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Objective: Colon cancer is associated with multiple levels of molecular heterogeneity. RNA processing converts primary transcriptional RNA to mature RNA, which drives tumourigenesis and its maintenance. The characterisation of RNA processing genes in colon cancer urgently needs to be elucidated. Methods: In this study, we obtained 1033 relevant samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to explore the heterogeneity of RNA processing phenotypes in colon cancer. Firstly, Unsupervised hierarchical cluster analysis detected 4 subtypes with specific clinical outcomes and biological features via analysis of 485 RNA processing genes. Next, we adopted the least absolute shrinkage and selection operator (LASSO) as well as Cox regression model with penalty to characterise RNA processing-related prognostic features. Results: An RNA processing-related prognostic risk model based on 10 genes including FXR1, MFAP1, RBM17, SAGE1, SNRPA1, SRRM4, ADAD1, DDX52, ERI1, and EXOSC7 was identified finally. A composite prognostic nomogram was constructed by combining this feature with the remaining clinical variables including TNM, age, sex, and stage. Genetic variation, pathway activation, and immune heterogeneity with risk signatures were also analysed via bioinformatics methods. The outcomes indicated that the high-risk subgroup was associated with higher genomic instability, increased proliferative and cycle characteristics, decreased tumour killer CD8+ T cells and poorer clinical prognosis than the low-risk group. Conclusion: This prognostic classifier based on RNA-edited genes facilitates stratification of colon cancer into specific subgroups according to TNM and clinical outcomes, genetic variation, pathway activation, and immune heterogeneity. It can be used for diagnosis, classification and targeted treatment strategies comparable to current standards in precision medicine. It provides a rationale for elucidation of the role of RNA editing genes and their clinical significance in colon cancer as prognostic markers. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a prevalent cancer with high morbidity and mortality rates worldwide. Late diagnosis is a significant contributor to low survival rates in a minority of cases. The study aimed to perform a robust pipeline using integrated bioinformatics tools that will enable us to identify potential diagnostic and prognostic biomarkers for early detection of CRC by exploring differentially expressed genes (DEGs). In addition to, testing the capability of replacing chemotherapy with plant extract in CRC treatment by validating it using real-time PCR. RNA-seq data from cancerous and adjacent normal tissues were pre-processed and analyzed using various tools such as FastQC, Kallisto, DESeq@ R package, g:Profiler, GNEMANIA-CytoScape and CytoHubba, resulting in the identification of 1641 DEGs enriched in various signaling routes. MMP7, TCF21, and VEGFD were found to be promising diagnostic biomarkers for CRC. An in vitro experiment was conducted to examine the potential anticancer properties of 5-fluorouracile, Withania somnifera extract, and their combination. The extract was found to exhibit a positive trend in gene expression and potential therapeutic value by targeting the three genes; however, further trials are required to regulate the methylation promoter. Molecular docking tests supported the findings by revealing a stable ligand-receptor complex. In conclusion, the study's analysis workflow is precise and robust in identifying DEGs in CRC that may serve as biomarkers for diagnosis and treatment. Additionally, the identified DEGs can be used in future research with larger sample sizes to analyze CRC survival. [ABSTRACT FROM AUTHOR]

Imatinib (IMA) and nilotinib are the first and second generations of BCR-ABL tyrosine kinase inhibitors, which widely applied in chronic myeloid leukemia (CML) treatment. Here we aimed to provide new targets for CML treatment by transcriptome analysis. Microarray data GSE19567 was downloaded and analyzed from Gene Expression Omnibus (GEO) to identify common genes, which are downregulated or upregulated in K562-imatinib and K562-nilotinib treated cells. The differentially expressed genes (DEGs) were assessed, and STRING and Cytoscape were used to create the protein–protein interaction (PPI) network. In imatinib and nilotinib treated groups' comparison, there were common 626 upregulated and 268 downregulated genes, which were differentially expressed. The GO analysis represented the enrichment of DEGs in iron ion binding, protein tyrosine kinase activity, transcription factor activity, ATP binding, sequence-specific DNA binding, cytokine activity, the mitochondrion, sequence-specific DNA binding, plasma membrane and cell-cell adherens junction. KEGG pathway analysis revealed that downregulated DEGs were associated with pathways including microRNAs in cancer and PI3K-Akt signaling pathway. Furthermore, upregulated DEGs were involved in hematopoietic cell lineage, lysosome and chemical carcinogenesis. Among the upregulated genes, MYH9, MYH14, MYL10, MYL7, MYL5, RXRA, CYP1A1, FECH, AKR1C3, ALAD, CAT, CITED2, CPT1A, CYP3A5, CYP3A7, FABP1, HBD, HMBS and PPOX genes were found as hub genes. Moreover, 20 downregulated genes, YARS, AARS, SARS, GARS, CARS, IARS, RRP79, CEBPB, RRP12, UTP14A, PNO1, CCND1, DDX10, MYC, WDR43, CEBPG, DDIT3, VEGFA, PIM1 and TRIB3 were identified as hub genes. These genes have the potential to become target genes for diagnosis and therapy of CML patients. [ABSTRACT FROM AUTHOR]

