathway enrichment analyses indicated that the effects of NMN were mediated by genes related to inflammation, apoptosis, and oxidative stress, as well as the FOXO and PI3K-Akt signaling pathways. Molecular docking and molecular dynamics simulations revealed good binding ability between the active compounds and screened targets. CONCLUSION: The possible targets and pathways of NMN in the treatment of HCM have been successfully predicted by this investigation. It provides a novel approach for further investigation into the molecular processes of NMN in HCM treatment.

Machine-learning prediction of a novel diagnostic model using mitochondria-related genes for patients with bladder cancer.

Bladder cancer (BC) is the ninth most-common cancer worldwide and it is associated with high morbidity and mortality. Mitochondrial Dysfunction is involved in the progression of BC. This study aimed to developed a novel diagnostic model based on mitochondria-related genes (MRGs) for BC patients using Machine Learning. In this study, we analyzed GSE13507 datasets and identified 752 DE-MRGs in BC specimens. Functional enrichment analysis uncovered the significant roles of 752 DE-MRGs in key processes such as cellular and organ development, as well as gene regulation. The analysis revealed the crucial functions of these genes in transcriptional regulation and protein-DNA interactions. Then, we performed LASSO and SVM-RFE, and identified four critical diagnostic genes including GLRX2, NMT1, OXSM and TRAF3IP3. Based on the above four genes, we developed a novel diagnostic model whose diagnostic value was confirmed in GSE13507, GSE3167 and GSE37816 datasets. Moreover, we reported the expressing pattern of GLRX2, NMT1, OXSM and TRAF3IP3 in BC samples. Immune cell infiltration analysis revealed that the four genes were associated with several immune cells. Finally, we performed RT-PCR and confirmed NMT1 was highly expressed in BC cells. Functional experiments revealed that knockdown of NMT1 suppressed the proliferation of BC cells. Overall, we have formulated a diagnostic potential that offered a comprehensive framework for delving into the underlying mechanisms of BC. Before proceeding with clinical implementation, it is essential to undertake further investigative efforts to validate its diagnostic effectiveness in BC patients.

Correlation of caecal microbiome endotoxins genes and intestinal immune cells in Eimeria tenella infection based on bioinformatics.

Introduction: The infection with Eimeria tenella (ET) can elicit expression of various intestinal immune cells, incite inflammation, disrupt intestinal homeostasis, and facilitate co-infection with diverse bacteria. However, the reciprocal interaction between intestinal immune cells and intestinal flora in the progression of ET-infection remains unclear. Objective: The aim of this study was to investigate the correlation between cecal microbial endotoxin (CME)-related genes and intestinal immunity in ET-infection, with subsequent identification of hub potential biomarker and immunotherapy target. Methods: Differential expression genes (DEGs) within ET-infection and hub genes related to CME were identified through GSE39602 dataset based on bioinformatic methods and Protein-protein interaction (PPI) network analysis. Moreover, immune infiltration was analyzed by CIBERSORT method. Subsequently, comprehensive functional enrichment analyses employing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis along with Gene Ontology (GO), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were performed. Results: A total of 1089 DEGs and 25 hub genes were identified and CXCR4 was ultimately identified as a essential CME related potential biomarker and immunotherapy target in the ET-infection. Furthermore, activated natural killer cells, M0 macrophages, M2 macrophages, and T regulatory cells were identified as expressed intestinal immune cells. The functional enrichment analysis revealed that both DEGs and hub genes were significantly enriched in immune-related signaling pathways. Conclusion: CXCR4 was identified as a pivotal CME-related potential biomarker and immunotherapy target for expression of intestinal immune cells during ET-infection. These findings have significant implications in elucidating the intricate interplay among ET-infection, CME, and intestinal immunity.

Investigating the mechanism of Sinisan formula in depression treatment: a comprehensive analysis using GEO datasets, network pharmacology, and molecular docking.

