Expression Levels Of Biomarkers Research Articles

The SELP, CD93, IL2RG, and VAV1 Genes Associated with Atherosclerosis May Be Potential Diagnostic Biomarkers for Psoriasis.

Psoriasis and atherosclerosis are immunometabolic diseases. This study aimed to integrate bioinformatics and updated public resources to find potential biological markers associated with atherosclerosis that can cause psoriasis. Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened, and functional enrichment analysis was performed. We identified psoriasis and atherosclerosis common immune-related genes (PA-IRGs) by overlapping immune-related genes (IRGs) with genes in the module most associated with psoriasis and atherosclerosis obtained by weighted gene co-expression network analysis (WGCNAs). Receiver operating characteristic (ROC) was conducted to evaluate the predictive ability. The skin expression levels of diagnostic biomarkers were further verified by immunohistochemical staining. CIBERSORT, single-sample gene set enrichment analysis (ssGSEA), and Pearson's correlation analysis were applied to evaluate immune and lipid metabolism relationships in psoriatic tissues. In addition, a lincRNA-miRNA-mRNA network was constructed to find the pathogenesis in which diagnostic markers may be involved. Four PA-IRGs (SELP, CD93, IL2RG, and VAV1) demonstrated the optimal diagnostic value, with an AUC above 0.8. The immune cell infiltration analysis showed that dendritic resting cells, NK cell activation, neutrophils, macrophages M2, macrophages M0, and B-cell memory were highly abundant in psoriasis. Immune response analysis showed that TNF family members, chemokine receptors, interferons, natural killer cells, and TGF-β family members might be involved in psoriasis. Diagnostic biomarkers are strongly associated with various infiltrating immune cells, immune responses, and lipid metabolism. A lincRNA-miRNA-mRNA regulatory network consisting of 31 lincRNAs and 23 miRNAs was constructed. LINC00662 is involved in modulating four diagnostic biomarkers. This study identified atherosclerosis-related genes SELP, CD93, VAV1, and IL2RG as potential psoriasis diagnostic markers. Provide novel insights into the possible regulatory mechanisms involved in psoriasis.

Shenkang recipe alleviates renal aging in diabetic kidney disease by interfering with the lysine-specific demethylase KDM6B to modulate the PPAR-γ signaling pathway

ObjectiveDiabetic kidney diseases (DKD) is one of the most common and serious microvascular complications of diabetes, and is also a major cause of end-stage renal diseases and death in diabetic patients. Shenkang Recipe (SKR) is a traditional Chinese medicine formula that has been shown to be effective against DKD in clinical practice, however, its mechanism of action remains unclear. The study is to explore the anti-aging effects and potential molecular mechanisms of SKR in the renal aging process. MethodsThe active ingredients of SKR were identified by UHPLC-MS/MS. A DKD mouse model was induced using unilateral nephrectomy combined with intraperitoneal injection of STZ (60 mg/kg), followed by gavage of SKR (14.18, 28.36, 56.72 g/kg/d, ig) for 12 weeks in DKD mice, Dagliflozin (5 mg/kg/d, ig) was used as a positive control. The renal pathological damage was observed by HE, PAS and Masson staining. The expression levels of aging-related biomarkers were measured by RT-qPCR and Western blot. In vitro experiments, D-galactose was used to induce senescence in HMC cells, and the molecular mechanisms involved in the improvement of senescence by SKR were explored through the administration of GSK-J1 and FH535. ResultsIt was found that SKR treatment significantly attenuated renal pathological damage, alleviated the renal aging process in DKD mice and inhibited the activation of histone demethylase KDM6B. Transcriptomic analysis suggested that the PPAR signaling pathway was identified to be most significantly changed in the kidneys of DKD mice, including PPAR-α, PPAR-β, and PPAR-γ. The pharmacological inhibition of KDM6B demethylase activity by GSK-J1 could alleviate D-galactose-induced senescence in HMC cells, but this effect could be reversed by FH535, an inhibitor of PPAR-γ. ConclusionThese results suggest that SKR plays a role in delaying the renal aging process in DKD mice by interfering KDM6B to promote the PPAR-γ signaling pathway.

Immune infiltration patterns and identification of new diagnostic biomarkers GDF10, NCKAP5, and RTKN2 in non-small cell lung cancer

