Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

HybridGWOSPEA2ABC: a novel feature selection algorithm for gene expression data analysis and cancer classification

BACKGROUND AND OBJECTIVE

DNA micro-array technology has a remarkable impact on biological research, particularly in categorizing and diagnosing cancer and studying gene features and functions. With the availability of extensive collections of cancer-related data, there has been an increased focus on developing optimized Machine Learning (ML) techniques for cancer classification through gene pattern analysis and the identification of specific genes for cancer type categorization. The relevant gene selection for diagnosing and treating cancer poses a significant challenge, which requires efficient feature selection methods.

METHODS

This study introduces a novel hybrid algorithm, for gene selection, integrating the Grey Wolf Optimizer (GWO), Strength Pareto Evolutionary Algorithm 2 (SPEA2), and Artificial Bee Colony (ABC). This combination uses intelligence and evolutionary computation to enhance solution diversity, convergence efficiency, and exploration and exploitation capabilities in high-dimensional gene expression data. The algorithm was compared with five bio-inspired algorithms using five different classifiers on various cancer datasets to validate its effectiveness in feature selection.

RESULTS

The HybridGWOSPEA2ABC algorithm demonstrated superior performance in identifying relevant cancer biomarkers compared to the conventional bio-inspired algorithms. Comparison with the benchmark algorithms has shown the hybrid approach's enhanced capability in addressing the challenges of high-dimensional data and advancing the gene selection problem for cancer classification.

CONCLUSION

The novel hybridization algorithm enhances performance by maintaining solution diversity, efficiently converging to optimal solutions, and improving the exploration and exploitation of the search space. This study provides a better understanding of relevant genes for cancer classification and promotes effective methodologies for disease detection and classification.

Brain connectivity and transcriptional changes induced by rTMS in first-episode major depressive disorder

Repetitive transcranial magnetic stimulation (rTMS) is a widely utilized non-invasive brain stimulation technique with demonstrated efficacy in treating major depressive disorder (MDD). However, the mechanisms underlying its therapeutic effects, particularly in modulating neural connectivity and influencing gene expression, remain incompletely understood. In this study, we investigated the voxel-wise degree centrality (DC) induced by 10 Hz rTMS targeting the left dorsolateral prefrontal cortex, as well as their associations with transcriptomic data from the Allen Human Brain Atlas. The results indicated that the active treatment significantly reduced clinical symptoms and increased DC in the left superior medial frontal gyrus, left middle occipital gyrus, and right anterior cingulate cortex. Partial least squares regression analysis revealed that genes associated with DC alternations were enriched biological processes related to neural plasticity and synaptic connectivity. Furthermore, protein-protein interaction (PPI) analysis identified key hub genes, including SCN1A, SNAP25, and PVALB, whose expression levels were positively correlated with DC changes. Notably, SCN1A emerged as a significant predictor on DC changes. These findings suggest that rTMS may exert its therapeutic effects in MDD by modulating specific molecular pathways and neural networks, providing valuable insights into its mechanisms of action.

The underlying neurobiological basis of gray matter volume alterations in schizophrenia with auditory verbal hallucinations: A meta-analytic investigation

Schizophrenia patients with auditory verbal hallucinations (AVH) frequently exhibit brain structural alterations, particularly reductions in gray matter volume (GMV).Understanding the neurobiological mechanisms underlying the changes is essential for advancing treatment strategies. To address this, a meta-analysis was conducted to identify GMV changes in schizophrenia patients with AVH and their associations with gene expression and neurotransmitter receptor profiles. The results indicated significant GMV reductions in the left and the right insula, as well as the left anterior cingulate cortex. Ontology analysis of genes associated with GMV alternations revealed enrichment in biological processes related to ion transport and synaptic transmission. Hub genes from the KCN, SCN, GN, and PRK families, along with neurotransmitter receptors such as D2, VAChT, and mGluR5, showed significant correlations with GMV changes. Furthermore, multivariate linear regression analysis demonstrated that GNB2, GNB4, PRKCG, D2, and mGluR5 significantly predicted GMV alternations. These findings suggest that GMV reductions in schizophrenia with AVH are linked to disruptions in neurobiological processes involving specific genes and neurotransmitter systems, highlighting the potential targets for therapeutic intervention.

Network pharmacology-integrated molecular modeling analysis of Aquilaria malaccensis L. (agarwood) essential oil phytocompounds

A network pharmacology approach was used to construct comprehensive pharmacological networks, elucidating the interactions between agarwood compounds and key biological targets associated with cancer pathways. We have employed a combination of network pharmacology, molecular docking and molecular dynamics to unravel agarwood plants' active components and potential mechanisms. Reported 23 molecules were collected from the agarwood plants and considered to identify molecular targets. Further, we identified ten potent targets related to cancer through network pharmacology analysis. The key targets include EGFR, JUN, TP53, SRC, MAPK3, ACTB, GAPDH, AKT1, MYC and CTNNB1. The biological processes include the negative regulation of fibroblast proliferation, metabolic, oxidative, and more. Subsequently, molecular docking results have indicated that 7-isopropenyl-1, 4a-dimethyl-4, 4a, 5,6,7,8-hexahydro-3 H-naphthalen-2-one showed an excellent binding affinity for all ten targets. This is the first study; we employed a novel integrated approach that combines network pharmacology, molecular docking and molecular dynamics simulation (MDS). The GO and KEGG, pathway enrichment analyses, shed light on biological processes relevant to cancer treatment. Moreover, molecular docking studies results indicated that the molecule 7-isopropenyl-1,4a-dimethyl-4,4a,5,6,7,8-hexahydro-3H-naphthalen-2-one exhibited strong binding affinity among all ten cancer targets, with a docking score ranging from - 9.9 to - 6.7 kcal/mol and found to have hydrogen bond interaction with Lys168, Ser322, Thr336 and Ala946 residues. MDS sheds light on the stability of their binding, the longevity of their interactions, and their overall effect on the enzyme's active site throughout the simulation. The current work signifies the initial report using bioinformatics approaches to assess the anticancer properties of compounds derived from the agarwood plant.