The herbal formula Sinisan (SNS) is a commonly used treatment for depression; however, its mechanism of action remains unclear. This article uses a combination of the GEO database, network pharmacology and molecular docking technologies to investigate the mechanism of action of SNS. The aim is to provide new insights and methods for future depression treatments. The study aims to extract effective compounds and targets for the treatment of depression from the T CMSP database. Relevant targets were searched using the GEO, Disgenet, Drugbank, PharmGKB and T T D databases, followed by screening of core targets. In addition, GO and KEGG pathway enrichment analyses were performed to explore potential pathways for the treatment of depression. Molecular docking was used to evaluate the potential targets and compounds and to identify the optimal core protein-compound complex. Molecular dynamics was used to further investigate the dynamic variability and stability of the complex. The study identified 118 active SNS components and 208 corresponding targets. Topological analysis of P P I networks identified 11 core targets. GO and KEGG pathway enrichment analyses revealed that the mechanism of action for depression involves genes associated with inflammation, apoptosis, oxidative stress, and the MAP K3 and P I3K-Akt signalling pathways. Molecular docking and dynamics simulations showed a strong binding affinity between these compounds and the screened targets, indicating promising biological activity. The present study investigated the active components, targets and pathways of SNS in the treatment of depression. Through a preliminary investigation, key signalling pathways and compounds were identified. These findings provide new directions and ideas for future research on the therapeutic mechanism of SNS and its clinical application in the treatment of depression.Communicated by Ramaswamy H. Sarma.

Influence of Pterostilbene on Gene Expression in Liver Cancer: An In Silico Analysis.

Background Liver cancer, in particular, is a serious threat to global health and has few viable treatments. One natural molecule that shows potential in cancer therapy is pterostilbene, especially for hepatocellular carcinoma (HCC). The molecular details of pterostilbene's interactions with liver cancer are uncovered in this study using an in silico method. Methodology This study determines the differentially expressed genes (DEGs) in HCC and the way pterostilbene affects them using data from Gene Expression Omnibus (GEO) datasets. To identify the intricate linkages and possible treatment targets, network pharmacology, protein-protein interaction (PPI) analysis, and pathway enrichment investigations were performed. Results The study revealed complex relationships between pterostilbene and liver cancer, identified important DEGs in HCC, and showed enriched pathways. Pterostilbene shows promise as a target for therapeutic approaches in HCC due to its modulation of important signaling pathways. Conclusions This work offers an extensive knowledge of pterostilbene's potential in liver cancer, despite intrinsic computational limitations. In addition to the importance of experimental validation, the pathways and DEGs that have been found provide insightful information for future investigation, highlighting the ongoing research that is necessary to create targeted therapeutics for HCC.

The necroptosis signature and molecular mechanism of lung squamous cell carcinoma.

BACKGROUND: Given the poor prognosis of lung squamous cell carcinoma (LUSC), the aim of this study was to screen for new prognostic biomarkers. METHODS: The TGCA_LUSC dataset was used as the training set, and GSE73403 was used as the validation set. The genes involved in necroptosis-related pathways were acquired from the KEGG database, and the differential genes between the LUSC and normal samples were identified using the GSEA. A necroptosis signature was constructed by survival analysis, and its correlation with patient prognosis and clinical features was evaluated. The molecular characteristics and drug response associated with the necroptosis signature were also identified. The drug candidates were then validated at the cellular level. RESULTS: The TCGA_LUSC dataset included 51 normal samples and 502 LUSC samples. The GSE73403 dataset included 69 samples. 159 genes involved in necroptosis pathways were acquired from the KEGG database, of which most showed significant differences between two groups in terms of genomic, transcriptional and methylation alterations. In particular, CHMP4C, IL1B, JAK1, PYGB and TNFRSF10B were significantly associated with the survival (p < 0.05) and were used to construct the necroptosis signature, which showed significant correlation with patient prognosis and clinical features in univariate and multivariate analyses (p < 0.05). Furthermore, CHMP4C, IL1B, JAK1 and PYGB were identified as potential targets of trametinib, selumetinib, SCH772984, PD 325901 and dasatinib. Finally, knockdown of these genes in LUSC cells increased chemosensitivity to those drugs. CONCLUSION: We identified a necroptosis signature in LUSC that can predict prognosis and identify patients who can benefit from targeted therapies.

Differential expression of miR-140-3p and its potential role during the development of the acute coronary syndrome.

