Implications of Human Antimicrobial Peptide Defensin Beta-1 in Clinical Oral Squamous Cell Carcinoma Patients via an Integrated Bioinformatics Approach.

BACKGROUND: The human antimicrobial peptide defensin beta 1 (DEFB1) has been found to play antimicrobial and anti-inflammatory roles in oral diseases; however, its tumor-regulating role in oral squamous cell carcinoma (OSCC) has not yet been researched by using an integrative bioinformatics approach. OBJECTIVE: To investigate the regulating mechanisms of the DEFB1 gene in OSCC in terms of its expression patterns, prognostic values, biological functions, and implication for tumor immunity. METHODS: The DEFB1 gene expression pattern and regulatory involvement in OSCC were investigated using publically accessible data from TCGA database. R software tools and public web servers were utilized to conduct statistical analysis of data from cancer and noncancerous samples. RESULTS: DEFB1 was found to be significantly downregulated in OSCC tumor samples compared with healthy control oral samples. The DEFB1 gene was found associated with the prognostic outcomes of OSCC, and its upregulation represented better survival outcome. Gene set enrichment analysis (GSEA) results showed that DEFB1-significantly correlated genes were mainly enriched in four signaling pathways mediating the antitumor role of DEFB1 in OSCC, including extracellular matrix-related pathway, RTK/PI3K/AKT/mTOR pathway, keratinization, and cytokine-related pathway. The gene-gene interaction network showed that DEFB1 was closely correlated with several genes, for example, CCR6 (C-C motif chemokine receptor 6), CXCL1 (C-X-C motif chemokine ligand 1), MAP4K2 (mitogen-activated protein kinase kinase kinase kinase 2), PTGER3 (prostaglandin E receptor 3), and MMP7 (matrix metallopeptidase 7). Moreover, DEFB1 was found to be involved in the tumor immunity of OSCC by regulating the function of tumor macrophage cells, mast cells, T cells, and NK cells. CONCLUSIONS: Given the dysregulation, prognostic value, and tumor progression-related biological pathway alteration, indicating the tumor immune-modulatory role of DEFB1 in OSCC, the DEFB1 gene should be regarded as a potential therapeutic target for treating oral cancer.

Reveals of quercetin's therapeutic effects on oral lichen planus based on network pharmacology approach and experimental validation.

Oral lichen planus (OLP) is a localized autoimmune disease of the oral mucosa, with an incidence of up to 2%. Although corticosteroids are the first-line treatment, they cause several adverse effects. Quercetin, a naturally occurring compound, has fewer side-effects and provides long-term benefits. Besides, it has powerful anti­inflammatory activities. Here, we combined network pharmacology with experimental verification to predict and verify the key targets of quercetin against OLP. First, 66 quercetin-OLP common targets were analyzed from various databases. The protein-protein interaction (PPI) network was constructed. Topology analysis and MCODE cluster analysis of common targets were conducted to identify 12 key targets including TP53, IL-6 and IFN-γ and their connections. Gene functions and key signaling pathways, including reactive oxygen species metabolism, IL-17 pathway and AGE-RAGE pathway, were enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then, in vitro experiments showed that quercetin interfered with Th1/Th2 balance by acting on IL-6 and IFN-γ to modulate the immune system in treating OLP. Quercetin considerably affected the apoptosis and migration of T lymphocytes in OLP patients. Our study reveals the potential therapeutic targets and signaling pathways of quercetin associated with OLP, and establishes the groundwork for future clinical applications.

Identification of Therapeutic Targets and Prognostic Biomarkers among Genes from the Mediator Complex Family in the Hepatocellular Carcinoma Tumour-Immune Microenvironment.

