STRING Database Research Articles

Proteomics for Biomarker Discovery in Gynecological Cancers: A Systematic Review.

The present study aims to summarize the current biomarker landscape in gynecological cancers (GCs) and incorporate bioinformatics analysis to highlight specific biological processes. The literature was retrieved from PubMed, Web of Science, Embase, Scopus, Ovid Medline, and Cochrane Library. The final search was conducted on December 7, 2022. Prospective registration was completed with the PROSPERO with registration number CRD42023477145. This systematic review covered proteomic research on biomarkers for cervical, endometrial, and ovarian cancers. The PANTHER classification system was used to classify the shortlisted candidate biomarkers (CBs), and the STRING database was utilized to visualize protein-protein interaction networks. A total of 23 articles were included in this systematic review. Consistently regulated CBs in the GCs include collagen alpha-2(I) chain, collagen alpha-1(III) chain, collagen alpha-2(V) chain, calreticulin, protein disulfide-isomerase A3, heat shock protein family A (Hsp70) member 5, prolyl 4-hydroxylase, beta polypeptide, fibrinogen alpha chain, fibrinogen gamma chain, apolipoprotein B-100, apolipoprotein C-IV, and apolipoprotein M. In conclusion, collagens, fibrinogens, chaperones, and apolipoproteins were revealed to be replicated in GCs and to be regulated consistently. These CBs contribute to GC etiology and physiology by participating in collagen fibril organization, blood coagulation, protein folding in endoplasmic reticulum, and lipid transporter activity.

Unveiling the shared genes between systemic sclerosis and lung cancer

The risk of lung cancer is significantly increased in patients with systemic sclerosis (SSc), yet the specific genes underlying this association remain unexplored. Our study aims to identify genes shared by SSc and lung cancer. We identified differentially expressed genes (DEGs) from SSc and lung adenocarcinoma (LUAD) datasets (SSc: GSE95065, LUAD: GSE136043) in the GEO database. We found shared genes by intersecting top genes in protein–protein interaction networks by the STRING database. The area under the ROC curve (AUC) was calculated for each shared gene in validation datasets (SSc: GSE231692; LUAD: GSE43458), identifying PRKG2 as the core shared gene. We used the UALCAN platform to assess PRKG2 expression in LUAD patients at various stages and lymph node metastasis states, and compared disease-free survival (DFS) between low and high PRKG2 expression LUAD groups. PRKG2 was overexpressed in A549 cells to study its impact on lung cancer cell proliferation and invasion in vitro. We identified seven shared genes (SCN7A, AGTR1, WIF1, PRKG2, LTF, AQP4, COL10A1), with the AUC for PRKG2 exceeding 0.93 in both diseases (SSc AUC = 0.973; LUAD AUC = 0.939). The PRKG2 expression levels of LUAD patients with different clinical stages and lymph node metastasis states were consistently lower than those observed in normal individuals. The DFS of LUAD patients in the high PRKG2 expression group was higher than that in the low expression group (p = 0.028). In vitro experiments confirmed elevated PRKG2 expression inhibits the proliferation and invasion of lung cancer cells. PRKG2 is one of the genes shared by SSc and lung cancer, affecting the proliferation and invasion of lung cancer cells.

Study on the Improvement of Cognitive Impairment Induced by Scopolamine in Mice by Forsythia suspensa Essential Oil

ABSTRACTIn this study, the mechanism of Forsythia suspensa essential oil (FSEO) on cognitive disorders was investigated based on network pharmacology and animal experiments. The common targets of AD diseases and the main components of FSEO were collected through GeneCards, PubChem, STRING and other databases, and protein–protein interaction (PPI) network maps were established. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed using DAVID and Metascape databases, and the enrichment results were verified using the scopolamine (SCO) cognitive impairment mouse model. The results of network pharmacological analysis showed that FSEO involved 107 potential therapeutic targets, among which MAPK1, MAPK3 and ESR1 were the core targets, which were closely related to the regulatory pathways of MAPK cascade, 5‐hydroxytryptaminergic synapses, dopaminergic synapses and oxidoreductase activity. The results of animal experiments showed that FSEO improved cognitively impaired behaviours, reduced central cholinergic neuron damage and oxidative stress in the mouse brain and increased the content of monoamine transmitters in the mouse brain. Animal experiments further validated the results of the FSEO network pharmacology analysis.

Platelets as key cells in endometriosis patients: Insights from small extracellular vesicles in peritoneal fluid and endometriotic lesions analysis.