Developing an Early Warning System for Financial Networks: An Explainable Machine Learning Approach

Identifying the influential variables that provide early warning of financial network instability is challenging, in part due to the complexity of the system, uncertainty of a failure, and nonlinear, time-varying relationships between network participants. In this study, we introduce a novel methodology to select variables that, from a data-driven and statistical modeling perspective, represent these relationships and may indicate that the financial network is trending toward instability. We introduce a novel variable selection methodology that leverages Shapley values and modified Borda counts, in combination with statistical and machine learning methods, to create an explainable linear model to predict relationship value weights between network participants. We validate this new approach with data collected from the March 2023 Silicon Valley Bank Failure. The models produced using this novel method successfully identified the instability trend using only 14 input variables out of a possible 3160. The use of parsimonious linear models developed by this method has the potential to identify key financial stability indicators while also increasing the transparency of this complex system.

The Effect of Vitamin D Deficiency on Immune-Related Hub Genes: A Network Analysis Associated With Type 1 Diabetes

Background Type 1 diabetes (T1D) is an autoimmune disorder that results in the destruction of pancreatic beta cells, causing a shortage of insulin secretion. The development of T1D is influenced by both genetic predisposition and environmental factors, such as vitamin D. This vitamin is known for its ability to regulate the immune system and has been associated with a decreased risk of T1D. However, the specific ways in which vitamin D affects immune regulation and the preservation of beta cells in T1D are not yet fully understood. Gaining a better understanding of these interactions is essential for identifying potential targets for preventing and treating T1D. Methods The analysis focused on two Gene Expression Omnibus (GEO) datasets, namely, GSE55098 and GSE50012, to detect differentially expressed genes (DEGs). Enrichr (Ma'ayan Laboratory, New York, NY) was used to perform enrichment analysis for the Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The Search Tool for the Retrieval of Interacting Genes 12.0 (STRING) database was used to generate a protein-protein interaction (PPI) network. The Cytoscape 3.10.1 (Cytoscape Team, San Diego, CA) was used to analyze the PPI network and discover the hub genes. Results The DEGs in both datasets were identified using the GEO2R tool, with a particular focus on genes exhibiting contrasting regulations. Enrichment analysis unveiled the participation of these oppositely regulated DEGs in processes relevant to the immune system. Cytoscape analysis of the PPI network revealed five hub genes, MNDA, LILRB2, FPR2, HCK, and FCGR2A, suggesting their potential role in the pathogenesis of T1D and the response to vitamin D. Conclusion The study elucidates the complex interaction between vitamin D metabolism and immune regulation in T1D. The identified hub genes provide important knowledge on the molecular pathways that underlie T1D and have the potential to be targeted for therapeutic intervention. This research underscores the importance of vitamin D in the immune system's modulation and its impact on T1D development.

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

The Potential MicroRNA Diagnostic Biomarkers in Oral Squamous Cell Carcinoma of the Tongue

Oral squamous cell carcinoma (OSCC) of the tongue is a common type of head and neck malignancy with a poor prognosis, underscoring the urgency for early detection. MicroRNAs (miRNAs) have remarkable stability and are easily measurable. Thus, miRNAs may be a promising biomarker candidate among biomarkers in cancer diagnosis. Biomarkers have the potential to facilitate personalized medicine approaches by guiding treatment decisions and optimizing therapy regimens for individual patients. Utilizing data from The Cancer Genome Atlas, we identified 13 differentially expressed upregulated miRNAs in OSCC of the tongue. Differentially expressed miRNAs were analyzed by enrichment analysis to reveal underlying biological processes, pathways, or functions. Furthermore, we identified miRNAs associated with the progression of OSCC of the tongue, utilizing receiver operating characteristic analysis to evaluate their potential as diagnostic biomarkers. A total of 13 upregulated miRNAs were identified as differentially expressed in OSCC of the tongue. Five of these miRNAs had high diagnostic power. In particular, miR-196b has the potential to serve as one of the most effective diagnostic biomarkers. Then, functional enrichment analysis for the target gene of miR-196b was performed, and a protein-protein interaction network was constructed. This study assessed an effective approach for identifying miRNAs as early diagnostic markers for OSCC of the tongue.

Integrated transcriptomic analysis reveals immune signatures distinguishing persistent versus resolving outcomes in MRSA bacteremia

Introduction

Staphylococcus aureus bacteremia (SAB) is a life-threatening infection particularly involving methicillin-resistant S. aureus (MRSA). In contrast to resolving MRSA bacteremia (RB), persistent MRSA bacteremia (PB) blood cultures remain positive despite appropriate antibiotic treatment. Host immune responses distinguishing PB vs. RB outcomes are poorly understood. Here, integrated transcriptomic, IL-10 cytokine levels, and genomic analyses sought to identify signatures differentiating PB vs. RB outcomes.

Methods

Whole-blood transcriptomes of propensity-matched PB (n=28) versus RB (n=30) patients treated with vancomycin were compared in one independent training patient cohort. Gene expression (GE) modules were analyzed and prioritized relative to host IL-10 cytokine levels and DNA methyltransferase-3A (DNMT3A) genotype.

