UBE2C: A pan-cancer diagnostic and prognostic biomarker revealed through bioinformatics analysis.

BACKGROUND: The diverse and complex attributes of cancer have made it a daunting challenge to overcome globally and remains to endanger human life. Detection of critical cancer-related gene alterations in solid tumor samples better defines patient diagnosis and prognosis, and indicates what targeted therapies must be administered to improve cancer patients' outcome. MATERIALS AND METHODS: To identify genes that have aberrant expression across different cancer types, differential expressed genes were detected within the TCGA datasets. Subsequently, the DEGs common to all pan cancers were determined. Furthermore, various methods were employed to gain genetic alterations, co-expression genes network and protein-protein interaction (PPI) network, pathway enrichment analysis of common genes. Finally, the gene regulatory network was constructed. RESULTS: Intersectional analysis identified UBE2C as a common DEG between all 28 types of studied cancers. Upregulated UBE2C expression was significantly correlated with OS and DFS of 10 and 9 types of cancer patients. Also, UBE2C can be a diagnostic factor in CESC, CHOL, GBM, and UCS with AUC = 100% and diagnose 19 cancer types with AUC ≥90%. A ceRNA network constructed including UBE2C, 41 TFs, 10 shared miRNAs, and 21 circRNAs and 128 lncRNAs. CONCLUSION: In summary, UBE2C can be a theranostic gene, which may serve as a reliable biomarker in diagnosing cancers, improving treatment responses and increasing the overall survival of cancer patients and can be a promising gene to be target by cancer drugs in the future.

Exploring the mechanism of ShenGui capsule in treating heart failure based on network pharmacology and molecular docking: A review.

ShenGui capsule (SGC), as a herbal compound, has significant effects on the treatment of heart failure (HF), but its mechanism of action is unclear. In this study, we aimed to explore the potential pharmacological targets and mechanisms of SGC in the treatment of HF using network pharmacology and molecular docking approaches. Potential active ingredients of SGC were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform database and screened by pharmacokinetic parameters. Target genes of HF were identified by comparing the toxicogenomics database, GeneCards, and DisGeNET databases. Protein interaction networks and gene-disorder-target networks were constructed using Cytoscape for visual analysis. Gene ontology and Kyoto Encyclopedia of Genes and Genomes were also performed to identify protein functional annotations and potential target signaling pathways through the DAVID database. CB-DOCK was used for molecular docking to explore the role of IL-1ß with SGC compounds. Sixteen active ingredients in SGC were screened from the traditional Chinese medicine systems pharmacology database and analysis platform, of which 36 target genes intersected with HF target genes. Protein-protein interactions suggested that each target gene was closely related, and interleukin-1ß (IL-1ß) was identified as Hub gene. The network pharmacology analysis suggested that these active ingredients were well correlated with HF. Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that target genes were highly enriched in pathways such as inflammation. Molecular docking results showed that IL-1ß binds tightly to SGC active components. This experiment provides an important research basis for the mechanism of action of SGC in the treatment of HF. In this study, the active compounds of SGC were found to bind IL-1ß for the treatment of heart failure.

Unraveling the molecular landscape of Ataxia Telangiectasia: Insights into Neuroinflammation, immune dysfunction, and potential therapeutic target.

BACKGROUND: Ataxia Telangiectasia (AT) is a genetic disorder characterized by compromised DNA repair, cerebellar degeneration, and immune dysfunction. Understanding the molecular mechanisms driving AT pathology is crucial for developing targeted therapies. METHODS: In this study, we conducted a comprehensive analysis to elucidate the molecular mechanisms underlying AT pathology. Using publicly available RNA-seq datasets comparing control and AT samples, we employed in silico transcriptomics to identify potential genes and pathways. We performed differential gene expression analysis with DESeq2 to reveal dysregulated genes associated with AT. Additionally, we constructed a Protein-Protein Interaction (PPI) network to explore the interactions between proteins implicated in AT. RESULTS: The network analysis identified hub genes, including TYROBP and PCP2, crucial in immune regulation and cerebellar function, respectively. Furthermore, pathway enrichment analysis unveiled dysregulated pathways linked to AT pathology, providing insights into disease progression. CONCLUSION: Our integrated approach offers a holistic understanding of the complex molecular landscape of AT and identifies potential targets for therapeutic intervention. By combining transcriptomic analysis with network-based methods, we provide valuable insights into the underlying mechanisms of AT pathogenesis.