Endometriosis is a chronic inflammatory condition characterized by the presence of endometrium-like tissue outside the uterus, primarily affecting pelvic organs and tissues. In this study, we explored platelet activation in endometriosis. We utilized the STRING database to analyze the functional interactions among proteins previously identified in small extracellular vesicles (EVs) isolated from the peritoneal fluid of endometriosis patients and controls. The bioinformatic analysis indicated enriched signaling pathways related to platelet activation, hemostasis, and neutrophil degranulation. Double immunohistochemistry analysis for CD61 and MPO revealed a significant presence of neutrophils and platelets in close contact infiltrating endometriotic lesions, suggesting potential cell-cell interactions. Subsequently, we isolated small EVs from the peritoneal fluid of women diagnosed with endometriosis and from women without endometriosis who underwent surgery for non-inflammatory benign diseases. We performed single-particle phenotyping analysis based on platelet biomarkers GPIIb/IIIa and PF4 using nanoflow cytometry, as well as single-particle morphological and nanomechanical characterization through atomic force microscopy. The study demonstrated that patients with endometriosis had a notably higher proportion of particles testing positive for platelet biomarkers compared to the total number of EVs. This finding implies a potential role for platelets in the pathogenesis of endometriosis. Further research is necessary to delve into the mechanisms underlying this phenomenon and its implications for disease progression.

SMOC2, OGN, FCN3, and SERPINA3 could be biomarkers for the evaluation of acute decompensated heart failure caused by venous congestion

BackgroundVenous congestion (VC) sets in weeks before visible clinical decompensation, progressively increasing cardiac strain and leading to acute heart failure (HF) decompensation. Currently, the field lacks a universally acknowledged gold standard and early detection methods for VC.MethodsUsing data from the GEO database, we identified VC's impact on HF through key genes using Limma and STRING databases. The potential mechanisms of HF exacerbation were explored via GO and KEGG enrichment analyses. Diagnostic genes for acute decompensated HF were discovered using LASSO, RF, and SVM-REF machine learning algorithms, complemented by single-gene GSEA analysis. A nomogram tool was developed for the diagnostic model's evaluation and application, with validation conducted on external datasets.ResultsOur findings reveal that VC influences 37 genes impacting HF via 8 genes, primarily affecting oxygen transport, binding, and extracellular matrix stability. Four diagnostic genes for HF's pre-decompensation phase were identified: SMOC2, OGN, FCN3, and SERPINA3. These genes showed high diagnostic potential, with AUCs for each gene exceeding 0.9 and a genomic AUC of 0.942.ConclusionsOur study identifies four critical diagnostic genes for HF's pre-decompensated phase using bioinformatics and machine learning, shedding light on the molecular mechanisms through which VC worsens HF. It offers a novel approach for clinical evaluation of acute decompensated HF patient congestion status, presenting fresh insights into its pathogenesis, diagnosis, and treatment.

Revealing the core active pharmaceutical ingredients and targets of Jie-gu capsules for fracture treatment through network pharmacology and mendelian randomization.

Jie-gu capsules are widely used for the treatment of fractures in China. However, the core active pharmaceutical ingredients of Jie-gu capsules and the potential mechanisms for treating fractures remain unclear. This study aims to preliminarily elucidate the potential mechanisms of Jie-gu capsules in the treatment of fractures through network pharmacology and mendelian randomization methods. Data of fracture patients were obtained from the GEO database (GSE93215), and the active pharmaceutical ingredients and therapeutic targets of Jie-gu capsules were retrieved from the TCMSP and TCMID databases to identify the intersection genes. Subsequently, a protein-protein interaction network of the intersection genes was constructed using the STRING database. Then, GO and KEGG analyses were conducted on the intersection genes. In addition, mendelian randomization was employed to identify core targets. Finally, molecular docking techniques were used to perform molecular docking of the core active pharmaceutical ingredients and core targets for Jie-gu capsules in the treatment of fractures. In this study, a total of 65 intersection genes involved in Jie-gu capsule treatment of fractures were identified. GO and KEGG results indicated that these 65 intersection genes were primarily associated with biological processes such as response to tumor necrosis factor and are involved in signaling pathways, especially the regulation of the MAPK signaling pathway. We identified 5 core active ingredients of Jie-gu capsules (quercetin, baicalein, kaempferol, luteolin, and succinic acid). Mendelian randomization confirmed 2 core targets (ALOX12 and EGF). Molecular docking results demonstrated that the core active pharmaceutical ingredients (quercetin, baicalein, kaempferol, luteolin, and succinic acid) exhibit high affinities with the core targets (ALOX12 and EGF). This study has unveiled the core active pharmaceutical ingredients and potential action targets of the Jie-gu capsules in treating fractures, offering valuable insights for subsequent foundational research and the development of new medications.

The therapeutic effects of Paeoniae Radix Rubra on chronic hepatitis through network pharmacology and molecular docking.

