Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington's disease using single-cell RNA sequencing data.

BACKGROUND: Huntington's disease (HD) is a hereditary neurological disorder caused by mutations in HTT, leading to neuronal degeneration. Traditionally, HD is associated with the misfolding and aggregation of mutant huntingtin due to an extended polyglutamine domain encoded by an expanded CAG tract. However, recent research has also highlighted the role of global transcriptional dysregulation in HD pathology. However, understanding the intricate relationship between mRNA expression and HD at the cellular level remains challenging. Our study aimed to elucidate the underlying mechanisms of HD pathology using single-cell sequencing data. RESULTS: We used single-cell RNA sequencing analysis to determine differential gene expression patterns between healthy and HD cells. HD cells were effectively modeled using a residual neural network (ResNet), which outperformed traditional and convolutional neural networks. Despite the efficacy of our approach, the F1 score for the test set was 96.53%. Using the SHapley Additive exPlanations (SHAP) algorithm, we identified genes influencing HD prediction and revealed their roles in HD pathobiology, such as in the regulation of cellular iron metabolism and mitochondrial function. SHAP analysis also revealed low-abundance genes that were overlooked by traditional differential expression analysis, emphasizing its effectiveness in identifying biologically relevant genes for distinguishing between healthy and HD cells. Overall, the integration of single-cell RNA sequencing data and deep learning models provides valuable insights into HD pathology. CONCLUSION: We developed the model capable of analyzing HD at single-cell transcriptomic level.

Relevance of pyroptosis-associated genes in nasopharyngeal carcinoma diagnosis and subtype classification.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a highly aggressive and metastatic malignancy originating in the nasopharyngeal tissue. Pyroptosis is a relatively newly discovered, regulated form of necrotic cell death induced by inflammatory caspases that is associated with a variety of diseases. However, the role and mechanism of pyroptosis in NPC are not fully understood. METHODS: We analyzed the differential expression of pyroptosis-related genes (PRGs) between patients with and without NPC from the GSE53819 and GSE64634 datasets of the Gene Expression Omnibus (GEO) database. We mapped receptor operating characteristic profiles for these key PRGs to assess the accuracy of the genes for disease diagnosis and prediction of patient prognosis. In addition, we constructed a nomogram based on these key PRGs and carried out a decision curve analysis. The NPC patients were classified into different pyroptosis gene clusters by the consensus clustering method based on key PRGs, whereas the expression profiles of the key PRGs were analyzed by applying principal component analysis. We also analyzed the differences in key PRGs, immune cell infiltration and NPC-related genes between the clusters. Finally, we performed differential expression analysis for pyroptosis clusters and obtained differentially expressed genes (DEGs) and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. RESULTS: We obtained 14 differentially expressed PRGs from GEO database. Based on these 14 differentially expressed PRGs, we applied least absolute shrinkage and selection operator analysis and the random forest algorithm to obtain four key PRGs (CHMP7, IL1A, TP63 and GSDMB). We completely distinguished the NPC patients into two pyroptosis gene clusters (pyroptosis clusters A and B) based on four key PRGs. Furthermore, we determined the immune cell abundance of each NPC sample, estimated the association between the four PRGs and immune cells, and determined the difference in immune cell infiltration between the two pyroptosis gene clusters. Finally, we obtained and functional enrichment analyses 259 DEGs by differential expression analysis for both pyroptosis clusters. CONCLUSIONS: PRGs are critical in the development of NPC, and our research on the pyroptosis gene cluster may help direct future NPC therapeutic approaches.

Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes.

BACKGROUND: Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction. METHODS: The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes. RESULTS: Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer. CONCLUSION: The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

Screening of key modules and key genes in the prevention of skin cancer development based on gene volcano plot and WGCNA.

BACKGROUND: Skin cancer, a prevalent form of cancer that is on the rise worldwide, requires proactive prevention strategies to reduce the burden of screening, treatment, and mortality. The KEGG research highlighted the significant involvement of red module genes in protein digestion and absorption. These findings provide valuable insights into the underlying molecular mechanisms associated with skin cancer susceptibility, offering potential targets for further research and development of preventive strategies. MATERIALS AND METHODS: Hub genes numbered 130. "limma" in R found 600 DEGs from GSE66359 dataset. DEGs are enriched in BP: chromosome segregation, CC: chromosomal region, and MF: DNA replication origin binding, according to GO analysis. Cell cycle was enriched in DEGs by KEGG and GSEA. Finally, significant genes were COL5A1, CTHRC1, ECM1, FSTL1, KDELR3, and WIPI1. RESULTS: ECM1 and WIPI1 greatly prevented skin cancer. This study created a coexpression network using WGCNA to investigate skin cancer susceptibility modules and cardiovascular disease genes. CONCLUSION: Our study finds a module and many important genes that are essential building blocks in the etiology of skin cancer, which may help us understand the molecular mechanisms of disease prevention.

