Identification of key genes and molecular mechanisms of chronic urticaria based on bioinformatics.

Chronic urticaria (CU) is characterized by persistent skin hives, redness, and itching, enhanced by immune dysregulation and inflammation. Our main objective is identifying key genes and molecular mechanisms of chronic urticaria based on bioinformatics. We used the Gene Expression Omnibus (GEO) database and retrieved two GEO datasets, GSE57178 and GSE72540. The raw data were extracted, pre-processed, and analyzed using the GEO2R tool to identify the differentially expressed genes (DEGs). The samples were divided into two groups: healthy samples and CU samples. We defined cut-off values of log2 fold change ≥1 and p < .05. Analyses were performed in the Kyoto Encyclopaedia of Genes and Genomes (KEGG), the Database for Annotation, Visualization and Integrated Discovery (DAVID), Metascape, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and CIBERSOFT databases. We obtained 1613 differentially expressed genes. There were 114 overlapping genes in both datasets, out of which 102 genes were up-regulated while 12 were down-regulated. The biological processes included activation of myeloid leukocytes, response to inflammations, and response to organic substances. Moreover, the KEGG pathways of CU were enriched in the Nuclear Factor-Kappa B (NF-kB) signaling pathway, Tumor Necrosis Factor (TNF) signaling pathway, and Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway. We identified 27 hub genes that were implicated in the pathogenesis of CU, such as interleukin-6 (IL-6), Prostaglandin-endoperoxide synthase 2 (PTGS2), and intercellular adhesion molecule-1 (ICAM1). The complex interplay between immune responses, inflammatory pathways, cytokine networks, and specific genes enhances CU. Understanding these mechanisms paves the way for potential interventions to mitigate symptoms and improve the quality of life of CU patients.

Taxonomic resolution of different 16S rRNA variable regions varies strongly across plant-associated bacteria.

Plant-microbiome research plays a pivotal role in understanding the relationships between plants and their associated microbial communities, with implications for agriculture and ecosystem dynamics. Metabarcoding analysis on variable regions of the 16S ribosomal RNA (rRNA) gene remains the dominant technology to study microbiome diversity in this field. However, the choice of the targeted variable region might affect the outcome of the microbiome studies. In our in silico analysis, we have evaluated whether the targeted variable region has an impact on taxonomic resolution in 16 plant-related microbial genera. Through a comparison of 16S rRNA gene variable regions with whole-genome data, our findings suggest that the V1-V3 region is generally a more suitable option than the widely used V3-V4 region for targeting microbiome analysis in plant-related genera. However, sole reliance on one region could introduce detection biases for specific genera. Thus, we are suggesting that while transitioning to full-length 16S rRNA gene and whole-genome sequencing for plant-microbiome analysis, the usage of genus-specific variable regions can achieve more precise taxonomic assignments. More broadly, our approach provides a blueprint to identify the most discriminating variable regions of the 16S rRNA gene for genera of interest.

Computational insights into the circular permutation roles on ConA binding and structural stability.

The mechanisms behind Concanavalin A (ConA) circular permutation have been under investigation since 1985. Although a vast amount of information is available about this lectin and its applications, the exact purpose of its processing remains unclear. To shed light on this, this study employed computer simulations to compare the unprocessed ProConA with the mature ConA. This approach aimed to reveal the importance of the post-translational modifications, especially how they affect the lectin stability and carbohydrate-binding properties. To achieve these goals, we conducted 200 ns molecular dynamics simulations and trajectory analyses on the monomeric forms of ProConA and ConA (both unbound and in complex with D-mannose and the GlcNAc2Man9 N-glycan), as well as on their oligomeric forms. Our findings reveal significant stability differences between ProConA and ConA at both the monomeric and tetrameric levels, with ProConA exhibiting consistently lower stability parameters compared to ConA. In terms of carbohydrate binding properties, however, both lectins showed remarkable similarities in their interaction profiles, contact numbers, and binding free energies with D-mannose and the high-mannose N-glycan. Overall, our results suggest that the processing of ProConA significantly enhances the stability of the mature lectin, especially in maintaining the tetrameric oligomer, without substantially affecting its carbohydrate-binding properties.