Chronic hepatitis (CH) refers to liver inflammation lasting at least 6 months caused by various factors, significantly impacting patients' daily lives. Paeoniae Radix Rubra (CS) is a classic blood-activating and stasis-dissolving herb known for its protective effects on the liver. This research seeks to investigate the underlying mechanisms by which CS treat CH, employing network pharmacology and molecular docking. The active constituents of CS for CH treatment were identified through the TCMSP database. Targets associated with CH were gathered from GeneCards, the Therapeutic Target Database, and OMIM databases. The intersecting genes between these targets and the components of CS were considered potential therapeutic targets. Protein-protein interaction analysis was performed with the use of the STRING database and Cytoscape software, leading to the identification of core targets. These core targets underwent KEGG and GO enrichment analysis, and the top 10 pathways were chosen for building a drug-compound-target-pathway-disease' network. Finally, molecular docking was utilized to evaluate the binding affinities between the compounds and the core targets. From the TCMSP database, 29 compounds were screened, and 101 potential intersection targets of CS for treating CH were identified. The protein-protein interaction network analysis revealed that the core targets included EGFR, HSP90AA1, SRC, TNF, ALB, ESR1, CASP3, PTGS2, ERBB2, and FGF2. Pathway analysis indicated that CS's treatment of CH is mainly associated with the pathway in cancer. Molecular docking results indicated that Paeoniflorin and Baicalin exhibited strong binding affinity with EGFR and HSP90AA1. This research uncovers the possible mechanisms of CS in CH treatment, offering new avenues for future studies.

Integrative multi-omics approach using random forest and artificial neural network models for early diagnosis and immune infiltration characterization in ischemic stroke.

Ischemic stroke (IS) is a significant global health issue, causing high rates of morbidity, mortality, and disability. Since conventional Diagnosis methods for IS have several shortcomings. It is critical to create new Diagnosis models in order to enhance existing Diagnosis approaches. We utilized gene expression data from the Gene Expression Omnibus (GEO) databases GSE16561 and GSE22255 to identify differentially expressed genes (DEGs) associated with IS. DEGs analysis using the Limma package, as well as GO and KEGG enrichment analyses, were performed. Furthermore, PPI networks were constructed using DEGs from the String database, and Random Forest models were utilized to screen key DEGs. Additionally, an artificial neural network model was developed for IS classification. Use the GSE58294 dataset to evaluate the effectiveness of the scoring model on healthy controls and ischemic stroke samples. The effectiveness of the scoring model was evaluated through AUC analysis, and CIBERSORT analysis was conducted to estimate the immune landscape and explore the correlation between gene expression and immune cell infiltration. A total of 26 significant DEGs associated with IS were identified. Metascape analysis revealed enriched biological processes and pathways related to IS. 10 key DEGs (ARG1, DUSP1, F13A1, NFIL3, CCR7, ADM, PTGS2, ID3, FAIM3, HLA-DQB1) were selected using Random Forest and artificial neural network models. The area under the ROC curve (AUC) for the IS classification model was found to be near 1, indicating its high accuracy. Additionally, the analysis of the immune landscape demonstrated elevated immune-related networks in IS patients compared to healthy controls. The study uncovers the involvement of specific genes and immune cells in the pathogenesis of IS, suggesting their importance in understanding and potentially targeting the disease.

Potential molecular mechanisms of tobacco smoke exposure in Alzheimer's disease.

Smoking is detrimental to health, with tobacco use being a critical factor in the development of various neurodegenerative diseases, including Alzheimer's disease (AD), which progressively impairs brain function and poses a significant threat to public health. This study aims to examine the potential genetic alterations induced by smoking that are associated with AD and to investigate the underlying regulatory mechanisms. The research will provide theoretical foundations for targeted prevention and treatment strategies for AD. This study analyzed datasets from the Gene Expression Omnibus (GEO) and the Comparative Toxicogenomics Database (CTD) to identify genes affected by tobacco smoke exposure and those altered in patients with AD relative to normal controls. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using OmicShare tools to screen for key pathways. Key genes were identified by constructing protein-protein interaction networks (PPI) in the STRING database with the aid of CytoHubba. Additionally, the binding activity of the proteins encoded by these key genes to nicotine, the main component of tobacco, was analyzed using molecular docking techniques. Finally, the analytical results were verified using Quantitative Real-Time Polymerase Chain Reaction. The CTD identified 12,164 CE-related genes affected by tobacco smoke exposure. A comparison of these datasets yielded 94 common genes that were both influenced by tobacco and differentially expressed across all brain regions. The GO and KEGG pathway enrichment analyses showed that these common differentially expressed genes (DEGs) were predominantly enriched in the Wnt/β-catenin and PI3K-AKT signaling pathways. The DEGs' PPI network, constructed using the STRING database, highlighted key genes such as HSP90AB1, SOS2, MAGI1, and YWHAZ. Molecular docking studies demonstrated that nicotine binds effectively to the protein structures of these key genes, primarily through amino acid residues such as Ser and Glu. Experimental validation showed that HSP90AB1 and YWHAZ exhibited notable expression discrepancies under varying concentrations of cigarette smoke extract (CSE) treatments, particularly demonstrating a pronounced down-regulation trend at elevated concentrations. The study indicates that tobacco may impact the function of transmembrane transporter proteins and contribute to the development of AD by affecting key genes such as HSP90AB1 and YWHAZ, as well as signaling pathways like PI3K-AKT.