This study aimed to identify potential biomarkers for non-small cell lung cancer (NSCLC) and analyze the role of immune cell infiltration in NSCLC. R software was used to screen differentially expressed genes (DEGs) from NSCLC datasets obtained from the Gene Expression Omnibus (GEO) database, and functional correlation analysis was performed. The machine learning algorithms were used to screen the potential biomarkers of NSCLC. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. The protein and mRNA expression levels of potential biomarkers were verified based on the Human Protein Atlas (HPA) database and qRT-PCR. CIBERSORT was used to evaluate the infiltration of immune cells in NSCLC tissues, and the correlation between potential biomarkers and infiltrated immune cell was analyzed. Finally, specific siRNAs were utilized to reduce the GDF10, NCKAP5, and RTKN2 expression in A549 and H1975 cells. The proliferation ability of A549 and H1975 cells was detected by MTT assay. A total of 848 upregulated DEGs and 1308 downregulated DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the DEGs were mainly related to cell division. Disease ontology (DO) enrichment analysis showed that the diseases with these DEGs were mainly lung diseases, including NSCLC. In addition,three potential biomarkers were identified: GDF10, NCKAP5, and RTKN2. Immune cell infiltration analysis showed that resting NK cells, activated dendritic cells, and Tregs may be involved in the pathogenesis of NSCLC. Meanwhile, GDF10, NCKAP5, and RTKN2 were negatively correlated with Tregs and naïve B cells but were positively correlated with activated dendritic cells and resting NK cells. Immunohistochemical staining showed that the expression of GDF10, NCKAP5, and RTKN2 in the lung tissue of patients with NSCLC was lower than that of normal lung tissue. qRT-PCR also confirmed that the mRNA expression of three biomarkers in NSCLC cell lines A549 and H1975 were significantly lower than those in human normal lung epithelial cells BEAS-2B. An MTT assay showed that GDF10, NCKAP5, and RTKN2 knockdown significantly promoted the proliferation of A549 and H1975 cells. The in vitro experiments showed that GDF10, NCKAP5, and RTKN2 played the inhibitory effects on NSCLC cell lines proliferation. Hence, GDF10, NCKAP5, and RTKN2 can be used as diagnostic biomarkers for NSCLC.

PLEKHG2 Promotes NSCLC Cell Growth by Increasing Glycolysis via Activated PI3K/AKT Pathway.

Purpose: PLEKHG2 is a member of the diffuse B-cell lymphoma family. The function of PLEKHG2 in NSCLC was still unclear. This study aimed to investigate the relationship between the upregulated expression of PLEKHG2 and the prognosis of NSCLC and to revealed its mechanisms. Materials and methods: The expression of PLEKHG2 in NSCLC patients and its relationship with prognosis were first determined by analyzing public databases. Validation was performed in NSCLC cell lines and patient`s tumor tissues. PLEKHG2-silenced H1299 cells and PLEKHG2 overexpressing PC9 cells were constructed and used to validate its function. Glycolysis was evaluated by assaying cellular metabolites, glucose uptake and the expression levels of biomarkers of glycolysis. The relationship of PLEKHG2 and the PI3K/Akt pathway was demonstrated by small molecule inhibitors. The function of PLEKHG2 was evaluated in vivo by a H1299 cell derived xenograft (CDX) model. Results: PLEKEHG2 was highly expressed in NSCLC tissues and associated with poor prognosis. In PLEKHG2 knockdown H1299 cells, ATP and lactate production and glucose uptake were significantly inhibited. The opposite results were observed in PC9 cells with PLEKHG2 overexpression. The increased glycolysis following PLEKHG2 overexpression was abolished by adding the PI3K/AKT pathway inhibitor LY294002, suggesting that PLEKHG2 promotes glycolysis in NSCLC cells via activation of the PI3K/AKT pathway. Finally, we found that PLEKHG2 knockdown inhibited the tumor growth in the H1299 CDX model. Conclusion: PLEKHG2 contributed to NSCLC development by promoting glycolysis via activation of the PI3K/AKT pathway. PLEKHG2 was a potential therapeutic target and biomarker for poor prognosis of NSCLC.

Use of clinicopathological factors to predict prognosis of fertility-sparing treatment for endometrial endometrioid carcinoma and atypical hyperplasia.

The incidence of endometrial endometrioid carcinoma (EEC) has been gradually increasing over the past decade. Fertility-sparing therapy with progestin is a treatment option for EEC or endometrial atypical hyperplasia (AH). The present study evaluated the role of numerous prognostic factors following fertility-sparing therapy for EEC or AH. Furthermore, the present study assessed the strength of various clinicopathological indicators for the prediction of treatment efficacy. A retrospective analysis was performed of patients with EEC and AH who received fertility-sparing therapy between August 2013 and September 2021 at Peking University People's Hospital (Beijing, China). Endometrial specimens were obtained from each patient after 3 months of treatment and at the end of the fertility-sparing therapy, before treatment efficacy and prognosis were evaluated using the χ2 test. Furthermore, the protein expression levels of EEC biomarkers, such as estrogen receptor (ER), progesterone receptor (PR), paired box 2 (PAX2), PTEN and p53 were assessed using immunohistochemistry. The overall complete response (CR) rate of fertility-sparing treatment in the EEC group was 67.39% (31/46), whereas that in the AH group was 86.49% (32/37). The difference between the CR rates in the EEC and AH groups was statistically significant (P<0.05). There was no association between prognosis after treatment and ER, PAX2, PTEN or Ki-67 expression in the initially untreated AH or EEC groups. However, tissues with >50% positive PR expression were demonstrated to have a higher CR rate compared with those with ≤50% positive PR expression in both the EEC and AH groups. Furthermore, the PAX2-positive group tended to demonstrate higher CR rates compared with the PAX2-negative group in the patients with EEC. In conclusion, these data suggested that fertility-sparing therapy is effective for patients with EEC and AH who wish to remain fertile after treatment. Specifically, in the AH group, a higher proportion of patients achieved a CR whilst also achieving this more rapidly. Furthermore, PR was demonstrated to be a useful marker for the evaluation of EEC and AH.

