Differential expression analysis identifies a prognostically significant extracellular matrix-enriched gene signature in hyaluronan-positive clear cell renal cell carcinoma.

Hyaluronan (HA) accumulation in clear cell renal cell carcinoma (ccRCC) is associated with poor prognosis; however, its biology and role in tumorigenesis are unknown. RNA sequencing of 48 HA-positive and 48 HA-negative formalin-fixed paraffin-embedded (FFPE) samples was performed to identify differentially expressed genes (DEG). The DEGs were subjected to pathway and gene enrichment analyses. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data and DEGs were used for the cluster analysis. In total, 129 DEGs were identified. HA-positive tumors exhibited enhanced expression of genes related to extracellular matrix (ECM) organization and ECM receptor interaction pathways. Gene set enrichment analysis showed that epithelial-mesenchymal transition-associated genes were highly enriched in the HA-positive phenotype. A protein-protein interaction network was constructed, and 17 hub genes were discovered. Heatmap analysis of TCGA-KIRC data identified two prognostic clusters corresponding to HA-positive and HA-negative phenotypes. These clusters were used to verify the expression levels and conduct survival analysis of the hub genes, 11 of which were linked to poor prognosis. These findings enhance our understanding of hyaluronan in ccRCC.

Identification and validation of a prognostic anoikis-related gene signature in papillary thyroid carcinoma by integrated analysis of single-cell and bulk RNA-sequencing.

Papillary thyroid carcinoma (PTC) prognosis may be deteriorated due to the metastases, and anoikis palys an essential role in the tumor metastasis. However, the potential effect of anoikis-related genes on the prognosis of PTC was unclear. The mRNA and clinical information were obtained from the cancer genome atlas database. Hub genes were identified and risk model was constructed using Cox regression analysis. Kaplan-Meier (K-M) curve was applied for the survival analysis. Immune infiltration and immune therapy response were calculated using CIBERSORT and TIDE. The identification of cell types and cell interaction was performed by Seurat, SingleR and CellChat packages. GO, KEGG, and GSVA were applied for the enrichment analysis. Protein-protein interaction network was constructed in STRING and Cytoscape. Drug sensitivity was assessed in GSCA. Based on bulk RNA data, we identified 4 anoikis-related risk signatures, which were oncogenes, and constructed a risk model. The enrichment analysis found high risk group was enriched in some immune-related pathways. High risk group had higher infiltration of Tregs, higher TIDE score and lower levels of monocytes and CD8 T cells. Based on scRNA data, we found that 4 hub genes were mainly expressed in monocytes and macrophages, and they interacted with T cells. Hub genes were significantly related to immune escape-related genes. Drug sensitivity analysis suggested that cyclin dependent kinase inhibitor 2A may be a better chemotherapy target. We constructed a risk model which could effectively and steadily predict the prognosis of PTC. We inferred that the immune escape may be involved in the development of PTC.

Identification of potential biomarkers of leprosy: A study based on GEO datasets.

Leprosy has a high rate of cripplehood and lacks available early effective diagnosis methods for prevention and treatment, thus novel effective molecule markers are urgently required. In this study, we conducted bioinformatics analysis with leprosy and normal samples acquired from the GEO database(GSE84893, GSE74481, GSE17763, GSE16844 and GSE443). Through WGCNA analysis, 85 hub genes were screened(GS > 0.7 and MM > 0.8). Through DEG analysis, 82 up-regulated and 3 down-regulated genes were screened(|Log2FC| > 3 and FDR < 0.05). Then 49 intersection genes were considered as crucial and subjected to GO annotation, KEGG pathway and PPI analysis to determine the biological significance in the pathogenesis of leprosy. Finally, we identified a gene-pathway network, suggesting ITK, CD48, IL2RG, CCR5, FGR, JAK3, STAT1, LCK, PTPRC, CXCR4 can be used as biomarkers and these genes are active in 6 immune system pathways, including Chemokine signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation, T cell receptor signaling pathway, Natural killer cell mediated cytotoxicity and Leukocyte transendothelial migration. We identified 10 crucial gene markers and related important pathways that acted as essential components in the etiology of leprosy. Our study provides potential targets for diagnostic biomarkers and therapy of leprosy.

