Protein-protein Interaction Network Analysis Research Articles

Assessing the causal relationship between plasma proteins and osteoporosis: novel insights into pathological mechanisms and therapeutic implications.

This study aims to identify potential therapeutic targets for OP using summary data-based Mendelian randomization (SMR) and colocalization analysis methods. Furthermore, we seek to explore the biological significance and pharmacological value of these drug targets. To identify potential therapeutic targets for OP, we conducted SMR and colocalization analysis. Plasma protein (pQTL, exposure) data were sourced from the study by Ferkingstad et al. (n = 35,559). Summary statistics for bone mineral density (BMD, outcome) were obtained from the GWAS Catalog (n = 56,284). Additionally, we utilized enrichment analysis, protein-protein interaction (PPI) network analysis, drug prediction, and molecular docking to further analyze the biological significance and pharmacological value of these drug targets. In the SMR analysis, while 20 proteins showed significance, only 8 potential drug targets (GCKR, ERBB3, CFHR1, GPN1, SDF2, VTN, BET1L, and SERPING1) received support from colocalization (PP.H4 > 0.8). These proteins are closely associated with immune function in terms of biological significance. Molecular docking also demonstrated favorable binding of drugs to proteins, consistent with existing structural data, further substantiating the pharmacological value of these targets. The study identified 8 potential drug targets for OP. These prospective targets are believed to have a higher chance of success in clinical trials, thus aiding in prioritizing OP drug development and reducing development costs.

Database-assisted screening of autism spectrum disorder related gene set

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social and communication difficulties, along with repetitive behaviors. While genetic factors play a significant role in ASD, the precise genetic landscape remains complex and not fully understood, particularly in non-syndromic cases. The study performed an in silico comparison of three genetic databases. ClinVar, SFARI Gene, and AutDB were utilized to identify relevant gene subset and genetic variations associated with non-syndromic ASD. Gene set enrichment analysis (GSEA) and protein–protein interaction (PPI) network analysis were conducted to elucidate the biological significance of the identified genes. The integrity of ASD-related gene subset and the distribution of their variations were statistically assessed. A subset of twenty overlapping genes potentially specific for non-syndromic ASD was identified. GSEA revealed enrichment of biological processes related to neuronal development and differentiation, synaptic function, and social skills, highlighting their importance in ASD pathogenesis. PPI network analysis demonstrated functional relationships among the identified genes. Analysis of genetic variations showed predominance of rare variants and database-specific distribution patterns. The results provide valuable insights into the genetic landscape of ASD and outline the genes and biological processes involved in the condition, while taking into account that the study relied exclusively on in silico analyses, which may be subject to biases inherent to database methodologies. Further research incorporating multi-omics data and experimental validation is warranted to enhance our understanding of non-syndromic ASD genetics and facilitate the development of targeted research, interventions and therapies.

Identification of COL3A1 as a candidate protein involved in the crosstalk between obesity and diarrhea using quantitative proteomics and machine learning

BackgroundIncreasing epidemiologic studies have shown a positive correlation between obesity and chronic diarrhea. Nevertheless, the precise etiology remains uncertain. MethodsWe performed a comprehensive proteomics analysis utilizing the data-independent acquisition (DIA) technique on jejunal tissues from patients with obesity and chronic diarrhea (OD, n = 33), obese patients (OB, n = 10), and healthy controls (n = 8). Differentially expressed proteins (DEPs) in OD vs. control and OD vs. OB comparisons were subjected to pathway enrichment and protein-protein interaction (PPI) network analysis. Machine learning algorithms were adopted on overlapping DEPs in both comparisons. The candidate protein was further validated using Western blot, immunohistochemistry (IHC), and in vitro experiments. ResultsWe identified 189 and 228 DEPs in OD vs. control and OD vs. OB comparisons, respectively. DEPs in both comparisons were co-enriched in extracellular matrix (ECM) organization. Downregulated DEPs were associated with tight junction and ECM-receptor interaction in OD vs. control and OD vs. OB comparisons, respectively. Machine learning algorithms selected 3 proteins from 14 overlapping DEPs in both comparisons, among which collagen alpha-1(III) chain (COL3A1) was identified as a core protein in PPI networks. Western blot and IHC verified the expression of COL3A1. Moreover, the tight junction-related proteins decreased after the knockdown of COL3A1 in Caco2 intestinal cells upon PA challenge, consistent with the proteomics results. ConclusionsWe generated in-depth profiling of a proteomic dataset from samples of OD patients and provided unique insights into disease pathogenesis. COL3A1 was involved in the crosstalk between obesity and intestinal homeostasis via the ECM-receptor interaction pathway.