A prognostic model of idiopathic pulmonary fibrosis constructed based on macrophage and mitochondria-related genes.

BACKGROUND: Studies have shown that mitochondrial function and macrophages may play a role in the development of idiopathic pulmonary fibrosis (IPF). However, the understanding of the interactions and specific mechanisms between mitochondrial function and macrophages in pulmonary fibrosis is still very limited. METHODS: To construct a prognostic model for IPF based on Macrophage- related genes (MaRGs) and Mitochondria-related genes (MitoRGs), differential analysis was performed to achieve differentially expressed genes (DEGs) between IPF and Control groups in the GSE28042 dataset. Then, MitoRGs, MaRGs and DEGs were overlapped to screen out the signature genes. The univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) algorithm were implemented to achieve key genes. Furthermore, the independent prognostic analysis was employed. The ingenuity pathway analysis (IPA) was employed to further understand the molecular mechanisms of key genes.Next, the immune infiltration analysis was implemented to identify differential immune cells between two risk subgroups. RESULTS: There were 4791 DEGs between IPF and Control groups. Furthermore, 26 signature genes were achieved by the intersection processing. Three key genes including ALDH2, MCL1, and BCL2A1 were achieved, and the risk model based on the key genes was created. In addition, a nomogram for survival forecasting of IPF patients was created based on riskScore, Age, and Gender, and we found that key genes were associated with classical pathways including 'Apoptosis Signaling', 'PI3K/AKT Signaling', and so on. Next, two differential immune cells including Monocytes and CD8 T cells were identified between two risk subgroups. Moreover, we found that MIR29B2CHG and hsa-mir-1-3p could regulate the expression of ALDH2. CONCLUSION: We achieved 3 key genes including ALDH2, MCL1,, and BCL2A1 associated with IPF, providing a new theoretical basis for clinical treatment of IPF.

Bioinformatic Analysis Identifies Potential Extracellular Matrix Related Genes in the Pathogenesis of Early Onset Preeclampsia.

Early-onset preeclampsia (EOPE) is a complex pregnancy complication that poses significant risks to the health of both mothers and fetuses, and research on its pathogenesis and pathophysiology remains insuffcient. This study aims to explore the role of candidate genes and their potential interaction mechanisms in EOPE through bioinformatics analysis techniques. Two gene expression datasets, GSE44711 and GSE74341, were obtained from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) between EOPE and gestational age-matched preterm control samples. Functional enrichment analysis was performed utilizing the kyoto encyclopedia of genes and genomes (KEGG), gene ontology (GO), and gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed using the STRING database, and hub DEGs were identified through Cytoscape software and comparative toxicogenomics database (CTD) analysis. Furthermore, a diagnostic logistic model was established using these hub genes, which were confirmed through reverse transcription polymerase chain reaction (RT-PCR). Finally, immune cell infiltration was analyzed using CIBERSORT. In total, 807 DEGs were identified in the GSE44711 dataset (451 upregulated genes and 356 downregulated genes), and 787 DEGs were identified in the GSE74341 dataset (446 upregulated genes and 341 downregulated genes). These DEGs were significantly enriched in various molecular functions such as extracellular matrix structural constituent, receptor-ligand activity binding, cytokine activity, and platelet-derived growth factor. KEGG and GSEA annotation revealed significant enrichment in pathways related to ECM-receptor interaction, PI3K-AKT signaling, and focal adhesion. Ten hub genes were identified through the CytoHubba plugin in Cytoscape. Among these hub genes, three key DEGs (COL1A1, SPP1, and THY1) were selected using CTD analysis and various topological methods in Cytoscape. The diagnostic logistic model based on these three genes exhibited high efficiency in predicting EOPE (AUC = 0.922). RT-PCR analysis confirmed the downregulation of these genes in EOPE, and immune cell infiltration analysis suggested the significant role of M1 and M2 macrophages in EOPE. In conclusion, this study highlights the association of three key genes (COL1A1, SPP1, and THY1) with EOPE and their contribution to high diagnostic efficiency in the logistic model. Additionally, it provides new insights for future research on EOPE and emphasizes the diagnostic value of these identified genes. More research is needed to explore their functional and diagnostic significance in EOPE.