Identification of hub glutamine metabolism-associated genes and immune characteristics in pre-eclampsia.

OBJECTIVE: Preeclampsia (PE) is a severe complication of unclear pathogenesis associated with pregnancy. This research aimed to elucidate the properties of immune cell infiltration and potential biomarkers of PE based on bioinformatics analysis. MATERIALS AND METHODS: Two PE datasets were imported from the Gene ExpressioOmnibus (GEO) and screened to identify differentially expressed genes (DEGs). Significant module genes were identified by weighted gene co-expression network analysis (WGCNA). DEGs that interacted with key module genes (GLu-DEGs) were analyzed further by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. The diagnostic value of the genes was assessed using receiver operating characteristic (ROC) curves and protein-protein interaction (PPI) networks were constructed using GeneMANIA, and GSVA analysis was performed using the MSigDB database. Immune cell infiltration was analyzed using the TISIDB database, and StarBase and Cytoscape were used to construct an RBP-mRNA network. The identified hub genes were validated in two independent datasets. For further confirmation, placental tissue from healthy pregnant women and women with PE were collected and analyzed using both RT-qPCR and immunohistochemistry. RESULTS: A total of seven GLu-DEGs were obtained and were found to be involved in pathways associated with the transport of sulfur compounds, PPAR signaling, and energy metabolism, shown by GO and KEGG analyses. GSVA indicated significant increases in adipocytokine signaling. Furthermore, single-sample Gene Set Enrichment Analysis (ssGSEA) indicated that the levels of activated B cells and T follicular helper cells were significantly increased in the PE group and were negatively correlated with GLu-DEGs, suggesting their potential importance. CONCLUSION: In summary, the results showed a correlation between glutamine metabolism and immune cells, providing new insights into the understandingPE pathogenesis and furnishing evidence for future advances in the treatment of this disease.

Genetic-based patient stratification in Alzheimer's disease.

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.

MYC and NCAPG2 as molecular targets of colorectal cancer and gastric cancer in nursing.

Colorectal cancer is a common malignant tumor in intestinal tract, the early symptoms are not obvious. Gastric cancer is a malignant tumor originating from the gastric mucosal epithelium. However, the role of MYC and non-SMC condensin II complex subunit G2 (NCAPG2) in colorectal cancer and gastric cancer remains unclear. The colorectal cancer datasets GSE49355 and gastric cancer datasets GSE19826 were downloaded from gene expression omnibus database. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis (WGCNA) was performed. Functional enrichment analysis, gene set enrichment analysis (GSEA) and immune infiltration analysis was performed. Construction and analysis of protein-protein interactions (PPI) network. Survival analysis and comparative toxicogenomics database (CTD) were performed. A heat map of gene expression was drawn. A total of 751 DEGs were obtained. According to the gene ontology (GO) analysis, in Biological process (BP) analysis, they are mainly enriched in cell differentiation, cartilage development, and skeletal development. In cellular component (CC) analysis, they are mainly enriched in the cytoskeleton of muscle cells and actin filaments. In molecular function (MF) analysis, they are mainly concentrated in Rho GTPase binding, DNA binding, and fibronectin binding. In Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, they are mainly enriched in the MAPK signaling pathway, apoptosis, and cancer pathways. The soft threshold power for WGCNA analysis was set to 9, resulting in the generation of 40 modules. Ultimately, 2 core genes (MYC and NCAPG2) were identified. The heatmap of core gene expression showed high expression of MYC and NCAPG2 in colorectal cancer tissue samples and low expression in normal tissue samples, while they were core molecules in gastric cancer. Survival analysis indicated that MYC and NCAPG2 were risk factors, showing an upregulation trend with increasing risk scores. CTD analysis revealed associations of MYC and NCAPG2 with colorectal cancer, gastric cancer, inflammation, and immune system diseases. MYC and NCAPG2 are highly expressed in colorectal cancer. The higher the expression of MYC and NCAPG2, the worse the prognosis. MYC and NCAPG2 are core molecules in gastric cancer.

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy.