Rosiglitazone attenuates Acute Kidney Injury from hepatic ischemia-reperfusion in mice by inhibiting arachidonic acid metabolism through the PPAR-γ/NF-κB pathway.

Acute Kidney Injury (AKI), a prevalent complication of Liver Transplantation (LT) that occurs during the perioperative period has been established to profoundly impact the prognosis of transplant recipients. This study aimed to investigate the mechanism of the hepatic IRI-induced AKI and to identify potential therapeutic targets for treating this condition and improving the prognosis of LT patients. An integrated transcriptomics and proteomics approach was employed to investigate transcriptional and proteomic alterations in hepatic IRI-induced AKI and the hypoxia-reoxygenation (H/R) model using TCMK-1 cells and the hepatic IRI-induced AKI mouse model using male C57BL/6J mice were employed to elucidate the underlying mechanisms. Hematoxylin-eosin staining, reverse transcription quantitative polymerase chain reaction, enzyme-linked immunosorbent assay and Western blot were used to assess the effect of Rosiglitazone(RGZ) on hepatic IRI-induced AKI in vitro and in vivo. According to the results, 322 genes and 128 proteins were differentially expressed between the sham and AKI groups. Furthermore, Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway analyses revealed significant enrichment in pathways related to amino acid and lipid metabolism. Additionally, the Protein-Protein Interaction (PPI) network analysis of the kidney tissues obtained from a hepatic IRI-induced AKI mouse model highlighted arachidonic acid metabolism as the most prominent pathway. Animal and cellular analyses further revealed that RGZ, a PPAR-γ agonist, could inhibit the expression of the PPAR-γ/NF-κB signaling pathway-associated proteins in in vitro and in vivo. These findings collectively suggest that RGZ ameliorates hepatic IRI-induced AKI via PPAR-γ/NF-κB signaling pathway modulation, highlighting PPAR-γ as a crucial therapeutic target for AKI prevention post-LT.

Pathogenic gene connections in type 2 diabetes and non-alcoholic fatty liver disease: a bioinformatics analysis and mouse model investigations experiments

BackgroundType 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) are prevalent metabolic disorders with overlapping pathophysiological mechanisms. A comprehensive understanding of the shared molecular pathways involved in these conditions can advance the development of effective therapeutic interventions.MethodsWe used two datasets sourced from the Gene Expression Omnibus (GEO) database to identify common differentially expressed genes (DEGs) between T2D and NAFLD. Subsequently, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify the enriched biological processes and signaling pathways. In addition, we performed a protein-protein interaction (PPI) network analysis to identify hub genes with pivotal roles. To validate our findings, we established a type 2 diabetic mouse model with NAFLD.ResultsOur analysis identified 53 DEGs shared between T2D and NAFLD. Enrichment analysis revealed their involvement in signal transduction, transcriptional regulation, and cell proliferation as well as in the ferroptosis signaling pathways. PPI network analysis identified ten hub genes, namely CD44, CASP3, FYN, KLF4, HNRNPM, HNRNPU, FUBP1, RUNX1, NOTCH3, and ANXA2. We validated the differential expression of FYN, HNRNPU, and FUBP1 in liver tissues of a type 2 diabetic mouse model with NAFLD.ConclusionsOur study offers valuable insights into the shared molecular mechanisms underlying T2D and NAFLD. The identified hub genes and pathways present promising prospects as therapeutic targets to address these prevalent metabolic disorders.

Identification of oxidative stress-related diagnostic markers and immune infiltration features for idiopathic pulmonary fibrosis by bibliometrics and bioinformatics.