DPP6 and MFAP5 are associated with immune infiltration as diagnostic biomarkers in distinguishing uterine leiomyosarcoma from leiomyoma.

Uterine leiomyosarcoma (ULMS) is the most common subtype of uterine sarcoma and is difficult todiscernfrom uterine leiomyoma (ULM) preoperatively. The aim of the study was to determine the potential and significance of immune-related diagnostic biomarkers in distinguishingULMS from ULM. Two public gene expression profiles (GSE36610 and GSE64763) from the GEO datasets containing ULMS and ULM samples were downloaded. Differentially expressed genes (DEGs) were selected and determined among 37 ULMS and 25 ULM control samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Disease Ontology (DO) enrichment analyses as well as gene set enrichment analysis (GSEA). The candidate biomarkers were identified by least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analyses. The receiver operating characteristic curve (ROC) was applied to evaluate diagnosticability. Forfurther confirmation, the biomarker expression levels and diagnostic value in ULMS were verified in the GSE9511 and GSE68295 datasets (12 ULMS and 10 ULM), and validated by immunohistochemistry (IHC). TheCIBERSORTalgorithm was used to calculate the compositional patterns of 22 types of immune cells in ULMS. In total,55DEGs wererecognized via GO analysis, and KEGG analyses revealed that the DEGs were enriched in nuclear division, and cell cycle. The recognized DEGs were primarily implicated in non-small cell lung carcinoma and breast carcinoma. Gene sets related to the cell cycle and DNA replication were activated in ULMS. DPP6 and MFAP5 were distinguished as diagnostic biomarkers of ULMS (AUC = 0.957, AUC = 0.899, respectively), and they were verified in the GSE9511 and GSE68295 datasets (AUC = 0.983, AUC = 0.942, respectively). The low expression of DPP6 and MFAP5 were associated with ULMS. In addition, the analysis of the immune microenvironment indicated that resting mast cells were positively correlated with DPP6 and MFAP5 expression and that eosinophils and M0 macrophages were negatively correlated with DPP6 expression (P<0.05). These findings indicated that DPP6 and MFAP5 are diagnostic biomarkers of ULMS, thereby offering a novel perspective for future studies on the occurrence, function and molecular mechanisms of ULMS.

Bioinformatics identification and validation of biomarkers and infiltrating immune cells in endometriosis.

Endometriosis (EM) is a common gynecological disorder that often leads to irregular menstruation and infertility. The pathogenesis of EM remains unclear and delays in diagnosis are common. Thus, it is urgent to explore potential biomarkers and underlying molecular mechanisms for EM diagnosis and therapies. Three EM-related datasets (GSE11691, GSE25628, and GSE86534) were downloaded from the Gene Expression Omnibus (GEO) which were integrated into a combined dataset after removing batch effect. Differentially expressed immune cell-related genes were obtained by CIBERSORT, WGCNA, and the identification of differentially expressed genes. Random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM) were then constructed and the biomarkers for EM were determined. A nomogram evaluating the risk of disease was constructed and the validity was assessed by the calibration curve, DCA curve, and clinical impact curve. Single-gene Gene Set Enrichment Analysis (GSEA)was performed to explore the molecular mechanisms of biomarkers. The ceRNA regulatory network of biomarkers was created by Cytoscape and potential target drugs were obtained in the DGIdb database (Drug-Gene Interaction database).The expression levels of biomarkers from clinical samples was quantified by RT-qPCR. The ratio of eight immune cells was significantly different between the eutopic and ectopic endometrium samples. A total of eight differentially expressed immune cell-related genes were investigated. The SVM model was a relatively suitable model for the prediction of EM and five genes (CXCL12, PDGFRL, AGTR1, PTGER3, and S1PR1) were selected from the model as biomarkers. The calibration curve, DCA curve, and clinical impact curve indicated that the nomogram based on the five biomarkers had a robust ability to predict disease. Single gene GSEA result suggested that all five biomarkers were involved in labyrinthine layer morphogenesis and transmembrane transport-related biological processes in EM. A ceRNA regulatory network containing 184 nodes and 251 edges was constructed. Seven drugs targeting CXCL12, 49 drugs targeting AGTR1, 16 drugs targeting PTGER3, and 21 drugs targeting S1PR1 were extracted as potential drugs for EM therapy. Finally, the expression of PDGFRL and S1PR1 in clinical samples was validated by RT-qPCR, which was consistent with the result of public database. In summary, we identified five biomarkers (CXCL12, PDGFRL, AGTR1, PTGER3, and S1PR1) and constructed diagnostic model, furthermore predicted the potential therapeutic drugs for EM. Collectively, these findings provide new insights into EM diagnosis and treatment.

Identification of diagnostic gene biomarkers related to immune infiltration in patients with idiopathic pulmonary fibrosis based on bioinformatics strategies.