BACKGROUND: Hepatocellular carcinoma (HCC) is predominant among all types of primary liver cancers characterised by high morbidity and mortality. Genes in the mediator complex (MED) family are engaged in the tumour-immune microenvironment and function as regulatory hubs mediating carcinogenesis and progression across diverse cancer types. Whereas research studies have been conducted to examine the mechanisms in several cancers, studies that systematically focused on the therapeutic and prognostic values of MED in patients with HCC are limited. METHODS: The online databases ONCOMINE, GEPIA, UALCAN, GeneMANIA, cBioPortal, OmicStudio, STING, Metascape, and TIMER were used in this study. RESULTS: The transcriptional levels of all members of the MED family in HCC presented an aberrant high expression pattern. Significant correlations were found between the MED1, MED6, MED8, MED10, MED12, MED15, MED17, MED19, MED20, MED21, MED22, MED23, MED24, MED25, MED26, and MED27 expression levels and the pathological stage in the patients with HCC. The patients with high expression levels of MED6, MED8, MED10, MED17, MED19, MED20, MED21, MED22, MED24, and MED25 were significantly associated with poor prognosis. Functional enrichment analysis revealed that the members of the MED family were mainly enriched in the nucleobase-containing compound catabolic process, regulation of chromosome organisation, and transcriptional regulation by TP53. Significant correlations were found between the MED6, MED8, MED10, MED17, MED19, MED20, MED21, MED22, MED24, and MED25 expression levels and all types of immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells). B cells and MED8 were independent predictors of overall survival. We found significant correlations between the somatic copy number alterations of the MED6, MED8, MED10, MED20, MED21, MED22, MED24, and MED25 molecules and the abundance of immune infiltrates. CONCLUSIONS: Our study delineated a thorough landscape to investigate the therapeutic and prognostic potentials of the MED family for HCC cases, which yielded promising results for the development of immunotherapeutic drugs and construction of a prognostic stratification model.

Identification of immune features of HIV-infected patients with antiretroviral therapy through bioinformatics analysis.

BACKGROUND: Acquired immunodeficiency syndrome (AIDS) is a disease arising from human immunodeficiency virus (HIV). Antiretroviral therapy (ART) is a main therapeutic regimen for inhibiting HIV proliferation and viability. Identification of differentially expressed genes (DEGs) in HIV-infected patients with and without ART could provide theoretical evidence for deep research into the efficacy of ART and corresponding mechanism. METHODS: In this study, mRNA microarray data (GSE108296) of HIV-infected patients who received and didn't receive ART were downloaded from Gene Expression Omnibus (GEO) database. DEGs were obtained through differential analysis with R package limma. Then, protein-protein interaction (PPI) analysis was performed to identify hub genes and functional modules. Besides, immune-related DEGs were screened, followed by GO annotation and KEGG pathway enrichment analysis. Moreover, various immune cells and immune functions in samples were analyzed by ESTIMATE, ssGSEA and CIBERSORT, based on which the immune function of HIV-infected patients who received and didn't receive ART was evaluated. RESULTS: A total of 109 DEGs were obtained from differential analysis. Among them, 19 immune-related DEGs were identified and subjected to GO and KEGG enrichment analyses. Furthermore, PPI network analysis was undertaken on the 109 DEGs. 10 hub genes and 3 functional modules were further screened. It was shown that these genes and functional modules were correlated with immune functions and relevant signaling pathways. ESTIMATE, ssGSEA and CIBERSORT results displayed that HIV-infected patients with ART presented a relatively high immune level. CONCLUSION: According to bioinformatics analysis, we reasonably posited that HIV-infected patients who received ART had an increased immune level relative to patients who didn't receive ART.

Transcriptome Analysis of Large to Giant Congenital Melanocytic Nevus Reveals Cell Cycle Arrest and Immune Evasion: Identifying Potential Targets for Treatment.

