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

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

Identification of differentially expressed genes, pathways, and immune infiltration in diabetes.

This study aimed to perform exhaustive bioinformatic analysis by using GSE29221 micro-array maps obtained from healthy controls and Type 2 Diabetes (T2DM) patients. Raw data are downloaded from the Gene Expression Omnibus database and processed by the limma package in R software to identify Differentially Expressed Genes (DEGs). Gene ontology functional analysis and Kyoto Gene Encyclopedia and Genome Pathway analysis are performed to determine the biological functions and pathways of DEGs. A protein interaction network is constructed using the STRING database and Cytoscape software to identify key genes. Finally, immune infiltration analysis is performed using the Cibersort method. This study has implications for understanding the underlying molecular mechanism of T2DM and provides potential targets for further research.

Identification of prostate cancer bone metastasis related genes and potential therapy targets by bioinformatics and in vitro experiments.

The aetiology of bone metastasis in prostate cancer (PCa) remains unclear. This study aims to identify hub genes involved in this process. We utilized machine learning, GO, KEGG, GSEA, Single-cell analysis, ROC methods to identify hub genes for bone metastasis in PCa using the TCGA and GEO databases. Potential drugs targeting these genes were identified. We validated these results using 16 specimens from patients with PCa and analysed the relationship between the hub genes and clinical features. The impact of APOC1 on PCa was assessed through in vitro experiments. Seven hub genes with AUC values of 0.727-0.926 were identified. APOC1, CFH, NUSAP1 and LGALS1 were highly expressed in bone metastasis tissues, while NR4A2, ADRB2 and ZNF331 exhibited an opposite trend. Immunohistochemistry further confirmed these results. The oxidative phosphorylation pathway was significantly enriched by the identified genes. Aflatoxin B1, benzo(a)pyrene, cyclosporine were identified as potential drugs. APOC1 expression was correlated with clinical features of PCa metastasis. Silencing APOC1 significantly inhibited PCa cell proliferation, clonality, and migration in vitro. This study identified 7 hub genes that potentially facilitate bone metastasis in PCa through mitochondrial metabolic reprogramming. APOC1 emerged as a promising therapeutic target and prognostic marker for PCa with bone metastasis.

Understanding the Molecular Landscape of Endometriosis: A Bioinformatics Approach to Uncover Signaling Pathways and Hub Genes.

Background: Endometriosis is a chronic gynecological disorder characterized by the ectopic growth of endometrial tissue outside the uterus, leading to debilitating pain and infertility in affected women. Despite its prevalence and clinical significance, the molecular mechanisms underlying the progression of endometriosis remain poorly understood. This study employs bioinformatics tools and molecular docking simulations to unravel the intricate genetic and molecular networks associated with endometriosis progression. Objectives: The primary objectives of this research are to identify differentially expressed genes (DEGs) linked to endometriosis, elucidate associated biological pathways using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), construct a Protein-Protein Interaction (PPI) network to identify hub genes, and perform molecular docking simulations to explore potential ligand-protein interactions associated with endometriosis. Methods: Microarray data from Homo sapiens, specifically Accession: GDS3092 Series = GSE5108 (Platform: GPL2895), were retrieved from the NCBI Gene Expression Omnibus (GEO). The data underwent rigorous preprocessing and DEG analysis using NCBI GEO2. Database for Annotation, Visualization, and Integrated Discovery analysis was employed for functional annotation, and a PPI network was constructed using the STITCH database and Cytoscape 3.8.2. Molecular docking simulations against target proteins associated with endometriosis were conducted using MVD 7.0. Results: A total of 1 911 unique elements were identified as DEGs associated with endometriosis from the microarray data. Database for Annotation, Visualization, and Integrated Discovery analysis revealed pathways and biological characteristics positively and negatively correlated with endometriosis. Hub genes, including BCL2, CCNA2, CDK7, EGF, GAS6, MAP3K7, and TAB2, were identified through PPI network analysis. Molecular docking simulations highlighted potential ligands, such as Quercetin-3-o-galactopyranoside and Kushenol E, exhibiting favorable interactions with target proteins associated with endometriosis. Conclusions: This study provides insights into the molecular signatures, pathways, and hub genes associated with endometriosis. Utilizing DAVID in this study clarifies biological pathways associated with endometriosis, revealing insights into intricate genetic networks. Molecular docking simulations identified ligands for further exploration in therapeutic interventions. The consistent efficacy of these ligands across diverse targets suggests broad-spectrum effectiveness, encouraging further exploration for potential therapeutic interventions. The study contributes to a deeper understanding of endometriosis pathogenesis, paving the way for targeted therapies and precision medicine approaches to improve patient outcomes. These findings advance our understanding of the molecular mechanisms in endometriosis (EMS), offering promising avenues for future research and therapeutic development in addressing this complex condition.