A protein network refinement method based on module discovery and biological information.

BACKGROUND: The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs. METHODS: Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules. RESULTS: To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

[Screening of differentially expressed genes in gastric cancer based on GEO database and function and pathway enrichment analysis].

OBJECTIVE: To explore the core genes related to the diagnosis and prognosis of gastric cancer (GC) based on Gene Expression Omnibus (GEO) database and screen the molecular targets involved in the occurrence and development of GC. METHODS: GC microarray data GSE118916, GSE54129 and GSE79973 were downloaded from GEO database, and the differentially expressed genes (DEGs) were screened. Enrichment analysis of the signaling pathways and molecular functions were preformed and protein-protein interaction networks (PPI) were constructed to identify the hub genes, whose expression levels and diagnostic and prognostic values were verifies based on gastric adenocarcinoma data from TCGA. The expression levels of these core genes were also detected in different GC cell lines using qRT- PCR. RESULTS: Seventy-seven DEGs were identified, which encodes proteins located mainly in the extracellular matrix and basement membrane with activities of oxidoreductase and extracellular matrix receptor and ligand, involving the biological processes of digestion and hormone metabolism and the signaling pathways in retinol metabolism and gastric acid secretion. Nine hub genes were obtained, among which SPARC, TIMP1, THBS2, COL6A3 and THY1 were significantly up- regulated and TFF1, GKN1, TFF2 and PGC were significantly down-regulated in GC. The abnormal expressions of SPARC, TIMP1, THBS2, COL6A3, TFF2 and THY1 were significantly correlated with the survival time of GC patients. ROC curve analysis showed that aberrant expression of TIMP1 SPARC, THY1 and THBS2 had high diagnostic value for GC. High expressions of SPARC, TIMP1, THBS2 and COL6A3 were detected in GC tissues. In the GC cell lines, qRT- PCR revealed different expression patterns of these hub genes, but their expressions were largely consistent with those found in bioinformatics analyses. CONCLUSION: SPARC, TIMP1, THBS2 and other DEGs are probably involved in GC occurrence and progression and may serve as potential candidate molecular markers for early diagnosis and prognostic evaluation of GC.

Unveiling the link between lactate metabolism and rheumatoid arthritis through integration of bioinformatics and machine learning.

Rheumatoid arthritis (RA) is a persistent autoimmune condition characterized by synovitis and joint damage. Recent findings suggest a potential link to abnormal lactate metabolism. This study aims to identify lactate metabolism-related genes (LMRGs) in RA and investigate their correlation with the molecular mechanisms of RA immunity. Data on the gene expression profiles of RA synovial tissue samples were acquired from the gene expression omnibus (GEO) database. The RA database was acquired by obtaining the common LMRDEGs, and selecting the gene collection through an SVM model. Conducting the functional enrichment analysis, followed by immuno-infiltration analysis and protein-protein interaction networks. The results revealed that as possible markers associated with lactate metabolism in RA, KCNN4 and SLC25A4 may be involved in regulating macrophage function in the immune response to RA, whereas GATA2 is involved in the immune mechanism of DC cells. In conclusion, this study utilized bioinformatics analysis and machine learning to identify biomarkers associated with lactate metabolism in RA and examined their relationship with immune cell infiltration. These findings offer novel perspectives on potential diagnostic and therapeutic targets for RA.

Unveiling the role of HIST2H2AC in stroke through single-cell and transcriptome analysis.