Results

Differential expression of T and B lymphocyte gene expression early in MRSA bacteremia discriminated RB from PB outcomes. Significant increases in effector T and B cell signaling pathways correlated with RB, lower IL-10 cytokine levels and DNMT3A heterozygous A/C genotype. Importantly, a second PB and RB patient cohort analyzed in a masked manner demonstrated high predictive accuracy of differential signatures.

Discussion

Collectively, the present findings indicate that human PB involves dysregulated immunity characterized by impaired T and B cell responses associated with excessive IL-10 expression in context of the DNMT3A A/A genotype. These findings reveal distinct immunologic programs in PB vs. RB outcomes, enable future studies to define mechanisms by which host and/or pathogen drive differential signatures and may accelerate prediction of PB outcomes. Such prognostic assessment of host risk could significantly enhance early anti-infective interventions to avert PB and improve patient outcomes.

Globospiramine from Voacanga globosa Exerts Robust Cytotoxic and Antiproliferative Activities on Cancer Cells by Inducing Caspase-Dependent Apoptosis in A549 Cells and Inhibiting MAPK14 (p38α): In Vitro and Computational Investigations

Bisindole alkaloids are a source of inspiration for the design and discovery of new-generation anticancer agents. In this study, we investigated the cytotoxic and antiproliferative activities of three spirobisindole alkaloids from the traditional anticancer Philippine medicinal plant Voacanga globosa, along with their mechanisms of action. Thus, the alkaloids globospiramine (1), deoxyvobtusine (2), and vobtusine lactone (3) showed in vitro cytotoxicity and antiproliferative activities against the tested cell lines (L929, KB3.1, A431, MCF-7, A549, PC-3, and SKOV-3) using MTT and CellTiter-Blue assays. Globospiramine (1) was also screened against a panel of breast cancer cell lines using the sulforhodamine B (SRB) assay and showed moderate cytotoxicity. It also promoted the activation of apoptotic effector caspases 3 and 7 using Caspase-Glo 3/7 and CellEvent-3/7 apoptosis assays. Increased expressions of cleaved caspase 3 and PARP in A549 cells treated with 1 were also observed. Apoptotic activity was also confirmed when globospiramine (1) failed to promote the rapid loss of membrane integrity according to the HeLa cell membrane permeability assay. Network pharmacology analysis, molecular docking, and molecular dynamics simulations identified MAPK14 (p38α), a pharmacological target leading to cancer cell apoptosis, as a putative target. Low toxicity risks and favorable drug-likeness were also predicted for 1. Overall, our study demonstrated the anticancer potentials and apoptotic mechanisms of globospiramine (1), validating the traditional medicinal use of Voacanga globosa.

Insights into dietary phytochemicals targeting Parkinson's disease key genes and pathways: A network pharmacology approach

Parkinson's disease (PD) is a complex neurological disease associated with the degeneration of dopaminergic neurons. Oxidative stress is a key player in instigating apoptosis in dopaminergic neurons. To improve the survival of neurons many dietary phytochemicals have gathered significant attention recently. Thus, the present study implements a comprehensive network pharmacology approach to unravel the mechanisms of action of dietary phytochemicals that benefit disease management. A literature search was performed to identify ligands (i.e., comprising dietary phytochemicals and Food and Drug Administration pre-approved PD drugs) in the PubMed database. Targets associated with selected ligands were extracted from the search tool for interactions of chemicals (STITCH) database. Then, the construction of a gene-gene interaction (GGI) network, analysis of hub-gene, functional and pathway enrichment, associated transcription factors, miRNAs, ligand-target interaction network, docking were performed using various bioinformatics tools together with molecular dynamics (MD) simulations. The database search resulted in 69 ligands and 144 unique targets. GGI and subsequent topological measures indicate histone acetyltransferase p300 (EP300), mitogen-activated protein kinase 1 (MAPK1) or extracellular signal-regulated kinase (ERK)2, and CREB-binding protein (CREBBP) as hub genes. Neurodegeneration, MAPK signaling, apoptosis, and zinc binding are key pathways and gene ontology terms. hsa-miR-5692a and SCNA gene-associated transcription factors interact with all the 3 hub genes. Ligand-target interaction (LTI) network analysis suggest rasagiline and baicalein as candidate ligands targeting MAPK1. Rasagiline and baicalein form stable complexes with the Y205, K330, and V173 residues of MAPK1. Computational molecular insights suggest that baicalein and rasagiline are promising preclinical candidates for PD management.

Identification of hub genes distinguishing subtypes in endometrial stromal sarcoma through comprehensive bioinformatics analysis

Diagnosing low-grade and high-grade endometrial stromal sarcoma (LG-ESS and HG-ESS) is a challenge. This study aimed to identify biomarkers. 22 ESS cases were analyzed using Illumina microarrays. Differentially expressed genes (DEGs) were identified via Limma. DEGs were analyzed with String and Cytoscape. Core genes were enriched with GO and KEGG, their pan-cancer implications and immune aspects were studied. 413 DEGs were found by exome sequencing, 2174 by GSE85383 microarray. 36 common genes were identified by Venn analysis, and 10 core genes including RBFOX1, PCDH7, FAT1 were selected. Core gene GO enrichment included cell adhesion, T cell proliferation, and KEGG focused on related pathways. Expression was evaluated across 34 cancers, identifying immune DEGs IGF1 and AVPR1A. Identifying the DEGs not only helps improve our understanding of LG-ESS, HG-ESS but also promises to be potential biomarkers for differential diagnosis between LG-ESS and HG-ESS and new therapeutic targets.