Identification and verification of AK4 as a protective immune-related biomarker in adipose-derived stem cells and breast cancer.

Background: Breast cancer (BC) remains the most common cancer among women, and novel post-surgical reconstruction techniques, including autologous fat transplantation, have emerged. While Adipose-derived stem cells (ADSCs) are known to impact the viability of fat grafts, their influence on breast cancer progression remains unclear. This study aims to elucidate the genetic interplay between ADSCs and breast cancer, focusing on potential therapeutic targets. Methods: Using the GEO and TCGA databases, we pinpointed differentially expressed (DE) mRNAs, miRNAs, lncRNAs, and pseudogenes of ADSCs and BC. We performed functional enrichment analysis and constructed protein-protein interaction (PPI), RNA binding protein (RBP)-pseudogene-mRNA, and lncRNA-miRNA-transcription factor (TF)-gene networks. Our study delved into the correlation of AK4 expression with 33 different malignancies and examined its impact on prognostic outcomes across a pan-cancer cohort. Additionally, we scrutinized immune infiltration, microsatellite instability, and tumor mutational burden, and conducted single-cell analysis to further understand the implications of AK4 expression. We identified novel sample subtypes based on hub genes using the ConsensusClusterPlus package and examined their association with immune infiltration. The random forest algorithm was used to screen DE mRNAs between subtypes to validate the powerful prognostic prediction ability of the artificial neural network. Results: Our analysis identified 395 DE mRNAs, 3 DE miRNAs, 84 DE lncRNAs, and 26 DE pseudogenes associated with ADSCs and BC. Of these, 173 mRNAs were commonly regulated in both ADSCs and breast cancer, and 222 exhibited differential regulation. The PPI, RBP-pseudogene-mRNA, and lncRNA-miRNA-TF-gene networks suggested AK4 as a key regulator. Our findings support AK4 as a promising immune-related therapeutic target for a wide range of malignancies. We identified 14 characteristic genes based on the AK4-related cluster using the random forest algorithm. Our artificial neural network yielded excellent diagnostic performance in the testing cohort with AUC values of 0.994, 0.973, and 0.995, indicating its ability to distinguish between breast cancer and non-breast cancer cases. Conclusions: Our research sheds light on the dual role of ADSCs in BC at the genetic level and identifies AK4 as a key protective mRNA in breast cancer. We found that AK4 significantly predicts cancer prognosis and immunotherapy, indicating its potential as a therapeutic target.

Molecular network mechanism in cerebral ischemia-reperfusion rats treated with human urine stem cells.

Objective: To explore the effect of human urine-derived stem cells (husc) in improving the neurological function of rats with cerebral ischemia-reperfusion (CIR), and report new molecular network by bioinformatics, combined with experiment validation. Methods: After CIR model was established, and husc were transplanted into the lateral ventricle of rats，neurological severe score (NSS) andgene network analysis were performed. Firstly, we input the keywords "Cerebral reperfusion" and "human urine stem cells" into Genecard database and merged data with findings from PubMed so as to get their targets genes, and downloaded them to make Venny intersection plot. Then, Gene ontology (GO) analysis, kyoto encyclopedia of genes and genomes (KEGG) pathway analysis and protein-protein interaction (PPI) were performed to construct molecular network of core genes. Lastly, the expressional level of core genes was validated via quantitative real-time polymerase chain reaction (qRT-PCR), and localized by immunofluorescence. Results: Compared with the Sham group, the neurological function of CIR rats was significantly improved after the injection of husc into the lateral ventricle; at 14 days, P = 0.028, which was statistically significant. There were 258 overlapping genes between CIR and husc, and integrated with 252 genes screened from PubMed and CNKI. GO enrichment analysis were mainly involved neutrophil degranulation, neutrophil activation in immune response and platelet positive regulation of degranulation, Hemostasis, blood coagulation, coagulation, etc. KEGG pathway analysis was mainly involved in complement and coagulation cascades, ECM-receptor. Hub genes screened by Cytoscape consist ofCD44, ACTB, FN1, ITGB1, PLG, CASP3, ALB, HSP90AA1, EGF, GAPDH. Lastly, qRT-PCR results showed statistic significance (P < 0.05) in ALB, CD44 and EGF before and after treatment, and EGF immunostaining was localized in neuron of cortex. Conclusion: husc transplantation showed a positive effect in improving neural function of CIR rats, and underlying mechanism is involved in CD44, ALB, and EGF network.

