Identifying the key hub genes linked with lung squamous cell carcinoma by examining the differentially expressed and survival genes.

Lung Squamous Cell Carcinoma is characterised by significant alterations in RNA expression patterns, and a lack of early symptoms and diagnosis results in poor survival rates. Our study aimed to identify the hub genes involved in LUSC by differential expression analysis and their influence on overall survival rates in patients. Thus, identifying genes with the potential to serve as biomarkers and therapeutic targets. RNA sequence data for LUSC was obtained from TCGA and analysed using R Studio. Survival analysis was performed on DE genes. PPI network and hub gene analysis was performed on survival-relevant genes. Enrichment analysis was conducted on the PPI network to elucidate the functional roles of hub genes. Our analysis identified 2774 DEGs in LUSC patient datasets. Survival analysis revealed 511 genes with a significant impact on patient survival. Among these, 20 hub genes-FN1, ACTB, HGF, PDGFRB, PTEN, SNAI1, TGFBR1, ESR1, SERPINE1, THBS1, PDGFRA, VWF, BMP2, LEP, VTN, PXN, ABL1, ITGA3 and ANXA5-were found to have lower expression levels associated with better patient survival, whereas high expression of SOX2 correlated with longer survival. Enrichment analysis indicated that these hub genes are involved in critical cellular and cancer-related pathways. Our study has identified six key hub genes that are differentially expressed and exhibit significant influence over LUSC patient survival outcomes. Further, in vitro and in vivo studies must be conducted on the key genes for their utilisation as therapeutic targets and biomarkers in LUSC.

Construction of a panoramic mRNA map of adult noncystic fibrosis bronchiectasis and a preliminary study of the underlying molecular mechanisms.

BACKGROUND: The pathogenesis of noncystic fibrosis bronchiectasis in adults is complex, and the relevant molecular mechanisms remain unclear. In this study, we constructed a panoramic map of bronchiectasis mRNA, explored the potential molecular mechanisms, and identified potential therapeutic targets, thus providing a new clinical perspective for the preventive management of bronchiectasis and its acute exacerbation. METHODS: The mRNA profiles of peripheral blood and bronchiectasis tissues were obtained through transcriptome sequencing and public databases, and bioinformatics methods were used to screen for differentially expressed genes (DEGs). The DEGs were then subjected to biological function and pathway analyses. Some DEGs were validated using a real-time quantitative polymerase chain reaction (RT-qPCR) in peripheral blood. Spearman's correlation analysis was used to analyse the correlation between DEGs and clinical indicators. RESULTS: Based on transcriptome sequencing and public databases, the mRNA profile of bronchiectasis was determined. DEGs were obtained from the peripheral blood sequencing dataset (985 DEGs), tissue sequencing dataset (2919 DEGs), and GSE97258 dataset (1083 DEGs). Bioinformatics analysis showed that upregulated DEGs had enriched neutrophil-related pathways, and downregulated DEGs had enriched ribosome-related pathways. RT-qPCR testing confirmed the upregulated expression of VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 in bronchiectasis. These genes were related to many clinical parameters, such as neutrophils, C-reactive protein, and procalcitonin (P < 0.05). CONCLUSIONS: Transcriptomic methods were used to construct a panoramic map of bronchiectasis mRNA expression. The findings showed that neutrophil activation, chronic inflammation, immune regulation, impaired ribosomal function, oxidative phosphorylation, and energy metabolism disorders are important factors in the development of bronchiectasis. VCAN, SESTD1, SLC12A1, CD177, IFI44L, SIGLEC1, and RSAD2 may play important roles in the pathogenesis of bronchiectasis and are potential therapeutic targets.

Investigation of differentially expressed genes related to cellular senescence between high-risk and non-high-risk groups in neuroblastoma.