BACKGROUND: Sepsis-induced myopathy (SIM) a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study was performed to identify potential key oxidative stress-related genes (OS-genes) as biomarkers for the diagnosis of SIM using bioinformatics. METHODS: The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. The intersection of the genes selected from the GO database and the genes from the GSE13205 was considered as OS-genes of SIM, where the differential genes were regarded as OS-DEGs. OS-DEGs were analyzed using GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in OS-DEGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed. RESULTS: A total of 1089 DEGs were screened from the GSE13205, and 453 OS-genes were identified from the GO database. The overlapping DEGs and OS-genes constituted 25 OS-DEGs, including 15 significantly upregulated and 10 significantly downregulated genes. The top 10 hub genes, including CD36, GPX3, NQO1, GSR, TP53, IDH1, BCL2, HMOX1, JAK2, and FOXO1, were screened. Furthermore, 5 diagnostic genes were identified: CD36, GPX3, NQO1, GSR, and TP53. The ROC analysis showed that the respective area under the curves (AUCs) of CD36, GPX3, NQO1, GSR, and TP53 were 0.990, 0.981, 0.971, 0.971, and 0.971, which meant these genes had very high diagnostic values of SIM. Finally, based on these 5 diagnostic genes, we found that miR-124-3p and miR-16-5p may be potential targets for the treatment of SIM. CONCLUSIONS: The results of this study suggest that OS-genes might play an important role in SIM. CD36, GPX3, NQO1, GSR, and TP53 have potential as specific biomarkers for the diagnosis of SIM.

Identification and verification of a novel signature that combines cuproptosis-related genes with ferroptosis-related genes in osteoarthritis using bioinformatics analysis and experimental validation.

BACKGROUND: Exploring the pathogenesis of osteoarthritis (OA) is important for its prevention, diagnosis, and treatment. Therefore, we aimed to construct novel signature genes (c-FRGs) combining cuproptosis-related genes (CRGs) with ferroptosis-related genes (FRGs) to explore the pathogenesis of OA and aid in its treatment. MATERIALS AND METHODS: Differentially expressed c-FRGs (c-FDEGs) were obtained using R software. Enrichment analysis was performed and a protein-protein interaction (PPI) network was constructed based on these c-FDEGs. Then, seven hub genes were screened. Three machine learning methods and verification experiments were used to identify four signature biomarkers from c-FDEGs, after which gene set enrichment analysis, gene set variation analysis, single-sample gene set enrichment analysis, immune function analysis, drug prediction, and ceRNA network analysis were performed based on these signature biomarkers. Subsequently, a disease model of OA was constructed using these biomarkers and validated on the GSE82107 dataset. Finally, we analyzed the distribution of the expression of these c-FDEGs in various cell populations. RESULTS: A total of 63 FRGs were found to be closely associated with 11 CRGs, and 40 c-FDEGs were identified. Bioenrichment analysis showed that they were mainly associated with inflammation, external cellular stimulation, and autophagy. CDKN1A, FZD7, GABARAPL2, and SLC39A14 were identified as OA signature biomarkers, and their corresponding miRNAs and lncRNAs were predicted. Finally, scRNA-seq data analysis showed that the differentially expressed c-FRGs had significantly different expression distributions across the cell populations. CONCLUSION: Four genes, namely CDKN1A, FZD7, GABARAPL2, and SLC39A14, are excellent biomarkers and prospective therapeutic targets for OA.

Unraveling transcriptomic signatures and dysregulated pathways in systemic lupus erythematosus across disease states.

OBJECTIVES: This study aims to elucidate the transcriptomic signatures and dysregulated pathways in patients with Systemic Lupus Erythematosus (SLE), with a particular focus on those persisting during disease remission. METHODS: We conducted bulk RNA-sequencing of peripheral blood mononuclear cells (PBMCs) from a well-defined cohort comprising 26 remission patients meeting the Low Lupus Disease Activity State (LLDAS) criteria, 76 patients experiencing disease flares, and 15 healthy controls. To elucidate immune signature changes associated with varying disease states, we performed extensive analyses, including the identification of differentially expressed genes and pathways, as well as the construction of protein-protein interaction networks. RESULTS: Several transcriptomic features recovered during remission compared to the active disease state, including down-regulation of plasma and cell cycle signatures, as well as up-regulation of lymphocytes. However, specific innate immune response signatures, such as the interferon (IFN) signature, and gene modules involved in chromatin structure modification, persisted across different disease states. Drug repurposing analysis revealed certain drug classes that can target these persistent signatures, potentially preventing disease relapse. CONCLUSION: Our comprehensive transcriptomic study revealed gene expression signatures for SLE in both active and remission states. The discovery of gene expression modules persisting in the remission stage may shed light on the underlying mechanisms of vulnerability to relapse in these patients, providing valuable insights for their treatment.