Integrated analysis of inflammatory mRNAs, miRNAs, and lncRNAs elucidates the molecular interactome behind bovine mastitis

Mastitis is known as intramammary inflammation, which has a multifactorial complex phenotype. However, the underlying molecular pathogenesis of mastitis remains poorly understood. In this study, we utilized a combination of RNA-seq and miRNA-seq techniques, along with computational systems biology approaches, to gain a deeper understanding of the molecular interactome involved in mastitis. We retrieved and processed one hundred transcriptomic libraries, consisting of 50 RNA-seq and 50 matched miRNA-seq data, obtained from milk-isolated monocytes of Holstein-Friesian cows, both infected with Streptococcus uberis and non-infected controls. Using the weighted gene co-expression network analysis (WGCNA) approach, we constructed co-expressed RNA-seq-based and miRNA-seq-based modules separately. Module-trait relationship analysis was then performed on the RNA-seq-based modules to identify highly-correlated modules associated with clinical traits of mastitis. Functional enrichment analysis was conducted to understand the functional behavior of these modules. Additionally, we assigned the RNA-seq-based modules to the miRNA-seq-based modules and constructed an integrated regulatory network based on the modules of interest. To enhance the reliability of our findings, we conducted further analyses, including hub RNA detection, protein-protein interaction (PPI) network construction, screening of hub-hub RNAs, and target prediction analysis on the detected modules. We identified a total of 17 RNA-seq-based modules and 3 miRNA-seq-based modules. Among the significant highly-correlated RNA-seq-based modules, six modules showed strong associations with clinical characteristics of mastitis. Functional enrichment analysis revealed that the turquoise module was directly related to inflammation persistence and mastitis development. Furthermore, module assignment analysis demonstrated that the blue miRNA-seq-based module post-transcriptionally regulates the turquoise RNA-seq-based module. We also identified a set of different RNAs, including hub-hub genes, hub-hub TFs (transcription factors), hub-hub lncRNAs (long non-coding RNAs), and hub miRNAs within the modules of interest, indicating their central role in the molecular interactome underlying the pathogenic mechanisms of S. uberis infection. This study provides a comprehensive insight into the molecular crosstalk between immunoregulatory mRNAs, miRNAs, and lncRNAs during S. uberis infection. These findings offer valuable directions for the development of molecular diagnosis and biological therapies for mastitis.

Hierarchical based classification method based on fusion of Gaussian map descriptors for Alzheimer diagnosis using T1-weighted magnetic resonance imaging

Alzheimer's disease (AD) is considered one of the most spouting elderly diseases. In 2015, AD is reported the US's sixth cause of death. Substantially, non-invasive imaging is widely employed to provide biomarkers supporting AD screening, diagnosis, and progression. In this study, Gaussian descriptors-based features are proposed to be efficient new biomarkers using Magnetic Resonance Imaging (MRI) T1-weighted images to differentiate between Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal controls (NC). Several Gaussian map-based features are extracted such as Gaussian shape operator, Gaussian curvature, and mean curvature. The aforementioned features are then introduced to the Support Vector Machine (SVM). They were, first, calculated separately for the Hippocampus and Amygdala. Followed by the fusion of the features. Moreover, Fusion of the regions before feature extraction was also employed. Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, formed of 45, 55, and 65 cases for AD, MCI, and NC respectively, is appointed in this study. The shape operator feature outperformed the other features, with 74.6%, and 98.9% accuracy in the case of normal vs. abnormal, and AD vs. MCI classification respectively.

Vinculin Identified as a Potential Biomarker in Hand-Arm Vibration Syndrome Based on iTRAQ and LC-MS/MS-Based Proteomic Analysis

Local vibration can induce vascular injuries, one example is the hand-arm vibration syndrome (HAVS) caused by hand-transmitted vibration (HTV). Little is known about the molecular mechanism of HAVS-induced vascular injuries. Herein, the iTRAQ (isobaric tags for relative and absolute quantitation) followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics approach was applied to conduct the quantitative proteomic analysis of plasma from specimens with HTV exposure or HAVS diagnosis. Overall, 726 proteins were identified in iTRAQ. 37 proteins upregulated and 43 downregulated in HAVS. Moreover, 37 upregulated and 40 downregulated when comparing severe HAVS and mild HAVS. Among them, Vinculin (VCL) was found to be downregulated in the whole process of HAVS. The concentration of vinculin was further verified by ELISA, and the results suggested that the proteomics data was reliable. Bioinformative analyses were used, and those proteins mainly engaged in specific biological processes like binding, focal adhesion, and integrins. The potential of vinculin application in HAVS diagnosis was validated by the receiver operating characteristic curve.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

Objective

Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

Methods

RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

Results

As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

Conclusion

The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.

Coexpression network analysis of human candida infection reveals key modules and hub genes responsible for host-pathogen interactions

Invasive fungal infections are a significant reason for morbidity and mortality among organ transplant recipients. Therefore, it is critical to investigate the host and candida niches to understand the epidemiology of fungal infections in transplantation. Candida albicans is an opportunistic fungal pathogen that causes fatal invasive mucosal infections, particularly in solid organ transplant patients. Therefore, identifying and characterizing these genes would play a vital role in understanding the complex regulation of host-pathogen interactions. Using 32 RNA-sequencing samples of human cells infected with C. albicans, we developed WGCNA coexpression networks and performed DESeq2 differential gene expression analysis to identify the genes that positively correlate with human candida infection. Using hierarchical clustering, we identified 5 distinct modules. We studied the inter- and intramodular gene network properties in the context of sample status traits and identified the highly enriched genes in the correlated modules. We identified 52 genes that were common in the most significant WGCNA turquoise module and differentially expressed genes in human endothelial cells (HUVEC) infection vs. control samples. As a validation step, we identified the differentially expressed genes from the independent Candida-infected human oral keratinocytes (OKF6) samples and validated 30 of the 52 common genes. We then performed the functional enrichment analysis using KEGG and GO. Finally, we performed protein-protein interaction (PPI) analysis using STRING and CytoHubba from 30 validated genes. We identified 8 hub genes (JUN, ATF3, VEGFA, SLC2A1, HK2, PTGS2, PFKFB3, and KLF6) that were enriched in response to hypoxia, angiogenesis, vasculogenesis, hypoxia-induced signaling, cancer, diabetes, and transplant-related disease pathways. The discovery of genes and functional pathways related to the immune system and gene coexpression and differential gene expression analyses may serve as novel diagnostic markers and potential therapeutic targets.