Molecular insights into epoxyazadiradione induced death in triple-negative breast cancer cells: A system biology approach.

Epoxyazadiradione is an important limonoid with immense pharmacological potential. We have reported previously that epoxyazadiradione (EAD) induces apoptosis in triple negative breast cancer cells (MDA-MB 231) by modulating diverse cellular targets. Here, we identify the key genes/pathways responsible for this effect through next-generation sequencing of the transcriptome from EAD treated cells and integrated molecular data analysis using bioinformatics. In silico analysis indicated that EAD displayed favourable drug-like properties and could target multiple macromolecules relevant to TNBC. RNA sequencing revealed that EAD treatment results in the differential expression of 1838 genes in MDA-MB 231 cells, with 752 downregulated and 1086 upregulated. Gene set enrichment analysis of these genes suggested that EAD disrupts protein folding in the endoplasmic reticulum, triggering the unfolded protein response (UPR) and potentially leading to cell death. EAD also induced oxidative stress and DNA damage, downregulated pathways linked to metabolism, cell cycle progression, pro-survival signalling, cell adhesion, motility and inflammatory response. The identification of protein cluster and hub genes were also done. The validation of the identified hub genes gave an inverse correlation between their expression in EAD treated cells and TNBC patient samples. Thus, the identified hub genes could be explored as therapeutic or diagnostic markers for TNBC. Hence, EAD appears to be a promising therapeutic candidate for TNBC by targeting various hallmarks of cancer, including cell death resistance, uncontrolled proliferation and metastasis. To conclude, the identified pathways and validated targets for EAD will provide a roadmap for further in vivo studies and preclinical/clinical validation required for potential drug development.

Analysis of miR-497/195 cluster identifies new therapeutic targets in cervical cancer.

OBJECTIVE: miR-497/195, located at 17p13.1, is a highly conserved miRNA cluster whose abnormal expression is a key regulator of carcinogenesis. We performed a comprehensive analysis of the miR-497/195 cluster to determine its prognostic utility and role in cervical cancer (CC) using publicly available datasets. RESULTS: In silico analysis and validation revealed that this cluster is downregulated in CC. A total of 60 target genes of miR-497/195 cluster were identified as differentially expressed between normal and CC samples. ShinyGO, STRING, CytoHubba, Timer 2.0, HPA, and HCMBD were used for functional enrichment, PPIN network construction, hub gene identification, immune infiltration correlation, histopathological expression, and determination of the metastatic potential of miR-497/195 cluster and their target genes. PPIN analysis identified CCNE1, CCNE2, ANLN, RACGAP1, KIF23, CHEK1, CDC25A, E2F7, CDK1, and CEP55 as the top 10 hub genes (HGs). Furthermore, the upregulation of RECK, ATD5, and BCL2, downregulation of OSBPL3, RCAN3, and HIST1H3H effected overall survival of CC patients. We identified 6 targets (TFAP2A, CLSPN, RASEF, HIST1H3H, AKT3, and ITPR1) of miR-497/195 cluster to influence metastasis. In addition, 8 druggable genes and 38 potential drugs were also identified. Our study identified miR-497/195 cluster target genes and pathways that could be used for prognostic and therapeutic applications in CC.