Object: This study aims to identify differentially expressed genes (DEGs) between high-risk and non-high-risk groups in neuroblastoma (NB), construct a prognostic model, and establish a risk score formula. Materials and methods: The NB dataset GSE49710 (n = 498) from the GEO database served as the training cohort to select DEGs between high-risk and non-high-risk NB groups. Cellular senescence-related genes were obtained from the Aging Atlas database. Intersection genes from both datasets were identified as key genes of cellular senescence-related genes (SRGs). A prognostic model was constructed using Univariate Cox regression analysis and the Lasso algorithm with SRGs. Validation was performed using the E-MTAB-8248 cohort (n = 223). The expression levels of AURKA and CENPA were evaluated via RT-qPCR in two clinical NB sample groups. Results: Eight SRGs were identified, and a prognostic model comprising five genes related to cellular senescence was constructed. AURKA and CENPA showed significant expression in clinical samples and were closely associated with cellular senescence. Conclusion: The prognostic model consisted with five cellular senescence related genes effectively predicts the prognosis of NB patients. AURKA and CENPA represent promising targets in NB for predicting cellular senescence, offering potential insights for NB therapy.

Comparative global profiling of Perilla leaf and stem via transcriptomics and metabolomics.

Perilla (Perilla frutescens L.) is a time-honored herbal plant with widespread applications in both medicine and culinary practices around the world. Profiling the essential organs and tissues with medicinal significance on a global scale offers valuable insights for enhancing the yield of desirable compounds in Perilla and other medicinal plants. In the present study, genome-wide RNA-sequencing (RNA-seq) and assessing the global spectrum of metabolites were carried out in the two major organs/tissues of stem (PfST) and leaf (PfLE) in Perilla. The results showed a total of 18,490 transcripts as the DEGs (differentially expressed genes) and 144 metabolites as the DAMs (differentially accumulated metabolites) through the comparative profiling of PfST vs PfLE, and all the DEGs and DAMs exhibited tissue-specific trends. An association analysis between the transcriptomics and metabolomics revealed 14 significantly enriched pathways for both DEGs and DAMs, among which the pathways of Glycine, serine and threonine metabolism (ko00260), Glyoxylate and dicarboxylate metabolism (ko00630), and Glucagon signaling pathway (ko04922) involved relatively more DEGs and DAMs. The results of qRT-PCR assays of 18 selected DEGs confirmed the distinct tissue-specific characteristics of all identified DEGs between PfST and PfLE. Notably, all eight genes associated with the flavonoid biosynthesis/metabolism pathways exhibited significantly elevated expression levels in PfLE compared to PfST. This observation suggests a heightened accumulation of metabolites related to flavonoids in Perilla leaves. The findings of this study offer a comprehensive overview of the organs and tissues in Perilla that have medicinal significance.

Unlocking the Transcriptional Reprogramming Repertoire between Variety-Dependent Responses of Grapevine Berries to Infection by Aspergillus carbonarius.

Aspergillus carbonarius causes severe decays on berries in vineyards and is among the main fungal species responsible for grape contamination by ochratoxin A (OTA), which is the foremost mycotoxin produced by this fungus. The main goal of this study was to investigate at the transcriptome level the comparative profiles between two table grape varieties (Victoria and Fraoula, the white and red variety, respectively) after their inoculation with a virulent OTA-producing A. carbonarius strain. The two varieties revealed quite different transcriptomic signatures and the expression profiles of the differential expressed genes (DEGs) highlighted distinct and variety-specific responses during the infection period. The significant enrichment of pathways related to the modulation of transcriptional dynamics towards the activation of defence responses, the triggering of the metabolic shunt for the biosynthesis of secondary metabolites, mainly phenylpropanoids, and the upregulation of DEGs encoding phytoalexins, transcription factors, and genes involved in plant-pathogen interaction and immune signaling transduction was revealed in an early time point in Fraoula, whereas, in Victoria, any transcriptional reprogramming was observed after a delay. However, both varieties, to some extent, also showed common expression dynamics for specific DEG families, such as those encoding for laccases and stilbene synthases. Jasmonate (JA) may play a critical modulator role in the defence machinery as various JA-biosynthetic DEGs were upregulated. Along with the broader modulation of the transcriptome that was observed in white grape, expression profiles of specific A. carbonarius genes related to pathogenesis, fungal sporulation, and conidiation highlight the higher susceptibility of Victoria. Furthermore, the A. carbonarius transcriptional patterns directly associated with the regulation of the pathogen OTA-biosynthesis gene cluster were more highly induced in Victoria than in Fraoula. The latter was less contaminated by OTA and showed substantially lower sporulation. These findings contribute to uncovering the interplay beyond this plant-microbe interaction.