Idiopathic pulmonary fibrosis (IPF) garners considerable attention due to its high fatality rate and profound impact on quality of life. Our study conducts a comprehensive literature review on IPF using bibliometric analysis to explore existing hot research topics, and identifies novel diagnostic and therapeutic targets for IPF using bioinformatics analysis. Publications related to IPF from 2013 to 2023 were searched on the Web of Science Core Collection (WoSCC) database. Data analysis and visualization were conducted using CiteSpace and VOSviewer software primarily. The gene expression profiles GSE24206 and GSE53845 were employed as the training dataset. The GSE110147 dataset was employed as the validation dataset. We identified differentially expressed genes (DEGs) and differentially expressed genes related to oxidative stress (DEOSGs) between IPF and normal samples. Then, we conducted Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were screened by protein-protein interaction (PPI) networks and machine learning algorithms. The CIBERSORT was used to analyze the immune infiltration of 22 kinds of immune cells. Finally, we conducted the expression and validation of hub genes. The diagnostic efficacy of hub genes was evaluated by employing Receiver Operating Characteristic (ROC) curves and the associations between hub genes and immune cells were analyzed. A total of 6,500 articles were identified, and the annual number of articles exhibited an upward trend. The United States emerged as the leading contributor in terms of publication count, institutional affiliations, highly cited articles, and prolific authorship. According to co-occurrence analysis, oxidative stress and inflammation are hot topics in IPF research. A total of 1,140 DEGs were identified, and 72 genes were classified as DEOSGs. By employing PPI network analysis and machine learning algorithms, PON2 and TLR4 were identified as hub genes. A total of 10 immune cells exhibited significant differences between IPF and normal samples. PON2 and TLR4, as oxidative stress-related genes, not only exhibit high diagnostic efficacy but also show close associations with immune cells. In summary, our study highlights oxidative stress and inflammation are hot topics in IPF research. Oxidative stress and immune cells play a vital role in the pathogenesis of IPF. Our findings suggest the potential of PON2 and TLR4 as novel diagnostic and therapeutic targets for IPF.

Identification of TFRC as a biomarker for pulmonary arterial hypertension based on bioinformatics and experimental verification

BackgroundPulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there is a paucity of studies that reflect the available biomarkers from separate gene expression profiles in PAH.MethodsThe GSE131793 and GSE113439 datasets were combined for subsequent analyses, and batch effects were removed. Bioinformatic analysis was then performed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and a protein-protein interaction (PPI) network analysis were then used to further filter the hub genes. Functional enrichment analysis of the intersection genes was performed using Gene Ontology (GO), Disease Ontology (DO), Kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA). The expression level and diagnostic value of hub gene expression in pulmonary arterial hypertension (PAH) patients were also analyzed in the validation datasets GSE53408 and GSE22356. In addition, target gene expression was validated in the lungs of a monocrotaline (MCT)-induced pulmonary hypertension (PH) rat model and in the serum of PAH patients.ResultsA total of 914 differentially expressed genes (DEGs) were identified, with 722 upregulated and 192 downregulated genes. The key module relevant to PAH was selected using WGCNA. By combining the DEGs and the key module of WGCNA, 807 genes were selected. Furthermore, protein–protein interaction (PPI) network analysis identified HSP90AA1, CD8A, HIF1A, CXCL8, EPRS1, POLR2B, TFRC, and PTGS2 as hub genes. The GSE53408 and GSE22356 datasets were used to evaluate the expression of TFRC, which also showed robust diagnostic value. According to GSEA enrichment analysis, PAH-relevant biological functions and pathways were enriched in patients with high TFRC levels. Furthermore, TFRC expression was found to be upregulated in the lung tissues of our experimental PH rat model compared to those of the controls, and the same conclusion was reached in the serum of the PAH patients.ConclusionsAccording to our bioinformatics analysis, the observed increase of TFRC in the lung tissue of human PAH patients, as indicated by transcriptomic data, is consistent with the alterations observed in PAH patients and rodent models. These data suggest that TFRC may serve as a potential biomarker for PAH.

Interaction between microglial cells and CD1C+ B dendritic cells leads to CD8+ T cells depletion during the early stages of renal clear cell carcinoma.