Stroke is a leading cause of death and disability, and genetic risk factors play a significant role in its development. Unfortunately, effective therapies for stroke are currently limited. Early detection and diagnosis are critical for improving outcomes and developing new treatment strategies. In this study, we aimed to identify potential biomarkers and effective prevention and treatment strategies for stroke by conducting transcriptome and single-cell analyses. Our analysis included screening for biomarkers, functional enrichment analysis, immune infiltration, cell-cell communication, and single-cell metabolism. Through differential expression analysis, enrichment analysis, and protein-protein interaction (PPI) network construction, we identified HIST2H2AC as a potential biomarker for stroke. Our study also highlighted the diagnostic role of HIST2H2AC in stroke, its relationship with immune cells in the stroke environment, and our improved understanding of metabolic pathways after stroke. Overall, our research provided important insights into the pathogenesis of stroke, including potential biomarkers and treatment strategies that can be explored further to improve outcomes for stroke patients.

Analysis and prediction of interactions between transmembrane and non-transmembrane proteins.

BACKGROUND: Most of the important biological mechanisms and functions of transmembrane proteins (TMPs) are realized through their interactions with non-transmembrane proteins(nonTMPs). The interactions between TMPs and nonTMPs in cells play vital roles in intracellular signaling, energy metabolism, investigating membrane-crossing mechanisms, correlations between disease and drugs. RESULTS: Despite the importance of TMP-nonTMP interactions, the study of them remains in the wet experimental stage, lacking specific and comprehensive studies in the field of bioinformatics. To fill this gap, we performed a comprehensive statistical analysis of known TMP-nonTMP interactions and constructed a deep learning-based predictor to identify potential interactions. The statistical analysis describes known TMP-nonTMP interactions from various perspectives, such as distributions of species and protein families, enrichment of GO and KEGG pathways, as well as hub proteins and subnetwork modules in the PPI network. The predictor implemented by an end-to-end deep learning model can identify potential interactions from protein primary sequence information. The experimental results over the independent validation demonstrated considerable prediction performance with an MCC of 0.541. CONCLUSIONS: To our knowledge, we were the first to focus on TMP-nonTMP interactions. We comprehensively analyzed them using bioinformatics methods and predicted them via deep learning-based solely on their sequence. This research completes a key link in the protein network, benefits the understanding of protein functions, and helps in pathogenesis studies of diseases and associated drug development.

Mendelian randomization and colocalization analysis reveal novel drug targets for myasthenia gravis.

OBJECTIVE: Myasthenia gravis (MG) is a complex autoimmune disease affecting the neuromuscular junction with limited drug options, but the field of MG treatment recently benefits from novel biological agents. We performed a drug-targeted Mendelian randomization (MR) study to identify novel therapeutic targets of MG. METHODS: Cis-expression quantitative loci (cis-eQTL), which proxy expression levels for 2176 druggable genes, were used for MR analysis. Causal relationships between genes and disease, identified by eQTL MR analysis, were verified by comprehensive sensitivity, colocalization, and protein quantitative loci (pQTL) MR analyses. The protein-protein interaction (PPI) analysis was also performed to extend targets, followed by enzyme-linked immunosorbent assay (ELISA) to explore the serum level of drug targets in MG patients. A phenome-wide MR analysis was then performed to assess side effects with a clinical trial review assessing druggability. RESULTS: The eQTL MR analysis has identified eight potential targets for MG, one for early-onset MG and seven for late-onset MG. Further colocalization analyses indicated that CD226, CDC42BPB, PRSS36, and TNFSF12 possess evidence for colocalization with MG or late-onset MG. pQTL MR analyses identified the causal relations of TNFSF12 and CD226 with MG and late-onset MG. Furthermore, PPI analysis has revealed the protein interaction between TNFSF12-TNFSF13(APRIL) and TNFSF12-TNFSF13B(BLyS). Elevated TNFSF13 serum level of MG patients was also identified by ELISA experiments. This study has ultimately proposed three promising therapeutic targets (TNFSF12, TNFSF13, TNFSF13B) of MG. CONCLUSIONS: Three drug targets associated with the BLyS/APRIL pathway have been identified. Multiple biological agents, including telitacicept and belimumab, are promising for MG therapy.

Experimentally validated oxidative stress -associated prognostic signatures describe the immune landscape and predict the drug response and prognosis of SKCM.