Large to giant congenital melanocytic nevus (lgCMN) is a benign cutaneous tumor that develops during embryogenesis. A large number of lgCMN patients are ineligible for surgical treatment; hence, there is an urgent need to develop pharmacological treatments. Clinically, tumorigenesis and progression essentially halt after birth, resulting in the homeostasis of growth arrest and survival. Numerous studies have employed whole-genome or whole-exome sequencing to clarify the etiology of lgCMN; however, transcriptome sequencing of lgCMN is still lacking. Through comprehensive transcriptome analysis, this study elucidated the ongoing regulation and homeostasis of lgCMN and identified potential targets for treatment. Transcriptome sequencing, identification of differentially expressed genes and hub genes, protein-protein network construction, functional enrichment, pathway analysis, and gene annotations were performed in this study. Immunohistochemistry, real-time quantitative PCR, immunocytofluorescence, and cell cycle assays were employed for further validation. The results revealed several intriguing phenomena in lgCMN, including P16-induced cell cycle arrest, antiapoptotic activity, and immune evasion caused by malfunction of tumor antigen processing. The arrested cell cycle in lgCMN is consistent with its phenotype and rare malignant transformation. Antiapoptotic activity and immune evasion might explain how such heterogeneous cells have avoided elimination. Major histocompatibility complex (MHC) class I-mediated tumor antigen processing was the hub pathway that was significantly downregulated in lgCMN, and ITCH, FBXW7, HECW2, and WWP1 were identified as candidate hub genes. In conclusion, our research provides new perspectives for immunotherapy and targeted therapy.

RNA-Binding Protein Expression Alters Upon Differentiation of Human B Cells and T Cells.

B cells and T cells are key players in the defence against infections and malignancies. To exert their function, B cells and T cells differentiate into effector and memory cells. Tight regulation of these differentiation processes is key to prevent their malfunction, which can result in life-threatening disease. Lymphocyte differentiation relies on the appropriate timing and dosage of regulatory molecules, and post-transcriptional gene regulation (PTR) is a key player herein. PTR includes the regulation through RNA-binding proteins (RBPs), which control the fate of RNA and its translation into proteins. To date, a comprehensive overview of the RBP expression throughout lymphocyte differentiation is lacking. Using transcriptome and proteome analyses, we here catalogued the RBP expression for human B cells and T cells. We observed that even though the overall RBP expression is conserved, the relative RBP expression is distinct between B cells and T cells. Differentiation into effector and memory cells alters the RBP expression, resulting into preferential expression of different classes of RBPs. For instance, whereas naive T cells express high levels of translation-regulating RBPs, effector T cells preferentially express RBPs that modulate mRNA stability. Lastly, we found that cytotoxic CD8+ and CD4+ T cells express a common RBP repertoire. Combined, our study reveals a cell type-specific and differentiation-dependent RBP expression landscape in human lymphocytes, which will help unravel the role of RBPs in lymphocyte function.

Analysis of immune-related key genes in Alzheimer's disease.

This research revealed that 15 modules were obtained through weighted gene co-expression network analysis, among which the magenta and blue modules were significantly associated with Alzheimer's Disease (AD). There were 121 genes in the magenta module and 1022 genes in the blue module. Through differently expressed genes analysis, significant differences were shown in 134 genes (88 were up-regulated and 46 were down-regulated). 34 immune-key genes were obtained after three types of genes were crossed. Functional enrichment analysis showed that genes were mainly enriched in cytokine receptor activity and immune receptor activity. Through protein-protein interaction (PPI) network analysis, 10 hub genes were obtained: SERPINE1, ZBTB16, CD44, BCL6, HMOX1, SLC11A1, CEACAM8, ITGA5, SOCS3, and IL4R. Through immune-infiltration analysis, significant differences were demonstrated in four immune cells: CD8 + T cells, resting NK cells, M2 macrophages, and activated dendritic cells, and a significant positive correlation was shown between CD8 + T cells and macrophages M2, or between the other two cells. CEACAM8 was positively correlated with CD8 + T cells and macrophages M2, and negatively correlated with the other two cells while the remaining nine genes showed the opposite. Receiver operating characteristic (ROC) curve analysis demonstrated that both the differential immune cells and 10 hub genes had good diagnostic values. In GSE122063, the hub genes were verified and BCL6, CD44, HMOX1, IL4R, ITGA5, and SOCS3 were up-regulated. Meanwhile, hub genes was up-regulated in the brain tissues of AD rats. This study is of great significance for the diagnosis and therapy of AD.