Renal clear cell carcinoma (RCC) is a type of malignant tumor, which, in addition to surgical resection, radiotherapy, and chemotherapy, has been widely treated through immunotherapy recently. However, the influence of the tumor microenvironment and the infiltrating immune cells within it on immunotherapy remains unclear. It is imperative to study the interactions between various immune cells of RCC. The scRNA-seq dataset from GEO's database was used to analyze the immune cells present in tumor tissue and peripheral blood samples. Through quality control, clustering, and identification, the types and proportions of infiltrating immune cells were determined. The cellular differences were determined, and gene expression levels of the differentially present cells were investigated. A protein-protein interaction network analysis was performed using string. KEGG and GO analyses were performed to investigate abnormal activities. The microglia marker CD68 and CD1C+ B dendritic cells marker CD11C were detected using multiplex immunofluorescence staining. Many depleted CD8+ T cells (exhausted CD8+ T cells) appeared in tumor tissues as well as microglia. CD1C+ B dendritic cells did not infiltrate tumor tissues. HSPA1A was correlated with DNAJB1 in microglia. Compared with Paracancer tissues, microglia increased while CD1C+ B dendritic cells decreased in pathological stages I and I-II in cancerous tissues. An altered tumor microenvironment caused by increases in microglia in RCC in the early stage resulted in an inability of CD1C+ B dendritic cells to infiltrate, resulting in CD8+ T cells being unable to receive the antigens presented by them, and in turn being depleted in large quantities.

Hypoxia-related signature to risk stratify patients for the benefit of immune checkpoint inhibitors therapy in head and neck squamous cell carcinoma: An experimental study.

Increasing evidence has shown that hypoxia is a biomarker of tumor proliferation and metastasis. This research aimed to identify a hypoxia-associated gene prognostic index (HAGPI) in head and neck squamous cell carcinoma (HNSCC) and based on HAGPI-defined subgroups to predict prognosis and response to immune checkpoint inhibitors therapy. RNA-sequencing transcriptomic data for patients with HNSCC were downloaded from The Cancer Genome Atlas (TCGA). Protein-protein interaction network analysis was performed to select hypoxia-related hub genes. Univariate and multivariate cox regression analyses were used to identify hub genes to develop the HAGPI. Afterward expression data were imported into CIBERSORT to evaluate the relative proportion of 22 immune cells and compared the relative proportions of immune cells between the 2 HAGPI subgroups. The relationship between immunopheno score (IPS) and HAGPI was validated for immune checkpoint inhibitors (ICIs) response in TCGA cohorts. The HAGPI was constructed based on HS3ST1, HK1, PGK1, STC2, SERPINE1, PKLR genes. In high-HAGPI patients, the primary and secondary endpoint events in TCGA and GEO cohorts were significantly lower than low-HAGPI groups (P < .05). HAGPI-high patients exhibited a poorer prognosis than HAGPI-low patients did. The abundance of M2 macrophages and NK cell were significantly enhanced in the high-HAGPI while T cells regulatory and T cells CD8, were markedly elevated in the low-HAGPI. Meanwhile, patients in the low-HAGPI patients had higher levels of immunosuppressant expression and less aggressive phenotypes. Furthermore, IPS analysis showed that the low-HAGPI group with higher IPS represented a more immunogenic phenotype. The current study developed and verified a HAPGI model that can be considered as an independent prognostic biomarker and elucidated the tumor immune microenvironment of HNSCC.

Identification and verification of immune-related genes for diagnosing the progression of atherosclerosis and metabolic syndrome

BackgroundAtherosclerosis and metabolic syndrome are the main causes of cardiovascular events, but their underlying mechanisms are not clear. In this study, we focused on identifying genes associated with diagnostic biomarkers and effective therapeutic targets associated with these two diseases.MethodsTranscriptional data sets of atherosclerosis and metabolic syndrome were obtained from GEO database. The differentially expressed genes were analyzed by RStudio software, and the function-rich and protein-protein interactions of the common differentially expressed genes were analyzed.Furthermore, the hub gene was screened by Cytoscape software, and the immune infiltration of hub gens was analyzed. Finally, relevant clinical blood samples were collected for qRT-PCR verification of the three most important hub genes.ResultsA total of 1242 differential genes (778 up-regulated genes and 464 down-regulated genes) were screened from GSE28829 data set. A total of 1021 differential genes (492 up-regulated genes and 529 down-regulated genes) were screened from the data set GSE98895. Then 23 up-regulated genes and 11 down-regulated genes were screened by venn diagram. Functional enrichment analysis showed that cytokines and immune activation were involved in the occurrence and development of these two diseases. Through the construction of the Protein–Protein Interaction(PPI) network and Cytoscape software analysis, we finally screened 10 hub genes. The immune infiltration analysis was further improved. The results showed that the infiltration scores of 7 kinds of immune cells in GSE28829 were significantly different among groups (Wilcoxon Test < 0.05), while in GSE98895, the infiltration scores of 4 kinds of immune cells were significantly different between groups (Wilcoxon Test < 0.05). Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets. The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05). There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05). Finally, our results identified 10 small molecules with the highest absolute enrichment value, and the three most significant key genes (CX3CR1, TLR5, IL32) were further verified in the data expression matrix and clinical blood samples.ConclusionWe have established a co-expression network between atherosclerotic progression and metabolic syndrome, and identified key genes between the two diseases. Through the method of bioinformatics, we finally obtained 10 hub genes in As and MS, and selected 3 of the most significant genes (CX3CR1, IL32, TLR5) for blood PCR verification. This may be helpful to provide new research ideas for the diagnosis and treatment of AS complicated with MS.