BACKGROUND: Acute coronary syndrome (ACS) is a category of cardiovascular disease with a high fatality rate. AIMS: We searched the differential expressed miRNAs (DEmiRNAs) in ACS based on bioinformatic analysis and investigated the diagnostic value of plasma miR-140-3p in patients with ACS and its potential functional role in ACS. METHODS: The miRNAs (GSE94605, GSE49823, and GSE185729) microarray datasets of ACS were downloaded from the GEO datasets. After integrating the miRNA and mRNA interaction, a protein-protein interaction (PPI) network was constructed with 36 overlapped target mRNAs using STRING database. The plasma levels of miR-140-3p were detected by RT-qPCR, and its clinical diagnostic value was evaluated using the ROC curve. The potential effects of the miR-140-3p/RHOA axis in ACS were explored using human coronary endothelial cells (HCAECs). RESULTS: After overlapping the GEO datasets, miR-140-3p was identified in the microarray datasets of ACS. The plasma miR-140-3p expression levels were highly expressed in ACS patients than in healthy control and had diagnostic significance. The target mRNAs of miR-140-3p were predicted using TargetScan, miRWalk, TarBase, and miRDB databases. The PPI network identified ten hub genes. miR-140-3p could decrease the HCAECs' cell viability, while RHOA reversed the inhibition effect of miR-140-3p. CONCLUSIONS: The plasma expression of miR-140-3p was upregulated in ACS patients. miR-140-3p could decrease the HCAECs' cell viability, while RHOA reversed the inhibition effect of miR-140-3p. The miR-140-3p may be a potential diagnostic biomarker for the early detection of ACS.

The Integrated Analysis Identifies Three Critical Genes as Novel Diagnostic Biomarkers Involved in Immune Infiltration in Atherosclerosis.

Atherosclerosis (AS), a chronic inflammatory disease of the blood vessels, is the primary cause of cardiovascular disease, the leading cause of death worldwide. This study aimed to identify possible diagnostic markers for AS and determine their correlation with the infiltration of immune cells in AS. In total, 10 serum samples from AS patients and 10 samples from healthy subjects were collected. The original gene expression profiles of GSE43292 and GSE57691 were downloaded from the Gene Expression Omnibus database. Least absolute shrinkage and selection operator regression model and support vector machine recursive feature elimination analyses were carried out to identify candidate markers. The diagnostic values of the identified biomarkers were determined using receiver operating characteristic assays. The compositional patterns of the 22 types of immune cell fraction in AS were estimated using CIBERSORT. RT-PCR was performed to further determine the expression of the critical genes. This study identified 17 differentially expressed genes (DEGs) in AS samples. The identified DEGs were mainly involved in non-small cell lung carcinoma, pulmonary fibrosis, polycystic ovary syndrome, glucose intolerance, and T-cell leukemia. FHL5, IBSP, and SCRG1 have been identified as the diagnostic genes in AS. The expression of SCRG1 and FHL5 was distinctly downregulated in AS samples, and the expression of IBSP was distinctly upregulated in AS samples, which was further confirmed using our cohort by RT-PCR. Moreover, immune assays revealed that FHL5, IBSP, and SCRG1 were associated with several immune cells, such as CD8 T cells, naïve B cells, macrophage M0, activated memory CD4 T cells, and activated NK cells. Overall, future investigations into the occurrence and molecular mechanisms of AS may benefit from using the genes FHL5, IBSP, and SCRG1 as diagnostic markers for the condition.

A novel prognostic 6-gene signature for osteoporosis.

Introduction: The incidence of osteoporosis (OP) keeps increasing due to global aging of the population. Therefore, identifying the diagnostic and prognostic biomarkers of OP is of great significance. Methods: mRNA data from OP and non-OP samples were obtained from GEO database, which were divided into training set (GSE35959) and testing sets (GSE7158, GSE62402, GSE7429 and GSE56815). Gene modules most significantly related to OP were revealed using weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) between OP and normal samples in training set were identified using limma R package. Thereafter, above two gene sets were intersected to obtain the genes potentially related to OP. Protein-protein interaction (PPI) pairs were screened by STRING database and visualized using Cytoscape, while the plug-in cytoHubba was used to screen hub genes by determining their topological parameters. Afterwards, a diagnostic model was constructed using those hub genes, whose creditability was further evaluated by testing sets. Results: The results of WGCNA analysis found the Black module was most significantly related to OP, which included altogether 1286 genes. Meanwhile, 2771 DEGs were discovered between OP patients and the normal controls. After taking the intersection, 479 genes were identified potentially correlated with the development of OP. Subsequently, six hub genes were discovered through PPI network construction and node topological analysis. Finally, we constructed a support vector machine model based on these six genes, which can accurately classified training and testing set samples into OP and normal groups. Conclusion: Our current study constructed a six hub genes-based diagnostic model for OP. Our findings may shed some light on the research of the early diagnosis for OP and had certain practical significance.