Identifying allergic-rhinitis-associated genes with random-walk-based method in PPI network.

Allergic rhinitis is a common allergic disease with a complex pathogenesis and many unresolved issues. Studies have shown that the incidence of allergic rhinitis is closely related to genetic factors, and research on the related genes could help further understand its pathogenesis and develop new treatment methods. In this study, 446 allergic rhinitis-related genes were obtained on the basis of the DisGeNET database. The protein-protein interaction network was searched using the random-walk-with-restart algorithm with these 446 genes as seed nodes to assess the linkages between other genes and allergic rhinitis. Then, this result was further examined by three screening tests, including permutation, interaction, and enrichment tests, which aimed to pick up genes that have strong and special associations with allergic rhinitis. 52 novel genes were finally obtained. The functional enrichment test confirmed their relationships to the biological processes and pathways related to allergic rhinitis. Furthermore, some genes were extensively analyzed to uncover their special or latent associations to allergic rhinitis, including IRAK2 and MAPK, which are involved in the pathogenesis of allergic rhinitis and the inhibition of allergic inflammation via the p38-MAPK pathway, respectively. The new found genes may help the following investigations for understanding the underlying molecular mechanisms of allergic rhinitis and developing effective treatments.

Integration of multi-omics data for survival prediction of lung adenocarcinoma.

BACKGROUND AND OBJECTIVE: The morbidity of lung adenocarcinoma (LUAD) has been increasing year by year and the prognosis is poor. This has prompted researchers to study the survival of LUAD patients to ensure that patients can be cured in time or survive after appropriate treatment. There is still no fully valid model that can be applied to clinical practice. METHODS: We introduced struc2vec-based multi-omics data integration (SBMOI), which could integrate gene expression, somatic mutations and clinical data to construct mutation gene vectors representing LUAD patient features. Based on the patient features, the random survival forest (RSF) model was used to predict the long- and short-term survival of LUAD patients. To further demonstrate the superiority of SBMOI, we simultaneously replaced scale-free gene co-expression network (FCN) with a protein-protein interaction (PPI) network and a significant co-expression network (SCN) to compare accuracy in predicting LUAD patient survival under the same conditions. RESULTS: Our results suggested that compared with SCN and PPI network, the FCN based SBMOI combined with RSF model had better performance in long- and short-term survival prediction tasks for LUAD patients. The AUC of 1-year, 5-year, and 10-year survival in the validation dataset were 0.791, 0.825, and 0.917, respectively. CONCLUSIONS: This study provided a powerful network-based method to multi-omics data integration. SBMOI combined with RSF successfully predicted long- and short-term survival of LUAD patients, especially with high accuracy on long-term survival. Besides, SBMOI algorithm has the potential to combine with other machine learning models to complete clustering or stratificational tasks, and being applied to other diseases.

ITGAM is a critical gene in ischemic stroke.

BACKGROUND: Globally, ischemic stroke (IS) is ranked as the second most prevailing cause of mortality and is considered lethal to human health. This study aimed to identify genes and pathways involved in the onset and progression of IS. METHODS: GSE16561 and GSE22255 were downloaded from the Gene Expression Omnibus (GEO) database, merged, and subjected to batch effect removal using the ComBat method. The limma package was employed to identify the differentially expressed genes (DEGs), followed by enrichment analysis and protein-protein interaction (PPI) network construction. Afterward, the cytoHubba plugin was utilized to screen the hub genes. Finally, a ROC curve was generated to investigate the diagnostic value of hub genes. Validation analysis through a series of experiments including qPCR, Western blotting, TUNEL, and flow cytometry was performed. RESULTS: The analysis incorporated 59 IS samples and 44 control samples, revealing 226 DEGs, of which 152 were up-regulated and 74 were down-regulated. These DEGs were revealed to be linked with the inflammatory and immune responses through enrichment analyses. Overall, the ROC analysis revealed the remarkable diagnostic potential of ITGAM and MMP9 for IS. Quantitative assessment of these genes showed significant overexpression in IS patients. ITGAM modulation influenced the secretion of critical inflammatory cytokines, such as IL-1ß, IL-6, and TNF-α, and had a distinct impact on neuronal apoptosis. CONCLUSIONS: The inflammation and immune response were identified as potential pathological mechanisms of IS by bioinformatics and experiments. In addition, ITGAM may be considered a potential therapeutic target for IS.