A Network Pharmacology-based Study on the Anti-aging Properties of Traditional Chinese Medicine Sisheng Bulao Elixir.

Traditional Chinese Medicine (TCM) has a rich history of use in preventing senescence for millennia in China. Nonetheless, a systematic method to study the antiaging properties and the underlying molecular mechanism of TCM remains absent. The objective of this study is to decipher the anti-aging targets and mechanisms of Sisheng Bulao Elixir (SBE) using a systematic approach based on a novel aging database and network pharmacology. Bioactive compounds and target proteins in SBE were identified via the Traditional Chinese Medicine System Pharmacology (TCMSP) database. Aging-related proteins were uncovered through alignment with the Ageing Alta database. A compound-target (CT) protein network analysis highlighted key flavonoids targeting aging. Core aging-related proteins were extracted through protein-protein interaction (PPI) network analysis. Molecular docking validated binding activities between core compounds and aging-related proteins. The antioxidant activity of SBE was confirmed using an in vitro senescent cells model. A total of 39 active compounds were extracted from a pool of 639 compounds in SBE. Through a matching process with the Aging Alta, 88 target proteins associated with the aging process were identified. Impressively, 80 out of these 88 proteins were found to be targeted by flavonoids. Subsequently, an analysis using CT methodology highlighted 11 top bioactive flavonoids. Notably, core aging-related proteins, including AKT1, MAPK3, TP53, VEGFA, IL6, and HSP90AA1, emerged through the PPI network analysis. Moreover, three flavonoids, namely quercetin, kaempferol, and luteolin, exhibited interactions with over 100 aging-related proteins. Molecular docking studies were conducted on these flavonoids with their shared three target proteins, namely AKT1, HSP90AA1, and IL6, to assess their binding activities. Finally, the antioxidant properties of SBE were validated using an in vitro model of senescent cells. This study offers novel insights into SBE's anti-aging attributes, providing evidence of its molecular mechanisms. It enhances our understanding of traditional remedies in anti-aging research.

RBBP4: A novel diagnostic and prognostic biomarker for non-small-cell lung cancer correlated with autophagic cell death.

Non-small-cell lung cancer (NSCLC) often presents at later stages, typically associated with poor prognosis. Autophagy genes play a role in the progression of tumors. This study investigated the clinical relevance, prognostic value, and biological significance of RBBP4 in NSCLC. We assessed RBBP4 expression using the GSE30219 and TCGA NSCLC datasets and NSCLC cells, exploring its links with clinical outcomes, tumor immunity, and autophagy genes through bioinformatics analysis after transcriptome sequencing of RBBP4-knockdown and control PC9 cells. We identified differentially expressed genes (DEGs) and conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and protein-protein interaction network analyses. The significance of autophagy-related DEGs was evaluated for diagnosis and prognosis using the GSE30219 dataset. Experiments both invivo and invitro explored the biological mechanisms behind RBBP4-mediated autophagic cell death in NSCLC. RBBP4 overexpression in NSCLC correlates with a poorer prognosis. Eighteen types of immune cell were significantly enriched in cultures that had low RBBP4 expression compared high expression. DEGs associated with RBBP4 are enriched in autophagy pathways. Transcriptomic profiling of the PC9 cell line identified autophagy-related DEGs associated with RBBP4 that exhibited differential expression in NSCLC, suggesting prognostic applications. Invitro experiments demonstrated that RBBP4 knockdown induced autophagy and apoptosis in PC9 cells, promoting cell death, which was inhibited by 3-MA. Invivo, targeted siRNA against RBBP4 significantly reduced tumor development in PC9 cell-injected nude mice, elevating autophagy-related protein levels and inducing apoptosis and necrosis in tumor tissues. In NSCLC, RBBP4 upregulation correlates with poor prognosis and altered immunity. Its knockdown induces autophagic cell death in NSCLC cells. These results indicate RBBP4 as a potential NSCLC diagnostic marker and its autophagy modulation as a prospective therapeutic target.