Deciphering the Molecular Complexity of Hepatocellular Carcinoma: Unveiling Novel Biomarkers and Therapeutic Targets Through Advanced Bioinformatics Analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) represents a primary liver tumor characterized by a bleak prognosis and elevated mortality rates, yet its precise molecular mechanisms have not been fully elucidated. This study uses advanced bioinformatics techniques to discern differentially expressed genes (DEGs) implicated in the pathogenesis of HCC. The primary objective is to discover novel biomarkers and potential therapeutic targets that can contribute to the advancement of HCC research. METHODS: The bioinformatics analysis in this study primarily utilized the Gene Expression Omnibus (GEO) database as data source. Initially, the Transcriptome analysis console (TAC) screened for DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network of the proteins associated to the identified DEGs with the STRING database. We obtained our hub genes using Cytoscape and confirmed the results through the GEPIA database. Furthermore, we assessed the prognostic significance of the identified hub genes using the GEPIA database. To explore the regulatory interactions, a miRNA-gene interaction network was also constructed, incorporating information from the miRDB database. For predicting the impact of gene overexpression on drug effects, we utilized CANCER DP. RESULTS: A comprehensive analysis of HCC gene expression profiles revealed a total of 4716 DEGs, consisting of 2430 upregulated genes and 2313 downregulated genes in HCC sample compared to healthy control group. These DEGs exhibited significant enrichment in key pathways such as the PI3K-Akt signaling pathway, nuclear receptors meta-pathway, and various metabolism-related pathways. Further exploration of the PPI network unveiled the P53 signaling pathway and pyrimidine metabolism as the most prominent pathways. We identified 10 hub genes (ASPM, RRM2, CCNB1, KIF14, MKI67, SHCBP1, CENPF, ANLN, HMMR, and EZH2) that exhibited significant upregulation in HCC samples compared to healthy control group. Survival analysis indicated that elevated expression levels of these genes were strongly associated with changes in overall survival in HCC patients. Lastly, we identified specific miRNAs that were found to influence the expression of these genes, providing valuable insights into potential regulatory mechanisms underlying HCC progression. CONCLUSION: The findings of this study have successfully identified pivotal genes and pathways implicated in the pathogenesis of HCC. These novel discoveries have the potential to significantly enhance our understanding of HCC at the molecular level, opening new ways for the development of targeted therapies and improved prognosis evaluation.

Uncovering chikungunya virus-encoded miRNAs and host-specific targeted genes associated with antiviral immune responses: an integrated bioinformatics approach.

Chikungunya virus (CHIKV) is a single-stranded RNA virus belonging to the genus Alphavirus and is responsible for causing Chikungunya fever, a type of arboviral fever. Despite extensive research, the pathogenic mechanism of CHIKV within host cells remains unclear. In this study, an in-silico approach was used to predict that CHIKV produces micro-RNAs that target host-specific genes associated with host cellular regulatory pathways. Putative micro-RNAs of CHIKV were predicted using the miRNAFold and Vmir RNA structure web servers, and secondary structure prediction was performed using RNAfold. Host-specific target genes were then predicted, and hub genes were identified using CytoHubba and module selection through MCODE. Functional annotations of hub genes revealed their association with various pathways, including osteoclast differentiation, neuroactive ligand-receptor interaction, and mRNA surveillance. We used the freely available dataset GSE49985 to determine the level of expression of host-specific target genes and found that two genes, F-box and leucine-rich repeat protein 16 (FBXL16) and retinoic acid receptor alpha (RARA), were down-regulated, while four genes, RNA binding protein with serine-rich domain 1 (RNPS1), RNA helicase and ATPase (UPF1), neuropeptide S receptor 1 (NPSR1), and vasoactive intestinal peptide receptor 1 (VIPR1), were up-regulated. These findings provide insight into novel miRNAs and hub genes associated with CHIKV infection and suggest potential targets for therapeutic intervention. Further experimental validation of these targets could lead to the development of effective treatments for CHIKV-mediated diseases.