The study aims to identify potential diagnostic markers of idiopathic pulmonary fibrosis (IPF) and analyze the significance of immune cell infiltration in this pathology. Download two publicly available gene expression profiles (GSE10667 and GSE24206 datasets) from the GEO database including 48 Idiopathic pulmonary fibrosis (IPF) samples and 21 human control samples and select for distinctly expressed genes (DEG) from them. Lasso regression model and support vector machine recursive feature elimination S,V,R,F analysis were used to check candidate biomarkers. The area under the subject's work characteristic curve (AUC) value is used to evaluate its recognition ability. The GSE53845 dataset (40 IPF patients and 8 controls) continue to validate the expression level and diagnostic value of biomarkers in IPF. Comprehensive analysis of immune infiltrated cells of IPF was performed using R software and immune cell infiltration estimation analysis tool- deconvolution algorithm (CIBERSORT). 43 DEGs were identified in total. The identified DEGs mostly involve pneumonia, lung disease, collagen disease, obstructive pulmonary disease and other diseases. The activation of IL-17 signaling pathways, amoebic disease, interaction of viral proteins with cytokines and cytokine receptors, protein digestion and absorption, and flaccid hormone signaling pathways in IPF were different from the control group. The expression degree of CRTAC1, COL10A1, COMP, RPS4Y1, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 in IPF tissue were prominently higher than the normal group. Immune cell infiltration analysis showed that CRTAC1, COL10A1, COMP, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 were associated with monocytes, plasma cells, neutrophils, and regulatory (treg) T cells. CRTAC1, COL10A1, COMP, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 can be used as diagnostic markers for IPF, providing new ideas for the future study of IPF occurrence and molecular mechanisms.

Identification of fatty acid metabolism-related molecular subtype biomarkers and their correlation with immune checkpoints in cutaneous melanoma.

Fatty acid metabolism (FAM) affects the immune phenotype in a metabolically dynamic tumor microenvironment (TME), but the use of FAM-related genes (FAMGs) to predict the prognosis and immunotherapy response of cutaneous melanoma (CM) patients has not been investigated. In this study, we aimed to construct FAM molecular subtypes and identify key prognostic biomarkers in CM. We used a CM dataset in The Cancer Genome Atlas (TCGA) to construct FAM molecular subtypes. We performed Kaplan-Meier (K-M) analysis, gene set enrichment analysis (GSEA), and TME analysis to assess differences in the prognosis and immune phenotype between subtypes. We used weighted gene co-expression network analysis (WGCNA) to identify key biomarkers that regulate tumor metabolism and immunity between the subtypes. We compared overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) between CM patients with high or low biomarker expression. We applied univariable and multivariable Cox analyses to verify the independent prognostic value of the FAM biomarkers. We used GSEA and TME analysis to investigate the immune-related regulation mechanism of the FAM subtype biomarker. We evaluated the immune checkpoint inhibition (ICI) response and chemotherapy sensitivity between CM patients with high or low biomarker expression. We performed real-time fluorescent quantitative PCR (qRT-PCR) and semi-quantitative analysis of the immunohistochemical (IHC) data from the Human Protein Atlas to evaluate the mRNA and protein expression levels of the FAM biomarkers in CM. We identified 2 FAM molecular subtypes (cluster 1 and cluster 2). K-M analysis showed that cluster 2 had better OS and PFS than cluster 1 did. GSEA showed that, compared with cluster 1, cluster 2 had significantly upregulated immune response pathways. The TME analysis indicated that immune cell subpopulations and immune functions were highly enriched in cluster 2 as compared with cluster 1. WGCNA identified 6 hub genes (ACSL5, ALOX5AP, CD1D, CD74, IL4I1, and TBXAS1) as FAM biomarkers. CM patients with high expression levels of the six biomarkers had better OS, PFS, and DSS than those with low expression levels of the biomarkers. The Cox regression analyses verified that the 6 FAM biomarkers can be independent prognostic factors for CM patients. The single-gene GSEA showed that the high expression levels of the 6 genes were mainly enriched in T-cell antigen presentation, the PD-1 signaling pathway, and tumor escape. The TME analysis confirmed that the FAM subtype biomarkers were not only related to immune infiltration but also highly correlated with immune checkpoints such as PD-1, PD-L1, and CTLA-4. TIDE scores confirmed that patients with high expression levels of the 6 biomarkers had worse immunotherapy responses. The 6 genes conveyed significant sensitivity to some chemotherapy drugs. qRT-PCR and IHC analyses verified the expression levels of the 6 biomarkers in CM cells. Our FAM subtypes verify that different FAM reprogramming affects the function and phenotype of infiltrating immune cells in the CM TME. The FAM molecular subtype biomarkers can be independent predictors of prognosis and immunotherapy response in CM patients.

Dual-Modality Molecular Imaging of Tumor via Quantum Dots-Liposome-Microbubble Complexes.