Identification of Central Genes and Regulatory Pathways Associated with Hyperlipidemia in Rats.

Hyperlipidemia is an independent risk factor for cardiovascular and cerebrovascular diseases. The transcriptomic data and the gene regulatory networks of hyperlipidemia are largely unclear. We analyzed the changes in liver gene expression and the serum levels of biochemical indicators in rats with hyperlipidemia induced by high-fat diet (HFD). The body weight, liver weight, and the serum levels of TG, TC, HDL-C, LDL-C, ALT, and AST were significantly higher in the hyperlipidemic rats compared to the healthy controls (P < 0.05). In addition, HFD feeding decreased the antioxidant capacity of the liver tissues and significantly increased the arteriosclerosis index (AI) (P < 0.05). There were 584 differentially expressed genes (DEGs) in the hyperlipidemia model compared to the control, with |log2FC|≥ 1 and P-adjust ≤ 0.05 as the thresholds. GO analysis of the DEGs revealed significant enrichment of 382 biological processes (BP), 18 cellular components (CC), and 40 molecular functions (MF). In addition, pathways related to bile secretion, cholesterol metabolism, and steroid hormone biosynthesis were significantly associated with hyperlipidemia. The key genes potentially involved in the blood lipid changes were Agt, Src, Gnai3, Cyp2c7, Cyp2c11, Cyp2c22, Apoa1, Apoe, and Srebf1. The genes and pathways identified in this study are potential intervention targets for hyperlipidemia and warrant further investigation.

Comprehensive analysis of lncRNA-miRNA-mRNA ceRNA network and key genes in granulosa cells of patients with biochemical primary ovarian insufficiency.

Primary ovarian insufficiency (POI) is a common condition leading to the pathological decline of ovarian function in women of reproductive age, resulting in amenorrhea, hypogonadism, and infertility. Biochemical premature ovarian insufficiency (bPOI) is an intermediate stage in the pathogenesis of POI in which the fertility of patients has been reduced. Previous studies suggest that granulosa cells (GCs) play an essential role in the pathogenesis of POI, but their pathogenetic mechanisms remain unclear. To further explore the potential pathophysiological mechanisms of GCs in POI, we constructed a molecular long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) network using GC expression data collected from biochemical premature ovarian failure (bPOI) patients in the GEO database. We discovered that the GCs of bPOI patients had differential expression of 131 mRNAs, 191 lncRNAs, and 28 miRNAs. By systematic network analysis, we identified six key genes, including SRSF1, PDIA5, NEURL1B, UNK, CELF2, and CFL2, and five hub miRNAs, namely hsa-miR-27a-3p, hsa-miR-24-3p, hsa-miR-22-3p, hsa-miR-129-5p, and hsa-miR-17-5p, and the results suggest that the expression of these key genes may be regulated by two hub miRNAs, hsa-miR-27a-3p and hsa-miR-17-5p. Additionally, a POI model in vitro was created to confirm the expression of a few important genes. In this study, we discovered a unique lncRNA-miRNA-mRNA network based on the ceRNA mechanism in bPOI for the first time, and we screened important associated molecules, providing a partial theoretical foundation to better understand the pathogenesis of POI.

Integrated Bioinformatics Analysis Identified ASNS and DDIT3 as the Therapeutic Target in Castrate-Resistant Prostate Cancer.

Many studies have demonstrated the mechanisms of progression to castration-resistant prostate cancer (CRPC) and novel strategies for its treatment. Despite these advances, the molecular mechanisms underlying the progression to CRPC remain unclear, and currently, no effective treatments for CRPC are available. Here, we characterized the key genes involved in CRPC progression to gain insight into potential therapeutic targets. Bicalutamide-resistant prostate cancer cells derived from LNCaP were generated and named Bical R. RNA sequencing was used to identify differentially expressed genes (DEGs) between LNCaP and Bical R. In total, 631 DEGs (302 upregulated genes and 329 downregulated genes) were identified. The Cytohubba plug-in in Cytoscape was used to identify seven hub genes (ASNS, AGT, ATF3, ATF4, DDIT3, EFNA5, and VEGFA) associated with CRPC progression. Among these hub genes, ASNS and DDIT3 were markedly upregulated in CRPC cell lines and CRPC patient samples. The patients with high expression of ASNS and DDIT3 showed worse disease-free survival in patients with The Cancer Genome Atlas (TCGA)-prostate adenocarcinoma (PRAD) datasets. Our study revealed a potential association between ASNS and DDIT3 and the progression to CRPC. These results may contribute to the development of potential therapeutic targets and mechanisms underlying CRPC progression, aiming to improve clinical efficacy in CRPC treatment.