Analyzing large scale gene expression data in colorectal cancer reveals important clues; CLCA1 and SELENBP1 downregulated in CRC not in normal and not in adenoma.

Early detection of colorectal cancer (CRC) increases the chances of survival and reduces the therapeutic problems and costs of treatment. Since molecular biomarkers can help us diagnose colorectal cancer early, we need to identify novel gene for predicting the early stages of tumorigenesis. Here, we integrated five independent CRC gene expression datasets derived from expression profiling by array comparing CRC with normal samples in: GSE21510, GSE4107, GSE25071, GSE15781 dataset, and GSE8671 dataset, including 64 samples from 32 patients comparing 32 colonic normal mucosa with 32 colorectal adenoma. To detect genes that expressed differentially in experimental circumstances of these datasets, we used web tool of GEO2R to compare groups of samples in the GEO data series. Furthermore, we constructed the protein-protein interactions network by STRING database for mostly downregulated genes and the expression of their members in PPI network were studied into five datasets separately. Also, the level of expression of selected biomarker genes in different stages of CRC compared to normal was studied. Our data revealed 17 common downregulated genes (average fold change (FC) in five tests ≥6) in CRC in comparison with normal (Test 1 to Test 4) and in adenoma compared with normal (Test 5). Studying of gene expression of PPI network members of these downregulated genes led to identifying of CLCA1, SELENBP1, CWC25, ACOT11, GUCY2C and ALDH1A1 as suppressor genes and PTGS2, PROCR, MOCS3 and NFS1 as oncogenes which respectively downregulated and upregulated in CRC. Since decreasing of gene expression was seen in CRC comparing with normal and due to no different expression seen for these 10 genes in adenoma, they, especially CLCA1 and SELENBP1, could be considered as biomarkers for early detection of CRC. Before using these signature genes in the clinic; however, further validations are required.

The Highly Expressed IFIT1 in Nasopharyngeal Carcinoma Enhances Proliferation, Migration, and Invasion of Nasopharyngeal Carcinoma Cells.

In this study, we aimed to identify potential targets modulating the progression of nasopharyngeal carcinoma (NPC) using integrated bioinformatics analysis and functional assays. Differentially expressed genes (DEGs) between NPC and normal tissues samples were obtained from publicly availably microarray datasets (GSE68799, GSE34573, and GSE53819) in the Gene Expression Omnibus (GEO) database. The bioinformatics analysis identified 49 common DEGs from three GEO datasets, which were mainly enriched in cytokine/chemokine pathways and extracellular matrix organization pathway. Further protein-protein interaction network analysis identified 11 hub genes from the 49 DEGs. The 11 hub genes were significantly up-regulated in the NPC tissues when compared to normal tissues by analyzing the Oncomine database. The 8 hub genes including COL5A1, COL7A1, COL22A1, CXCL11, IFI44L, IFIT1, RSAD2, and USP18 were significantly up-regulated in the NPC tissues when compared to normal tissues by using the Oncomine database. Further validation studies showed that IFIT1 was up-regulated in the NPC cells. Knockdown of IFI1T1 suppressed the proliferation, migration, and invasion of NPC cells; while IFIT1 overexpression promoted the proliferation, migration, and invasion of NPC cells. In conclusion, a total of 49 DEGs and 11 hub genes in NPC using the integrated bioinformatics analysis. IFIT1 was up-regulated in the NPC cells lines, and IFIT1 may act as an oncogene by promoting NPC cell proliferation, migration, and invasion.

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm.