Background: Skin Cutaneous Melanoma (SKCM) incidence is continually increasing, with chemotherapy and immunotherapy being among the most common cancer treatment modalities. This study aims to identify novel biomarkers for chemotherapy and immunotherapy response in SKCM and explore their association with oxidative stress. Methods: Utilizing TCGA-SKCM RNA-seq data, we employed Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) networks to identify six core genes. Gene co-expression analysis and immune-related analysis were conducted, and specific markers associated with oxidative stress were identified using Gene Set Variation Analysis (GSVA). Single-cell analysis revealed the expression patterns of Oxidative Stress-Associated Genes (OSAG) in the tumor microenvironment. TIDE analysis was employed to explore the association between immune therapy response and OSAG, while CIBERSORT was used to analyze the tumor immune microenvironment. The BEST database demonstrated the impact of the Oxidative Stress signaling pathway on chemotherapy drug resistance. Immunohistochemical staining and ROC curve evaluation were performed to assess the protein expression levels of core genes in SKCM and normal samples, with survival analysis utilized to determine their diagnostic value. Results: We identified six central genes associated with SKCM metastasis, among which the expression of DSC2 and DSC3 involved in the oxidative stress pathway was closely related to immune cell infiltration. DSC2 influenced drug resistance in SKMC patients. Furthermore, downregulation of DSC2 and DSC3 expression enhanced the response of SKCM patients to immunotherapy. Conclusion: This study identified two Oxidative Stress-Associated genes as novel biomarkers for SKCM. Additionally, targeting the oxidative stress pathway may serve as a new strategy in clinical practice to enhance SKCM chemotherapy and sensitivity.

Potential shared pathogenic mechanisms between endometriosis and inflammatory bowel disease indicate a strong initial effect of immune factors.

Introduction: Over the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD. Methods: Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects. Results: 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD. Discussion: Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

RNA-seq analysis-based study on the effects of gestational diabetes mellitus on macrosomia.

Background: Both the mother and the infant are negatively impacted by macrosomia. Macrosomia is three times as common in hyperglycemic mothers as in normal mothers. This study sought to determine why hyperglycemic mothers experienced higher macrosomia. Methods: Hematoxylin and Eosin staining was used to detect the placental structure of normal mother(NN), mothers who gave birth to macrosomia(NM), and mothers who gave birth to macrosomia and had hyperglycemia (DM). The gene expressions of different groups were detected by RNA-seq. The differentially expressed genes (DEGs) were screened with DESeq2 R software and verified by qRT-PCR. The STRING database was used to build protein-protein interaction networks of DEGs. The Cytoscape was used to screen the Hub genes of the different group. Results: The NN group's placental weight differed significantly from that of the other groups. The structure of NN group's placenta is different from that of the other group, too. 614 and 3207 DEGs of NM and DM, respectively, were examined in comparison to the NN group. Additionally, 394 DEGs of DM were examined in comparison to NM. qRT-PCR verified the results of RNA-seq. Nucleolar stress appears to be an important factor in macrosomia, according on the results of KEGG and GO analyses. The results revealed 74 overlapped DEGs that acted as links between hyperglycemia and macrosomia, and 10 of these, known as Hub genes, were key players in this process. Additionally, this analysis believes that due of their close connections, non-overlapping Hubs shouldn't be discounted. Conclusion: In diabetic mother, ten Hub genes (RPL36, RPS29, RPL8 and so on) are key factors in the increased macrosomia in hyperglycemia. Hyperglycemia and macrosomia are linked by 74 overlapping DEGs. Additionally, this approach contends that non-overlapping Hubs shouldn't be ignored because of their tight relationships.

Identification of hub genes and key pathways in spinal cord injury via bioinformatics analysis.

This study aimed to explore the hub genes and related key pathways in Spinal Cord Injury (SCI) based on the bioinformatics analysis. Two microarray datasets (GSE45006, GSE45550) were obtained from the GEO database and were merged and batch-corrected. The differentially expressed genes (DEGs) in SCI were explored with the Limma, and the weighted gene co-expression network analysis (WGCNA) was conducted to explore the module genes. Functional enrichment analysis and Gene set variation analysis (GSVA) were used to investigate the biological functions and key pathways of the key genes related to SCI. Then the protein-protein interaction (PPI) network was generated using the STING online tool, and the hub genes in SCI were identified. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic value of the selected hub genes. We identified 554 DEGs in SCI, and 1236 key genes in SCI were selected via WGCNA. Totally 111 key genes related to SCI were discovered. Furthermore, the functional enrichment analysis showed that these key mRNAs were primarily enriched in the extracellular matrix (ECM)-related pathways and processes associated with wound healing and cell growth. The PPI network further filtered six hub genes (Cd44, Timp1, Loxl1, Col6a1, Col3a1, Col5a1) ranked by the degree, and the diagnostic value of the six hub genes was confirmed by the ROC curves. Six hub genes including Cd44, Timp1, Loxl1, Col6a1, Col3a1, and Col5a1 were identified in SCI, with differential expression and excellent diagnostic value, which might provide insight into the targeted therapy of SCI.

