The Adaptations of E. coli SM10\u03bbpir (pUCP24T) Under Constant Sub\u2010MIC Gentamicin Treatment

Aberrant expression of cell cycle and material metabolism related genes contributes to hepatocellular carcinoma occurrence

Spinal cord injury (SCI) significantly impacts patients, with mitochondrial dysfunction playing a critical role in its pathology. Identifying mitochondria-related genes may offer new therapeutic and prognostic insights. RNA sequencing data from the GEO database were analyzed to identify differentially expressed genes (DEGs). Functional enrichment analyses were conducted, and weighted gene coexpression network analysis (WGCNA) alongside machine learning algorithms was used to identify key mitochondria-related genes. Immune infiltration was assessed using the EPIC algorithm, and single-cell RNA sequencing (scRNA-seq) data were analyzed for cellular diversity. A total of 2566 upregulated and 2634 downregulated genes were identified in SCI versus control samples. GO and KEGG enrichment analyses revealed these DEGs were primarily involved in oxidative stress, mitochondrial function, and immune pathways, including necroptosis and T cell receptor signaling. Then, 1578 genes with the strongest correlation to SCI were selected by WGCNA. By integrating DEGs, WGCNA module genes, and mitochondria-related genes, 76 candidate genes were obtained and used to construct a PPI network. Six hub genes (NDUFB3, SLC25A24, SLC25A40, GSTZ1, MAOA, and MRPL12) were identified by machine learning, all showing strong diagnostic potential (AUC > 0.77). Immune infiltration analysis indicated reduced B and T cell infiltration and increased macrophage activity in SCI samples. scRNA-seq analysis further revealed higher expression of NDUFB3 in dendritic cells and MAOA in pro-B cells, suggesting their involvement in immune regulation and mitochondrial dysfunction. These six genes represent potential biomarkers and therapeutic targets for SCI, providing insights into its molecular mechanisms and immune response.

This study aims to investigate the effect of maternal nicotine exposure on the gene expression profiles in the liver of offspring mice. Pregnant mice were subcutaneously injected with either saline vehicle or nicotine twice a day on gestational days 11-21. Total RNA from the liver samples which collected from the offspring mice of postnatal day 7 and 21 was subjected to RNA sequencing. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis were conducted to identify the functions of differentially expressed genes (DEGs). Four genes were selected for further validation by quantitative reverse transcription polymerase chain reaction (qRT-PCR). A total of 448 DEGs and 186 DEGs were identified on postnatal day 7 and 21, respectively. GO analysis revealed that the DEGs on postnatal day 7 mainly participated in the biological functions of cell growth and proliferation, and the DEGs on postnatal day 21 mainly participated in ion transport/activity. KEGG enrichment analysis showed that the DEGs on postnatal day 7 were mainly enriched in the cell cycle, cytokine-cytokine receptor interactions, hypertrophic cardiomyopathy, and the p53 signaling pathway, while the DEGs on postnatal day 21 were mainly enriched in neuroactive ligand-receptor interactions, the calcium signaling pathway, retinol metabolism, and axon guidance. The qRT-PCR results were consistent with the RNA sequencing data. The DEGs may affect the growth of liver in early postnatal period while may affect ion transport/activity in late postnatal period.