Genome-wide analysis of the PYL-PP2C-SnRK2s family in the ABA signaling pathway of pitaya reveals its expression profiles under canker disease stress

BackgroundAbscisic acid (ABA) plays a crucial role in seed dormancy, germination, and growth, as well as in regulating plant responses to environmental stresses during plant growth and development. However, detailed information about the PYL-PP2C-SnRK2s family, a central component of the ABA signaling pathway, is not known in pitaya.ResultsIn this study, we identified 19 pyrabactin resistance-likes (PYLs), 70 type 2 C protein phosphatases (PP2Cs), and 14 SNF1-related protein kinase 2s (SnRK2s) from pitaya. In pitaya, tandem duplication was the primary mechanism for amplifying the PYL-PP2C-SnRK2s family. Co-linearity analysis revealed more homologous PYL-PP2C-SnRK2s gene pairs located in collinear blocks between pitaya and Beta vulgaris L. than that between pitaya and Arabidopsis. Transcriptome analysis showed that the PYL-PP2C-SnRK2s gene family plays a role in pitaya’s response to infection by N. dimidiatum. By spraying ABA on pitaya and subsequently inoculating it with N. dimidiatum, we conducted qRT-PCR experiments to observe the response of the PYL-PP2C-SnRK2s gene family and disease resistance-related genes to ABA. These treatments significantly enhanced pitaya’s resistance to pitaya canker. Further protein interaction network analysis helped us identify five key PYLs genes that were upregulated during the interaction between pitaya and N. dimidiatum, and their expression patterns were verified by qRT-PCR. Subcellular localization analysis revealed that the PYL (Hp1879) gene is primarily distributed in the nucleus.ConclusionThis study enhances our understanding of the response of PYL-PP2C-SnRK2s to ABA and also offers a new perspective on pitaya disease resistance.

Fc receptor-like A promotes malignant behavior in renal cell carcinoma and correlates with tumor immune infiltration.

Our study aims to investigate the mechanisms through which Fc receptor-like A (FCRLA) promotes renal cell carcinoma (RCC) and to examine its significance in relation to tumor immune infiltration. The correlation between FCRLA and data clinically related to RCC was explored using The Cancer Genome Atlas (TCGA), then validated using Gene Expression Omnibus (GEO) gene chip data. Enrichment and protein-protein interaction (PPI) network analyses were performed for FCRLA and its co-expressed genes. FCRLA was knocked down in RCC cell lines to evaluate its impact on biological behavior. Then the potential downstream regulators of FCRLA were determined by western blotting, and rescue experiments were performed for verification. The relevance between FCRLA and various immune cells was analyzed through GSEA, TIMER, and GEPIA tools. TIDE and ESTIMATE algorithms were used to predict the effect of FCRLA in immunotherapy. Fc receptor-like A was associated with clinical and T stages and could predict the M stage (AUC = 0.692) and 1-3- and 5-year survival rates (AUC = 0.823, 0.834, and 0.862) of RCC patients. Higher expression of FCLRA predicted an unfavorable overall survival (OS) in TCGA-RCC and GSE167573 datasets (p = 0.03, p = 0.04). FCRLA promoted the malignant biological behavior of RCC cells through the pERK1/2/-MMP2 pathway and was associated with tumor immune microenvironment in RCC. Fc receptor-like A is positively correlated with poor outcomes in RCC patients and plays an oncogenic role in RCC through the pERK1/2-MMP2 pathway. Patients with RCC might benefit from immunotherapy targeting FCRLA.

Network Pharmacology and Experimental Validation Explore the Pharmacological Mechanisms of Herb Pair for Treating Rheumatoid Arthritis.