Rheumatoid arthritis(RA) is the most common inflammatory arthritis, and a significant cause of morbidity and mortality. RA patients' synovial inflammation contains a variety of genes and signalling pathways that are poorly understood. It was the goal of this research to discover the major biomarkers related to the course of RA and how they connect to immune cell infiltration. The Gene Expression Omnibus was used to download gene microarray data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression were used to identify hub markers for RA. Single-sample GSEA was used to examine the infiltration levels of 28 immune cells and their connection to hub gene markers. The hub genes' expression in RA-HFLS and HFLS cells was verified by RT-PCR. The CCK-8 assay was applied to determine the roles of hub genes in RA. In this study, we identified 21 differentially expressed genes (DEGs) in RA. WGCNA yielded two co-expression modules, one of which exhibited the strongest connection with RA. Using a combination of differential genes, a total of 6 intersecting genes was discovered. Six hub genes were identified as possible biomarkers for RA after a lasso analysis was performed on the data. Three hub genes, CKS2, CSTA, and LY96, were found to have high diagnostic value using ROC curve analysis. They were shown to be closely related to the concentrations of several immune cells. RT-PCR confirmed that the expressions of CKS2, CSTA and LY96 were distinctly upregulated in RA-HFLS cells compared with HFLS cells. More importantly, knockdown of CKS2 suppressed the proliferation of RA-HFLS cells. Overall, to help diagnose and treat RA, it's expected that CKS2, CSTA, and LY96 will be available, and the aforementioned infiltration of immune cells may have a significant impact on the onset and progression of the disease.

Transcriptomic profiling of hepatic tissues for drug metabolism genes in nonalcoholic fatty liver disease: A study of human and animals.

Background: Drug metabolism genes are involved in the in vivo metabolic processing of drugs. In previous research, we found that a high-fat diet affected the transcript levels of mouse hepatic genes responsible for drug metabolism. Aims: Our research intends to discover the drug metabolism genes that are dysregulated at the transcriptome level in nonalcoholic fatty liver disease (NAFLD). Methods: We analyzed the transcriptome for drug metabolism genes of 35 human liver tissues obtained during laparoscopic cholecystectomy. Additionally, we imported transcriptome data from mice fed a high-fat diet in previous research and two open-access Gene Expression Omnibus (GEO) datasets (GSE63067 and GSE89632). Then, using quantitative real-time polymerase chain reaction (qRT-PCR), we cross-linked the differentially expressed genes (DEGs) in clinical and animal samples and validated the common genes. Results: In this study, we identified 35 DEGs, of which 33 were up-regulated and two were down-regulated. Moreover, we found 71 DEGs (39 up- and 32 down-regulated), 276 DEGs (157 up- and 119 down-regulated), and 158 DEGs (117 up- and 41 down-regulated) in the GSE63067, GSE89632, and high-fat diet mice, respectively. Of the 35 DEGs, nine co-regulated DEGs were found in the Venn diagram (CYP20A1, CYP2U1, SLC9A6, SLC26A6, SLC31A1, SLC46A1, SLC46A3, SULT1B1, and UGT2A3). Conclusion: Nine significant drug metabolism genes were identified in NAFLD. Future research should investigate the impacts of these genes on drug dose adjustment in patients with NAFLD. Clinical Trial Registration: http://www.chictr.org.cn, identifier ChiCTR2100041714.

Mechanisms underlying the therapeutic effects of Qingfeiyin in treating acute lung injury based on GEO datasets, network pharmacology and molecular docking.

BACKGROUND: Qingfeiyin (QFY) is a common Chinese herbal formula for the treatment of acute lung injury (ALI). However, its mechanisms of action are unclear. In this study, we systematically explored the effects and mechanism of action of QFY in ALI using network pharmacology and molecular docking. METHODS: Active compounds and targets of QFY were obtained from TCMSP and TCMID. ALI-related targets were retrieved from GEO datasets combined with GeneCards, OMIM, and TTD databases. A protein-protein interaction (PPI) network was built to screen the core targets. DAVID was used for GO and KEGG pathway enrichment analyses. The tissue and organ distribution of targets was evaluated. Interactions between potential targets and active compounds were assessed by molecular docking. A molecular dynamics simulation was conducted for the optimal core protein-compound complexes obtained by molecular docking. RESULTS: In total, 128 active compounds and 121 targets of QFY were identified. A topological analysis of the PPI network revealed 13 core targets. GO and KEGG pathway enrichment analyses indicated that the effects of QFY are mediated by genes related to inflammation, apoptosis, and oxidative stress as well as the MAPK and PI3K-Akt signaling pathways. Molecular docking and molecular dynamics simulations revealed good binding ability between the active compounds and screened targets. CONCLUSIONS: This study successfully predict the effective components and potential targets and pathways involved in the treatment of ALI for QFY. We provided a novel strategy for future research of molecular mechanisms of QFY in ALI treatment. Moreover, the potential active ingredients provide a reliable source for drug screening for ALI.