An integrated analysis of the anticarcinogenic role of forkhead box protein 1 in oesophageal squamous cell carcinoma.

Forkhead box protein 1 (FOXP1) serves as a tumour promoter or suppressor depending on different cancers, but its effect in oesophageal squamous cell carcinoma has not been fully elucidated. This study investigated the role of FOXP1 in oesophageal squamous cell carcinoma through bioinformatics analysis and experimental verification. We determined through public databases that FOXP1 expresses low in oesophageal squamous cell carcinoma compared with normal tissues, while high expression of FOXP1 indicates a better prognosis. We identified potential target genes regulated by FOXP1, and explored the potential biological processes and signalling pathways involved in FOXP1 in oesophageal squamous cell carcinoma through GO and KEGG enrichment, gene co-expression analysis, and protein interaction network construction. We also analysed the correlation between FOXP1 and tumour immune infiltration levels. We further validated the inhibitory effect of FOXP1 on the proliferation of oesophageal squamous cell carcinoma cells through CCK-8, colony formation and subcutaneous tumour formation assays. This study revealed the anticarcinogenic effect of FOXP1 in oesophageal squamous cell carcinoma, which may serve as a novel biological target for the treatment of tumour.

Molecular subtype identification of cerebral ischemic stroke based on ferroptosis-related genes.

Cerebral ischemic stroke (CIS) has the characteristics of a high incidence, disability, and mortality rate. Here, we aimed to explore the potential pathogenic mechanisms of ferroptosis-related genes (FRGs) in CIS. Three microarray datasets from the Gene Expression Omnibus (GEO) database were utilized to analyze differentially expressed genes (DEGs) between CIS and normal controls. FRGs were obtained from a literature report and the FerrDb database. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were used to screen hub genes. The receiver operating characteristic (ROC) curve was adopted to evaluate the diagnostic value of key genes in CIS, followed by analysis of immune microenvironment, transcription factor (TF) regulatory network, drug prediction, and molecular docking. In total, 128 CIS samples were divided into 2 subgroups after clustering analysis. Compared with cluster A, 1560 DEGs were identified in cluster B. After the construction of the WGCNA and PPI network, 5 hub genes, including MAPK3, WAS, DNAJC5, PRKCD, and GRB2, were identified for CIS. Interestingly, MAPK3 was a FRG that differentially expressed between cluster A and cluster B. The expression levels of 5 hub genes were all specifically highly in cluster A subtype. It is noted that neutrophils were the most positively correlated with all 5 real hub genes. PRKCD was one of the target genes of FASUDIL. In conclusion, five real hub genes were identified as potential diagnostic markers, which can distinguish the two subtypes well.

Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease and enhanced likelihood of therapeutic drug targets success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. Here, we investigated gene essentiality scores and found that they tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein-protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in their cancer genes research.

Multiple transcriptome analyses reveal mouse testis developmental dynamics.