Banxia Xiexin Decoction Prevents HT22 Cells from High Glucose-induced Neurotoxicity via JNK/SIRT1/Foxo3a Signaling Pathway.

Type 2 diabetes-associated cognitive dysfunction (DCD) is a chronic complication of diabetes that has gained international attention. The medicinal compound Banxia Xiexin Decoction (BXXXD) from traditional Chinese medicine (TCM) has shown potential in improving insulin resistance, regulating endoplasmic reticulum stress (ERS), and inhibiting cell apoptosis through various pathways. However, the specific mechanism of action and medical value of BXXXD remain unclear. We utilized TCMSP databases to screen the chemical constituents of BXXXD and identified DCD disease targets through relevant databases. By using Stitch and String databases, we imported the data into Cytoscape 3.8.0 software to construct a protein-protein interaction (PPI) network and subsequently identified core targets through network topology analysis. The core targets were subjected to Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The results were further validated through in vitro experiments. Network pharmacology analysis revealed the screening of 1490 DCD-related targets and 190 agents present in BXXXD. The topological analysis and enrichment analysis conducted using Cytoscape software identified 34 core targets. Additionally, GO and KEGG pathway analyses yielded 104 biological targets and 97 pathways, respectively. BXXXD exhibited its potential in treating DCD by controlling synaptic plasticity and conduction, suppressing apoptosis, reducing inflammation, and acting as an antioxidant. In a high glucose (HG) environment, the expression of JNK, Foxo3a, SIRT1, ATG7, Lamp2, and LC3 was downregulated. BXXXD intervention on HT22 cells potentially involved inhibiting excessive oxidative stress, promoting neuronal autophagy, and increasing the expression levels of JNK, SIRT1, Foxo3a, ATG7, Lamp2, and LC3. Furthermore, the neuroprotective effect of BXXXD was partially blocked by SP600125, while quercetin enhanced the favorable role of BXXXD in the HG environment. BXXXD exerts its effects on DCD through multiple components, targets, levels, and pathways. It modulates the JNK/SIRT1/Foxo3a signaling pathway to mitigate autophagy inhibition and apoptotic damage in HT22 cells induced by HG. These findings provide valuable perspectives and concepts for future clinical trials and fundamental research.

Comprehensive Multi-Omics Analysis Reveals NPC2 and ITGAV Genes as Potential Prognostic Biomarkers in Gastrointestinal Cancers.

Gastrointestinal cancers (GICs) continue to dominate in terms of both incidence and mortality worldwide. Due to the absence of efficient and accurate prognostic biomarkers, the prognosis and treatment outcomes of many GICs are poor. Identifying biomarkers to predict individual clinical outcomes efficiently is a fundamental challenge in clinical oncology. Although several biomarkers have been continually discovered, their predictive accuracy is relatively modest, and their therapeutic use is restricted. In light of this, the discovery of reliable biomarkers for predicting prognosis and outcome in GIC is urgently required. We evaluated the Human Protein Atlas dataset and identified NPC Intracellular Cholesterol Transporter 2 (NPC2) and Integrin Subunit Alpha V (ITGAV) as probable poor predictive genes for these cancers. In addition, we used the GEPIA2, cBioPortal, UALCAN, LinkedOmics, STRING, Enrichr, TISDB, TIMER2.0, hTFTarget, miRTarBase, circBank, and drug-gene interaction database databases to conduct a comprehensive and systematic analysis of the NPC2 and ITGAV genes. Our results found high expression levels of NPC2 and ITGAV in most GICs. The aforementioned gene expressions were linked to several clinicopathological characteristics of GICs as well as poorer prognosis in LIHC and STAD. The most common alteration type of NPC2 was amplification, and for ITGAV was deep deletion. Significant promotor hypermethylation was also seen in NPC2 and ITGAV in PAAD and COAD, respectively. For the immunologic significance, NPC2 and ITGAV were positively correlated with the abundance of tumor-infiltrating lymphocytes and macrophages. Furthermore, various immunomodulators showed strong correlations with the expression of these genes. There were currently 10 small molecule drugs targeting ITGAV. Consequently, our bioinformatics analysis showed that NPC2 and ITGAV might be used as potential biomarkers to determine the prognosis of various GICs and are also related to immune infiltration.