This study aimed to elucidate the multitarget mechanism of the Mori Ramulus - Taxilli Herba (MT) herb pair in treating rheumatoid arthritis (RA). The targets of the herb pair and RA were predicted from databases and screened through cross-analysis. The core targets were obtained using protein-protein interaction (PPI) network analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. Finally, animal experiments were conducted to validate the anti-RA effect and mechanism of this herb pair. This approach successfully identified 9 active compounds of MT that interacted with 6 core targets (AKT1, TNF, IL6, TP53, VEGFA, and IL1β). Pathway and functional enrichment analyses revealed that MT had significant effects on the TNF and IL-17 signaling pathways. The consistency of interactions between active components and targets in these pathways was confirmed through molecular docking. Moreover, the potential therapeutic effect of MT was verified in vivo, demonstrating its ability to effectively relieve inflammation by regulating these targeted genes and pathways. The present work suggests that the therapeutic effect of MT herb pair on RA may be attributed to its ability to regulate the TNF signaling pathway and IL-17 signaling pathway.

Identification of Key Prognostic Alternative Splicing Events of Costimulatory Molecule-Related Genes in Colon Cancer.

This study aimed to explore the key alternative splicing events in costimulatory molecule-related genes in colon cancer and to determine their correlation with prognosis. Gene expression RNA-sequencing data, clinical data, and SpliceSeq data of colon cancer were obtained from The Cancer Genome Atlas. Differentially expressed alternative splicing events in genes were identified, Followed by correlation analysis of genes corresponding to differentially expressed alternative splicing events with costimulatory molecule-related genes. Survival analysis was conducted using differentially expressed alternative splicing events in these genes and a prognostic model was constructed. Functional enrichment, proteinprotein interaction network, and splicing factor analyses were performed. In total, 6504 differentially expressed alternative splicing events in 3949 genes were identified between tumor and normal tissues. Correlation analysis revealed 3499 differentially expressed alternative splicing events in 2168 costimulatory molecule-related genes. Moreover, 328 differentially expressed alternative splicing events in 288 costimulatory molecule-related genes were associated with overall survival. The prognostic models constructed using these showed considerable power in predicting survival. The ubiquitin A-52 residue ribosomal protein fusion product 1 and ribosomal protein S9 were the hub nodes in the protein-protein interaction network. Furthermore, one splicing factor, splicing factor proline and glutamine-rich, was significantly associated with patient prognosis. Four splicing factor-alternative splicing pairs were obtained from four alternative splicing events in three genes: TBC1 domain family member 8 B, complement factor H, and mitochondrial fission 1. The identified differentially expressed alternative splicing events of costimulatory molecule-related genes may be used to predict patient prognosis and immunotherapy responses in colon cancer.

Bipolar disorder: Construction and analysis of a joint diagnostic model using random forest and feedforward neural networks

BackgroundTo construct a diagnostic model for Bipolar Disorder (BD) depressive phase using peripheral tissue RNA data from patients and combining Random Forest with Feedforward Neural Network methods. MethodsDatasets GSE23848, GSE39653, and GSE69486 were selected, and differential gene expression analysis was conducted using the limma package in R. Key genes from the differentially expressed genes were identified using the Random Forest method. These key genes' expression levels in each sample were used to train a Feedforward Neural Network model. Techniques like L1 regularization, early stopping, and dropout layers were employed to prevent model overfitting. Model performance was then validated, followed by GO, KEGG, and protein-protein interaction network analyses. ResultsThe final model was a Feedforward Neural Network with two hidden layers and two dropout layers, comprising 2345 trainable parameters. Model performance on the validation set, assessed through 1000 bootstrap resampling iterations, demonstrated a specificity of 0.769 (95 % CI 0.571–1.000), sensitivity of 0.818 (95 % CI 0.533–1.000), AUC value of 0.832 (95 % CI 0.642–0.979), and accuracy of 0.792 (95 % CI 0.625–0.958). Enrichment analysis of key genes indicated no significant enrichment in any known pathways. ConclusionKey genes with biological significance were identified based on the decrease in Gini coefficient within the Random Forest model. The combined use of Random Forest and Feedforward Neural Network to establish a diagnostic model showed good classification performance in Bipolar Disorder.