Development of a cell adhesion-based prognostic model for multiple myeloma: Insights into chemotherapy response and potential reversal of adhesion effects.

Multiple myeloma (MM) is a hematologic malignancy notorious for its high relapse rate and development of drug resistance, in which cell adhesion-mediated drug resistance plays a critical role. This study integrated four RNA sequencing datasets (CoMMpass, GSE136337, GSE9782, and GSE2658) and focused on analyzing 1706 adhesion-related genes. Rigorous univariate Cox regression analysis identified 18 key prognosis-related genes, including KIF14, TROAP, FLNA, MSN, LGALS1, PECAM1, and ALCAM, which demonstrated the strongest associations with poor overall survival (OS) in MM patients. To comprehensively evaluate the impact of cell adhesion on MM prognosis, an adhesion-related risk score (ARRS) model was constructed using Lasso Cox regression analysis. The ARRS model emerged as an independent prognostic factor for predicting OS. Furthermore, our findings revealed that a heightened cell adhesion effect correlated with tumor resistance to DNA-damaging drugs, protein kinase inhibitors, and drugs targeting the PI3K/Akt/mTOR signaling pathway. Nevertheless, we identified promising drug candidates, such as tirofiban, pirenzepine, erlotinib, and bosutinib, which exhibit potential in reversing this resistance. In vitro, experiments employing NCIH929, RPMI8226, and AMO1 cell lines confirmed that MM cell lines with high ARRS exhibited poor sensitivity to the aforementioned candidate drugs. By employing siRNA-mediated knockdown of the key ARRS model gene KIF14, we observed suppressed proliferation of NCIH929 cells, along with decreased adhesion to BMSCs and fibronectin. This study presents compelling evidence establishing cell adhesion as a significant prognostic factor in MM. Additionally, potential molecular mechanisms underlying adhesion-related resistance are proposed, along with viable strategies to overcome such resistance. These findings provide a solid scientific foundation for facilitating clinically stratified treatment of MM.

Integrative analysis of bioinformatics and machine learning to identify cuprotosis-related biomarkers and immunological characteristics in heart failure.

Backgrounds: Cuprotosis is a newly discovered programmed cell death by modulating tricarboxylic acid cycle. Emerging evidence showed that cuprotosis-related genes (CRGs) are implicated in the occurrence and progression of multiple diseases. However, the mechanism of cuprotosis in heart failure (HF) has not been investigated yet. Methods: The HF microarray datasets GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed CRGs between HF patients and nonfailing donors (NFDs). Four machine learning models were used to identify key CRGs features for HF diagnosis. The expression profiles of key CRGs were further validated in a merged GEO external validation dataset and human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). In addition, Gene Ontology (GO) function enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and immune infiltration analysis were used to investigate potential biological functions of key CRGs. Results: We discovered nine differentially expressed CRGs in heart tissues from HF patients and NFDs. With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties. In addition, their differential expression between HF patients and NFDs was confirmed through qRT-PCR. Moreover, the results of enrichment analyses and immune infiltration exhibited that these diagnostic markers of CRGs were strongly correlated to energy metabolism and immune activity. Conclusions: Our study discovered that cuprotosis was strongly related to the pathogenesis of HF, probably by regulating energy metabolism-associated and immune-associated signaling pathways.

Transcriptome profiling and analysis of patients with esophageal squamous cell carcinoma from Kazakhstan.