Differences in T cell-associated serum markers between ischemic cardiomyopathy and dilated cardiomyopathy.

Background: Ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM) have similar clinical manifestations but differ in pathogenesis. We aimed to identify T cell-associated serum markers that can be used to distinguish between ICM and DCM. Methods: We identified differentially expressed genes (DEGs) with transcriptome sequencing data in GSE116250, and then conducted enrichment analysis of DEGs in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Protein-protein interaction (PPI) networks were used to analyze the relationship between T cells-related genes and identify hub genes. Enzyme-linked immunosorbent assay (ELISA) kits were used to detect T cell-associated proteins in serum, and receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of these serum markers. Results: Using the limma package and Venn plots, we found that the non-failing donors (NFD) and DCM groups shared many of the same DEGs and DEGs-enriched functions compared to the ICM group, which were involved in T cell activation and differentiation, among other functions. Subsequently, the immune cell score showed no difference between NFD and DCM, but they were significantly different from ICM patients in CD8 T cells CD4 T cells memory resting and activated, T cells follicular helper, and M1 macrophage. After analyzing T cell-associated DEGs, it was found that 4 DEGs encoding secreted proteins were highly expressed in the ICM group compared with the NFD and DCM groups, namely chemokine (C-C motif) ligand 21 (CCL21), interleukin (IL)-1ß, lymphocyte-activation gene 3 (LAG3), and vascular cell adhesion molecule-1 (VCAM-1). Importantly, the serum levels of CCL21, IL-1ß, LAG3, and VCAM-1 in ICM patients were all significantly higher than those in DCM patients. The ROC curves showed that the area under the curve (AUC) values of serum CCL21, IL-1ß, LAG3, and VCAM-1 were 0.775, 0.868, 0.934, and 0.903, respectively. Conclusions: We have identified four T cell-associated serum markers, CCL21, IL-1ß, LAG3, and VCAM-1, as potential diagnostic serum markers that differentiate ICM from DCM.

Role of 14-3-3 protein family in the pathobiology of EBV in immortalized B cells and Alzheimer's disease.

Background and Aims: Several studies have revealed that Epstein-Barr virus (EBV) infection raised the likelihood of developing Alzheimer's disease (AD) via infecting B lymphocytes. The purpose of the current investigation was to assess the possible association between EBV infection and AD. Methods: The microarray datasets GSE49628, GSE126379, GSE122063, and GSE132903 were utilized to extract DEGs by using the GEO2R tool of the GEO platform. The STRING tool was used to determine the interaction between the DEGs, and Cytoscape was used to visualize the results. The DEGs that were found underwent function analysis, including pathway and GO, using the DAVID 2021 and ClueGo/CluePedia. By using MNC, MCC, Degree, and Radiality of cytoHubba, we identified seven common key genes. Gene co-expression analysis was performed through the GeneMANIA web tool. Furthermore, expression analysis of key genes was performed through GTEx software, which have been identified in various human brain regions. The miRNA-gene interaction was performed through the miRNet v 2.0 tool. DsigDB on the Enrichr platform was utilized to extract therapeutic drugs connected to key genes. Results: In GEO2R analysis of datasets with |log2FC|≥ 0.5 and p-value <0.05, 8386, 10,434, 7408, and 759 genes were identified. A total of 141 common DEGs were identified by combining the extracted genes of different datasets. A total of 141 nodes and 207 edges were found during the PPI analysis. The DEG GO analysis with substantial alterations disclosed that they are associated to molecular functions and biological processes, such as positive regulation of neuron death, autophagy regulation of mitochondrion, response of cell to insulin stimulus, calcium signaling regulation, organelle transport along microtubules, protein kinase activity, and phosphoserine binding. Kyoto Encyclopedia of Genes and Genomes analysis discovered the correlation between the DEGs in pathways of neurodegeneration: multiple disease, cell cycle, and cGMP-PKG signaling pathway. Finally, YWHAH, YWHAG, YWHAB, YWHAZ, MAP2K1, PPP2CA, and TUBB genes were identified that are strongly linked to EBV and AD. Three miRNAs, i.e., hsa-mir-15a-5p, hsa-let-7a-5p, and hsa-mir-7-5p, were identified to regulate most of hub genes that are associated with EBV and AD. Further top 10 significant therapeutic drugs were predicted. Conclusion: We have discovered new biomarkers and therapeutic targets for AD, as well as the possible biological mechanisms whereby infection with EBV may be involved in AD susceptibility for the first time.