Novel necroptosis-related gene signature for predicting the prognosis of pancreatic adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a deadly digestive system tumor with a poor prognosis. Recently, necroptosis has been considered as a type of inflammatory programmed cell death. However, the expression of necroptosis-related genes (NRGs) in PAAD and their associations with prognosis remain unclear. NRGs' prediction potential in PAAD samples from The TCGA and GEO datasets was investigated. The prediction model was constructed using Lasso regression. Co-expression analysis showed that gene expression was closely related to necroptosis. NRGs were shown to be somewhat overexpressed in high-risk people even when no other clinical symptoms were present, indicating that they may be utilized in a model to predict PAAD prognosis. GSEA showed immunological and tumor-related pathways in the high-risk group. Based on the findings, immune function and m6A genes differ significantly between the low-risk and high-risk groups. MET, AM25C, MROH9, MYEOV, FAM111B, Y6D, and PPP2R3A might be related to the oncology process for PAAD patients. Moreover, CASKIN2, TLE2, USP20, SPRN, ARSG, MIR106B, and MIR98 might be associated with low-risk patients with PAAD. NRGs and the relationship of the immune function, immune checkpoints, and m6A gene expression with NRGs in PAAD may be considered as potential therapeutic targets that should be further studied.

Low expression of CDHR1 is an independent unfavorable prognostic factor in glioma.

Background: Analysis of the differentially expressed genes between lower grade glioma (LGG) and glioblastoma (GBM) will identify genes involved in a more aggressive phenotype of glioma. Methods: Differentially expressed genes between GBM and LGG were identified using published datasets. Kaplan-Meier estimator was used to determine the overall survival of different groups of glioma patients. The biological functions of CDHR1 in glioma were tested using CCK-8 and trans-well assays. Results: CCDC109B, CD58, CLIC1, EFEMP2, EMP3, LAMC1, LGALS1, PDLIM1 and TNFRSF1A were over-expressed, while, CDHR1 was down-regulated in GBM in The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE4412 and GSE43378 datasets. Compared with normal brain tissues, CDHR1 was down-regulated in glioma tissues. And low expression of CDHR1 was an unfavorable prognostic factor in glioma. Moreover, CDHR1 was lowly expressed in mesenchymal GBM subtype and lower expression of CDHR1 was associated with the worse clinical prognosis of GBM. Furthermore, CDHR1 was down-regulated in astrocytoma LGG subtype and low expression of CDHR1 was a bad prognosis of LGG. CDHR1 expression levels were also associated with IDH mutation. IDH mutant LGG or GBM patients were with higher CDHR1 expression. High expression of CDHR1 was a favorable prognosis in IDH mutant or IDH wild type LGG patients. CHDR1 expression was associated with MGMT methylation and CDHR1 was down-regulated in chemotherapy un-responsive LGG patients. CDHR1 was an independent prognostic factor and negatively associated with EMP3 expression. Glioma patients with low CDHR1 and high EMP3 expression had worse clinical outcomes. At last, we showed that over-expression of CDHR1 could inhibit glioma cell growth and invasion. Conclusion: Low expression of CDHR1 was an independent unfavorable prognostic factor in glioma.

The role of PI3K signaling pathway and its associated genes in papillary thyroid cancer.

BACKGROUND: One of the well-differentiated types of thyroid cancer is papillary thyroid cancer (PTC), often developed by genetic mutations and radiation. METHODS: In this study, the public NCBI-GEO database was systematically searched. The eligible datasets were the targets for biomarker discovery associated with PI3K signaling pathway. RESULTS: Only two datasets were suitable and passed the inclusion criteria. The meta-analysis outcomes revealed eleven upregulation and thirteen downregulation genes differentially expressed between PTC and healthy tissues. Moreover, the outcomes for survival and disease-free rates for each gene were illustrated. CONCLUSIONS: The present research suggests a panel signature of 24 gene biomarkers in diagnosing the PTC.