IntroductionHead and neck squamous cell carcinoma (HNSCC) is the most common cancer of the head and neck region, including the oral cavity, larynx, pharynx, nasal cavity, and paranasal sinuses. Cancer arises because of cumulative genetic and epigenetic alterations in cancer-associated genes. It is important to understand the genetic/epigenetic background of the tumors to establish molecular targeted therapies. So far, the knowledge of key genes or molecules, which are closely associated with the carcinogenesis and development of HNSCC, is insufficient for targeted therapies. On the other hand, recent advances in next-generation sequencing (NGS) have greatly contributed to cancer genome research. In this research, using RNA sequence data of HNSCC stored in The Cancer Genome Atlas (TCGA) database, we identified differentially expressed genes (DEGs), functionally enriched gene sets, and new prognostic markers or candidate therapeutic targets. This exploratory study investigated whether novel prognostic markers and candidate therapeutic targets for HNSCC could be identified from TCGA RNA-seq data.MethodsThe RNA sequence data were downloaded from TCGA, including 504 cases from cancer and 44 cases from corresponding normal tissue. The DEGs between cancer and normal samples were detected using the DESeq2 package in R software. Differences with | log2 fold change (FC) | > 1.0 and p-value <0.05 were considered as DEGs. Functional enrichment analyses were performed by ShinyGO 0.85 with Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. A gene set enrichment analysis (GSEA) was also performed using GSEA software. We also analyzed the top 10 up- and down-regulated genes, which were sorted by adjusted p-value, by using Kaplan-Meier analysis to assess their potential as prognostic markers.ResultsUsing the DESeq2 package, 10,976 DEGs were detected, including 6,932 up-regulated genes and 4,044 down-regulated genes in cancer. As expected, functional enrichment analyses revealed enrichment of KEGG terms associated with cancers, including “Pathway in Cancer”, “Human Papillomavirus infection”, and “PI3K-Akt signaling pathway” in up-regulated genes, whereas KEGG terms enriched in down-regulated genes were mainly “Metabolic pathways”. GO terms for “Cell differentiation (GOBP)” and “Extracellular region (GOCC)” were enriched both in up- and down-regulated genes, suggesting aberrant expression of genes associated with cell differentiation and remodeling of the extracellular matrix. GSEA data supported the enrichment analyses data. Kaplan-Meier analyses revealed that high expression of homeobox C6 (HOXC6) (p=0.048), nucleobindin 2 (NUCB2) (p=0.007), IL12A antisense RNA 1 (IL12A-AS1) (p=0.001), calcium-binding protein 39-like (CAB39L)(p=0.038), nitric oxide synthase trafficking (NOSTRIN) (p=0.024), SLC8A1 antisense RNA 1 (SLC8A1-AS1) (p=0.016), were the significantly correlated with poorer prognosis.ConclusionsBased on bioinformatical approaches, we identified significantly enriched gene sets and novel candidates for prognostic markers or therapeutic targets in HNSCC. Further investigation would aid in determining the anti-cancer effects of these candidates.

Purpose: This study aimed to evaluate how well the ligature-induced periodontitis mouse model reflects the transcriptomic features of human periodontitis and to identify periodontitis-associated genes and functions con-served across species.Materials and Methods: RNA sequencing data from human and mouse gingival tissues were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using DESeq2. Func-tional enrichment analysis was performed using Gene Ontology and KEGG pathway databases. Cross-species transcriptomic similarity was evaluated by comparing DEG overlap and enrichment similarity between human and mouse.Results: A total of 223 human DEGs and 622 mouse DEGs were identified. Among these, 25 DEGs were shared between species, including 23 showing concordant regulation direction. The mouse-to-human overlap ratio was 3.70%. Functional enrichment analysis identified 189 significant terms or pathways in humans and 602 in mice, with 129 shared results. Specifically, 41.18% of mouse KEGG pathway results overlapped with human results. The shared concordant genes, including IL-1β, PTGS2 (also known as COX-2), and MMP13, were associated with im-mune and inflammatory functions.Conclusion: The ligature-induced periodontitis mouse model reflects the transcriptomic features of human peri-odontitis in a limited manner, showing low similarity at the DEG level and moderate similarity at the enrichment level. Conserved DEGs such as IL-1β, PTGS2, and MMP13 may represent fundamental molecular mechanisms of periodontitis.