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal cancer in Central Asia, often diagnosed at advanced stages. Understanding population-specific patterns of ESCC is crucial for tailored treatments. This study aimed to unravel ESCC's genetic basis in Kazakhstani patients and identify potential biomarkers for early diagnosis and targeted therapies. ESCC patients from Kazakhstan were studied. We analyzed histological subtypes and conducted in-depth transcriptome sequencing. Differential gene expression analysis was performed, and significantly dysregulated pathways were identified using KEGG pathway analysis (p-value < 0.05). Protein-protein interaction networks were constructed to elucidate key modules and their functions. Among Kazakhstani patients, ESCC with moderate dysplasia was the most prevalent subtype. We identified 42 significantly upregulated and two significantly downregulated KEGG pathways, highlighting molecular mechanisms driving ESCC pathogenesis. Immune-related pathways, such as viral protein interaction with cytokines, rheumatoid arthritis, and oxidative phosphorylation, were elevated, suggesting immune system involvement. Conversely, downregulated pathways were associated with extracellular matrix degradation, crucial in cancer invasion and metastasis. Protein-protein interaction network analysis revealed four distinct modules with specific functions, implicating pathways in esophageal cancer development. High-throughput transcriptome sequencing elucidated critical molecular pathways underlying esophageal carcinogenesis in Kazakhstani patients. Insights into dysregulated pathways offer potential for early diagnosis and precision treatment strategies for ESCC. Understanding population-specific patterns is essential for personalized approaches to ESCC management.

[Correlation study of NCF2 in chronic rhinosinusitis with nasal polyps].

Objective:After selecting NCF2 based on bioinformatics, clinical experiments were conducted to verify the expression of NCF2 in chronic rhinosinusitis with nasal polyps to study its correlation. Methods:The differentially expressed genes（DEGs） between CRSwNP and non-CRS patients were explored using the CRS-related dataset from the gene expression omnibus GEO database. The weighted gene co-expression network（WGCNA） was used for cluster analysis. The expression and cell distribution of NCF2 in the tissues were determined by single gene enrichment analysis（GSEA）, immune inflammatory infiltration analysis, and principal component（PCA） analysis. The expression degree of NCF2 in the tissues of the subjects was determined by immunohistochemistry, and the percentage of EOS in the peripheral blood of the subjects was detected and the correlation was analyzed. EOS in the tissues of the subjects were counted under a microscope and compared. Results:①The Venn diagram was obtained by crossing the module with the highest correlation between DEGs and WGCNA to determine the core gene NCF2. ②GSEA analysis showed that NCF2 was significantly related to the immunological processes such as allogeneic rejection and asthma. ③The area under the ROC curve was 1, indicating that NCF2 had diagnostic value for CRSwNP. ④NCF2 was highly expressed in nasal polyps, mainly distributed in monocytes and eosinophils. ⑤HE staining showed that the number of EOS in ECRSwNP tissues and the percentage of eosinophils in peripheral blood were higher than those in nonECRSwNP and control groups. ⑥The immunohistochemistry results showed that NCF2 was significantly expressed in the nasal polyps of ECRSwNP patients, which was higher than that in the nasal mucosa of nonECRSwNP group and control group. ⑦The expression of NCF2 in tissues was positively correlated with EOS count in ECRSwNP group and EOS expression in peripheral blood. Conclusion:The expression of NCF2 is increased in eosinophilic chronic rhinosinusitis with nasal polyps, and it is significantly correlated with the expression of eosinophils in peripheral blood and tissues, suggesting that NCF2 may be used as a basis for the intrinsic classification of ECRSwNP and a reference index for clinical diagnosis and treatment.

Xuefu Zhuyu decoction promotes synaptic plasticity by targeting miR-191a-5p/BDNF-TrkB axis in severe traumatic brain injury.