To investigate the function of age-related genes in different subtypes of asthma based on bioinformatics analysis.

Asthma is a heterogeneous airway inflammatory disease that can be classified according to the inflammatory phenotype. The pathogenesis, clinical features, response to hormone therapy, and prognosis of different inflammatory phenotypes differ significantly. This condition also refers to age-related chronic ailments. Here, we intend to identify the function of aging-related genes in different inflammatory phenotypes of asthma using bioinformatic analyses. Initially, the research adopted the GSEA analysis to understand the fundamental mechanisms that govern different inflammatory phenotypes of asthma pathogenesis and use the CIBERSORT algorithm to assess the immune cell composition. The differentially expressed genes (DEGs) of eosinophilic asthma (EA), neutrophilic asthma (NA), and paucigranulocytic asthma (PGA) were identified through the limma R package. Aging-related genes, screened from multiple databases, were intersected with DEGs of asthma to obtain the asthma-aging-related DEGs. Then, the GO and KEGG pathway enrichment analyses showed that the NA- and EA-aging-related DEGs are involved in the various cytokine-mediated signaling pathways. PPI network and correlation analysis were performed to identify and evaluate the correlation of the hub genes. Further, the clinical characteristics of asthma-aging-related DEGs were explored through ROC analysis. 3 and 12 aging-related DEGs in EA and NA patients show high diagnostic accuracy, respectively (AUC >0.7). This study provided valuable insights into aging-related gene therapy for phenotype-specific asthma. Moreover, the study suggests that effective interventions against asthma may operate by disrupting the detrimental cycle of "aging induces metabolic diseases, which exacerbate aging".

Identifying key pathogenic mechanisms and potential intervention targets for recurrence after laryngeal cancer treatment through bioinformatics screening.