Doxorubicin downregulates cell cycle regulatory hub genes in breast cancer cells.

Breast cancer (BC) is the leading commonly diagnosed cancer in the world, with complex mechanisms underlying its development. There is an urgent need to enlighten key genes as potential therapeutic targets crucial to advancing BC treatment. This study sought to investigate the influence of doxorubicin (DOX) on identified key genes consistent across numerous BC datasets obtained through bioinformatic analysis. To date, a meta-analysis of publicly available coding datasets for expression profiling by array from the Gene Expression Omnibus (GEO) has been carried out. Differentially Expressed Genes (DEGs) identified using GEO2R revealed a total of 23 common DEGs, including nine upregulated genes and 14 downregulated genes among the datasets of three platforms (GPL570, GPL6244, and GPL17586), and the commonly upregulated DEGs, showed significant enrichment in the cell cycle in KEGG analysis. The top nine genes, NUSAP1, CENPF, TPX2, PRC1, ANLN, BUB1B, AURKA, CCNB2, and CDK-1, with higher degree values and MCODE scores in the cytoscape program, were regarded as hub genes. The hub genes were activated in disease states commonly across all the subclasses of BC and correlated with the unfavorable overall survival of BC patients, as verified by the GEPIA and UALCAN databases. qRT-PCR confirmed that DOX treatment resulted in reduced expression of these genes in BC cell lines, which reinforces the evidence that DOX remains an effective drug for BC and suggests that developing modified formulations of doxorubicin to reduce toxicity and resistance, could enhance its efficacy as an effective therapeutic option for BC.

Identification of hub genes and key modules in laryngeal squamous cell carcinoma.

Background: Laryngeal squamous cell carcinoma (LSCC) is the prominent cancer in head and neck, which greatly affects life quality of patients. The pathogenesis of LSCC is not clear. Presently, the LSCC treatments include chemotherapy, surgery and radiotherapy; however, these methods have poor efficacy in patients with recurrent and persistent cancer. Therefore, the study identified the hub genes accompanied with LSCC, which may be a potential therapeutic target in the future. Methods: We extracted whole transcriptome high-throughput sequencing (HTS) LSCC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and calculate differentially expressed genes (DEGs) between LSCC and normal samples using statistical software RStudio. Through weighted gene co-expression network analysis (WGCNA), enrichment examination of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) functions, and examination of protein-protein interaction (PPI) network, we obtained network hub genes and validated the hub genes prognostic value and expression levels of protein. Results: Through analysis of differential gene expression, from the GEO and TCGA databases 2,139 and 2,774 DEGs were obtained, respectively, 13 and 15 modules were screened from TCGA-LSCC and GSE127165 datasets by WGCNA, respectively. The most significant positive and negative correlation modules in the WGCNA and DEG lists were overlapped, and overall 36 co-expressed overlapping genes were retrieved. Through enrichment analysis of GO and KEGG, it was found that the gene functions were highly concentrated in cell junction assembly, basement membrane, extracellular matrix (ECM) structural constituent etc., and the pathways were mainly concentrated in ECM receptor interaction, focal adhesion, small cell lung cancer, and toxoplasmosis. Through analysis of PPI network analysis, 10 network hub genes (SNAI2, ITGA6, LAMB3, LAMC2, CAV1, COL7A1, GJA1, EHF, OAT, and GPT) were obtained. Finally, survival analysis and protein expression validation of these genes confirmed that low OAT expression and high CAV1 expression remarkably influenced the survival of patient's prognosis with LSCC. Conclusions: We recognized the hub genes and key modules nearly associated to LSCC and these genes were validated by survival analysis and the database of Human Protein Atlas (HPA), which is of high importance for unveiling the pathogenesis of LSCC and probing for new precise biological marker and potential therapeutic targets.

Integrated Bioinformatics-Based Identification and Validation of Neuroinflammation-Related Hub Genes in Primary Open-Angle Glaucoma.