BACKGROUND: Xuefu Zhuyu decoction (XFZYD) is a traditional Chinese herbal formula known for its ability to eliminate blood stasis and improve blood circulation, providing neuroprotection against severe traumatic brain injury (sTBI). However, the underlying mechanism is still unclear. PURPOSE: We aim to investigate the neuroprotective effects of XFZYD in sTBI from a novel mechanistic perspective of miRNA-mRNA. Additionally, we sought to elucidate a potential specific mechanism by integrating transcriptomics, bioinformatics, and conducting both in vitro and in vivo experiments. METHODS: The sTBI rat model was established, and the rats were treated with XFZYD for 14 days. The neuroprotective effects of XFZYD were evaluated using a modified neurological severity score, hematoxylin and eosin staining, as well as Nissl staining. The anti-inflammatory effects of XFZYD were explored using quantitative real-time PCR (qRT-PCR), Western blot analysis, and immunofluorescence. Next, miRNA sequencing of the hippocampus was performed to determine which miRNAs were differentially expressed. Subsequently, qRT-PCR was used to validate the differentially expressed miRNAs. Target core mRNAs were determined using various methods, including miRNA prediction targets, mRNA sequencing, miRNA-mRNA network, and protein-protein interaction (PPI) analysis. The miRNA/mRNA regulatory axis were verified through qRT-PCR or Western blot analysis. Finally, morphological changes in the neural synapses were observed using transmission electron microscopy and immunofluorescence. RESULTS: XFZYD exhibited significant neuroprotective and anti-inflammatory effects on subacute sTBI rats' hippocampus. The analyses of miRNA/mRNA sequences combined with the PPI network revealed that the therapeutic effects of XFZYD on sTBI were associated with the regulation of the rno-miR-191a-5p/BDNF axis. Subsequently, qRT-PCR and Western blot analysis confirmed XFZYD reversed the decrease of BDNF and TrkB in the hippocampus caused by sTBI. Additionally, XFZYD treatment potentially increased the number of synaptic connections, and the expression of the synapse-related protein PSD95, axon-related protein GAP43 and neuron-specific protein TUBB3. CONCLUSIONS: XFZYD exerts neuroprotective effects by promoting hippocampal synaptic remodeling and improving cognition during the subacute phase of sTBI through downregulating of rno-miR-191a-5p/BDNF axis, further activating BDNF-TrkB signaling.

Robo-Therm, a pipeline to RNA Thermometer discovery and validation.

RNA thermometers are highly structured noncoding RNAs located in the 5' untranslated regions (UTR) of genes that regulate expression by undergoing conformational changes in response to temperature. The discovery of RNA thermometers through bioinformatics is difficult because there is little sequence conservation among their structural elements. Thus, the abundance of these thermo-sensitive regulatory structures remains unclear. Herein, to advance the discovery and validation of RNA thermometers, we developed Robo-Therm, a pipeline that combines an adaptive and user-friendly in silico motif search with a well-established reporter system. Through our application of Robo-Therm, we discovered two novel RNA thermometers in bacterial and bacteriophage genomes found in the human gut. One of these thermometers is present in 5'-UTR of a gene that codes for σ70 RNA polymerase subunit in the bacteria Mediterraneibacter gnavus and Bacteroides pectinophilus, and in the bacteriophage Caudoviricetes, which infects Bacteroides pectinophilus. The other thermometer is in the 5'-UTR of a tetracycline resistance gene (tetR) in the intestinal bacteria Escherichia coli and Shigella flexneri. Our Robo-Therm pipeline can be applied to discover multiple RNA thermometers across various genomes.

Integrated bioinformatics analysis identified leucine rich repeat containing 15 and secreted phosphoprotein 1 as hub genes for calcific aortic valve disease and osteoarthritis.

Calcific aortic valve disease (CAVD) and osteoarthritis (OA) are common diseases in the ageing population and share similar pathogenesis, especially in inflammation. This study aims to discover potential diagnostic and therapeutic targets in patients with CAVD and OA. Three CAVD datasets and one OA dataset were obtained from the Gene Expression Omnibus database. We used bioinformatics methods to search for key genes and immune infiltration, and established a ceRNA network. Immunohistochemical staining was performed to verify the expression of candidate genes in human and mice aortic valve tissues. Two key genes obtained, leucine rich repeat containing 15 (LRRC15) and secreted phosphoprotein 1 (SPP1), were further screened using machine learning and verified in human and mice aortic valve tissues. Compared to normal tissues, the infiltration of immune cells in CAVD tissues was significantly higher, and the expressions of LRRC15 and SPP1 were positively correlated with immune cells infiltration. Moreover, the ceRNA network showed extensive regulatory interactions based on LRRC15 and SPP1. The authors' findings identified LRRC15 and SPP1 as hub genes in immunological mechanisms during CAVD and OA initiation and progression, as well as potential targets for drug development.

Skin Microbial Composition and Genetic Mutation Analysis in Precision Medicine for Epidermolysis Bullosa.