The testes are the organs of gamete production and testosterone synthesis. Up to date, no model system is available for mammalian testicular development, and only few studies have characterized the mouse testis transcriptome from no more than three postnatal ages. To describe the transcriptome landscape of the developing mouse testis and identify the potential molecular mechanisms underlying testis maturation, we examined multiple RNA-seq data of mouse testes from 3-week-old (puberty) to 11-week-old (adult). Sperm cells appeared as expected in 5-week-old mouse testis, suggesting the proper sample collection. The principal components analysis revealed the genes from 3w to 4w clustered away from other timepoints, indicating they may be the important nodes for testicular development. The pairwise comparisons at two adjacent timepoints identified 7,612 differentially expressed genes (DEGs), resulting in 58 unique mRNA expression patterns. Enrichment analysis identified functions in tissue morphogenesis (3-4w), regulation of peptidase activity (4-5w), spermatogenesis (7-8w), and antigen processing (10-11w), suggesting distinct functions in different developmental periods. 50 hub genes and 10 gene cluster modules were identified in the testis maturation process by protein-protein interaction (PPI) network analysis, and the miRNA-lncRNA-mRNA, miRNA-circRNA-mRNA and miRNA-circRNA-lncRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed. The results suggest that testis maturation is a complex developmental process modulated by various molecules, and that some potential RNA-RNA interactions may be involved in specific developmental stages. In summary, this study provides an update on the molecular basis of testis development, which may help to understand the molecular mechanisms of mouse testis development and provide guidance for mouse reproduction.

Knockdown of TOP2A suppresses IL-17 signaling pathway and alleviates the progression of ulcerative colitis.

BACKGROUND: Ulcerative colitis (UC) is a chronic inflammatory disease of the colonic mucosa, with a gradually increasing incidence. Therefore, it is necessary to actively seek targets for the treatment of UC. METHODS: Common differentially expressed genes (DEGs) were screened from two microarray data sets related to UC. Protein-protein interaction network was constructed to find the hub genes. The UC mouse model and cell model were induced by dextran sulfate sodium (DSS). The pathological changes of colon tissue were observed by hematoxylin-eosin staining. Immunohistochemistry and immunofluorescence were performed to detect the expressions of Ki67 and Claudin-1. The performance of mice was observed by disease activity index (DAI). The effect of TOP2A on proliferation, inflammation, oxidative stress, and interleukin-17 (IL-17) signaling pathway in UC model was measured by cell counting kit-8, enzyme-linked immunosorbent assay, and western blot. RESULTS: Through bioinformatics analysis, 295 common DEGs were screened, and the hub gene TOP2A was selected. In UC model, there was obvious inflammatory cell infiltration in the colon and less goblet cells, while si-TOP2A lessened it. More Ki67 positive cells and less Claudin-1 positive cells were observed in UC model mice. Furthermore, knockdown of TOP2A increased the body weight and colon length of UC mice, while the DAI was decreased. Through in vivo and in vitro experiments, knockdown of TOP2A also inhibited inflammation and IL-17 signaling pathway, and promoted proliferation in DSS-induced NCM460 cells. CONCLUSION: Knockdown of TOP2A alleviated the progression of UC by suppressing inflammation and inhibited IL-17 signaling pathway.

EPI-SF: essential protein identification in protein interaction networks using sequence features.

Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Identification of Novel Prognostic Biomarkers for Colorectal Cancer by Bioinformatics Analysis.

BACKGROUND/AIMS: Colorectal cancer (CRC) ranks third among malignancies in terms of global incidence and has a poor prognosis. The identification of effective diagnostic and prognostic biomarkers is critical for CRC treatment. This study intends to explore novel genes associated with CRC progression via bioinformatics analysis. MATERIALS AND METHODS: Dataset GSE184093 was selected from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between CRC and noncancerous specimens. Functional enrichment analyses were implemented for probing the biological functions of DEGs. Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter databases were employed for gene expression detection and survival analysis, respectively. Western blotting and real-time quantitative polymerase chain reaction were employed for detecting molecular protein and messenger RNA levels, respectively. Flow cytometry, Transwell, and CCK-8 assays were utilized for examining the effects of GBA2 and ST3GAL5 on CRC cell behaviors. RESULTS: There were 6464 DEGs identified, comprising 3005 downregulated DEGs (dDEGs) and 3459 upregulated DEGs (uDEGs). Six dDEGs were significantly associated with the prognoses of CRC patients, including PLCE1, PTGS1, AMT, ST8SIA1, ST3GAL5, and GBA2. Upregulating ST3GAL5 or GBA2 repressed the malignant behaviors of CRC cells. CONCLUSION: We identified 6 genes related to CRC progression, which could improve the disease prognosis and treatment.