Comparative Transcriptomic Insights into the Mechanisms Underlying Maize (Zea mays L.) Embryogenic Callus Differentiation

The regeneration of plant somatic cells is a prerequisite for their biological breeding. Identification of key genes controlling embryogenic callus (EC) differentiation and investigation of the genetic mechanism of cell fate determination are important for improving plant variety. In this study, we used the maize inbred line KN5585 and its gene-edited mutants Zmprx19-1, Zmprx19-2 and Zmprx19-3 as plant materials. Three somatic regeneration-related traits, the embryogenic callus induction rate (EIR), green callus rate (GCR) and plantlet regeneration rate (PRR), were identified by tissue culture of immature embryos. Additionally, the ECs at different differentiation stages (0 d, 5 d, 10 d and 15 d) were subjected to RNA-seq, and comparative transcriptome analyses were performed. The results showed that the somatic regeneration traits of the mutants were all highly significantly lower than those of the wild type (p &lt; 0.01). The PRR value of KN5585 was 75.25%, while the highest PRR of the mutants was only 15.08%, indicating that knockdown of ZmPRX19 inhibited EC regeneration. Transcriptome sequencing yielded a total of 200.30 Gb of clean data from 24 libraries, with an average of 6.53 Gb of clean data per library. Mutant and wild-type gene expression data were compared separately at four differentiation stages, and 689 common differentially expressed genes (DEGs) were screened. WGCNA was used to classify these genes into nine modules, which were subsequently subjected to GO and KEGG enrichment analyses. In total, 40, 23, 17 and 5 genes were significantly (q &lt; 0.05) enriched in plant hormone signal transduction, the MAPK signaling pathway-plant, phenylpropanoid biosynthesis and photosynthesis, respectively. Moreover, protein–protein interaction (PPI) network analysis revealed five MAPKKK17_18 hub nodes involved in the MAPK pathway-plant, which may be the key genes controlling plantlet differentiation from ECs. The above results provide a basis for the final elucidation of the molecular mechanism of plant somatic regeneration.

Discovering the effect of combination of celecoxib and sorafenib on hepatocellular carcinoma

IntroductionHepatocellular carcinoma (HCC) is a common and fatal cancer, and its molecular mechanisms are still not fully understood. This study aimed to explore the potential molecular mechanisms and immune infiltration characteristics of celecoxib combined with sorafenib in the treatment of HCC by analyzing the differentially expressed genes (DEGs) from the GSE45340 dataset in the GEO database and identifying key genes.MethodsThe GSE45340 dataset was downloaded from the GEO database, and DEGs were screened using GEO2R, and visualization and statistical analysis were performed. Metascape was used to perform functional annotation and protein–protein interaction network analysis of DEGs. The immune infiltration was analyzed using the TIMER database, and the expression of key genes and their relationship with patient survival were analyzed and verified using the UALCAN database.ResultsA total of 2181 DEGs were screened through GEO2R analysis, and heat maps were drawn for the 50 genes with the highest expression. Metascape was used for enrichment analysis, and the enrichment results of KEGG and GO and the PPI network were obtained, and 44 core genes were screened. Analysis of the TIMER database found that 12 genes were closely related to tumor immune infiltration. UALCAN analysis further verified the differential expression of these genes in HCC and was closely related to the overall survival of patients.ConclusionsThrough comprehensive bioinformatics analysis, this study identified a group of key genes related to the treatment of HCC with celecoxib combined with sorafenib. These genes play an important role in tumor immune infiltration and patient survival, providing important clues for further studying the molecular mechanism of HCC and developing potential therapeutic targets.

Assessment of NB-UVB Effects on Skin of Atopic Dermatitis Patients: A Network Analysis.

Introduction: Atopic dermatitis is a common inflammatory skin disease which is treated with narrowband ultraviolet B (NB-UVB). Exploring the critical targeted genes in patients by UV radiation is the main aim of this study. Methods: Gene expression profiles of lesional and non-lesional skin samples of atopic dermatitis patients after treatment with NB-UVB and the non-irradiated samples were extracted from the Gene Expression Omnibus (GEO) database and analyzed via protein-protein interaction (PPI) network analysis to find the critical targeted genes. Results: A total of 357 significant differentially expressed genes (DEGs) were included in the PPI network. CTNNB1, SRSF1, YWHAB, SMC3, GNB2, ARF3, UBL7, RAB2A, YWHAE, EIF5B, SNRPE, PPIG, RC3H2, CFL1, SMARCB1. LAPTM5, PRPF40A, and RBBP4 were introduced as hub-bottlenecks. Conclusion: In conclusion, five central genes including SMC3, ARF3, EIF5B, SMARCB1, and LAPTM5 were highlighted as the critical genes in response to NB-UVB radiation in the skin of the treated atopic dermatitis patients. The introduced crucial genes are involved in essential cellular functions such as apoptosis, cell cycle, cell proliferation, and inflammation. It seems that applied NB-UVB radiation is a suitable therapeutic method for atopic dermatitis disease.