Epidermolysis bullosa (EB) is an inherited skin disease representing a spectrum of rare genetic disorders. These conditions share the common trait that causes fragile skin, resulting in the development of blisters and erosions. The inheritance follows an autosomal pattern, and the array of clinical presentations leads to significant physical suffering, considerable morbidity, and mortality. Despite EB having no cure, effectively managing EB remains an exceptional challenge due to its rarity and complexity, occasionally casting a profound impact on the lives of affected individuals. Considering that EB management requires a multidisciplinary approach, this sometimes worsens the condition of patients with EB due to inappropriate handling. Thus, more appropriate and precise treatment management of EB is essentially needed. Advanced technology in medicine and health comes into the bioinformatics era. Including treatment for skin diseases, omics-based approaches aim to evaluate and handle better disease management and treatment. In this work, we review several approaches regarding the implementation of omics-based technology, including genetics, pathogenic mutation, skin microbiomics, and metagenomics analysis for EB. In addition, we highlight recent updates on the potential of metagenomics analysis in precision medicine for EB.

Elucidating the role of FOS in modulating the immune microenvironment through fibroblast and myeloid cell regulation in locoregional recurrent HNSCC.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) presents a significant clinical challenge, particularly due to its high propensity for locoregional recurrence. Current research underscores the need to unravel the complex interactions within the tumor microenvironment. This study addresses the critical gap in understanding how FOS modulates the immune landscape in HNSCC, with a focus on its influence on fibroblast and myeloid cell dynamics. METHODS: Employing a comprehensive approach, we analyzed tissue samples from HNSCC patients and adjacent non-cancerous tissues using bulk RNA sequencing complemented by in-depth bioinformatics analyses, including gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and immune infiltration assessment. A pivotal aspect of our research involved dissecting single-cell RNA-seq data from GSE234933 to elucidate the cell-type-specific expression of FOS. RESULTS: We found that FOS expression varies significantly in different cell populations in the HNSCC tumor microenvironment, especially in fibroblasts and myeloid cells. This expression difference may reflect the different roles of these cells in tumor progression and their impact on the tumor microenvironment. CONCLUSION: Our results uncover a significant correlation between FOS expression and key immune and hypoxia-related pathways, suggesting its integral role in the tumor microenvironment. These findings not only enhance our understanding of HNSCC pathogenesis but also highlight FOS as a potential therapeutic target. This study marks a significant step towards addressing the urgent need for targeted interventions in HNSCC, particularly in the context of locoregional recurrence.

CCNA2 and KIF23 are molecular targets for the prognosis of adenoid cystic carcinoma.

OBJECTIVE: Adenoid cystic carcinoma (ACC) is a tumor type derived from glands. However, relationship between CCNA2 and KIF23, and adenoid cystic carcinoma remains unclear. METHODS: GSE36820 and GSE88804 profiles for ACC were obtained from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified, and Weighted Gene Co-expression Network Analysis (WGCNA) was conducted. Subsequently, the construction and analysis of protein-protein interaction (PPI) network, functional enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed. A gene expression heat map was generated to visually depict the expression difference of core genes between adenoid cystic carcinoma and normal samples. TargetScan was employed to identify miRNAs that regulated central DEGs. Western blotting (WB) was conducted for cell verification. RESULTS: A total of 885 DEGs were identified. GO and KEGG analyses revealed their main enrichment in responses to chemical stimuli, cell proliferation, tissue development, and regulation of cell proliferation. The GO and KEGG results indicated significant enrichment in aldosterone-regulated sodium reabsorption, the cell cycle, and the PPAR signaling pathway. Notably, core genes (CCNA2 and KIF23) were found to be highly expressed in Adenoid Cystic Carcinoma samples and expressed at low levels in normal samples. WB validated the overexpression of CCNA2 and KIF23 in the Adenoid Cystic Carcinoma group, confirming the protein-level changes associated with cell cycle, metastasis, apoptosis, and inflammatory factors in Adenoid Cystic Carcinoma groups with gene overexpression and knockout. CONCLUSIONS: CCNA2 and KIF23 exhibit high expression levels in ACC, suggesting their potential role as molecular targets for this malignancy.

Assessing the Chemical Profile and Biological Potentials of Tamarix aphylla (L.) H.Karst. and Tamarix senegalensis DC. by In Vitro, In Silico, and Network Methodologies.