MYO1B as a prognostic biomarker and a therapeutic target in Arecoline-associated oral carcinoma.

BACKGROUND: Arecoline, the main component of betel nut, induces malignant transformation of oral cells through complicated unclear mechanisms. Thus, we aimed to screen the key genes involved in Arecoline-induced oral cancer and further verify their expressions and roles. METHODS: This study included a data-mining part, a bioinformatics verification part, and an experimental verification one. First, the key gene related to oral cancer induced by Arecoline was screened. Then, the expression and clinical significance of the key gene in head and neck/oral cancer tissues were verified, and its downstream mechanisms of action were explored. Afterwards, the expression and roles of the key gene were verified by experiments at the histological and cytological levels. RESULTS: MYO1B was identified as the key gene. Overexpression of MYO1B was associated with lymph node metastasis and unfavorable outcomes in oral cancer. MYO1B may be mainly related to metastasis, angiogenesis, hypoxia, and differentiation. A positive correlation between MYO1B and the infiltration of macrophages, B cells, and dendritic cells was presented. MYO1B might have a close relationship with SMAD3, which may be enriched in the Wnt signaling pathway. MYO1B suppression markedly inhibited the proliferation, invasion, and metastasis abilities of both Arecoline-transformed oral cells and oral cancer cells. CONCLUSION: This study revealed MYO1B as a key gene in Arecoline-induced oral tumorigenesis. MYO1B might be a novel prognostic indicator and therapeutic target for oral cancer.

Identification of key genes in spontaneous cerebral hemorrhage and prevention of disease damage: LASSO and SVM regression.

Prevention is more important than treatment, and the incidence of intracerebral hemorrhage can be effectively reduced by intervening on the risk factors of intracerebral hemorrhage. By studying the risk factors of spontaneous intracerebral hemorrhage, we can identify the risk factors to achieve the target of treatment and prevention. Through the use of the Least Absolute Shrinkage and Selection Operator (LASSO) and the Support Vector Machine (SVM), the two essential SICH-related genes, NUAK1 and ERO1L, were eliminated from consideration. A Venn analysis was performed, and based on the two important modules, it found that SICH was related with four critical genes: VCM1, CRNDE, COL6A2, and HSPB6. One gene (NUAK1) was dramatically downregulated in the illness group compared to the control group, whereas three essential genes (ERO1L, VCAM1, and COL6A2) were significantly upregulated in the disease group. In the end, the genes ERO1L, VCAM1, COL6A2, and NUAK1 were shown to be the most important ones for SICH. It is anticipated that these genes will become novel biomarkers as well as targets for the development of new pharmacotherapies for SICH.

Identification of key lipid metabolism-related genes in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) represents profound degenerative conditions of the brain that cause significant deterioration in memory and cognitive function. Despite extensive research on the significant contribution of lipid metabolism to AD progression, the precise mechanisms remain incompletely understood. Hence, this study aimed to identify key differentially expressed lipid metabolism-related genes (DELMRGs) in AD progression. METHODS: Comprehensive analyses were performed to determine key DELMRGs in AD compared to controls in GSE122063 dataset from Gene Expression Omnibus. Additionally, the ssGSEA algorithm was utilized for estimating immune cell levels. Subsequently, correlations between key DELMRGs and each immune cell were calculated specifically in AD samples. The key DELMRGs expression levels were validated via two external datasets. Furthermore, gene set enrichment analysis (GSEA) was utilized for deriving associated pathways of key DELMRGs. Additionally, miRNA-TF regulatory networks of the key DELMRGs were constructed using the miRDB, NetworkAnalyst 3.0, and Cytoscape software. Finally, based on key DELMRGs, AD samples were further segmented into two subclusters via consensus clustering, and immune cell patterns and pathway differences between the two subclusters were examined. RESULTS: Seventy up-regulated and 100 down-regulated DELMRGs were identified. Subsequently, three key DELMRGs (DLD, PLPP2, and PLAAT4) were determined utilizing three algorithms [(i) LASSO, (ii) SVM-RFE, and (iii) random forest]. Specifically, PLPP2 and PLAAT4 were up-regulated, while DLD exhibited downregulation in AD cerebral cortex tissue. This was validated in two separate external datasets (GSE132903 and GSE33000). The AD group exhibited significantly altered immune cell composition compared to controls. In addition, GSEA identified various pathways commonly associated with three key DELMRGs. Moreover, the regulatory network of miRNA-TF for key DELMRGs was established. Finally, significant differences in immune cell levels and several pathways were identified between the two subclusters. CONCLUSION: This study identified DLD, PLPP2, and PLAAT4 as key DELMRGs in AD progression, providing novel insights for AD prevention/treatment.