Glaucoma is a leading cause of permanent blindness, affecting 80 million people worldwide. Recent studies have emphasized the importance of neuroinflammation in the early stages of glaucoma, involving immune and glial cells. To investigate this further, we used the GSE27276 dataset from the GEO (Gene Expression Omnibus) database and neuroinflammation genes from the GeneCards database to identify differentially expressed neuroinflammation-related genes associated with primary open-angle glaucoma (POAG). Subsequently, these genes were submitted to Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analyses. Hub genes were picked out through protein-protein interaction networks and further validated using the external datasets (GSE13534 and GSE9944) and real-time PCR analysis. The gene-miRNA regulatory network, receiver operating characteristic (ROC) curve, genome-wide association study (GWAS), and regional expression analysis were performed to further validate the involvement of hub genes in glaucoma. A total of 179 differentially expressed genes were identified, comprising 60 upregulated and 119 downregulated genes. Among them, 18 differentially expressed neuroinflammation-related genes were found to overlap between the differentially expressed genes and neuroinflammation-related genes, with six genes (SERPINA3, LCN2, MMP3, S100A9, IL1RN, and HP) identified as potential hub genes. These genes were related to the IL-17 signaling pathway and tyrosine metabolism. The gene-miRNA regulatory network showed that these hub genes were regulated by 118 miRNAs. Notably, GWAS data analysis successfully identified significant single nucleotide polymorphisms (SNPs) corresponding to these six hub genes. ROC curve analysis indicated that our genes showed significant accuracy in POAG. The expression of these genes was further confirmed in microglia, Müller cells, astrocytes, and retinal ganglion cells in the Spectacle database. Moreover, three hub genes, SERPINA3, IL1R1, and LCN2, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the OGD/R-induced glaucoma model. This study suggests that the identified hub genes may influence the development of POAG by regulation of neuroinflammation, and it may offer novel insights into the management of POAG.

Insights into the ameliorative effect of ZnONPs on arsenic toxicity in soybean mediated by hormonal regulation, transporter modulation, and stress responsive genes.

Arsenic (As) contamination of agricultural soils poses a serious threat to crop productivity and food safety. Zinc oxide nanoparticles (ZnONPs) have emerged as a potential amendment for mitigating the adverse effects of As stress in plants. Soybean crop is mostly grown on marginalized land and is known for high accumulation of As in roots than others tissue. Therefore, this study aimed to elucidate the underlying mechanisms of ZnONPs in ameliorating arsenic toxicity in soybean. Our results demonstrated that ZnOB significantly improved the growth performance of soybean plants exposed to arsenic. This improvement was accompanied by a decrease (55%) in As accumulation and an increase in photosynthetic efficiency. ZnOB also modulated hormonal balance, with a significant increase in auxin (149%), abscisic acid (118%), gibberellin (160%) and jasmonic acid content (92%) under As(V) stress assuring that ZnONPs may enhance root growth and development by regulating hormonal signaling. We then conducted a transcriptomic analysis to understand further the molecular mechanisms underlying the NPs-induced As(V) tolerance. This analysis identified genes differentially expressed in response to ZnONPs supplementation, including those involved in auxin, abscisic acid, gibberellin, and jasmonic acid biosynthesis and signaling pathways. Weighted gene co-expression network analysis identified 37 potential hub genes encoding stress responders, transporters, and signal transducers across six modules potentially facilitated the efflux of arsenic from cells, reducing its toxicity. Our study provides valuable insights into the molecular mechanisms associated with metalloid tolerance in soybean and offers new avenues for improving As tolerance in contaminated soils.

SP110 Could be Used as a Potential Predictive and Therapeutic Biomarker for Oral Cancer.