The present study aimed to investigate the chemical profile, antioxidant, and enzyme inhibition properties of extracts from fruits and aerial parts (leaves and twigs) of Tamarix aphylla and T. senegalensis. Hexane, dichloromethane, ethyl acetate (EtOAc), and methanol extracts were prepared sequentially by maceration. Results revealed that EtOAc extracts of T. senegalensis and T. aphylla fruits contained the highest total phenolic content (113.74 and 111.21 mg GAE/g) while that of T. senegalensis (38.47 mg RE/g) recorded the highest total flavonoids content. Among the quantified compounds; ellagic, gallic, 3-hydroxybenzoic, caffeic, syringic, p-coumaric acids, isorhamnetin, procyanidin B2, and kaempferol were the most abundant compounds in the two species. EtOAc extracts of the two organs of T. senegalensis in addition to MeOH extract of T. aphylla aerial parts displayed the highest chelating power (21.00-21.30 mg EDTAE/g, p > 0.05). The highest anti-AChE (3.11 mg GALAE/g) and anti-BChE (3.62 mg GALAE/g) activities were recorded from the hexane and EtOAc extracts of T. senegalensis aerial parts and fruits, respectively. EtOAc extracts of the fruits of the two species exerted the highest anti-tyrosinase (anti-Tyr) activity (99.44 and 98.65 mg KAE/g, p > 0.05). Also, the EtOAc extracts of the both organs of the two species exhibited highest anti-glucosidase activity (0.88-0.90 mmol ACAE/g, p > 0.05) while the best anti-α-amylase activity was recorded from the dichloromethane extract of T. senegalensis fruits (0.74 mmol ACAE/g). In this study, network pharmacology was employed to examine the connection between compounds from Tamarix and their potential effectiveness against Alzheimer's disease. The compounds demonstrated potential interactions with pivotal genes including APP, GSK3B, and CDK5, indicating a therapeutic potential. Molecular docking was carried out to understand the binding mode and interaction of the compounds with the target enzymes. Key interactions observed, such as H-bonds, promoted the binding, and weaker ones, such as van der Waals attractions, reinforced it. These findings suggest that these two Tamarix species possess bioactive properties with health-promoting effects.

Construction of a Cancer Stem Cell related Histone Acetylation Regulatory Genes Prognostic Model for Hepatocellular Carcinoma via Bioinformatics Analysis: Implications for Tumor Chemotherapy and Immunity.

BACKGROUND: Cancer stem cells (CSC) play an important role in the development of Liver Hepatocellular Carcinoma (LIHC). However, the regulatory mechanisms between acetylation- associated genes (HAGs) and liver cancer stem cells remain unclear. OBJECTIVE: To identify a set of histone acetylation genes (HAGs) with close associations to liver cancer stem cells (LCSCs), and to construct a prognostic model that facilitates more accurate prognosis assessments for LIHC patients. METHODS: LIHC expression data were downloaded from the public databases. Using mRNA expression- based stemness indices (mRNAsi) inferred by One-Class Logistic Regression (OCLR), Differentially Expressed Genes (DEGs) (mRNAsi-High VS. mRNAsi-Low groups) were intersected with DEGs (LIHC VS. normal samples), as well as histone acetylation-associated genes (HAGs), to obtain mRNAsi-HAGs. A risk model was constructed employing the prognostic genes, which were acquired through univariate Cox and Least Shrinkage and Selection Operator (LASSO) regression analyses. Subsequently, independent prognostic factors were identified via univariate and multivariate Cox regression analyses and then a nomogram for prediction of LIHC survival was developed. Additionally, immune infiltration and drug sensitivity analysis were performed to explore the relationships between prognostic genes and immune cells. Finally, the expressions of selected mRNAsi-HAGs were validated in the LIHC tumor sphere by quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) assay and western blot analysis. RESULTS: Among 13 identified mRNAsi-HAGs, 3 prognostic genes (HDAC1, HDAC11, and HAT1) were selected to construct a risk model (mRNAsi-HAGs risk score = 0.02 * HDAC1 + 0.09 * HAT1 + 0.05 * HDAC11). T-stage, mRNAsi, and mRNAsi-HAGs risk scores were identified as independent prognostic factors to construct the nomogram, which was proved to predict the survival probability of LIHC patients effectively. We subsequently observed strongly positive correlations between mRNAsi-HAGs risk score and tumor-infiltrating T cells, B cells and macrophages/monocytes. Moreover, we found 8 drugs (Mitomycin C, IPA 3, FTI 277, Bleomycin, Tipifarnib, GSK 650394, AICAR and EHT 1864) had significant correlations with mRNAsi-HAGs risk scores. The expression of HDAC1 and HDAC11 was higher in CSC-like cells in the tumor sphere. CONCLUSION: This study constructed a mRNAsi and HAGs-related prognostic model, which has implications for potential immunotherapy and drug treatment of LIHC.