Molecular imaging has demonstrated promise for evaluating the expression levels of biomarkers for the early prediction of tumor progression and metastasis. However, most of the commonly used molecular imaging modalities are relatively single and have difficulties imaging complex biological processes. Here, we fabricated αvβ3-integrin-targeted quantum-dots-loaded liposome-microbubble (iRGD-QDLM) complexes that combined ultrasound imaging with optical imaging. The resulting iRGD-QDLM has excellent binding capability to 4T1 breast cancer cells. Ultrasound molecular imaging of 4T1 tumors demonstrated that significantly enhanced ultrasound molecular signals could be observed in comparison with non-targeted QDLM. Importantly, our study also suggested that iRGD-QDL on the surface of microbubbles could be delivered into a tumor by ultrasound-mediated microbubble destruction and adhered to αvβ3 integrin on breast cancer cells, achieving transvascular fluorescent imaging. Our study provides a novel approach to dual-modality molecular imaging of αvβ3 integrin in the tumor tissue.

Comprehensive Diagnostics of Diabetic Nephropathy by Transcriptome RNA Sequencing.

BackgroundDiabetic nephropathy (DN) is a primary driver of end-stage renal disease. Given the heterogeneity of renal lesions and the complex mechanisms of DN, the present-day diagnostic approach remains highly controversial. We aimed to design a diagnostic model by bioinformatics methods for discriminating DN patients from normal subjects.MethodsIn this study, transcriptome sequencing was performed on 6 clinical samples (3 from DN patients and 3 from healthy volunteers) from the Second Affiliated Hospital of Kunming Medical University. Construction of a competing endogenous RNA (ceRNA) network based on differentially expressed (DE)-mRNAs and -long noncoding RNAs (lncRNAs). Subsequently, the CytoHubba plugin was used to identify hub genes from DE-mRNAs in the ceRNA network and to perform functional enrichment analysis on them. The least absolute shrinkage and selection operator (LASSO) regression analysis was responsible for screening the diagnostic biomarkers from hub genes and assessing their diagnostic power using ROC curves. The pathways involved in hub genes were revealed by single-gene Gene Set Enrichment Analysis (GSEA). Moreover, we verified the expression levels of diagnostic biomarkers by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot.ResultsA total of 10 hub genes were screened from the ceRNA network, which appeared to be associated with the viral infection, kidney development, and regulation of immune and inflammatory responses. Subsequently, LASSO regression analysis established a diagnostic model consisting of DDX58, SAMD9L, and TLR6 with a robust diagnostic potency (AUC = 1). Similarly, single-gene GSEA showed a strong association of these diagnostic biomarkers with the viral infection. Furthermore, PCR and Western blot demonstrated showed that DDX58, SAMD9L, and TLR6 were upregulated in DN patients at both transcriptome and protein levels compared to healthy controls.ConclusionWe confirmed that differentially expressed hub genes may be novel diagnostic biomarkers in DN.

Identification of effective diagnostic biomarker and immune cell infiltration characteristics in acute liver failure by integrating bioinformatics analysis and machine-learning strategies

Background: To determine effective biomarkers for the diagnosis of acute liver failure (ALF) and explore the characteristics of the immune cell infiltration of ALF. Methods: We analyzed the differentially expressed genes (DEGs) between ALF and control samples in GSE38941, GSE62029, GSE96851, GSE120652, and merged datasets. Co-expressed DEGs (co-DEGs) identified from the five datasets were analyzed for enrichment analysis. We further constructed a PPI network of co-DEGs using the STRING database. Then, we integrated the two kinds of machine-learning strategies to identify diagnostic biomarkers of top hub genes screened based on MCC and Degree methods. And the potential diagnostic performance of the biomarkers for ALF was estimated using the AUC values. Data from GSE14668, GSE74000, and GSE96851 databases was performed as external verification sets to validate the expression level of potential diagnostic biomarkers. Furthermore, we analyzed the difference in the protein level of diagnostic biomarkers between normal and ALF mice models. Finally, we used CIBERSORT to estimate relative infiltration levels of 22 immune cell subsets in ALF samples and further analyzed the relationships between the diagnostic biomarkers and infiltrated immune cells. Results: A total of 200 co-DEGs were screened. Enrichment analyses depicted that they are highly enriched in metabolism and matrix collagen production-associated processes. The top 28 hub genes were obtained by integrating MCC and Degree methods. Then, the collagen type IV alpha 2 chain (COL4A2) was regarded as the diagnostic biomarker and showed excellent specificity and sensitivity. COL4A2 also showed a statistically significant difference and excellent diagnostic effectiveness in the verification set. In addition, there was a significant upregulation in the COL4A2 protein level in ALF mice models compared with the normal group. CIBERSORT analysis showed that activated CD4 T cells, plasma cells, macrophages, and monocytes may be implicated in the progress of ALF. In addition, COL4A2 showed different degrees of correlation with immune cells. Conclusion: In conclusion, COL4A2 may be a diagnostic biomarker for ALF, and immune cell infiltration may have important implications for the occurrence and progression of ALF.