Background: Laryngeal cancer (LC), a prevalent malignant tumor of the head and neck, is characterized by a high rate of postoperative recurrence and significant treatment challenges upon recurrence, severely impacting patients' quality of life. There is a pressing need for effective biomarkers in clinical practice to predict the risk of LC recurrence and guide the development of personalized treatment plans. This study uses bioinformatics methods to explore potential biomarkers for LC recurrence, focusing on key genes and exploring their functions and mechanisms of action in LC recurrence. The aim is to provide new perspectives and evidence for clinical diagnosis, prognostic evaluation, and targeted treatment of LC. Methods: Gene expression profiles from the GSE25727 data set in the Gene Expression Omnibus database were analyzed to detect the differentially expressed genes (DEGs) between the tumor tissues of postoperative recurrent and non-recurrent early stage LC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were also conducted. A protein-protein interaction (PPI) network and transcription factor (TF)-DEG-microRNA (miRNA) network were developed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, with key genes selected using the Molecular Complex Detection (MCODE) plugin. A Gene Set Enrichment Analysis (GSEA) was carried out to investigate the possible mechanisms of the key genes. A retrospective analysis was conducted using the clinical data of 83 LC patients. Immunohistochemical staining was used to examine the transcription level of the key genes in the LC tumor tissues and the factors affecting postoperative recurrence. Results: A total of 248 upregulated and 34 downregulated DEGs were identified in the GSE25727 data set. The PPI network analysis identified a significant module and five candidate genes (i.e., RRAGA, SLC38A9, WDR24, ATP6V1B1, and LAMTOR3). The construction of the TF-DEG-miRNA network indicated that ATP6V1B1 might be regulated by one TF and interact with 17 miRNAs. The KEGG and GSEA analyses suggested that ATP6V1B1 may influence LC recurrence through the involvement of pro-inflammatory and pro-fibrotic mediators, glutathione metabolism, matrix metalloproteinases, immune regulation, and lymphocyte interactions. The recurrence rate of the 83 LC patients included in the study was 19.3% (16/83). The immunohistochemistry results indicated that ATP6V1B1 was highly expressed in patients with recurrent LC. The univariate and multivariate logistic regression analyses revealed that tumor stage T3 (P=0.04), tumor stage T4 (P=0.01), and a high expression of ATP6V1B1 (P=0.02) were risk factors for recurrence after surgical treatment in LC patients. Conclusions: The key genes and signaling pathways identified through the bioinformatics screening provide insights into the potential mechanisms of the pathogenesis of LC. ATP6V1B1 may promote the recurrence of LC by weakening the immune phenotype. Our findings provide a theoretical basis for further research into clinical diagnostics and treatment strategies for LC.

Date palm transcriptome analysis provides new insights on changes in response to high salt stress of colonized roots with the endophytic fungus Piriformospora indica.

Salinity is a significant threat that causes considerable yield losses in date palm. The root endophytic fungus Piriformospora indica has proven effective in providing salt stress tolerance to host plants. However, the underlying molecular mechanism facilitating the date palm's response to P. indica inoculation, and its involvement in the salt stress tolerance, remains unknown. In this study, the colonization of P. indica on date palm seedlings exposed to saline conditions was observed through confocal microscopy, and its impact on gene expressions was evaluated using the transcriptomic analysis. Our findings show that P. indica colonization reinforced the cortical cells, prevented them from plasmolysis and cell death under salinity. The RNAseq analysis produced clean reads ranging from 62,040,451 to 3,652,095 across the treatment groups, successfully assembling into 30,600 annotated genes. Out of them, the number of differentially expressed genes (DEGs) varied across the treatments: i.e., 2523, 2031, and 1936 DEGs were upregulated, while 2323, 959, and 3546 were downregulated in Salt, Fungi, and Fungi+Salt groups, respectively. Furthermore, principal component analysis based on transcriptome profiles revealed discrete clustering of samples from different treatment groups. KEGG and GO pathways enrichment analysis highlighted variation in the number and types of enriched pathways among the treatments. Our study indicated variations in gene expression related to plant hormone biosynthesis and signal transduction (auxin, abscisic acid, gibberellin, and ethylene), ABC transporters, sodium/hydrogen exchanger, cation HKT transporter, transcription factors such as WRKY and MYBs, and the plant immune system (lipoxygenase and jasmonate) of the date palm seedlings. By characterizing the transcriptome of date palm roots under salt stress and with colonization of P. indica, the present findings provide valuable perspectives on the molecular mechanisms responsible for inducing salinity stress tolerance in plants.

Enhancing Pseudomonas syringae pv. Actinidiae sensitivity in kiwifruit by repressing the NBS-LRR genes through miRNA-215-3p and miRNA-29-3p identification.

Kiwifruit bacterial canker, caused by Pseudomonas syringae pv. actinidiae (PSA), poses a grave threat to the global kiwifruit industry. In this study, we examined the role of microRNAs (miRNAs) in kiwifruit's response to PSA. Kiwifruit seedlings subjected to PSA treatment showed significant changes in both miRNA and gene expression compared to the control group. We identified 364 differentially expressed miRNAs (DEMs) and 7170 differentially expressed genes (DEGs). Further analysis revealed 180 miRNAs negatively regulating 641 mRNAs. Notably, two miRNAs from the miRNA482 family, miRNA-215-3p and miRNA-29-3p, were found to increase kiwifruit's sensitivity to PSA when overexpressed. These miRNAs were linked to the regulation of NBS-LRR target genes, shedding light on their role in kiwifruit's defence against PSA. This study offers insights into the miRNA482-NBS-LRR network as a crucial component in enhancing kiwifruit bioresistance to PSA infestation and provides promising candidate genes for further research.