Immune protection of three serine protease inhibitors vaccine in mice against Rhipicephalus sanguineus.

Bioactive molecules in tick saliva are considered to be key to successful feeding and further the transmission of tick-borne pathogens. Problems such as pathogen transmission and animal weight loss result in tick infestation can cause tremendous economic losses to the livestock industry. Therefore, the development of a universal tick vaccine is urgently needed. In this paper, three serine protease inhibitor (serpin) proteins RMS-3, L7LRK7 and L7LTU1 were analyzed with bioinformatics methods. Subsequently the proteins were expressed and purified, and inoculated into Kunming mice for immune protection analysis. The amino acid sequence similarities between RMS-3, L7LRK7 and L7LTU1 were up to 90% in Rhipicephalus sanguineus. The recombinant RMS-3 + L7LRK7 + L7LTU1 showed anticoagulant reaction function and could inhibit the activity of CD4+ lymphocytes, when inoculated into Kunming mice. Additionally, After the immunized mice were challenged with Rhipicephalus sanguineus, the percentage of larvae and nymphs that were fully engorged dropped to 40.87% (P < 0.05) and 87.68% (P > 0.05) in the RmS-3 + L7LRK7 immune group, 49.57% (P < 0.01) and 52.06% (P < 0.05) in the RmS-3 + L7LTU1 group, and 45.22% (P < 0.05) and 60.28% (P < 0.05) in the RmS-3 + L7LRK7 + L7LTU1 immune group, in comparison with the control group. These data indicate that RmS-3 + L7LRK7 + L7LTU1 has good immune protection and has the potential to be developed into a vaccine against the larvae and nymphs of R. sanguineus.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms.

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.

SH3GL2 and MMP17 as lung adenocarcinoma biomarkers: a machine-learning based approach.

Objective: Using bioinformatics machine learning methods, our research aims to identify the potential key genes associated with Lung adenocarcinoma (LUAD). Methods: We obtained two gene expression profiling microarrays (GSE68571 and GSE74706) from the public Gene Expression Omnibus (GEO) database at the National Centre for Biotechnology Information (NCBI). The purpose was to identify Differentially Expressed Genes (DEGs) between the lung adenocarcinoma group and the healthy control group. The limma R package in R was utilized for this analysis. For the differential gene diagnosis of lung adenocarcinoma, we employed the least absolute shrinkage and selection operator (LASSO) regression and SVM-RFE screening crossover. To evaluate the performance, ROC curves were plotted. We performed immuno-infiltration analysis using CIBERSORT. Finally, we validated the key genes through qRT-PCR and Western-blot verification, then downregulated MMP17 gene expression, upregulated SH3GL2 gene expression, and performed CCK8 experiments. Results: A total of 32 Differentially Expressed Genes (DEGs) were identified. Two diagnostic marker genes, SH3GL2 and MMP17, were selected by employing LASSO and SVM-RFE machine learning methods. In Lung adenocarcinoma cells, the expression of MMP17 was observed to be elevated compared to normal lung epithelial cells in the control group (P < 0.05). In contrast, a down-regulation of SH3GL2 was found in Lung adenocarcinoma cells (P < 0.05). Finally, we downregulated MMP17 and upregulated SH3GL2 gene expression, then the CCK8 showed that the proliferation of both lung cancer cells was inhibited. Conclusion: SH3GL2 and MMP17 are expected to be potential biomarkers for Lung adenocarcinoma.