Integrative analysis based on the cell cycle-related genes identifies TPX2 as a novel prognostic biomarker associated with tumor immunity in breast cancer.

BACKGROUND: This study aims to identify the essential cell cycle-related genes associated with prognosis in breast cancer (BRCA), and to verify the relationship between the central gene and immune infiltration, so as to provide detailed and comprehensive information for the treatment of BRCA. MATERIALS AND METHODS: Gene expression profiles (GSE10780, GSE21422, GSE61304) and the Cancer Genome Atlas (TCGA) BRCA data were used to identify differentially expressed genes (DEGs) and further functional enrichment analysis. STRING and Cytoscape were employed for the protein-protein interaction (PPI) network construction. TPX2 was viewed as the crucial prognostic gene by the Survival and Cox analysis. Furthermore, the connection between TPX2 expression and immune infiltrating cells and immune checkpoints in BRCA was also performed by the TIMER online database and R software. RESULTS: A total of 18 cell cycle-related DEGs were identified in this study. Subsequently, an intersection analysis based on TCGA-BRCA prognostic genes and the above DEGs identified three genes (TPX2, UBE2C, CCNE2) as crucial prognostic candidate biomarkers. Moreover, we also demonstrated that TPX2 is closely associated with immune infiltration in BRCA and a positive relation between TPX2 and PD-L1 expression was firstly detected. CONCLUSIONS: These results revealed that TPX2 is a potential prognostic biomarker and closely correlated with immune infiltration in BRCA, which could provide powerful and efficient strategies for breast cancer immunotherapy.

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.

Identification of mRNA expression profiles and their characterization in age-related hearing loss.

Age-related hearing loss (ARHL), is a pervasive health problem worldwide. ARHL seriously affects the quality of life and reportedly leads to social isolation and dementia in the elderly. ARHL is caused by the degeneration or disorders of cochlear hair cells and auditory neurons. Numerous studies have verified that genetic factors contributed to this impairment, however, the mechanism behind remains unclear. In this study, we analyzed an mRNA expression dataset (GSE49543) from the GEO database. Differentially expressed genes (DEGs) between young control mice and presbycusis mice were analyzed using limma in R and weighted gene co-expression network analysis (WGCNA) methods. Functional enrichment analyses of the DEGs were conducted with the clusterProfiler R package and the results were visualized using ggplot2 R package. The STRING database was used for the construction of the protein-protein interaction (PPI) network of the screened DEGs. Two machine learning algorithms LASSO and SVM-RFE were used to screen the hub genes. We identified 54 DEGs in presbycusis using limma and WGCNA. DEGs were associated with the synaptic vesicle cycle, distal axon, neurotransmitter transmembrane transporter activity in GO analysis, and alcoholic liver disease, pertussis, lysosome pathway according to KEGG analyses. PPI network analysis identified three significant modules. Five hub genes (CLEC4D, MS4A7, CTSS, LAPTM5, ALOX5AP) were screened by LASSO and SVM-RFE. These hub genes were highly expressed in presbycusis mice compared with young control mice. We screened DEGs and identified hub genes involved in ARHL development, which might provide novel clues to understanding the molecular basis of ARHL.

Genome-Wide Identification and Expression Analysis of Kiwifruit Leucine-Rich Repeat Receptor-Like Proteins Reveal Their Roles in Biotic and Abiotic Stress Responses.