Bioactive Constituents and the Molecular Mechanism of Melastoma dodecandrum Lour. in the Treatment of Inflammation Based on Network Pharmacology and Molecular Docking

Background: Melastoma dodecandrum Lour. (MD) is a component used in traditional Chinese medicine that is widely distributed in southern China. MD has long been used clinically to treat various diseases, such as inflammation. However, the potential anti-inflammatory mechanism of MD remains to be elucidated. Objective: In this study, network pharmacology and experimental validation have been used to explore the underlying mechanism of MD in inflammation. Methods: The chemical composition of MD was determined using ultra-high performance liquid chromatography-electrospray ionization-high resolution mass spectrometry (UHPLC-ESI-HRMS). The effects of MD on pro-inflammatory cytokines, such as NO, i-NOS, IL-1β, and TNF-α, in RAW264.7 cells stimulated by lipopolysaccharide (LPS) were determined by ELISA and QRT-PCR. Through the analysis of multiple databases, targets for the treatment of inflammation with MD were identified. Other extensive analyses included PPI, GO, and KEGG pathway enrichment, which were completed through the use of the STRING database, Cytoscape software, and the DAVID database. Key targets and key components have been selected for molecular docking. Results: A total of 33 active components were identified in MD, and 134 common targets were obtained and used to construct the networks. Of these, 10 core components and 10 core targets of MD in the treatment of inflammation were identified. The GO and KEGG enrichment analyses revealed that the common targets were involved in multiple signaling pathways, including the PI3K-Akt signaling pathway, NOD-like receptor signaling pathway, chemokine signaling pathway, and IL-17 signaling pathway. The molecular docking methods confirmed the high affinity between bioactive molecules of MD and their targets in inflammation. Two core targets (PIK3CA and AKT) and three core components (asiatic acid, apigenin, and kaempferol) were found to be closely related to MD in the treatment of inflammation. In vitro, MD exerted a significant effect on LPS-stimulated NO, IL- 1β, and TNF-α secretion, and iNOS, IL-1β, and TNF-α expressions in macrophages. Conclusion: This study has demonstrated the bioactive constituents and mechanisms of MD in inhibiting the secretion of inflammatory factors and the multicomponent, multitarget, and multipathway treatment characteristics involved in inflammation, but this still needs further in vivo/in vitro experiments.

Network Pharmacology, Molecular Docking and Experimental Verification Revealing the Mechanism of Fule Cream against Childhood Atopic Dermatitis.

The Fule Cream (FLC) is an herbal formula widely used for the treatment of pediatric atopic dermatitis (AD), however, the main active components and functional mechanisms of FLC remain unclear. This study performed an initial exploration of the potential acting mechanisms of FLC in childhood AD treatment through analyses of an AD mouse model using network pharmacology, molecular docking technology, and RNA-seq analysis. The main bioactive ingredients and potential targets of FLC were collected from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and SwissTargetPrediction databases. An herb-compound-target network was built using Cytoscape 3.7.2. The disease targets of pediatric AD were searched in the DisGeNET, Therapeutic Target Database (TTD), OMIM, DrugBank and GeneCards databases. The overlapping targets between the active compounds and the disease were imported into the STRING database for the construction of the protein-protein interaction (PPI) network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the intersection targets were performed, and molecular docking verification of the core compounds and targets was then performed using AutoDock Vina 1.1.2. The AD mouse model for experimental verification was induced by MC903. The herb-compound-target network included 415 nodes and 1990 edges. Quercetin, luteolin, beta-sitosterol, wogonin, ursolic acid, apigenin, stigmasterol, kaempferol, sitogluside and myricetin were key nodes. The targets with higher degree values were IL-4, IL-10, IL-1α, IL-1β, TNFα, CXCL8, CCL2, CXCL10, CSF2, and IL-6. GO enrichment and KEGG analyses illustrated that important biological functions involved response to extracellular stimulus, regulation of cell adhesion and migration, inflammatory response, cellular response to cytokine stimulus, and cytokine receptor binding. The signaling pathways in the FLC treatment of pediatric AD mainly involve the PI3K-Akt signaling pathway, cytokine‒cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and NF-κB signaling pathway. The binding energy scores of the compounds and targets indicate a good binding activity. Luteolin, quercetin, and kaempferol showed a strong binding activity with TNFα and IL-4. This study illustrates the main bioactive components and potential mechanisms of FLC in the treatment of childhood AD, and provides a basis and reference for subsequent exploration.

Pan-cancer analysis combined with experimental validation revealed that KTN1 is an immunological and prognostic biomarker.