Decision letter: Major transcriptional changes observed in the Fulani, an ethnic group less susceptible to malaria

The aim of the present study was to investigate the differentially expressed genes (DEGs) and target genes of the estrogen receptor (ER) in renal cell carcinoma. The data (GSE12090) were downloaded from the gene expression omnibus database. Data underwent preprocessing using the affy package for Bioconductor software, then the DEGs were selected via the significance analysis of microarray algorithm within the siggenes package. Subsequently, the DEGs underwent functional and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery software. Following data analysis, transcriptional regulatory networks between the DEGs and transcription factors were constructed. Finally, the ER target genes were subjected to gene ontology enrichment analysis. A total of 215 DEGs were identified between the chromophobe renal cell carcinoma samples and the oncocytoma samples, including 126 upregulated and 89 downregulated genes. Functional enrichment analysis indicated that 25% of the DEGs were significantly enriched in functions associated with the plasma membrane. Among those DEGs, 105 were regulated by the ER. Further regulatory network analysis indicated that the ER was mainly involved in the regulation of oncogenes and tumor suppressor genes, including protease serine 8, claudin 7 and Ras-related protein Rab-25. In the present study, the identified ER target genes were demonstrated to be closely associated with tumor development; this knowledge may improve the understanding of the ER regulatory mechanisms during tumor development and promote the discovery of predictive markers for renal cell carcinoma.

Objective To investigate the molecular function of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta (PIK3CB) underlying Alzheimer's disease (AD). Methods RNA sequencing data were used to filtrate differentially expressed genes (DEGs) in AD/nondementia control and PIK3CB-low/high groups. An unbiased coexpression network was established to evaluate module-trait relationships by using weight gene correlation network analysis (WGCNA). Global regulatory network was constructed to predict the protein-protein interaction. Further cross-talking pathways of PIK3CB were identified by functional enrichment analysis. Results The mean expression of PIK3CB in AD patients was significantly lower than those in nondementia controls. We identified 2,385 DEGs from 16,790 background genes in AD/control and PIK3CB-low/high groups. Five coexpression modules were established using WGCNA, which participated in apoptosis, axon guidance, long-term potentiation (LTP), regulation of actin cytoskeleton, synaptic vesicle cycle, FoxO, mitogen-activated protein kinase (MAPK), and vascular endothelial growth factor (VEGF) signaling pathways. DEGs with strong relation to AD and low PIK3CB expression were extracted to construct a global regulatory network, in which cross-talking pathways of PIK3CB were identified, such as apoptosis, axon guidance, and FoxO signaling pathway. The occurrence of AD could be accurately predicted by low PIK3CB based on the area under the curve of 71.7%. Conclusions These findings highlight downregulated PIK3CB as a potential causative factor of AD, possibly mediated via apoptosis, axon guidance, and FoxO signaling pathway.

Identification of differentially expressed genes in lung adenocarcinoma cells using single-cell RNA sequencing not detected using traditional RNA sequencing and microarray

We aimed to identify key genes associated with prostate cancer using RNA-sequencing (RNA-seq) data. RNA-seq data, including 1 cancer sample and 1 adjacent normal sample, were downloaded from the NCBI SRA database and the differentially expressed genes (DEGs) were identified with the software Cufflinks. Functional enrichment analysis was performed to uncover the biological functions of DEGs. Regulatory information was retrieved from the IPA database and a network was established. A total of 147 DEGs were obtained, including 96 downregulated and 51 upregulated DEGs. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that metabolism and signal transduction were the 2 major functions that were significantly influenced. Moreover, an interaction network was built. In conclusion, a number of DEGs was identified and their roles in the pathogenesis of cancer were supported by previous studies. More studies are necessary to further validate their usefulness in the diagnosis and treatment of prostate cancer.