Satellite-based QKD is currently being developed to revolutionize global cryptographic key exchange by facilitating secure communication among remote parties at a global scale. By overcoming the exponential loss of fiber transmission, satellite-to-Earth communication can seamlessly interconnect vast distances as the link budget of such links is sufficient to support QKD links. In terms of this direction, DV-QKD implementations seems to be technologically ahead since key exchange has been experimentally demonstrated to perform much more efficiently by providing key rates that are orders of magnitude higher compared to entanglement-based key exchange. However, the specific requirements to support effectively functional DV-QKD satellite-to-ground links are yet to be defined. This work attempts to define the satellite and ground segment system requirements needed in order to achieve functional QKD service for various satellites orbits (LEO, MEO, and GEO). Finite key size effects are being considered to determine the minimum block sizes that are required for secure key generation between a satellite node and a ground terminal for a single satellite pass. The atmospheric link channel is modeled with consideration of the most important degradation effects such as turbulence and atmospheric and pointing loss. Critical Tx and Rx system parameters, such as the source's intrinsic Quantum Bit Error Rate (iQBER), the Rx telescope aperture size, and detection efficiency, were investigated in order to define the minimum requirements to establish an operation satellite-to-ground QKD link under specific assumptions. The performance of each downlink scenario was evaluated for the wavelength of 1550 nm in terms of link availability, link budget, and in the distilling of secure key volumes over time. Finally, the feasibility and requirements for distributing the collected space photons via terrestrial telecom fibers was also studied and discussed, leading to the proposal of a more futuristic WDM-enabled satellite QKD architecture. This comprehensive analysis aims to contribute to the advancement and implementation of effective satellite-based QKD systems, which can further exploit the ground fiber segment to realize converged space/terrestrial QKD networks. [ABSTRACT FROM AUTHOR]

Abstract Cancer‐associated fibroblasts (CAFs) are the center of cross‐communication between various cells in the tumor stroma. However, how CAFs‐associated genes play an important role in Head and neck squamous cell carcinoma (HNSCC) prognosis has not been reported. Transcriptome data were downloaded from TCGA and GEO databases. Devtools, DPIC, xCell, MCPcounter, and Estimate packages were used to calculate CAFs scores and immune infiltration. Prognosis and weighted gene coexpression network analysis (WGCNA) analysis were performed between high or low risk populations based on CAF scores. Hub genes were identified, intersected, and enriched between TCGA and GEO databases. CAFs related genes were used to construct a prognostic model and the tumor immune dysfunction and exclusion database was used to evaluate the immune infiltration. Drug sensitivity, difference analysis and the HPA database were used to identify sensitive drugs and verify their expression. TCGA and GEO data suggested that CAFs scores had a role in HNSCC prognosis prediction. Based on CAFs scores, WGCNA and core gene enrichment analysis were performed to construct a CAFs‐related prognostic model. The prognostic model composed of a total of 12 CAFs genes could predict the prognosis well and was validated in the validation dataset, demonstrating its applicability to external data. According to the model, although there was no statistical difference in immune escape between the high and low risk groups, the proportion of patients who responded to immunotherapy was different. Drug sensitivity also differed between the two groups. This study suggests that CAFs associated genetic signatures may help to optimize risk stratification and provide new insights into individualized cancer treatment.