Leucine-rich repeat receptor-like proteins (LRR-RLPs), a major group of receptor-like proteins in plants, have diverse functions in plant physiology, including growth, development, signal transduction, and stress responses. Despite their importance, the specific roles of kiwifruit LRR-RLPs in response to biotic and abiotic stresses remain poorly understood. In this study, we performed family identification, characterization, transcriptome data analysis, and differential gene expression analysis of kiwifruit LRR-RLPs. We identified totals of 101, 164, and 105 LRR-RLPs in Actinidia chinensis 'Hongyang', Actinidia eriantha 'Huate', and Actinidia chinensis 'Red5', respectively. Synteny analysis revealed that the expansion of kiwifruit LRR-RLPs was primarily attributed to segmental duplication events. Based on RNA-seq data from pathogen-infected kiwifruits, we identified specific LRR-RLP genes potentially involved in different stages of pathogen infection. Additionally, we observed the potential involvement of kiwifruit LRR-RLPs in abiotic stress responses, with upstream transcription factors possibly regulating their expression. Furthermore, protein interaction network analysis unveiled the participation of kiwifruit LRR-RLP in the regulatory network of abiotic stress responses. These findings highlight the crucial roles of LRR-RLPs in mediating both biotic and abiotic stress responses in kiwifruit, offering valuable insights for the breeding of stress-resistant kiwifruit varieties.

Identification of core genes of craniopharyngioma angiogenesis based on single-cell nuclear transcriptome sequencing.

This study aimed to explore the core genes of craniopharyngioma angiogenesis for targeted vascular therapy based on single-cell nuclear transcriptome sequencing. For single-cell nuclear transcriptome sequencing, we collected six samples from the tumor center and adjacent hypothalamic tumor tissues from three patients with craniopharyngioma, as well as four normal brain tissues based on Gene Expression Omnibus. We screened genes with differential up-regulation between vascular endothelial cells of craniopharyngioma and those of normal brain tissues, performed GO and KEGG analysis, constructed the protein-protein interaction network, and selected key genes verified using immunofluorescence. After data cleaning and quality control, 623 craniopharyngioma endothelial cells and 439 healthy brain endothelial cells were obtained. Compared with normal brain endothelial cells, craniopharyngioma endothelial cells were screened for 394 differentially up-expressed genes (DEGs). GO and KEGG results showed that DEGs probably modulated endothelial cells, adherens junction, focal adhesion, migration, actin cytoskeleton, and invasion via the PI3K-AKT, Rap1, Ras, Wnt, and Hippo pathways. The core genes screened were CTNNB1, PTK2, ITGB1, STAT3, FYN, HIF1A, VCL, SMAD3, PECAM1, FOS, and CDH5. This study obtained possible anti-angiogenic genes in craniopharyngioma. Our results shed novel insights into molecular mechanisms and craniopharyngioma treatment.

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.

Identification of genes related to ribosomal proteins in colorectal cancer: exploring their potential as biomarkers, prognostic indicators, and therapeutic targets.

BACKGROUND: Colorectal cancer (CRC) ranks as the third most commonly diagnosed cancer in both females and males, underscoring the need for the identification of effective biomarkers. METHODS AND RESULTS: We assessed the expression levels of ribosomal proteins (RPs) at both mRNA and protein levels. Subsequently, leveraging the STRING database, we constructed a protein-protein interaction network and identified hub genes. The co-expression network of differentially expressed genes associated with CRC and their target hub RPs was constructed using the weighted gene co-expression network analysis algorithm. Gene ontology and molecular signatures database were conducted to gain insights into the biological roles of genes associated with the identified module. To confirm the results, the expression level of the candidate genes in the CRC samples compared to the adjacent healthy was evaluated by the RT-qPCR method. Our findings indicated that the genes related to RPs were predominantly enriched in biological processes associated with Myc Targets, Oxidative Phosphorylation, and cell proliferation. Also, results demonstrated that elevated levels of GRWD1, MCM5, IMP4, and RABEPK that related to RPs were associated with poor prognostic outcomes for CRC patients. Notably, IMP4 and RABEPK exhibited higher diagnostic value. Moreover, the expression of IMP4 and RABEPK showed a significant association with drug resistance using cancer cell line encyclopedia and genomics of drug sensitivity in cancer databases. Also, the results showed that the expression level of IMP4 and RABEPK in cancerous samples was significantly higher compared to the adjacent healthy ones. CONCLUSION: The general results of this study have shown that many genes related to RPs are increased in cancer and could be associated with the death rate of patients. We also highlighted the therapeutic and prognostic potentials of RPs genes in CRC.

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.