Identification of unique transcriptomic signatures and key genes through RNA sequencing and integrated WGCNA and PPI network analysis in HIV infected lung cancer

With the widespread use of highly active antiretroviral therapy (HARRT), the survival time of AIDS patients has been greatly extended. However, the incidence of lung cancer in HIV-infected patients is increasing and has become a major problem threatening the survival of AIDS patients. The aim of this study is to use Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene analysis to find possible key genes involved in HIV-infected lung cancer. In this study, using lung tissue samples from five pairs of HIV-infected lung cancer patients, second-generation sequencing was performed and transcriptomic data were obtained. A total of 132 HIV-infected lung cancer-related genes were screened out by WGCNA and differential gene expression analysis methods. Based on gene annotation analysis, these genes were mainly enriched in mitosis-related functions and pathways. In addition, in protein-protein interaction (PPI) analysis, a total of 39 hub genes were identified. Among them, five genes (ASPM, CDCA8, CENPF, CEP55, and PLK1) were present in both three hub gene lists (intersection gene, DEGs, and WCGNA module) suggesting that these five genes may become key genes involved in HIV-infected lung cancer.

Sex-Specific Transcriptomic Signatures in Brain Regions Critical for Neuropathic Pain-Induced Depression

Neuropathic pain is a chronic debilitating condition with a high comorbidity with depression. Clinical reports and animal studies have suggested that both the medial prefrontal cortex (mPFC) and the anterior cingulate cortex (ACC) are critically implicated in regulating the affective symptoms of neuropathic pain. Neuropathic pain induces differential long-term structural, functional, and biochemical changes in both regions, which are thought to be regulated by multiple waves of gene transcription. However, the differences in the transcriptomic profiles changed by neuropathic pain between these regions are largely unknown. Furthermore, women are more susceptible to pain and depression than men. The molecular mechanisms underlying this sexual dimorphism remain to be explored. Here, we performed RNA sequencing and analyzed the transcriptomic profiles of the mPFC and ACC of female and male mice at 2 weeks after spared nerve injury (SNI), an early time point when the mice began to show mild depressive symptoms. Our results showed that the SNI-induced transcriptomic changes in female and male mice were largely distinct. Interestingly, the female mice exhibited more robust transcriptomic changes in the ACC than male, whereas the opposite pattern occurred in the mPFC. Cell type enrichment analyses revealed that the differentially expressed genes involved genes enriched in neurons, various types of glia and endothelial cells. We further performed gene set enrichment analysis (GSEA), which revealed significant de-enrichment of myelin sheath development in both female and male mPFC after SNI. In the female ACC, gene sets for synaptic organization were enriched, and gene sets for extracellular matrix were de-enriched after SNI, while such signatures were absent in male ACC. Collectively, these findings revealed region-specific and sexual dimorphism at the transcriptional levels induced by neuropathic pain, and provided novel therapeutic targets for chronic pain and its associated affective disorders.

Discovering Common Pathophysiological Processes between COVID-19 and Cystic Fibrosis by Differential Gene Expression Pattern Analysis

Coronaviruses are a family of viruses that infect mammals and birds. Coronaviruses cause infections of the respiratory system in humans, which can be minor or fatal. A comparative transcriptomic analysis has been performed to establish essential profiles of the gene expression of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) linked to cystic fibrosis (CF). Transcriptomic studies have been carried out in relation to SARS-CoV-2 since a number of people have been diagnosed with CF. The recognition of differentially expressed genes demonstrated 8 concordant genes shared between the SARS-CoV-2 and CF. Extensive gene ontology analysis and the discovery of pathway enrichment demonstrated SARS-CoV-2 response to CF. The gene ontological terms and pathway enrichment mechanisms derived from this research may affect the production of successful drugs, especially for the people with the following disorder. Identification of TF-miRNA association network reveals the interconnection between TF genes and miRNAs, which may be effective to reveal the other influenced disease that occurs for SARS-CoV-2 to CF. The enrichment of pathways reveals SARS-CoV-2-associated CF mostly engaged with the type of innate immune system, Toll-like receptor signaling pathway, pantothenate and CoA biosynthesis, allograft rejection, graft-versus-host disease, intestinal immune network for IgA production, mineral absorption, autoimmune thyroid disease, legionellosis, viral myocarditis, inflammatory bowel disease (IBD), etc. The drug compound identification demonstrates that the drug targets of IMIQUIMOD and raloxifene are the most significant with the significant hub DEGs.

High expression of RASAL1, a hub gene in the progression of liver cancer, suggests a poor prognostis

OBJECTIVE

Liver cancer (LC) is a frequently occurring lethal malignancy worldwide, yet the molecular mechanisms of carcinogenesis and their development remain uncharacterized. In this study, bioinformatics methods were used to find candidate hub genes for prognosis assessment and clinical treatment of LC.