Kinectin 1 (KTN1) is a membrane protein involved in intracellular organelle motility. However, the role of KTN1 in human pan-cancer lacks systematic analysis and evaluation. The aim of this study is to evaluate the expression profile and clinical value in human cancers by performing a pan-cancer analysis of KTN1. The expression of KTN1 and its correlation with cancer-associated fibroblasts (CAFs) infiltration were analyzed in TIMER2.0. The survival analysis, pathological stage correlation, and co-expression gene correlation analysis of KTN1 were performed on GEPIA2.0. We investigated the protein expression level of KTN1 in tumors through UALCAN. The cBioportal platform was used to analyze the variation frequency and type of KTN1. We used STRING database for protein-protein interaction (PPI) network analysis. Cell Counting Kit-8 (CCK8) and colony formation and transwell assay were performed to investigate the proliferation and migration abilities of head and neck squamous cell carcinoma (HNSC) cells with KTN1 knockdown. KTN1 was differentially expressed in 12 kinds of cancer tissues compared with correspondent normal tissues. Meanwhile, the high expression of KTN1 was negatively correlated with the prognosis of HNSC, adrenocortical carcinoma (ACC), and liver hepatocellular carcinoma (LIHC). Further analysis suggested that patients with KTN1 mutations had better overall survival (OS) and progression-free survival than those without mutations among several cancers. Moreover, the level of CAFs and KTN1 expression were significantly correlated in 12 types of cancer. Mechanically, co-expression analysis showed the positive association between KTN1 and KTN1 antisense RNA 1 (KTN1-AS1), MNAT1 component of CDK activating kinase (MNAT1), N-alpha-acetyltransferase 30 (NAA30), protein phosphatase 2 regulatory subunit B'epsilon (PPP2R5E), and proteasome 26S subunit (PSMC6), which are mainly involved in the protein kinase AMP-activated catalytic subunit alpha 1 (AMPK) signaling pathway that regulates the progression of tumors. The functional experiment revealed that KTN1 promotes the proliferation and metastasis of HNSC cells. The pan-cancer analysis of KTN1 revealed its significance in different cancers, which provides a new marker for the diagnosis and prognosis of cancers.

Establishment and validation of diagnostic model in immunoglobulin A nephropathy based on weighted gene co-expression network analysis.

Bioinformatics analysis helps to understand the underlying mechanisms and adjust diagnostic and treatment strategies for immunoglobulin A nephropathy (IgAN) by screening gene expression datasets. We explored the biological function of IgAN, and established and validated a diagnostic model for IgAN using weighted gene co-expression network analysis. Using the GSE93798 and GSE37460 datasets, we performed differential expression analysis, Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, constructed a protein-protein network, and identified hub genes. A diagnostic model was built using a receiver operating characteristic curve, calibration plot, and decision curve analysis. Two Gene Expression Omnibus (GEO) datasets were integrated to screen 38 differentially expressed genes between patients with IgAN and normal kidney donors in glomerular samples. KEGG enrichment analysis showed that the differentially expressed genes were mainly enriched in the IL-17 and relaxin signaling pathways. We constructed a protein-protein interaction (PPI) network of differentially expressed genes using the STRING database and cross-compared it with the results of weighted gene correlation network analysis to screen out the top 10 key genes: FOS, EGR2, FOSB, NR4A1, BR4A3, FOSL1, NR4A2, ALB, CD53, C3AR1.We also found that the immune infiltration level was remarkably increased in IgAN tissues. We established a 5-gene panel diagnostic model (ACTA2, ALB, AFM, ALDH1L1, and ALDH6A1). The combined diagnostic ability was high, with the area under the curve (AUC) was 0.964. Based on these 5 genes, we also developed a risk-scoring evaluation system for individuals. The calibration plot indicated that the nomogram-predicted probability of nonadherence was highly correlated with actual diagnosed nonadherence, and the decision curve analysis indicated that patients had a relatively good net benefit. The model and gene expression were also validated using an external dataset. Our study provides directions for exploring the potential molecular mechanisms of IgAN as well as diagnostic and therapeutic strategies.

Proteomic analysis illustrates the potential involvement of dysregulated ribosome-related pathways and disrupted metabolism during retinoic acid-induced cleft palate development