Key genes and immune infiltration patterns and the clinical implications in psoriasis patients.

BACKGROUND: Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention. MATERIALS AND METHODS: Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI). RESULTS: A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores. CONCLUSION: High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

Identification and validation of CCL5 as a key gene in HIV infection and pulmonary arterial hypertension.

Background: The relationship between human immunodeficiency virus (HIV) infection and pulmonary arterial hypertension (PAH) has garnered significant scrutiny. Individuals with HIV infection have a higher risk of developing PAH. However, the specific mechanism of HIV-associated PAH remains unclear. Our study aims at investigating the shared biomarkers in HIV infection and PAH and predicting the potential therapeutic target for HIV-associated PAH. Methods: Data for HIV infection and PAH were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) analysis was performed to detect shared genes in HIV infection and PAH. Enrichment analysis was conducted to identify the function of common DEGs. Protein-protein interaction (PPI) analysis was used to detect key genes. These crucial genes were subsequently verified by RT-qPCR. Finally, candidate drugs were identified by using the Drug Signatures Database (DSigDB). Results: Nineteen common DEGs were identified in HIV infection and PAH. Enrichment analysis exhibited that the functions of these genes were mainly enriched in inflammatory responses, mainly including cellular immunity and interaction between viral proteins and cytokines. By constructing PPI networks, we identified the key gene CC-type chemokine ligand 5 (CCL5), and we verified that CCL5 was highly expressed in hypoxia induced human pulmonary artery endothelial cells (hPAECs) and human pulmonary artery smooth muscle cells (hPASMCs). In addition, we predicted 10 potential drugs targeting CCL5 by Autodock Vina. Conclusion: This study revealed that CCL5 might be a common biomarker of HIV infection and PAH and provided a new therapeutic target for HIV-associated PAH. However, further clinical validation is still indispensable.

FHL1: A novel diagnostic marker for papillary thyroid carcinoma.

Although there are clear morphologic criteria for the diagnosis of papillary thyroid carcinoma (PTC), when the morphology is untypical or overlaps, accurate diagnostic indicators are necessary. Since few studies investigated the role of down-regulated genes in PTC, this article aims to further explore the molecular markers associated with PTC. We conducted bioinformatics analysis of gene microarrays of PTC and normal adjacent tissues. Besides, quantitative real-time quantitative polymerase chain reaction array and immunohistochemical staining were used to investigate the expression of the major down-regulated genes. The results indicated that several important down-regulated genes, including TLE1, BCL2, FHL1, GHR, KIT, and PPARGC1A were involved in the process of PTC. Compared to normal adjacent tissues, the mRNA expression of the major genes was down-regulated in PTC (p＜0.05). Immunohistochemically, FHL1 shows negative or low expression in PTC tissues (p＜0.05). BCL2 did not show a significant difference between PTC and normal thyroid tissues (p > 0.05). TLE1, KIT, PPARGC1A and GHR showed negative expression in both tumor and normal tissues. These results suggested that FHL1 could serve as a novel tumor marker for precise diagnosis of PTC.

Identification of Differentially Expressed Genes and Protein-Protein Interaction in Patients With COVID-19 and Diabetes Peripheral Neuropathy: A Bioinformatics and System Biology Approach.

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.

Development and evaluation of a chronic kidney disease risk prediction model using random forest.