Silver Nanoparticle Enhances Secretion of Exosomes in SH-SY5Y Cells: Potential Therapeutic Strategy for Human Neuroblastoma Cancer

Background: Exosomes-a subset of extracellular vesicles (EVs)-are secreted by virtually all cells, including human neuroblastoma cancer (SH-SY5Y) cells, into bodily fluids. Oxidative stress is critically involved in exosome biogenesis and release. Silver nanoparticles (AgNPs) induce cytotoxicity, oxidative stress, and apoptosis in cancer and non-cancer cells. Methods: Here, we studied whether AgNPs-induced oxidative stress could enhance exosome biogenesis and release under low serum conditions in the presence of AgNPs. Although several studies have reported various mechanisms that contribute to EV biogenesis and release from cells, none exists on the involvement of external stimuli by controlling acetylcholinesterase (AChE) and neutralsphingomyelinase (n-SMase) activities, total protein concentration of exosomes, and exosome count. Results: Owing to cytotoxic and oxidative stresses, AgNPs-treated cells and exosome release were significantly facilitated, which strongly correlated with the AgNPs-induced oxidative stress. Moreover, the expression levels of some important exosome biomarkers were found to be significant under oxidative stress conditions. N-acetylcysteine prevented oxidative stress-induced exosome biogenesis and release. Furthermore, we identified the involvement of the ceramide pathway in exosome functions by inhibiting AChE and n-SMase activities, and exosome protein/counts. These data contribute to the understanding of how AgNPs and intracellular molecular pathways affect exosome biogenesis and release in SH-SY5Y cells. Conclusion: To the best of our knowledge, this is the first study showing that AgNPs stimulate exosome biogenesis and release by inducing oxidative stress and ceramide pathways.

Anti-inflammatory Effect of Cornus Officinalis fruit extract and Cornus Officinalis Fruit Cheonghyeol Plus in Human Umbilical Vein Endothelial Cell

Objectives: The purpose of this study was to investigate the anti-inflammatory effect of Cornus Officinalis fruit extract(CE) and Cornus Officinalis Fruit Cheonghyeol Plus(CCP) in Human Umbilical Vein Endothelial Cell.Methods: We measured cell viability of CE, CCP and treated HUVEC with TNF-α. We measured the mRNA expression levels of KLF2, eNOS, MCP-1, ICAM-1, VCAM-1, the protein expression levels of KLF2, eNOS, MCP-1, ICAM-1, VCAM-1, and the protein phosphorylation level of ERK, JNK, p38 and the biomarker expression levels of MCP-1, ICAM-1, VCAM-1.Results: 1.CE incresed the mRNA, protein expression levels of KLF2, eNOS at concentrations of 100㎍/㎖ compared to the control group. CE decresed the mRNA, protein and biomarker expression levels of MCP-1,ICAM-1,VCAM-1 at concentrations of 100㎍/㎖ compared to the control group. CE decresed the protein phosphorylation level of p38 at concentrations of 100㎍/㎖ compared to the control group. 2. CCP incresed the mRNA, protein expression levels of KLF2, eNOS at concentrations of 100㎍/㎖ or more compared to the control group. CCP decresed the mRNA, protein and biomarker expression levels of MCP-1, ICAM-1, VCAM-1 at concentrations of 100㎍/㎖ or more compared to the control group. CCP decresed the protein phosphorylation level of ERK at concentrations of 100㎍/㎖ or more, p38 at concentrations of 200㎍/㎖ or more, and JNK at concentrations of 400㎍/㎖ compared to the control group.Conclusions: These results present that CE and CCP has anti-inflammatory effect in HUVEC. So, it could help treat or prevent inflammation in vein caused by dyslipidemia and contribute prevention of cardiovascular and cerebrovascular cerebrovascular diseases.

Miniaturized microfluidic-based nucleic acid analyzer to identify new biomarkers of biopsy lung cancer samples for subtyping.

Identifying new biomarkers is necessary and important to diagnose and treat malignant lung cancer. However, existing protein marker detection methods usually require complex operation steps, leading to a lag time for diagnosis. Herein, we developed a rapid, minimally invasive, and convenient nucleic acid biomarker recognition method, which enabled the combined specific detection of 11 lung cancer typing markers in a microliter reaction system after only one sampling. The primers for the combined specific detection of 11 lung cancer typing markers were designed and screened, and the microfluidic chip for parallel detection of the multiple markers was designed and developed. Furthermore, a miniaturized microfluidic-based analyzer was also constructed. By developing a microfluidic chip and a miniaturized nucleic acid analyzer, we enabled the detection of the mRNA expression levels of multiple biomarkers in rice-sized tissue samples. The miniaturized nucleic acid analyzer could detect ≥10 copies of nucleic acids. The cell volume of the typing reaction on the microfluidic chip was only 0.94 μL, less than 1/25 of that of the conventional 25-μL Eppendorf tube PCR method, which significantly reduced the testing cost and significantly simplified the analysis of multiple biomarkers in parallel. With a simple injection operation and reverse transcription loop-mediated isothermal amplification (RT-LAMP), real-time detection of 11 lung cancer nucleic acid biomarkers was performed within 45 min. Given these compelling features, 86 clinical samples were tested using the miniaturized nucleic acid analyzer and classified according to the cutoff values of the 11 biomarkers. Furthermore, multi-biomarker analysis was conducted by a machine learning model to classify different subtypes of lung cancer, with an average area under the curve (AUC) of 0.934. This method shows great potential for the identification of new nucleic acid biomarkers and the accurate diagnosis of lung cancer.