The morbidity of oral squamous cell carcinoma (OSCC) has been rising year after year, making it a major global health issue. But the molecular pathogenesis of OSCC is currently unclear. To study the potential pathogenesis of OSCC, the differentially expressed genes (DEGs) were screened, and multiple databases were used to perform the tumor stage, expression, prognosis, protein-protein interaction (PPI) networks, modules, and the functional enrichment analysis. Moreover, we have identified SP110 as the key candidate gene and conducted various analyses on it using multiple databases. The research indicated that there were 211 common DEGs, and they were enriched in various GO terms and pathways. Meanwhile, one DEG is significantly related to short disease-free survival, four are associated with overall survival, and 12 DEGs have close ties with tumor staging. Additionally, the SP110 is significantly associated with methylation level, HPV status, tumor staging, gender, race, tumor grade, age, and overall/disease-free survival of oral cancer patients, as well as the immune process. The copy number variation of SP110 significantly affected the abundance of immune infiltration. Therefore, we speculate that SP110 could be used as the diagnostic and therapeutic biomarker for OSCC, and can help to further understand oral carcinogenesis.

Identification of key biomarkers of endothelial dysfunction in hypertension with carotid atherosclerosis based on WGCNA and the LASSO algorithm.

Background: Endothelial dysfunction is the early stage of carotid atherosclerosis (CAS) in patients with hypertension. It is worth identifying the potential hub genes of endothelial dysfunction to elucidate pathological mechanism in the progression of the disease. Method: We obtained gene expression profiles of GSE43292 from the Gene Expression Omnibus (GEO) database. Hub genes associated with CAS were identified through weighted gene correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore potential biological mechanisms and signaling pathways. Finally, in vitro experiments on human umbilical vein endothelial cells (HUVECs) were conducted to validate these hub genes. Results: The microarray dataset GSE43292 included 32 CAS plaques samples and corresponding macroscopically intact tissues from patients with hypertension. A total of 161 differentially expressed genes were discovered. Through WGCNA analysis, the gray60 module emerged as the most significant module associated with clinical features. The GO and KEGG enrichment analyses of genes in the gray60 module highlighted the substantial involvement of immune response-related signaling pathways. Two key hub genes (CCR1 and NCKAP1L) were pinpointed via LASSO regression. We found a significant increase in the mRNA expression level of the hub genes in oxidized low density lipoprotein (ox-LDL) treated HUVECs. Conclusions: Our study indicated that the hub genes related to immune responses are involved in the development of CAS. Two hub genes (CCR1 and NCKAP1L) of endothelial dysfunction were identified. These genes may provide a valuable therapeutic target of CAS in patients with hypertension.

Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment.

Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immunotherapy of intracranial aneurysms (IAs). The objective of this study was to identify monocyte/macrophage (Mo/MΦ)-associated gene signatures to elucidate their correlation with the pathogenesis and immune microenvironment of IAs, thereby offering potential avenues for targeted therapy against IAs. Single-cell RNA-sequencing (scRNA-seq) data of IAs were acquired from the Gene Expression Synthesis (GEO) database. The significant infiltration of monocyte subsets in the parietal tissue of IAs was identified using single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis (hdWGCNA). The integration of six machine learning algorithms identified four crucial genes linked to these Mo/MΦ. Subsequently, we developed a multilayer perceptron (MLP) neural model for the diagnosis of IAs (independent external test AUC=1.0, sensitivity =100%, specificity =100%). Furthermore, we employed the CIBERSORT method and MCP counter to establish the correlation between monocyte characteristics and immune cell infiltration as well as patient heterogeneity. Our findings offer valuable insights into the molecular characterization of monocyte infiltration in IAs, which plays a pivotal role in shaping the immune microenvironment of IAs. Recognizing this characterization is crucial for comprehending the limitations associated with targeted therapies for IAs. Ultimately, the results were verified by real-time fluorescence quantitative PCR and Immunohistochemistry.