METHODS

Differential analysis was carried out based on the evidence of gene expression profiling in LC on The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) were constructed into co-expression networks and divided into modules by virtue of weighted gene co-expression network analysis (WGCNA). Based on the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the module genes were subjected to functional enrichment analysis. The LC microarray (GSE105130) in the Gene Expression Omnibus was selected to verify the hub genes' expression profiles. The validity of the hub genes was verified via survival analysis, as well as expression correlation with the clinicopathological features. Thereafter, gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (GSEA) were applied to investigate the possible biological functions of the hub genes.

RESULTS

In total, 3780 DEGs and 17 co-expression modules were obtained. The blue module had the strongest correlation with the tumour stage and the module genes were principally enriched in tumour-associated GO terms, as well as pathways such as Ras protein signal transduction, ERK1/2 cascade, Ras signal pathway, and ECM-receptor interaction. RASAL1, which is highly expressed in LC, was identified as a hub gene for LC progression. Its high expression suggested unfavorable patient prognosis and was correlated with T stage, gender and tumour stage. Further analysis identified that the overexpression of RASAL1 was substantially enriched in cancer-associated gene sets.

CONCLUSION

RASAL1 is a hub gene that influences LC progression, constituting a novel biomarker and molecular target in the future diagnosis and therapy of LC.

Both simulation and sequencing data reveal coinfections with multiple SARS-CoV-2 variants in the COVID-19 pandemic

SARS-CoV-2 is a single-stranded RNA betacoronavirus with a high mutation rate. The rapidly emerging SARS-CoV-2 variants could increase transmissibility and diminish vaccine protection. However, whether coinfection with multiple SARS-CoV-2 variants exists remains controversial. This study collected 12,986 and 4,113 SARS-CoV-2 genomes from the GISAID database on May 11, 2020 (GISAID20May11), and Apr 1, 2021 (GISAID21Apr1), respectively. With single-nucleotide variant (SNV) and network clique analyses, we constructed single-nucleotide polymorphism (SNP) coexistence networks and discovered maximal SNP cliques of sizes 16 and 34 in the GISAID20May11 and GISAID21Apr1 datasets, respectively. Simulating the transmission routes and SNV accumulations, we discovered a linear relationship between the size of the maximal clique and the number of coinfected variants. We deduced that the COVID-19 cases in GISAID20May11 and GISAID21Apr1 were coinfections with 3.20 and 3.42 variants on average, respectively. Additionally, we performed Nanopore sequencing on 42 COVID-19 patients and discovered recurrent heterozygous SNPs in twenty of the patients, including loci 8,782 and 28,144, which were crucial for SARS-CoV-2 lineage divergence. In conclusion, our findings reported SARS-CoV-2 variants coinfection in COVID-19 patients and demonstrated the increasing number of coinfected variants.

Integrative Analyses of Genes Associated With Otologic Disorders in Turner Syndrome

Background: Loss or partial loss of one X chromosome induces Turner syndrome (TS) in females, causing major medical concerns, including otologic disorders. However, the underlying genetic pathophysiology of otologic disorders in TS is mostly unclear.

Methods: Ear-related genes of TS (TSEs) were identified by analyzing differentially expressed genes (DEGs) in two Gene Expression Omnibus (GEO)-derived expression profiles and ear-genes in the Comparative Toxicogenomic Database (CTD). Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) analyses; Gene Set Enrichment Analysis (GSEA); and Gene Set Variation Analysis (GSVA) were adopted to study biological functions. Moreover, hub genes within the TSEs were identified by assessing protein-protein interaction (PPI), gene-microRNA, and gene-transcription factor (TF) networks. Drug-Gene Interaction Database (DGIdb) analysis was performed to predict molecular drugs for TS. Furthermore, three machine-learning analysis outcomes were comprehensively compared to explore optimal biomarkers of otologic disorders in TS. Finally, immune cell infiltration was analyzed.

Results: The TSEs included 30 significantly upregulated genes and 14 significantly downregulated genes. Enrichment analyses suggested that TSEs play crucial roles in inflammatory responses, phospholipid and glycerolipid metabolism, transcriptional processes, and epigenetic processes, such as histone acetylation, and their importance for inner ear development. Subsequently, we described three hub genes in the PPI network and confirmed their involvement in Wnt/β-catenin signaling pathway and immune cell regulation and roles in maintaining normal auditory function. We also constructed gene-microRNA and gene-TF networks. A novel biomarker (SLC25A6) of the pathogenesis of otologic disorders in TS was identified by comprehensive comparisons of three machine-learning analyses with the best predictive performance. Potential therapeutic agents in TS were predicted using the DGIdb. Immune cell infiltration analysis showed that TSEs are related to immune-infiltrating cells.

Conclusion: Overall, our findings have deepened the understanding of the pathophysiology of otologic disorders in TS and made contributions to present a promising biomarker and treatment targets for in-depth research.

Identification of Prognostic Biomarker Candidates Associated With Melanoma Using High-Dimensional Genomic Data

Survival of patients with metastatic melanoma varies widely. Melanoma is a highly proliferative, chemo-resistant disease. With the recent availability of immunotherapies such as checkpoint inhibitors, durable response rates have improved but are often still limited to 2–3 years. Response rates to treatment range from 30 to 45% with combination therapy however no improvement in overall survival is frequently observed. Of the available therapies, many have targeted the BRAFV600E mutation that results in abnormal MAPK pathway activation which is important for regulating cell proliferation. Immune checkpoint inhibitors such as anti-PD-1 and anti-PD-L1 offer better success but response rates are still low. Identifying biomarkers to better target those who will respond and identify the right combination of treatment is the best approach. In this study, we utilize data from the Cancer Cell Line Encyclopedia (CCLE), including 62 samples, to examine features of gene expression (19K+) and copy number (20K+) in the melanoma cell lines. We perform a clustering analysis on the feature set to assess genetically similarity among the cell lines. We then discover which specific genes and combinations thereof maximize cluster density. We design a feature selection approach for high-dimensional datasets that integrates multiple disparate machine learning techniques into one cohesive pipeline. Our approach provides a small subset of genes that can accurately distinguish between the clusters of melanoma cell lines across multiple types of classifiers. In particular, we find only the 15 highest ranked genes among the original 19 K are necessary to achieve perfect or near-perfect test split classification performance. Of these 15 genes, some are known to be linked to melanoma or other cancer progressions, while others have not previously been linked to melanoma and are of interest for further examination.