Network pharmacology and bioinformatics approach reveals the therapeutic mechanism of action of curcumin in Alzheimer disease.

Curcumin is a natural anti-inflammatory and antioxidant substance which plays a major role in reducing the amyloid plaques formation, which is the major cause of Alzheimer's disease (AD). Consequently, a methodical approach was used to select the potential protein targets of curcumin in AD through network pharmacology. In this study, through integrative methods, AD targets of curcumin through SwissTargetPrediction database, STITCH database, BindingDB, PharmMapper, Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM) database were predicted followed by gene enrichment analysis, network construction, network topology, and docking studies. Gene ontology analysis facilitated identification of a list of possible AD targets of curcumin (74 targets genes). The correlation of the obtained targets with AD was analysed by using gene ontology (GO) pathway enrichment analyses and Kyoto Encyclopaedia of Genes and Genomes (KEGG). We have incorporated the applied network pharmacological approach to identify key genes. Furthermore, we have performed molecular docking for analysing the mechanism of curcumin. In order to validate the temporospatial expression of key genes in human central nervous system (CNS), we searched the Human Brain Transcriptome (HBT) dataset. We identified top five key genes namely, PPARγ, MAPK1, STAT3, KDR and APP. Further validated the expression profiling of these key genes in publicly available brain data expression profile databases. In context to a valuable addition in the treatment of AD, this study is concluded with novel insights into the therapeutic mechanisms of curcumin, will ease the treatment of AD with the clinical application of curcumin.

Identification and analysis of mitochondria-related key genes of heart failure.

Mitochondria-induced cell death is a vital mechanism of heart failure (HF). Thus, identification of mitochondria-related genes (Mito-RGs) based on transcriptome sequencing data of HF might provide novel diagnostic markers and therapeutic targets for HF. First, bioinformatics analysis was conducted on the GSE57338, GSE76701, GSE136547, and GSE77399 datasets in the Gene Expression Omnibus. Next, we analyzed HF-Mito differentially expressed genes (DEGs) using the protein-protein interaction (PPI) network for obtaining critical genes and exploring their functions. Subsequently, immune cell scores of the HF and normal groups were compared. The potential alteration mechanisms of the key genes were investigated by constructing a competing endogenous RNA network. Finally, we predicted potential therapeutic agents and validated the expression levels of the key genes. Twenty-three HF-Mito DEGs were acquired in the GSE57338 dataset, and the PPI network obtained four key genes, including IFIT3, XAF1, RSAD2, and MX1. According to gene set enrichment analysis, the key genes showed high enrichment in myogenesis and hypoxia. Immune cell analysis demonstrated that aDCs, B cells, and 20 other immune cell types varied between the HF and normal groups. Moreover, we observed that H19 might affect the expression of IFIT3, AXF1, and RSAD2. PCGEM1 might regulate RSAD2 expression. A total of 515 potential therapeutic drugs targeting the key genes, such as tretinoin, silicon dioxide, and bisphenol A, were acquired. Finally, IFIT3, RSAD2, and MX1 expression increased in HF samples compared with normal samples in the GSE76701 dataset, conforming to the GSE57338 dataset analysis. This work screened four key genes, namely, IFIT3, XAF1, RSAD2, and MX1, which can be further explored in subsequent studies for their specific molecular mechanisms in HF.

Research progress on inosine monophosphate deposition mechanism in chicken muscle.