Hepatocellular carcinoma (HCC) is a highly lethal cancer and a leading cause of cancer-related deaths globally. RNA 5-methylcytosine (m5C) modification plays a vital role in epigenetic regulation, yet its impact on prognosis and the tumor immune microenvironment (TIME) in HCC remains unclear. RNA sequencing and clinical data were obtained from the Cancer Genome Atlas (TCGA) database. We applied an unsupervised clustering algorithm for the cluster analysis of m5C RNA methylation regulators, and then performed survival analyses to determine the best prognosis for HCC samples. Univariate and multivariate Cox regression analyses were conducted to construct a prognostic model. HCC patients were classified into high- and low-risk groups based on risk scores. Model performance was evaluated using ROC curves and validated with the ICGC cohort. Immune infiltration, clinicopathological features, and functional enrichment analyses were also performed. We analyzed the differential expression patterns of the m5C-related regulators between HCC and normal tissue samples. Based on consensus clustering of these regulators, three distinct molecular subgroups were identified, each associated with differences in patient survival and immune cell infiltration. Furthermore, we developed a prognostic signature comprising NSUN3, NSUN5, and YBX1, and stratified HCC patients into low- and high-risk groups. Patients in the low-risk group exhibited significantly better overall survival (OS) than those in the high-risk group. The robustness of this risk model was validated using the ICGC database. When integrated with clinicopathological characteristics, the risk score emerged as an independent prognostic factor. Additionally, we performed functional annotation and enrichment analyses based on differentially expressed genes (DEGs) between the two risk subgroups to explore potential underlying biological mechanisms. Our study revealed the potential roles of these m5C-related regulators in TIME and identified their prognosis value and therapeutic potential for HCC patients.

The present study aimed to reveal the potential genes associated with the pathogenesis of intervertebral disc degeneration (IDD) by analyzing microarray data using bioinformatics. Gene expression profiles of two regions of the intervertebral disc were compared between patients with IDD and controls. GSE70362 containing two groups of gene expression profiles, 16 nucleus pulposus (NP) samples from patients with IDD and 8 from controls, and 16 annulus fibrosus (AF) samples from patients with IDD and 8 from controls, was downloaded from the Gene Expression Omnibus database. A total of 93 and 114 differentially expressed genes (DEGs) were identified in NP and AF samples, respectively, using a limma software package for the R programming environment. Gene Ontology (GO) function enrichment analysis was performed to identify the associated biological functions of DEGs in IDD, which indicated that the DEGs may be involved in various processes, including cell adhesion, biological adhesion and extracellular matrix organization. Pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that the identified DEGs were potentially involved in focal adhesion and the p53 signaling pathway. Further analysis revealed that there were 35 common DEGs observed between the two regions (NP and AF), which may be further regulated by 6 clusters of microRNAs (miRNAs) retrieved with WebGestalt. The genes in the DEG-miRNA regulatory network were annotated using GO function and KEGG pathway enrichment analysis, among which extracellular matrix organization was the most significant disrupted biological process and focal adhesion was the most significant dysregulated pathway. In addition, the result of protein-protein interaction network modules demonstrated the involvement of inflammatory cytokine interferon signaling in IDD. These findings may not only advance the understanding of the pathogenesis of IDD, but also identify novel potential biomarkers for this disease.

Glioma-related epilepsy (GRE) is a common symptom in patients with diffuse gliomas. However, the underlying mechanisms of GRE remain unclear. The current study aimed to investigate the underlying epileptogenic mechanisms of GRE through RNA sequencing analysis. Demographic, RNA sequencing, and follow-up data of 643 patients were reviewed. Patients were divided into test and validation groups (223 and 420 patients, respectively) by different time periods for RNA sequencing. The differentially expressed genes (DEGs) associated with preoperative GRE were identified using R software. Functional enrichment analysis was subsequently performed, and tissue-infiltrating immune cells were also estimated. Weighted correlation network analysis (WGCNA) was conducted to further identify key modules exhibiting the highest correlation with preoperative GRE. Overlapping genes between the DEG set and key gene set identified by WGCNA were selected and verified in the validation cohort. The protein-protein interaction (PPI) network analysis was then constructed to identify hub genes for preoperative GRE. A total of 219 DEGs were identified, among which 112 were upregulated and 107 downregulated in patients with GRE. Functional enrichment analysis revealed that upregulated DEGs were related to ion channel activity, while downregulated genes were related to immunity. Forty-two genes were further selected from overlapping DEGs and the key gene set. Among these genes, 31 genes showed significant differences in the validation cohort. Finally, the PPI network analysis identified six genes, including SCN3B, KCNIP2, KCNJ11, VEGFA, MMP9, and ANXA2, as hub genes for GRE. The current study revealed that ion channel activity and immunity dysfunction in diffuse glioma patients contributed to the occurrence of GRE, and SCN3B might be a shared therapeutic target for both diffuse gliomas and GRE. These findings could improve the understanding of the mechanisms of GRE and promote individualized medications for glioma management.