Machine Learning-Devised Immune-Related lncRNA Signature Panel Predicts the Prognosis and Immune Landscape in Breast Cancer Novel IRLP Signature in BRCA.

Long noncoding RNAs (lncRNAs) actively participate in breast cancer (BRCA) tumorigenesis via epigenetic mechanisms. Our study identified immune-related lncRNA (irlncRNA) pairs and compiled them into a set of noncoding gene signatures able to stratify subtypes of BRCA associated with variable degrees of survival and immune cell infiltration. A 40 immune-related lncRNA pair (IRLP) signature including 43 irlncRNAs was built, with high sensitivity and specificity for the prediction of survival in different molecular subtypes of BRCA. Results demonstrated that the low-risk group showed a significantly longer survival rate, and this novel IRLP signature was highly associated with survival status, T stage, metastatic disease, and overall stage in BRCA. Immune infiltrating analyses found that the low-risk group has a lower expression level of macrophage M2 and a higher expression level of immunosuppressed biomarkers than the high-risk group. DEirlncRNAs were further proven to be significantly related to the MAPK signaling, Jak-STAT signaling, and ErbB signaling pathways in BRCA. In conclusion, the 40 IRLP signature showed a promising clinical prediction value in the prognosis of different molecular subtypes and immunotherapy response in BRCA, and the underlying mechanism for these IRLPs warrants further investigations.

Scutellarin Protects Against Mitochondrial Reactive Oxygen Species-Dependent NLRP3 Inflammasome Activation to Attenuate Intervertebral Disc Degeneration.

Intervertebral disc degeneration (IVDD) is a predominant cause of disc herniation and is widespread worldwide. Inflammatory responses, mitochondrial dysfunction, and extracellular matrix degradation are known to be involved in IVDD. Scutellarin, an active ingredient extracted from Erigeron breviscapus (Vaniot) Ha, Hand-Mazz, is reported to exhibit therapeutic potential in several degenerative diseases by suppressing inflammation and regulating metabolism. However, whether scutellarin can improve IVDD remains unknown. Human primary nucleus pulposus cells (HNPCs) were cultured and stimulated with TNF-α in the presence or absence of scutellarin. Furthermore, a rat needle puncture model was established, and scutellarin was injected into the IVD to verify its protective function against IVDD. Scutellarin attenuated the inflammatory reaction and retained the production of major IVD components both in vitro and in vivo. Mechanistically, scutellarin reduced the amount of reactive oxygen species (ROS), alleviated mitochondrial damage, and decreased the expression levels of apoptosis-related biomarkers upon stimulation with TNF-α. In addition, scutellarin antagonized the activation of the nuclear factor κ-light-chain-enhancer of activated B (NF-κB) signaling pathway and the mitogen-activated protein kinase (MAPK) signaling pathway and suppressed the activity of the NLRP3 inflammasome mediated by TNF-α. This study reveals that scutellarin protects against degeneration of nucleus pulposus cells, which might shed light on treatment of IVDD in the future.

Double-Negative T Cells Attenuate Cisplatin-Induced Acute Kidney Injury via Upregulating IL-10/AT2R Axis.

Objective Our previous research showed that TCR+CD4-CD8-double-negative (DN) T cells protect renal epithelial cells from cisplatin-induced acute kidney injury (AKI). Therefore, this study is aimed at investigating the mechanism underlying the effect of DN T cells against Cis-induced AKI. Methods HK-2 cells cultured alone or with DN T cells were treated with or without Cis. After treatment, the cell viability and death were analyzed by a CCK-8 kit and flow cytometric assay with Annexin V/PI staining, respectively. The expressions of inflammatory factors in HK-2 and DN T cells were analyzed using qPCR. The expression levels of nephrotoxicity-associated biomarkers (KIM, calbindin, and TIMP-1), Bcl-2, and angiotensin AT2 receptor (AT2R) were determined by Western blot and qPCR. Results The administration of cisplatin significantly decreased the cell viability and AT2R expression, and increased cell death, inflammatory factors, and nephrotoxicity-associated biomarkers of HK-2 cells, while these effects were partly attenuated when cocultured with DN T cells. IL-10 expression was significantly increased in DN T cells after coculture, and cisplatin treatment aggravated this elevation. IL-10 supplementation exhibited a similar effect to coculture, whereas anti-IL-10 antibody reversed the effect of coculture on cisplatin-treated HK-2 cells. Finally, PD123319, an AT2R antagonist, also reversed the effect of IL-10 and coculture on the cell viability, death, and the expression of KIM, calbindin, TIMP-1, and Bcl-2 of cisplatin-treated HK-2 cells. Conclusions DN T cells protected HK-2 cells from cisplatin-induced injury through IL-10/AT2R axis, which may act as a potential target for the treatment of cisplatin-induced AKI.

ASPN Is a Potential Biomarker and Associated with Immune Infiltration in Endometriosis.