With the continuous improvements in human diet, there is an ever-increasing demand for high-quality chicken, so it is particularly important for poultry breeders to carry out the breeding of high-quality broilers in a timely fashion. Inosine monophosphate (IMP) is a flavor-enhancing substance, which plays a critical role in the umami taste of the muscle, making the content of IMP an important umami taste indicator. Currently, research on the deposition mechanism of IMP in chicken is not only necessary for chicken breeders to promote the production of high-quality meat and poultry but also to meet the human demand for chicken meat. In this paper, the research history of IMP, its structure and taste mechanisms, the pathway and influencing factors of de novo IMP synthesis, and the key genes regulating IMP synthesis and metabolism are briefly summarized. Our aim was to lay a theoretical foundation and provide scientific background and research directions for further research on high-quality broiler breeding.

Investigate the stemness of adult adipose-derived stromal cells based on single-cell RNA-sequencing.

The cellular heterogeneity and genetic features of stemness of adipose-derived stromal cells (ADSCs) remain unclear. Using single-cell RNA sequencing (scRNA-seq), we investigated the genomic features of the stemness gene in ADSCs with genetic variability. We cultured the ADSCs isolated from the fat waste of a healthy adult volunteers undergoing cosmetic plastic surgery to the third generation, used the BD Rhapsody platform to perform scRNA-seq, then used Monocle2 to analyze the growth and development trajectory of ADSCs, Cellular Trajectory Reconstruction Analysis Using Gene Counts and Expression (CytoTRACE) to evaluate the stemness gene characteristics in ADSCs clusters, and Beam to analyze the expression change characteristics of the main stemness related genes of ADSCs. According to the scRNA-seq data of 5325 ADSCs, they could be classified into nine cell clusters. According to CytoTRACE analysis, Cluster 3 of ADSCs had the highest stemness, whereas Cluster 8 had the lowest stemness. Pseudotime analysis revealed that Cluster 3 of ADSCs was primarily dispersed in the middle part of the growth and development trajectory, whereas Cluster 8 was primarily distributed at the end. We summarized the stemness of Cluster 3 in ADSCs with high expression of TPM1 and CCND1 genes in the metaphase of growth and development is the strongest, whereas the stemness of Cluster 8 with high expression of FICD, CREBRF, SDF2L1, HERPUD1, and HYOU1 genes in the telophase of growth and development is the weakest, providing a theoretical basis for screening and improving the therapeutic effect of ADSCs in cell transplantation research.

Identification of Immune Cells and Key Genes associated with Alzheimer's Disease.

Alzheimer's disease (AD) is an age-related neurodegenerative disorder characterized by cognitive impairment and memory loss, for which there is no effective cure to date. In the past several years, numerous studies have shown that increased inflammation in AD is a major cause of cognitive impairment. This study aimed to reveal 22 kinds of peripheral immune cell types and key genes associated with AD. The prefrontal cortex transcriptomic data from Gene Expression Omnibus (GEO) database were collected, and CIBERSORT was used to assess the composition of 22 kinds of immune cells in all samples. Weighted gene co-expression network analysis (WGCNA) was used to construct gene co-expression networks and identified candidate module genes associated with AD. The least absolute shrinkage and selection operator (LASSO) and random forest (RF) models were constructed to analyze candidate module genes, which were selected from the result of WGCNA. The results showed that the immune infiltration in the prefrontal cortex of AD patients was different from healthy samples. Of all 22 kinds of immune cells, M1 macrophages were the most relevant cell type to AD. We revealed 10 key genes associated with AD and M1 macrophages by LASSO and RF analysis, including ARMCX5, EDN3, GPR174, MRPL23, RAET1E, ROD1, TRAF1, WNT7B, OR4K2 and ZNF543. We verified these 10 genes by logistic regression and k-fold cross-validation. We also validated the key genes in an independent dataset, and found GPR174, TRAF1, ROD1, RAET1E, OR4K2, MRPL23, ARMCX5 and EDN3 were significantly different between the AD and healthy controls. Moreover, in the 5XFAD transgenic mice, the differential expression trends of Wnt7b, Gpr174, Ptbp3, Mrpl23, Armcx5 and Raet1e are consistent with them in independent dataset. Our results provided potential therapeutic targets for AD patients.

Identification of key genes and pathways involved in abdominal aortic aneurysm initiation and progression.