The Impact of Immune Microenvironment on the Prognosis of Pancreatic Ductal Adenocarcinoma Based on Multi-Omics Analysis.

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor characterized by rapid progression, early metastasis, high recurrence, and limited responsiveness to conventional therapies. The 5-year survival rate of PDAC is extremely low (<8%), which lacks effective prognostic evaluation indicators. In this study, we used xCell to analyze infiltrating immune cells in a tumor and through the univariate and multivariate Cox analyses screened out two prognosis-related immune cells, CD4+TN and common lymphoid progenitor (CLP), which were used to construct a Cox model and figure out the risk-score. It was found that the constructed model could greatly improve the sensitivity of prognostic evaluation, that the higher the risk-score, the worse the prognosis. In addition, the risk-score could also identify molecular subtypes with poor prognosis and immunotherapy sensitivity. Through transcriptome and whole-exome sequencing analysis of PDAC dataset from The Cancer Genome Atlas (TCGA), it was found that copy number deletion and low expression of CCL19 might be crucial factors to affect the risk-score. Lastly, validation of the above findings was confirmed not only in Gene Expression Omnibus (GEO) datasets but also in our PDAC patient samples, Peking2020 cohort.

Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer.

BACKGROUND: Infiltrating immune and stromal cells are important components of the endometrial cancer (EC) microenvironment, which has a significant effect on the biological behavior of EC, suggesting that unique immune-related genes may be associated with the prognosis of EC. However, the association of immune-related genes with the prognosis of EC has not been elucidated. We attempted to identify immune-related genes with potentially prognostic value in EC using The Cancer Genome Atlas database and the relationship between immune microenvironment and EC. METHODS: We analyzed 578 EC samples from TCGA database and used weighted gene co-expression network analysis to screen out immune-related genes. We constructed a protein-protein interaction network and analyzed it using STRING and Cytoscape. Immune-related genes were analyzed through conjoint Cox regression and random forest algorithm analysis were to identify a multi-gene prediction model and stratify low-risk and high-risk groups of EC patients. Based on these data, we constructed a nomogram prediction model to improve prognosis assessment. Evaluation of Immunological, gene mutations and gene enrichment analysis were applied on these groups to quantify additional differences. RESULTS: Using conjoint Cox regression and random forest algorithm, we found that TRBC2, TRAC, LPXN, and ARHGAP30 were associated with the prognosis of EC and constructed four gene risk models for overall survival and a consistent nomogram. The time-dependent receiver operating characteristic curve analysis revealed that the area under the curve for 1-, 3-, and 5-y overall survival was 0.687, 0.699, and 0.76, respectively. These results were validated using a validation cohort. Immune-related pathways were mostly enriched in the low-risk group, which had higher levels of immune infiltration and immune status. CONCLUSION: Our study provides new insights for novel biomarkers and immunotherapy targets in EC.

Two types of immune infiltrating cells and six hub genes can predict the occurrence of myasthenia gravis in patients with thymoma.

Thymoma is the most common primary mass in anterior mediastinum. Although associated with low malignancy, it is often accompanied by myasthenia gravis resulting in poor prognosis. Due to the dual factors of tumor immune tolerance and autoimmune reaction, it is urgent to understand the immune status of MG with thymoma. In this study, RNA sequencing data were obtained from the TCGA and GEO cohorts to identify differentially expressed messenger RNAs and infiltrated immune cells. A total of 121 samples in TCGA and 43 samples in GEO were screened out. The infiltrated immune cells were identified by CIBERSORT, in which Tfh cells and activated DC cells were abnormal in thymoma patients. The differently expressed genes were performed by package LIMMA. The functional characteristics of differently expression genes were analyzed by GO and KEGG; one GO and seven KEGG pathways were both found in both TCGA and GEO cohorts. Meanwhile, 27 common differently expressed genes were obtained and were displayed by a Venn diagram. The TRRUST was used to screen the hub genes for the common 27 different genes and 6 genes were found. Then, PPI networks were constructed. Subsequently, the relationship between SCNAs of common genes and related immune cells tested by TIMER. Kaplan-Meier plots, ROC curve and Cox's expression model for immune infiltration and hub genes were also tested. In conclusion, we found that two types of immune infiltrated cells and six hub genes can predict the occurrence of myasthenia gravis in thymoma patients.