Assessment of Acoustic Features and Machine Learning for Parkinson's Detection

This article presents a machine learning approach for Parkinson's disease detection. Potential multiple acoustic signal features of Parkinson's and control subjects are ascertained. A collaborated feature bank is created through correlated feature selection, Fisher score feature selection, and mutual information-based feature selection schemes. A detection model on top of the feature bank has been developed using the traditional Naïve Bayes, which proved state of the art. The Naïve Bayes detector on collaborative acoustic features can detect the presence of Parkinson's magnificently with a detection accuracy of 78.97% and precision of 0.926, under the hold-out cross validation. The collaborative feature bank on Naïve Bayes revealed distinguishable results as compared to many other recently proposed approaches. The simplicity of Naïve Bayes makes the system robust and effective throughout the detection process.

AURKB, CHEK1 and NEK2 as the Potential Target Proteins of Scutellaria barbata on Hepatocellular Carcinoma: An Integrated Bioinformatics Analysis

Objective

We aim to explore the potential anti-HCC mechanism of Scutellaria barbata through integrated bioinformatics analysis.

Methods

We searched active ingredients and related targets of Scutellaria barbata via TCMSP database, PubChem and SwissTargetPrediction database. Then, we identified HCC disease targets from GEO dataset by WGCNA. Next, the intersected targets of disease targets and drug targets were input into STRING database to construct PPI networking in order to obtain potential therapeutic targets of Scutellaria barbata. Cytoscape software was used to carry out network topology analysis of potential targets. We used the R package for GO analysis and KEGG analysis. Finally, we used AutoDock vina and PyMOL software for molecular docking.

Results

Sixteen active components from Scutellaria barbata were lastly selected for further investigation. A total of 442 component targets were identified from 16 active ingredients of Scutellaria barbata after the removal of duplicate targets. GSE45436 was selected for construction of WGCNA and screening of differentially expressed genes. A total of 354 genes were up-regulated in HCC samples and 100 were down-regulated in HCC patients. Twenty-one common genes were obtained by intersection and 10 critical targets were filtered for further investigation. The enrichment analysis showed that cell cycle, DNA replication, p53 signaling pathway were mainly involved. The molecular docking results showed that 4 potential combinations were with the best binding energy and molecular interactions.

Conclusion

AURKB, CHEK1 and NEK2 could be the potential target proteins of Scutellaria barbata in treating HCC. Cell cycle, DNA replication, p53 signaling pathway consist of the fundamental regulation cores in this mechanism.

Systematic Analysis of the Transcriptome Profiles and Co-Expression Networks of Tumour Endothelial Cells Identifies Several Tumour-Associated Modules and Potential Therapeutic Targets in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third most common cause of cancer-related death, with tumour associated liver endothelial cells being thought to be major drivers in HCC progression. This study aims to compare the gene expression profiles of tumour endothelial cells from the liver with endothelial cells from non-tumour liver tissue, to identify perturbed biologic functions, co-expression modules, and potentially drugable hub genes that could give rise to novel therapeutic targets and strategies. Gene Set Variation Analysis (GSVA) showed that cell growth-related pathways were upregulated, whereas apoptosis induction, immune and inflammatory-related pathways were downregulated in tumour endothelial cells. Weighted Gene Co-expression Network Analysis (WGCNA) identified several modules strongly associated to tumour endothelial cells or angiogenic activated endothelial cells with high endoglin (ENG) expression. In tumour cells, upregulated modules were associated with cell growth, cell proliferation, and DNA-replication, whereas downregulated modules were involved in immune functions, particularly complement activation. In ENG⁺ cells, upregulated modules were associated with cell adhesion and endothelial functions. One downregulated module was associated with immune system-related functions. Querying the STRING database revealed known functional-interaction networks underlying the modules. Several possible hub genes were identified, of which some (for example FEN1, BIRC5, NEK2, CDKN3, and TTK) are potentially druggable as determined by querying the Drug Gene Interaction database. In summary, our study provides a detailed picture of the transcriptomic differences between tumour and non-tumour endothelium in the liver on a co-expression network level, indicates several potential therapeutic targets and presents an analysis workflow that can be easily adapted to other projects.

How to predict choice using eye-movements data?

In recent decades, eye-movement detection technology has improved significantly, and eye-trackers are available not only as standalone research tools but also as computer peripherals. This rapid spread gives further opportunities to measure the eye-movements of participants. The current paper provides classification models for the prediction of food choice and selects the best one. Four choice sets were presented to 112 volunteered participants, each choice set consisting of four different choice tasks, resulting in altogether sixteen choice tasks. The choice sets followed the 2-, 4-, 6- and 8-alternative forced-choice paradigm. Tobii X2-60 eye-tracker and Tobii Studio software were used to capture and export gazing data, respectively. After variable filtering, thirteen classification models were elaborated and tested; moreover, eight performance parameters were computed. The models were compared based on the performance parameters using the sum of ranking differences algorithm. The algorithm ranks and groups the models by comparing the ranks of their performance metrics to a predefined gold standard. Techniques based on decision trees were superior in all cases, regardless of the choice tasks and food product categories. Among the classifiers, Quinlan's C4.5 and cost-sensitive decision trees proved to be the best-performing ones. Future studies should focus on the fine-tuning of these models as well as their applications with mobile eye-trackers.