Objective: Endometriosis is a benign gynecological disease characterized by distant metastasis. Previous studies have discovered abnormal numbers and function of immune cells in endometriotic lesions. We aimed to find potential biomarkers of endometriosis and to explore the relationship between ASPN and the immune microenvironment of endometriosis. Methods: We obtained the GSE141549 and GSE7305 datasets containing endometriosis and normal endometrial samples from the Gene Expression Omnibus database (GEO). In the GSE141549 dataset, differentially expressed genes (DEGs) were found. The Least Absolute Shrinkage and Selection Operator (Lasso) regression and generalized linear models (GLMs) were used to screen new biomarkers. The expression levels and diagnostic utility of biomarkers were assessed in GSE7305, and biomarker expression levels were further validated using qRT-PCR and western blot. We identified DEGs between high and low expression groups of key biomarkers. Enrichment analysis was carried out to discover the target gene’s biological function. We analyzed the relationship between key biomarker expression and patient clinical features. Finally, the immune cells that infiltrate endometriosis were assessed using the Microenvironment Cell Population-Counter (MCP-counter), and the correlation of biomarker expression with immune cell infiltration and immune checkpoints genes was studied. Results: There were a total of 38 DEGs discovered. Two machine learning techniques were used to identify 10 genes. Six biomarkers (SCG2, ASPN, SLIT2, GEM, EGR1, and FOS) had good diagnostic efficiency (AUC > 0.7) by internal and external validation. We excluded previously reported related genes (SLIT2, EGR1, and FOS). ASPN was the most significantly differentially expressed biomarker between normal and ectopic endometrial tissues, as verified by qPCR. The western blot assay revealed a significant upregulation of ASPN expression in endometriotic tissues. The investigation for DEGs in the ASPN high- and low-expression groups revealed that the DEGs were particularly enriched in extracellular matrix tissue, vascular smooth muscle contraction, cytokine interactions, the calcium signaling pathway, and the chemokine signaling pathway. High ASPN expression was related to r-AFS stage (p = 0.006), age (p = 0.03), and lesion location (p < 0.001). Univariate and multivariate logistic regression analysis showed that ASPN expression was an independent influencing factor in patients with endometriosis. Immune cell infiltration analysis revealed a significant increase in T-cell, B-cell, and fibroblast infiltration in endometriosis lesions; cytotoxic lymphocyte, NK-cell, and endothelial cell infiltration were reduced. Additionally, the percentage of T cells, B cells, fibroblasts, and endothelial cells was favorably connected with ASPN expression, while the percentage of cytotoxic lymphocytes and NK cells was negatively correlated. Immune checkpoint gene (CTLA4, LAG3, CD27, CD40, and ICOS) expression and ASPN expression were positively associated. Conclusions: Increased expression of ASPN is associated with immune infiltration in endometriosis, and ASPN can be used as a diagnostic biomarker as well as a potential immunotherapeutic target in endometriosis.

Systematic identification of key extracellular proteins as the potential biomarkers in lupus nephritis.

BackgroundLupus nephritis (LN) is the most common and severe clinical manifestation of systemic lupus erythematosus (SLE) with considerable morbidity/mortality and limited treatment options. Since kidney biopsy is a relative hysteretic indicator, it is indispensable to investigate potential biomarkers for early diagnosis and predicting clinical outcomes of LN patients. Extracellular proteins may become the promising biomarkers by the secretion into body fluid. Our study linked extracellular proteins with lupus nephritis to identify the emerging biomarkers.MethodsThe expression profiling data were acquired from the Gene Expression Omnibus (GEO) database. Meanwhile, the two gene lists encoding extracellular proteins were collected from the Human Protein Atlas (HPA) and UniProt database. Subsequently, the extracellular protein-differentially expressed genes (EP-DEGs) were screened out, and the key EP-DEGs were determined by MCODE, MCC, and Degree methods via the protein–protein interaction (PPI) network. The expression level, immune characteristics, and diagnostic value of these candidate biomarkers were investigated. Finally, the Nephroseq V5 tool was applied to evaluate the clinical significance of the key EP-DEGs.ResultsA total of 164 DEGs were acquired by comparing LN samples with healthy controls based on GSE32591 datasets. Then, 38 EP-DEGs were screened out through the intersection between DEGs and extracellular protein gene lists. Function enrichment analysis indicated that these EP-DEGs might participate in immune response and constitute the extracellular matrix. Four key EP-DEGs (LUM, TGFBI, COL1A2, and POSTN) were eventually identified as candidate biomarkers, and they were all overexpressed in LN samples. Except that LUM expression was negatively correlated with most of the immune regulatory genes, there was a positive correlation between the remaining three biomarkers and the immune regulatory genes. In addition, these biomarkers had high diagnostic value, especially the AUC value of the LUM–TGFBI combination which reached almost 1 (AUC = 0.973), demonstrating high accuracy in distinguishing LN from controls. Finally, we found a meaningful correlation of these biomarkers with sex, WHO class, and renal function such as glomerular filtration rate (GFR), serum creatinine level, and proteinuria.ConclusionIn summary, our study comprehensively identified four key EP-DEGs exerting a vital role in LN diagnosis and pathogenesis and serving as promising therapeutic targets.