Identification of most representative hub-genes for diagnosis, prognosis, and therapies of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths globally. To reduce HCC-related mortality, early diagnosis and therapeutic improvement are essential. Hub differentially expressed genes (HubGs) may serve as potential diagnostic and prognostic biomarkers, also offering therapeutic targets for precise therapies. Therefore, we aimed to identify top-ranked hub genes for the diagnosis, prognosis, and therapy of HCC. METHODS: Through a systematic literature review, 202 HCC-related HubGs were derived from 59 studies, yet consistent detection across these was lacking. Then, we identified top-ranked HubGs (tHubGs) by integrated bioinformatics analysis, highlighting their functions, pathways, and regulators that might be more representative of the diagnosis, prognosis, and therapies of HCC. RESULTS: In this study, eight HubGs (CDK1, AURKA, CDC20, CCNB2, TOP2A, PLK1, BUB1B, and BIRC5) were identified as the tHubGs through the protein-protein interaction (PPI) network and survival analysis. Their differential expression in different stages of HCC, validated using The Cancer Genome Atlas (TCGA) Program database, suggests their potential as early HCC markers. The enrichment analyses revealed some important roles in HCC-related biological processes (BPs), molecular functions (MFs), cellular components (CCs), and signaling pathways. Moreover, the gene regulatory network analysis highlighted key transcription factors (TFs) and microRNAs (miRNAs) that regulate these tHubGs at transcriptional and post-transcriptional. Finally, we selected three drugs (CD437, avrainvillamide, and LRRK2-IN-1) as candidate drugs for HCC treatment as they showed strong binding with all of our proposed and published protein receptors. CONCLUSIONS: The findings of this study may provide valuable resources for early diagnosis, prognosis, and therapies for HCC.

Discovery and Validation of Ferroptosis-Associated Genes of Ulcerative Colitis.

Background: Ulcerative colitis (UC) is a long-lasting idiopathic condition, but its precise mechanisms remain unclear. Meanwhile, evidence has demonstrated that ferroptosis seems to interlock with the progress of UC. This research sought to identify hub genes of UC related to ferroptosis. Methods: First, the relevant profiles for this article were obtained from GEO database. From the FerrDb, 479 genes linked to ferroptosis were retrieved. Using analysis of the difference and WGCNA on colonic samples from GSE73661, the remaining six hub genes linked to ferroptosis and UC were discovered. Through logistic regression analyses, the diagnostic model was constructed and was then evaluated by external validation using dataset GSE92415. Afterwards, the correlation between immune cell filtration in UC and hub genes was examined. Finally, a mice model of colitis was established, and the results were verified using qRT-PCR. Results: We acquired six hub genes linked to ferroptosis and UC. In order to create a diagnostic model for UC, we used logistic regression analysis to screen three of the six ferroptosis related genes (HIF1A, SLC7A11, and LPIN1). The ROC curve showed that the three hub genes had outstanding potential for disease diagnosis (AUC = 0.976), which was subsequently validated in samples from GSE92415 (AUC = 0.962) and blood samples from GSE3365 (AUC = 0.847) and GSE94648 (AUC = 0.769). These genes might be crucial for UC immunity based upon the results on the immune system. Furthermore, mouse samples examined using qRT-PCR also verified our findings. Conclusion: In conclusion, the findings have important implications for ferroptosis and UC, and these hub genes may also offer fresh perspectives on the aetiology and therapeutic approaches of UC.

Targeting the senescence-related genes MAPK12 and FOS to alleviate osteoarthritis.