Recent studies have unveiled disrupted metabolism in the progression of cleft palate (CP), a congenital anomaly characterized by defective fusion of facial structures. Nonetheless, the precise composition of this disrupted metabolism remains elusive, prompting us to identify these components and elucidate primary metabolic irregularities contributing to CP pathogenesis. We established a murine CP model by retinoic acid (RA) treatment and analyzed control and RA-treated embryonic palatal tissues by LC-MS-based proteomic approach. We identified 220 significantly upregulated and 224 significantly downregulated proteins. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that these differentially expressed proteins (DEPs) were involved in translation, ribosome assembly, mitochondrial function, mRNA binding, as well as key metabolic pathways like oxidative phosphorylation (OXPHOS), glycolysis/gluconeogenesis, and amino acid biosynthesis. These findings suggest that dysregulated ribosome-related pathways and disrupted metabolism play a critical role in CP development. Protein-protein interaction analysis using the STRING database revealed a tightly connected network of DEPs. Furthermore, we identified the top 10 hub proteins in CP using the Cytohubba plugin in Cytoscape. These hub proteins, including RPL8, RPS11, ALB, PA2G4, RPL23, RPS6, CCT7, EGFR, HSPD1, and RPS28, are potentially key regulators of CP pathogenesis. In conclusion, our comprehensive proteomic analysis provides insights into the molecular alterations associated with RA-induced CP in Kun Ming mice. These findings suggest potential therapeutic targets and pathways to understand and prevent congenital craniofacial anomalies.

NLRC3 affects the development of psoriasis by modulating the NF-κB signaling pathway mediated inflammatory response through its interaction with TRAF6

ObjectiveThe function and mechanism of NOD-like receptor family CARD-containing 3 (NLRC3) in psoriasis are not yet reported, even though it plays a crucial role in innate and adaptive immunity by inhibiting inflammation. Therefore, this research aims to investigate the role and mechanism of NLRC3 in psoriasis. MethodsHaCaT cells were induced to form a psoriasis cell model using 20 ng/mL IL-1β, 20 ng/mL IL-17A, 20 ng/mL IL-23, 50 ng/mL TNF-α, and 20 ng/mL oncostatin M. Cell Counting Kit-8 (CCK-8) assay and flow cytometry were assessed to determine the proliferation, cell cycle, and apoptosis of HaCaT cells. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was utilized to measure the knockdown efficiency of NLRC3 and TRAF6 interfering RNA in HaCaT cells. Western blot analysis was performed to determine the expression levels of NLRC3, TRAF6, and proteins associated with the NF-κB signaling pathway. A mouse model of psoriasis-like dermatitis was established by evenly applying miquimod cream (62.5 mg/day) to both ears. Hematoxylin-eosin staining was used to measure ear thickness and inflammatory infiltrates in mice. Histological analysis, immunohistochemistry, and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL were performed to detect cell proliferation and apoptosis. Interactions between NLRC3 and TRAF6 were predicted using the STRING database (https://cn.string-db.org/). Co-Immunoprecipitation was used to confirm interactions between NLRC3 and TRAF6. Ubiquitination of TRAF6 was assessed by Western blot. ResultsKnockdown of NLRC3 expression promoted cell proliferation and inhibited cell apoptosis in HaCaT cells. In vivo, knockdown of NLRC3 expression significantly increased the infiltration of inflammatory cells and the proliferation of Ki-67 positive cells within mouse ear epidermis, while decreasing the number of apoptotic cells. NLRC3 interacted with TRAF6 and influenced its K63 ubiquitination level. Knockdown of TRAF6 expression resulted in increased cell proliferation and decreased cell apoptosis in HaCaT cells. In vivo, knockdown of TRAF6 expression led to a significant increase in inflammatory cell infiltration and Ki-67 positive cells in mouse ear epidermis, and a decrease in apoptotic cells. Inhibiting the NF-κB signaling pathway alleviated the progression of psoriasis, and interfering with TRAF6 activated the NF-κB signaling axis, contributing to the onset and advancement of psoriasis. ConclusionNLRC3 affects the occurrence of psoriasis by regulating TRAF6 and influencing the NF-κB signaling axis-mediated inflammatory response. This finding offers a theoretical foundation for the treatment of psoriasis.

Identification of tumor-antigen signatures and immune subtypes for mRNA vaccine selection in muscle-invasive bladder cancer