Identification of UBE2C as hub gene in driving prostate cancer by integrated bioinformatics analysis

BACKGROUND

The aim of this study was to identify novel genes in promoting primary prostate cancer (PCa) progression and to explore its role in the prognosis of prostate cancer.

METHODS

Four microarray datasets containing primary prostate cancer samples and benign prostate samples were downloaded from Gene Expression Omnibus (GEO), then differentially expressed genes (DEGs) were identified by R software (version 3.6.2). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to identify the function of DEGs. Using STRING and Cytoscape (version 3.7.1), we constructed a protein-protein interaction (PPI) network and identified the hub gene of prostate cancer. Clinical data on GSE70770 and TCGA was collected to show the role of hub gene in prostate cancer progression. The correlations between hub gene and clinical parameters were also indicated by cox regression analysis. Gene Set Enrichment Analysis (GSEA) was performed to highlight the function of Ubiquitin-conjugating enzyme complex (UBE2C) in prostate cancer.

RESULTS

243 upregulated genes and 298 downregulated genes that changed in at least two microarrays have been identified. GO and KEGG analysis indicated significant changes in the oxidation-reduction process, angiogenesis, TGF-beta signaling pathway. UBE2C, PDZ-binding kinase (PBK), cyclin B1 (CCNB1), Cyclin-dependent kinase inhibitor 3 (CDKN3), topoisomerase II alpha (TOP2A), Aurora kinase A (AURKA) and MKI67 were identified as the candidate hub genes, which were all correlated with prostate cancer patient' disease-free survival in TCGA. In fact, only UBE2C was highly expressed in prostate cancer when compared with benign prostate tissue in TCGA and the expression of UBE2C was also in parallel with the Gleason score of prostate cancer. Cox regression analysis has indicated UBE2C could function as the independent prognostic factor of prostate cancer. GSEA showed UBE2C had played an important role in the pathway of prostate cancer, such as NOTCH signaling pathway, WNT-β-catenin signaling pathway.

CONCLUSIONS

UBE2C was pivotal for the progression of prostate cancer and the level of UBE2C was important to predict the prognosis of patients.

Ursolic Acid Regulates Cell Cycle and Proliferation in Colon Adenocarcinoma by Suppressing Cyclin B1

Aims: The biological functions of cyclin B1 (CCNB1) in colon adenocarcinoma (COAD) will be explored in this study. Furthermore, the therapeutic effects and potential molecular mechanisms of ursolic acid (UA) in COAD cells will also be investigated in vitro.

Methods: COAD data were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Differentially expressed genes (DEGs) were determined with differential analysis. The biological functions of CCNB1 were analyzed through the GeneCards, the Search Tool for the Retrieval of Interacting Genes (STRING), and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) databases. Therapeutic effects of UA on COAD cell lines HCT-116 and SW-480 were analyzed by CCK-8 and high-content screening (HCS) imaging assay. Flow cytometry was utilized to detect cell cycle changes of SW-480 and HCT-116 cells. Levels of mRNA and expression proteins of HCT-116, SW-480, and normal colon epithelial cells NCM-460 were determined by qRT-PCR and western blot.

Results: CCNB1 was highly expressed and acted as an oncogene in COAD patients. CCNB1 and its interacting genes were significantly enriched in the cell cycle pathway. UA effectively inhibited the proliferation and injured COAD cells. In addition, UA arrested cell cycle of COAD cells in S phase. With regard to the molecular mechanisms of UA, we demonstrated that UA can significantly downregulate CCNB1 and its interacting genes and proteins, including CDK1, CDC20, CCND1, and CCNA2, which contributed to cell cycle blocking and COAD treatment.

Conclusion: Results from this study revealed that UA possesses therapeutic effects on COAD. The anti-COAD activities of UA are tightly related to suppression of CCNB1 and its interacting targets, which is crucial in abnormal cell cycle process.

Hub Genes Identification in a Murine Model of Allergic Rhinitis Based on Bioinformatics Analysis

This study aimed to identify allergic rhinitis (AR)-related hub genes and functionally enriched pathways in a murine model. Dataset GSE52804 (including three normal controls and three AR mice) was downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) analyses of DEGs were performed to identify the hub genes in AR. The DEGs were classified into different modules by using the weighted gene co-expression network analysis (WGCNA). Moreover, to verify the potential hub genes, nasal mucosa tissues were obtained from murine AR models (n = 5) and controls (n = 5), and qRT-PCR and Western blot were performed. In this study, a total of 634 DEGs were identified. They were significantly enriched in 14 GO terms, such as integral component of membrane, plasma membrane, and G-protein-coupled receptor signaling pathway. Meanwhile, there were eight terms of KEGG pathways significantly enriched, such as Olfactory transduction, Cytokine-cytokine receptor interaction, and TNF signaling pathway. The top 10 hub genes (Rtp1, Rps27a, Penk, Cxcl2, Gng8, Gng3, Cxcl1, Cxcr2, Ccl9, and Anxa1) were identified by the PPI network. DEGs were classified into seven modules by WGCNA. According to qRT-PCR validation of the five genes of interest (Rtp1, Rps27a, Penk, Cxcl2, and Anxa1), the expression level of Rtp1 mRNA was significantly decreased in the AR group compared with the control group, while there are enhanced Rps27a, Penk, Cxcl2, and Anxa1 mRNA expressions in the AR mice group compared with the control group. Western blot was also performed to further explore the expression of Anxa1 in the protein level, and the results showed a similar expression trend.