Identification and Validation of TREM2 in Intracranial Aneurysms.

Background: Intracranial aneurysm (IA) is a cerebrovascular disease that seriously endangers human heath and life. However, the pathogenesis of IA has not been clarified. Objective: In this study, we explored the role of the triggering receptor expressed on myeloid cells-2 (TREM2) gene to explore a novel mechanism underlying IA. Methods: First, we verified the role of the candidate gene, TREM2 in a modified mouse model of IA. Second, we verified elevated expression of TREM2 using the Gene Expression Omnibus (GEO) database (GSE54083 and GSE75436) and developed protein interaction (PPI) network analysis using the top one hundred DEGs from GSE75436 dataset. Finally, we predicted a likely mechanism by which TREM2 is involved in the pathology of IA using single-gene Gene Set Enrichment Analysis (GSEA). Results: The expression of TREM2 and inflammatory factors was significantly increased in the modified mouse IA model, and showed a positive correlation. Elevated expression of TREM2 was also found in IA patients tissues from the GSE54083 and GSE75436 data sets. PPI network analyses suggested that the DEGs were involved in a variety of inflammatory processes. The GSEA results suggest that TREM2 may participate in IA progression by regulating macrophage function. Conclusion: TREM2 is highly expressed in both human and mouse IA tissues, and may participate in IA progression by regulating macrophage function and inflammatory factor expression. The molecular mechanism of TREM2 involvement in the IA process can be further studied using our modified mouse IA model.

Metabolic Analysis of Potential Key Genes Associated with Systemic Lupus Erythematosus Using Liquid Chromatography-Mass Spectrometry.

The biological mechanism underlying the pathogenesis of systemic lupus erythematosus (SLE) remains unclear. In this study, we found 21 proteins upregulated and 38 proteins downregulated by SLE relative to normal protein metabolism in our samples using liquid chromatography-mass spectrometry. By PPI network analysis, we identified 9 key proteins of SLE, including AHSG, VWF, IGF1, ORM2, ORM1, SERPINA1, IGF2, IGFBP3, and LEP. In addition, we identified 4569 differentially expressed metabolites in SLE sera, including 1145 reduced metabolites and 3424 induced metabolites. Bioinformatics analysis showed that protein alterations in SLE were associated with modulation of multiple immune pathways, TP53 signaling, and AMPK signaling. In addition, we found altered metabolites associated with valine, leucine, and isoleucine biosynthesis; one carbon pool by folate; tyrosine metabolism; arginine and proline metabolism; glycine, serine, and threonine metabolism; limonene and pinene degradation; tryptophan metabolism; caffeine metabolism; vitamin B6 metabolism. We also constructed differently expressed protein-metabolite network to reveal the interaction among differently expressed proteins and metabolites in SLE. A total of 481 proteins and 327 metabolites were included in this network. Although the role of altered metabolites and proteins in the diagnosis and therapy of SLE needs to be further investigated, the present study may provide new insights into the role of metabolites in SLE.

Identification of Immune-Related Genes in Sepsis due to Community-Acquired Pneumonia.