This research aims to advance the detection of Chronic Kidney Disease (CKD) through a novel gene-based predictive model, leveraging recent breakthroughs in gene sequencing. We sourced and merged gene expression profiles of CKD-affected renal tissues from the Gene Expression Omnibus (GEO) database, classifying them into two sets for training and validation in a 7:3 ratio. The training set included 141 CKD and 33 non-CKD specimens, while the validation set had 60 and 14, respectively. The disease risk prediction model was constructed using the training dataset, while the validation dataset confirmed the model's identification capabilities. The development of our predictive model began with evaluating differentially expressed genes (DEGs) between the two groups. We isolated six genes using Lasso and random forest (RF) methods-DUSP1, GADD45B, IFI44L, IFI30, ATF3, and LYZ-which are critical in differentiating CKD from non-CKD tissues. We refined our random forest (RF) model through 10-fold cross-validation, repeated five times, to optimize the mtry parameter. The performance of our model was robust, with an average AUC of 0.979 across the folds, translating to a 91.18% accuracy. Validation tests further confirmed its efficacy, with a 94.59% accuracy and an AUC of 0.990. External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model's reliability. In summary, the study identified critical genetic biomarkers and successfully developed a novel disease risk prediction model for CKD. This model can serve as a valuable tool for CKD disease risk assessment and contribute significantly to CKD identification.

In silico exploration of anti-prostate cancer compounds from differential expressed genes.

Prostate cancer (PCa) is a complex and biologically diverse disease with no curative treatment options at present. This study aims to utilize computational methods to explore potential anti-PCa compounds based on differentially expressed genes (DEGs), with the goal of identifying novel therapeutic indications or repurposing existing drugs. The methods employed in this study include DEGs-to-drug prediction, pharmacokinetics prediction, target prediction, network analysis, and molecular docking. The findings revealed a total of 79 upregulated DEGs and 110 downregulated DEGs in PCa, which were used to identify drug compounds capable of reversing the dysregulated conditions (dexverapamil, emetine, parthenolide, dobutamine, terfenadine, pimozide, mefloquine, ellipticine, and trifluoperazine) at a threshold probability of 20% on several molecular targets, such as serotonin receptors 2a/2b/2c, HERG protein, adrenergic receptors alpha-1a/2a, dopamine D3 receptor, inducible nitric oxide synthase (iNOS), epidermal growth factor receptor erbB1 (EGFR), tyrosine-protein kinases, and C-C chemokine receptor type 5 (CCR5). Molecular docking analysis revealed that terfenadine binding to inducible nitric oxide synthase (-7.833 kcal.mol-1) and pimozide binding to HERG (-7.636 kcal.mol-1). Overall, binding energy ΔGbind (Total) at 0 ns was lower than that of 100 ns for both the Terfenadine-iNOS complex (-101.707 to -103.302 kcal.mol-1) and Ellipticine-TOPIIα complex (-42.229 to -58.780 kcal.mol-1). In conclusion, this study provides insight on molecular targets that could possibly contribute to the molecular mechanisms underlying PCa. Further preclinical and clinical studies are required to validate the therapeutic effectiveness of these identified drugs in PCa disease.

Comparative Analysis of Hulless Barley Transcriptomes to Regulatory Effects of Phosphorous Deficiency.

Hulless barley is a cold-resistant crop widely planted in the northwest plateau of China. It is also the main food crop in this region. Phosphorus (P), as one of the important essential nutrient elements, regulates plant growth and defense. This study aimed to analyze the development and related molecular mechanisms of hulless barley under P deficiency and explore the regulatory genes so as to provide a basis for subsequent molecular breeding research. Transcriptome analysis was performed on the root and leaf samples of hulless barley cultured with different concentrations of KH2PO4 (1 mM and 10 µM) Hoagland solution. A total of 46,439 genes were finally obtained by the combined analysis of leaf and root samples. Among them, 325 and 453 genes had more than twofold differences in expression. These differentially expressed genes (DEGs) mainly participated in the abiotic stress biosynthetic process through Gene Ontology prediction. Moreover, the Kyoto Encyclopedia of Genes and Genomes showed that DEGs were mainly involved in photosynthesis, plant hormone signal transduction, glycolysis, phenylpropanoid biosynthesis, and synthesis of metabolites. These pathways also appeared in other abiotic stresses. Plants initiated multiple hormone synergistic regulatory mechanisms to maintain growth under P-deficient conditions. Transcription factors (TFs) also proved these predictions. The enrichment of ARR-B TFs, which positively regulated the phosphorelay-mediated cytokinin signal transduction, and some other TFs (AP2, GRAS, and ARF) was related to plant hormone regulation. Some DEGs showed different values in their FPKM (fragment per kilobase of transcript per million mapped reads), but the expression trends of genes responding to stress and phosphorylation remained highly consistent. Therefore, in the case of P deficiency, the first response of plants was the expression of stress-related genes. The effects of this stress on plant metabolites need to be further studied to improve the relevant regulatory mechanisms so as to further understand the importance of P in the development and stress resistance of hulless barley.