OBJECTIVE: The study aimed to assess the gene expression profile of biopsies obtained from the neck of human abdominal aortic aneurysm (AAA) and the main site of AAA dilatation and to investigate the molecular mechanism underlying the development of AAA. METHODS: The microarray profile of GSE47472 and GSE57691 were obtained from the Gene Expression Omnibus (GEO) database. The GSE47472 was a microarray dataset of tissues from the aortic neck of AAA patients versus normal controls. The GSE57691 was a microarray dataset including the tissues from main site of AAA dilatation versus normal controls. Differentially expressed genes (DEGs) were chosen using the R package and annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG). The hub genes were identified in the protein-protein interaction (PPI) network. RESULTS: 342 upregulated DEGs and 949 downregulated DEGs were obtained from GSE47472. The upregulated DEGs were mainly enriched in biological regulation (ontology: BP), the membrane (ontology: CC), and protein binding (ontology: MF), and the downregulated genes were mainly enriched in biological regulation (ontology: BP), the membrane (ontology: CC), and protein blinding (ontology: MF). In the KEGG enrichment analysis, the DEGs mainly involved glycosaminoglycan degradation, vasopressin-regulated water reabsorption, and pyruvate metabolism. The hub genes in GSE47472 mainly include VAMP8, PTPRC, DYNLL1, RPL38, RPS4X, HNRNPA1, PRMT1, TGOLN2, PA2G4, and CUL2. From GSE57691, 248 upregulated DEGs and 1120 downregulated DEGs were selected. The upregulated DEGs of GSE57691 were mainly enriched in biological regulation (ontology: BP), the membrane (ontology: CC), and protein binding (ontology: MF), and the downregulated genes were mainly enriched in metabolic process (ontology: BP), the membrane (ontology: CC), and protein blinding (ontology: MF). In the KEGG enrichment analysis, the DEGs mainly involved the mitochondrial respiratory, respiratory chain complex, and respiratory chain. RPS15A, RPS5, RPL23, RPL27A, RPS24, RPL35A, RPS4X, RPL7, RPS25, and RPL21 were identified as the hub genes. CONCLUSION: At the early stage of AAA, the current study indicated the importance of glycosaminoglycan degradation and anaerobic metabolism. We also identified several hub genes closely related to AAA (VAMP8, PTPRC, DYNLL1, etc.). At the progression of the AAA, the dysfunctional mitochondria played a critical role in AAA formation and the RPS15A, RPS5, RPL23, etc., were identified as the hub genes.

Low Temperature Affects Fatty Acids Profiling and Key Synthesis Genes Expression Patterns in Zanthoxylum bungeanum Maxim.

Zanthoxylum bungeanum is one of the most important medicinal and edible homologous plants because of its potential health benefits and unique flavors. The chemical components in compositions and contents vary with plant genotype variations and various environmental stress conditions. Fatty acids participate in various important metabolic pathways in organisms to resist biotic and abiotic stresses. To determine the variations in metabolic profiling and genotypes, the fatty acid profiling and key differential genes under low temperature stress in two Z. bungeanum varieties, cold-tolerant (FG) and sensitive (FX), were investigated. Twelve main fatty acids were found in two Z. bungeanum varieties under cold stress. Results showed that the contents of total fatty acids and unsaturated fatty acids in FG were higher than those in FX, which made FG more resistant to low temperature. Based on the result of orthogonal partial least squares discriminant analysis, palmitic acid, isostearic acid, linolenic acid and eicosenoic acid were the important differential fatty acids in FG under cold stress, while isomyristic acid, palmitic acid, isostearic acid, stearic acid, oleic acid, linolenic acid and eicosenoic acid were the important differential fatty acids in FX. Furthermore, fatty acid synthesis pathway genes fatty acyl-ACP thioesterase A (FATA), Delta (8)-fatty-acid desaturase 2 (SLD2), protein ECERIFERUM 3 (CER3), fatty acid desaturase 3 (FAD3) and fatty acid desaturase 5 (FAD5) played key roles in FG, and SLD2, FAD5, 3-oxoacyl-[acyl-carrier-protein] synthase I (KAS I), fatty acyl-ACP thioesterase B (FATB) and acetyl-CoA carboxylase (ACC) were the key genes responding to low temperature in FX. The variation and strategies of fatty acids in two varieties of Z. bungeanum were revealed at the metabolic and molecular level. This work provides a reference for the study of chemical components in plant stress resistance.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer.

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein-protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.