BACKGROUND: Immunosuppression has a key function in sepsis pathogenesis, so it is of great significance to find immune-related markers for the treatment of sepsis. METHODS: Datasets of community-acquired pneumonia (CAP) with sepsis from the ArrayExpress database were extracted. Differentially expressed genes (DEGs) between the CAP group and normal group by Limma package were performed. After calculation of immune score through the ESTIMATE algorithm, the DEGs were selected between the high immune score group and the low immune score group. Enrichment analysis of the intersected DEGs was conducted. Further, the protein-protein interaction (PPI) of the intersected DEGs was drawn by Metascape tools. Related publications of the key DEGs were searched in NCBI PubMed through Biopython models, and RT-qPCR was used to verify the expression of key genes. RESULTS: 360 intersected DEGs (157 upregulated and 203 downregulated) were obtained between the two groups. Meanwhile, the intersected DEGs were enriched in 157 immune-related terms. The PPI of the DEGs was performed, and 8 models were obtained. In sepsis-related research, eight genes were obtained with degree ≥ 10, included in the models. CONCLUSION: CXCR3, CCR7, HLA-DMA, and GPR18 might participate in the mechanism of CAP with sepsis.

Comprehensive analysis of pivotal biomarkers, immune cell infiltration and therapeutic drugs for steroid-induced osteonecrosis of the femoral head.

Steroid-induced osteonecrosis of the femoral head (SONFH) is a progressive disease that leads to an increased disability rate. This study aimed to ascertain biomarkers, infiltrating immune cells, and therapeutic drugs for SONFH. The gene expression profile of the GSE123568 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the NetworkAnalyst platform. Functional enrichment, protein-protein interaction network (PPI), and module analyses were performed using Metascape tools. An immune cell abundance identifier was used to explore immune cell infiltration. Furthermore, hub genes were identified based on maximal clique centrality (MCC) evaluation using cytoHubba application and confirmed by qRT-PCR using clinical samples. Finally, the L1000 platform was used to determine potential drugs for SONFH treatment. The SONFH mouse model was used to determine the therapeutic effects of aspirin. In total, 429 DEGs were identified in SONFH samples. Functional enrichment analysis showed that these DEGs were enriched in myeloid leukocyte activation and osteoclast differentiation processes. A set of nine immune cell types was confirmed to be markedly different between the SONFH and control samples. All 10 hub genes were significantly highly expressed in the serum of SONFH patients, as shown by qRT-PCR. Finally, the therapeutic effect of aspirin on SONFH was examined in animal experiments. Taken together, our data revealed the hub genes and infiltrating immune cells in SONFH, and we also screened potential drugs for use in SONFH treatment.

Evaluation of tumor microenvironmental immune regulation and prognostic in lung adenocarcinoma from the perspective of purinergic receptor P2Y13.

Tumor-infiltrating immune cells (TICs) can serve as an important indicator to evaluate the prognosis and therapeutic response in lung adenocarcinoma (LUAD). The identification of mutated genes that can affect the abundance of TICs and prognosis has practical implications. In the presented study, tumor microenvironment (TME) scoring was performed by the ESTIMATE scoring system on 598 RNA transcripts selected from the TCGA database to determine the proportions of immune cells and stromal cells. The infiltration difference of TICs in LUAD samples was obtained by CIBERSORT. The 'immuneeconv' R software package, which integrates six latest algorithms, including TIMER, xCell, MCP-counter, CIBERSORT, EPIC and quanTIseq were used to verify the correlation between purinergic receptor P2Y13 (P2RY13) and immune cells. Based on RNA sequencing analysis of the Lewis lung cancer-bearing model in C57BL/6 mice and immunohistochemistry (IHC) of human LUAD tissues, the expression of P2RY13 and associated pathways were verified. It was shown that differentially expressed genes (DEGs) obtained by interactive analysis based on Immunescore and Stromalscore were significantly enriched in immune-related pathways. The expression of P2RY13 was significantly associated with prognosis and clinicopathological characteristics of LUAD patients. More importantly, this gene played an important role in maintaining the immune dominant environment and changing the regulation of TICs. P2RY13 expression was positively correlated with the infiltration of dendritic cells (DCs) in various of tumor tissues as validated by the PanglaoDB scRNA-seq database. Therefore, P2RY13 is expected to be a potential biomarker for predicting TME and the prognosis of LUAD after verification.

Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma.

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

Cytokines and chemokines expression pattern in herpes simplex virus type-1 encephalitis.