Transcriptome Analysis of Beta-Catenin-Related Genes in CD34+ Haematopoietic Stem and Progenitor Cells from Patients with AML.

Background: Acute myeloid leukaemia (AML) is a disease of the haematopoietic stem cells(HSCs) that is characterised by the uncontrolled proliferation and impaired differentiation of normal haematopoietic stem/progenitor cells. Several pathways that control the proliferation and differentiation of HSCs are impaired in AML. Activation of the Wnt/beta-catenin signalling pathway has been shown in AML and beta-catenin, which is thought to be the key element of this pathway, has been frequently highlighted. The present study was designed to determine beta-catenin expression levels and beta-catenin-related genes in AML. Methods: In this study, beta-catenin gene expression levels were determined in 19 AML patients and 3 controls by qRT-PCR. Transcriptome analysis was performed on AML grouped according to beta-catenin expression levels. Differentially expressed genes(DEGs) were investigated in detail using the Database for Annotation Visualisation and Integrated Discovery(DAVID), Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), STRING online tools. Results: The transcriptome profiles of our AML samples showed different molecular signature profiles according to their beta-catenin levels(high-low). A total of 20 genes have been identified as hub genes. Among these, TTK, HJURP, KIF14, BTF3, RPL17 and RSL1D1 were found to be associated with beta-catenin and poor survival in AML. Furthermore, for the first time in our study, the ELOV6 gene, which is the most highly up-regulated gene in human AML samples, was correlated with a poor prognosis via high beta-catenin levels. Conclusion: It is suggested that the identification of beta-catenin-related gene profiles in AML may help to select new therapeutic targets for the treatment of AML.

Transcriptome profiling reveals dysregulation of inflammatory and protein synthesis genes in PCOS.

To analyze the differential expression genes of polycystic ovary syndrome (PCOS), clarify their functions and pathways, as well as the protein-protein interaction network, identify HUB genes, and explore the pathological mechanism. PCOS microarray datasets were screened from the GEO database. Common differentially expressed genes (co-DEGs) were obtained using GEO2R and Venn analysis. Enrichment and pathway analyses were conducted using the DAVID online tool, with results presented in bubble charts. Protein-protein interaction analysis was performed using the STRING tool. HUB genes were identified using Cytoscape software and further interpreted with the assistance of the GeneCards database. A total of two sets of co-DEGs (108 and 102), key proteins (15 and 55), and hub genes (10 and 10) were obtained. The co-DEGs: (1) regulated inflammatory responses and extracellular matrix, TNF, and IL-17 signaling pathways; (2) regulated ribosomes and protein translation, ribosome and immune pathways. The key proteins: (1) regulated inflammation, immunity, transcription, matrix metabolism, proliferation/differentiation, energy, and repair; (2) regulated ubiquitination, enzymes, companion proteins, respiratory chain components, and fusion proteins. The Hub genes: (1) encoded transcription factors and cytokines, playing vital roles in development and proliferation; (2) encoded ribosomes and protein synthesis, influencing hormone and protein synthesis, associated with development and infertility. The dysregulated expression of inflammation and protein synthesis genes in PCOS may be the key mechanism underlying its onset and progression.