Background: The mechanism by which chondrocyte senescence aggravate OA progression has not yet been well elucidated. The aim of this study was to investigate the chondrocyte senescence related gene biosignatures in OA, and to analyze on the underlying mechanisms of senescence in OA. Materials and methods: We intersected osteoarthritis dataset GSE82107 from GEO database and senescence dataset from CellAge database of human senescence-associated genes based on genetic manipulations experiments plus gene expression profilin, and screened out 4 overlapping genes. The hub genes were verified in vitro and in human OA cartilage tissues by qRT-PCR. We further confirmed the function of mitogen-activated protein kinase 12 (MAPK12) and Fos proto-oncogene (FOS) in OA in vitro and in vivo by qRT-PCR, western blotting, Edu staining, immunofluorescence, SA-ß-gal staining, HE, IHC, von frey test, and hot plate. Results: 1458 downregulated and 218 upregulated DEGs were determined from GSE82107, and 279 human senescence-associated genes were downloaded from CellAge database. After intersection assay, we screened out 4 overlapping genes, of which FOS, CYR61 and TNFSF15 were upregulated, MAPK12 was downregulated. The expression of MAPK12 was obviously downregulated, whereas the expression profiles of FOS, CYR61 and TNFSF15 were remarkedly upregulated in H2O2- or IL-1ß-stimulated C28/I2 cells, human OA cartilage tissues, and knee cartilage of aging mice. Furthermore, both MAPK12 over-expression and FOS knock-down can promote cell proliferation and cartilage anabolism, inhibit cell senescence and cartilage catabolism, relieve joint pain in H2O2- or IL-1ß-stimulated C28/I2 cells and mouse primary chondrocytes, destabilization of the medial meniscus (DMM) mice. Conclusion: This study explored that MAPK12 and FOS are involved in the occurrence and development of OA through modulating chondrocyte senescence. They might be biomarkers of OA chondrocyte senescence, and provides some evidence as subsequent possible therapeutic targets for OA. The translational potential of this article: The translation potential of this article is that we revealed MAPK12 and FOS can effectively alleviate OA by regulating chondrocyte senescence, and thus provided potential therapeutic targets for prevention or treatment of OA in the future.

Bioinformatics and systems biology approaches to identify potential common pathogeneses for sarcopenia and osteoarthritis.

Sarcopenia, a geriatric syndrome characterized by progressive loss of muscle mass and strength, and osteoarthritis, a common degenerative joint disease, are both prevalent in elderly individuals. However, the relationship and molecular mechanisms underlying these two diseases have not been fully elucidated. In this study, we screened microarray data from the Gene Expression Omnibus to identify associations between sarcopenia and osteoarthritis. We employed multiple statistical methods and bioinformatics tools to analyze the shared DEGs (differentially expressed genes). Additionally, we identified 8 hub genes through functional enrichment analysis, protein-protein interaction analysis, transcription factor-gene interaction network analysis, and TF-miRNA coregulatory network analysis. We also discovered potential shared pathways between the two diseases, such as transcriptional misregulation in cancer, the FOXO signalling pathway, and endometrial cancer. Furthermore, based on common DEGs, we found that strophanthidin may be an optimal drug for treating sarcopenia and osteoarthritis, as indicated by the Drug Signatures database. Immune infiltration analysis was also performed on the sarcopenia and osteoarthritis datasets. Finally, receiver operating characteristic (ROC) curves were plotted to verify the reliability of our results. Our findings provide a theoretical foundation for future research on the potential common pathogenesis and molecular mechanisms of sarcopenia and osteoarthritis.

Key gene modules and hub genes associated with pyrethroid and organophosphate resistance in Anopheles mosquitoes: a systems biology approach.

Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main methods used to control mosquito populations for malaria prevention. The efficacy of these strategies is threatened by the spread of insecticide resistance (IR), limiting the success of malaria control. Studies of the genetic evolution leading to insecticide resistance could enable the identification of molecular markers that can be used for IR surveillance and an improved understanding of the molecular mechanisms associated with IR. This study used a weighted gene co-expression network analysis (WGCNA) algorithm, a systems biology approach, to identify genes with similar co-expression patterns (modules) and hub genes that are potential molecular markers for insecticide resistance surveillance in Kenya and Benin. A total of 20 and 26 gene co-expression modules were identified via average linkage hierarchical clustering from Anopheles arabiensis and An. gambiae, respectively, and hub genes (highly connected genes) were identified within each module. Three specific genes stood out: serine protease, E3 ubiquitin-protein ligase, and cuticular proteins, which were top hub genes in both species and could serve as potential markers and targets for monitoring IR in these malaria vectors. In addition to the identified markers, we explored molecular mechanisms using enrichment maps that revealed a complex process involving multiple steps, from odorant binding and neuronal signaling to cellular responses, immune modulation, cellular metabolism, and gene regulation. Incorporation of these dynamics into the development of new insecticides and the tracking of insecticide resistance could improve the sustainable and cost-effective deployment of interventions.