Improved insight into the molecular characteristics of hepatocellular carcinoma (HCC) is required to predict prognosis and to develop a new rationale for targeted therapeutic strategy. Bioinformatics methods, including functional enrichment and network analysis combined with survival analysis, are required to process a large volume of data to obtain further information on differentially expressed genes (DEGs). The RNA sequencing data related to HCC in The Cancer Genome Atlas (TCGA) database were analyzed to screen DEGs, which were separately submitted to perform gene enrichment analysis to identify gene sets and signaling pathways, and to construct a protein-protein interaction (PPI) network. Subsequently, hub genes were selected by the core level in the network, and the top hub genes were focused on gene expression analysis and survival analysis. A total of 610 DEGs were identified, including 444 upregulated and 166 downregulated genes. The upregulated DEGs were significantly enriched in the Gene Ontology analysis (GO): Cell division and in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway: Cell cycle, whereas the downregulated DEGs were enriched in GO: Negative regulation of growth and in the KEGG pathway: Retinol metabolism, with significant differences. Cyclin-dependent kinase (CDK)1 was selected as the top hub gene by the PPI network, which exhibited a similar expression trend with the data from the Gene Expression Omnibus (GEO) database. Survival analysis revealed a significantly negative correlation between CDK1 expression level and overall survival in the TCGA group (P<0.01) and the GEO group (P<0.01). Therefore, high-throughput TCGA data analysis appears to be an effective method for screening tumor molecular markers, and high expression of CDK1 is a prognostic factor for HCC.

Chemotactic cytokines play a crucial role in the development of acute myeloid leukemia (AML). Thus, investigating the mechanisms of chemotactic cytokine-related genes (CCRGs) in AML is of paramount importance. Using the TCGA-AML, GSE114868, and GSE12417 datasets, differential expression analysis identified differentially expressed CCRGs (DE-CCRGs). These genes were screened by overlapping differentially expressed genes (DEGs) between AML and control groups with CCRGs. Subsequently, functional enrichment analysis and the construction of a protein-protein interaction (PPI) network were conducted to explore the functions of the DE-CCRGs. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses identified relevant prognostic genes and developed a prognostic model. Survival analysis of the prognostic gene was performed, followed by functional similarity analysis, immune analysis, enrichment analysis, and drug prediction analysis. Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study. Functional enrichment analysis indicated that DE-CCRGs were primarily involved in chemotactic cytokine-related functions and pathways. Six prognostic genes (CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2) were identified and incorporated into the risk model. The model's performance was validated using the GSE12417 dataset. Survival analysis showed significant differences in AML overall survival (OS) between prognostic gene high and low expression groups, indicating that prognostic gene might be significantly associated with patient survival. Additionally, nine different immune cells were identified between the two risk groups. Correlation analysis revealed that CCR2 had the most significant positive correlation with monocytes and the most significant negative correlation with resting mast cells. The tumor immune dysfunction and exclusion score was lower in the high-risk group. CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2 were identified as prognostic genes correlated to AML and the tumor immune microenvironment. These findings offerred novel insights into the prevention and treatment of AML.