To explore the differently expressed cytokines and chemokines to understand the pathways that lead to herpes simplex encephalitis (HSE). Mice in the experimental group were inoculated intracranially with HSV-1. A high-throughput cytokine chip assay was employed to assess the expression of cytokines/chemokines in the mice brain. GO, KEGG, and PPIs analyses were used to investigate the biological process (BP), pathways and interaction network of the differently expressed proteins (DEPs) in HSE. 13 DEPs and various proteins-related signal pathways were identified in HSE, including three new factors (IL-1α, MIP-1γ, and sTNF RI). The proteins were mainly implicated in leukocyte activation and chemotaxis. Additionally, the DEPs constituted a pivotal protein interaction network where IL-6 might be a mediator. 13 DEPs and a series of related signal pathways were associated with the pathophysiological mechanisms responsible for HSE. IL-6 might be a key mediator in the inflammatory responses to the disease.

PD-L1 correlated gene expression profiles and tumor infiltrating lymphocytes in pancreatic cancer.

Objective: To study the expression and clinical value of PD-L1 gene in pancreatic cancer, and to predict the role of PD-L1 gene in the development of pancreatic cancer. Methods: The pancreatic cancer datasets were downloaded from the Cancer Genome Atlas (TCGA) and the Oncomine to obtain the PD-L1 gene expression profile and clinical information. Bioinformatics methods were used to analyze the correlation between the expression level of PD-L1 gene in pancreatic cancer and clinicopathological indicators, as well as its influence on prognosis. GSEA and WGCNA analysis was performed to predict the possible pathways of PD-L1 gene regulation in pancreatic cancer. TIMER and MCP-counter were used for PD-L1 with immune infiltration. The genes interact with PD-L1 were also investigated by STING and immunoco-precipitation combined with mass spectrometry analysis (IP-MS). Results: In TCGA database, the overall survival of patients with high expression of PD-L1 gene was significantly lower than that of patients with low expression of PD-L1 gene (χ2 = 12.52, P < 0.001). The samples with high expression of PD-L1 gene showed enrichment of 8 pathways including toll-like receptor signaling pathway and NOD receptor signaling pathway (P < 0.01, FDR < 0.05). Immune infiltration analysis suggested that PD-L1 were associated with monocytic lineage (r = 0.5). The proteins interacting with PD-L1 are mainly concentrated in RNA binding, ribosome, spliceosome and other biological processes or pathways. Conclusion: PD-L1 gene may play an important role in the development of pancreatic cancer and is expected to be a prognostic indicator of pancreatic cancer.

Preliminary therapeutic and mechanistic evaluation of S-allylmercapto-N-acetylcysteine in the treatment of pulmonary emphysema.

The objective of this work was to study the effects and mechanisms of S-allylmercapto-N-acetylcysteine (ASSNAC) in the treatment of pulmonary emphysema based on network pharmacology analysis and other techniques. Firstly, the potential targets associated with ASSNAC and COPD were integrated using public databases. Then, a protein-protein interaction network was constructed using String database and Cytoscape software. The Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed on DAVID platform. The molecular docking of ASSNAC with some key disease targets was implemented on the SwissDock platform. To verify the results of the network pharmacology, a pulmonary emphysema mice model was established and treated with ASSNAC. Besides, the expressions of the predicted targets were detected by immunohistochemistry, Western blot analysis or enzyme-linked immunosorbent assay. Results showed that 33 overlapping targets are achieved, including CXCL8, ICAM1, MAP2K1, PTGS2, ACE and so on. The critical pathways of ASSNAC against COPD involved arachidonic acid metabolism, chemokine pathway, MAPK pathway, renin-angiotensin system, and others. Pharmacodynamic experiments demonstrated that ASSNAC decreased the pulmonary emphysema and inflammation in the pulmonary emphysema mice. Therefore, these results confirm the perspective of network pharmacology in the target verification, and indicate the treatment potential of ASSNAC against COPD.