BackgroundMuscle-invasive bladder cancer continues to lack reliable diagnostic and prognostic biomarkers. Recently, tumor vaccines targeting specific molecules have emerged as a promising treatment in inhibiting tumor progression, which was rekindled under the background of coronavirus disease-2019 pandemic. However, the application of mRNA vaccine targeting muscle-invasive bladder cancer–specific antigens remains limited, and there has been a lack of comprehensive studies or validations to identify suitable patient subgroups for vaccination. This study aims to explore novel muscle-invasive bladder cancer antigen signatures to identify patients most likely to benefit from vaccination. MethodsGene expression profiles of muscle-invasive bladder cancer samples, along with corresponding clinical data, were retrieved from the Cancer Genome Atlas Program. The least absolute shrinkage and selection operator model was applied to develop signatures for stratifying muscle-invasive bladder cancer patients. Prognostic accuracy of each factor was assessed using receiver operating characteristic analysis. Tumor Immune Estimation Resource was employed to visualize the relationship between the proportion of antigen-presenting cells and the expression of selected genes. The CIBERSORT and WGCNA R packages were used to identify differences in immune infiltration levels across muscle-invasive bladder cancer subgroups. Additionally, the STRING database and Cytoscape were used to construct the protein-protein interaction network. CCK-8 and colony formation assays were employed in invitro experiments. ResultsA total of 49 potential tumor antigens were identified. Using least absolute shrinkage and selection operator Cox regression, 14 tumor antigens were selected to develop a risk evaluation signature. The risk score signature can serve as a valuable tool for predicting the outcomes of muscle-invasive bladder cancer patients. Based on differential clinical, molecular, and immune-related gene profiles, muscle-invasive bladder cancer patients were classified into 2 subtypes: the immune “cold” subtype (immune score 1) and the immune “hot” subtype (immune score 2). The immune score signature, developed using a logistic score model, effectively distinguishes between patients more likely to belong to immune score 1 or 2. Notably, patients with a high risk score exhibited a higher proportion of immune score 2 compared to those with a low risk score. Additionally, the prognostic accuracy was significantly enhanced when the risk score and immune score were combined. Different tumor subtypes displayed distinct immune landscapes and signaling pathways. Moreover, novel tumor antigens associated with oxidative stress were identified. ConclusionThe risk score and immune score signatures based on tumor antigens have identified potential effective neo-antigens for the development of mRNA vaccines targeting muscle-invasive bladder cancer. Patients with low risk score and immune score 1 subtype are more likely to benefit from mRNA vaccination. Additionally, this study highlights the critical role of oxidative stress in modulating the efficacy of the mRNA vaccine.

Systems biology and molecular modeling assisted exploration of the underlying mechanism of Safflower (Carthamus tinctorius L.) in the treatment of hearing loss

Hearing loss is the incapability to hear sound, either partially or fully. One potential natural remedy for hearing loss is the use of Carthamus tinctorius, commonly known as safflower, contains bioactive compounds, including flavonoids, phenolic acids, and terpenoids, which possess potent antioxidant and anti-inflammatory activities. This study uses network pharmacology to identify the potential therapeutic effects of these compounds on hearing loss. We identified 17 active compounds of C. tinctorius with favorable ADME properties through a litrature search. Potential targets for these compounds were found using databases like STITCH and Swiss Target Prediction, as well as the GeneCards database to retrieve hearing loss-related potential targets. A Venn diagram was drawn to determine shared targets, and GO enrichment and KEGG pathways analysis of 64 key targets were conducted DAVID database. Protein-protein interactions (PPIs) were analyzed via STRING database, and a compound-target-pathway network was created in Cytoscape. Molecular docking studies focused on one hub gene, namely, tumor necrosis factor (TNF) revealing that C. tinctorius’s compounds have high affinity for TNF, suggesting a potential therapeutic link to hearing loss. Finally, molecular dynamics simulations and calculations of binding free energy showed TNF_Beta Sitosterol and TNF_Campesterol were more stable than TNF_Cholesterol. Our findings indicate that C. tinctorius may have therapeutic potential for hearing loss by targeting key genes and pathways. However, further in-vivo and in-vitro studies are needed to confirm these findings and determine optimal treatment parameters.

Screening and validation of key genes associated with osteoarthritis

BackgroundOsteoarthritis is recognized as a common geriatric condition characterized by irregular chronic pain. Its prevalence is steadily increasing, posing significant challenges to global public health, while some studies indicate a trend towards younger individuals being affected. This condition severely impacts patients’ quality of life.MethodsUsing the Gene Expression Omnibus (GEO) database, we downloaded datasets GSE114007, GSE169077, and GSE206848. We utilized R software to screen and confirm differentially expressed genes (DEGs) related to the development of osteoarthritis. A cross-analysis of the three datasets was conducted, with the least overlapping dataset, GSE206848, selected as the validation set. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the DEGs from GSE114007 and GSE169077. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to identify modules closely associated with osteoarthritis, and genes from these intersecting modules were entered into the STRING database to construct Protein-Protein Interaction Networks. The top ten genes by connectivity were identified and validated using GSE206848. Key genes were identified and preliminarily validated using Quantitative Real-Time PCR (QPCR). Subsequent validation of related genes was carried out through Western Blot (WB) analysis.ResultsDifferentially expressed genes were identified from the GSE114007 and GSE169077 datasets and validated in the GSE206848 dataset, with ANGPTL4 selected as the key gene. QPCR results indicated a significant difference in ANGPTL4 expression levels between normal and osteoarthritic chondrocytes. Western Blot analysis confirmed a significant difference in ANGPTL4 protein expression between normal and osteoarthritic chondrocytes.ConclusionBased on the experimental findings, ANGPTL4 appears to be a potential key gene in osteoarthritis.