Carnosine dipeptidase 1 (CNDP1), an enzyme integral to the hydrolysis of dipeptides containing histidine, plays an indispensable role in myriad physiological processes, including hydrolysis of proteins, maturation of specific biochemical functionalities within proteins, tissue regeneration, and regulation of cell cycle. However, the implications of CNDP1 in oncogenesis and its prognostic value are not yet fully elucidated. Initially, we procured the GSE40367 dataset from the Gene Expression Omnibus and established a protein–protein interaction network. Thereafter, we conducted functional and pathway enrichment analyses utilizing GO, KEGG, and GSEA. Moreover, we undertook an association analysis concerning the expression of CNDP1 with immune infiltration, along with survival analysis across various cancers and specifically in hepatocellular carcinoma (HCC). Our study uncovered a total of 2,248 differentially expressed genes, with a down-regulation of CNDP1 in HCC and other cancers. Our explorations into the relationship between CNDP1 and immune infiltration disclosed a negative correlation between CNDP1 expression and the presence of immune cells in HCC. Survival analyses revealed that diminished expression of CNDP1 correlates with an adverse prognosis in HCC and several other types of cancer. These observations intimate that CNDP1 holds promise as a novel prognostic biomarker for both pan-cancer and HCC.

Introduction Mesenchymal stem cells can develop into osteoblasts, making them a promising cell-based osteoporosis treatment. Despite their therapeutic potential, their molecular processes are little known. Bioinformatics and experimental analysis were used to determine the molecular processes of bone marrow mesenchymal stem cell (BMSC) therapy for postmenopausal osteoporosis (PMO). Material and methods We used weighted gene co-expression network analysis (WGCNA) to isolate core gene sets from two GEO microarray datasets (GSE7158 and GSE56815). GeneCards found PMO-related genes. GO, KEGG, Lasso regression, and ROC curve analysis refined our candidate genes. Using the GSE105145 dataset, we evaluated KLF2 expression in BMSCs and examined the link between KLF2 and PIK3CA using Pearson correlation analysis. We created a protein-protein interaction network of essential genes involved in osteoblast differentiation and validated the functional roles of KLF2 and PIK3CA in BMSC osteoblast differentiation in vitro. Results We created 6 co-expression modules from 10 419 differentially expressed genes (DEGs). PIK3CA, the key gene in the PI3K-Akt pathway, was among 197 PMO-associated DEGs. KLF2 also induced PIK3CA transcription in PMO. BMSCs also expressed elevated KLF2. BMSC osteoblast differentiation involved the PI3K-Akt pathway. In vitro, KLF2 increased PIK3CA transcription and activated the PI3K-Akt pathway to differentiate BMSCs into osteoblasts. Conclusions BMSCs release KLF2, which stimulates the PIK3CA-dependent PI3K-Akt pathway to treat PMO. Our findings illuminates the involvement of KLF2 and the PI3K-Akt pathway in BMSC osteoblast development, which may lead to better PMO treatments.

From its early days as a fledgling technology, satellite communication has come a long way to become a flourishing component of the global technological ecosystem that determines our increasingly interdependent world. This scholarly essay provides a comprehensive analysis of current developments in satellite communication technology and the several fields in which they might be applied. The essay dives into major inventions that have catapulted this discipline to unparalleled heights, and it spans from the historical origins to the modern accomplishments. This overview elucidates the enormous influence that satellite communication has had on modern civilization, highlighting its central position in allowing global connection, data dissemination, and transformational applications across a variety of industries.

Abstract Background The mechanism of mitochondria-related genes (MRGs) in childhood allergic asthma (CAS) was unclear. The aim of this study was to find new biomarkers related to MRGs in CAS. Methods This research utilized two CAS-related datasets (GSE40888 and GSE40732) and extracted 40 MRGs from the MitoCarta3.0 Database. Initially, differential expression analysis was performed on CAS and control samples in the GSE40888 dataset to obtain the differentially expressed genes (DEGs). Differentially expressed MRGs (DE-MRGs) were obtained by overlapping the DEGs and MRGs. Protein protein interactions (PPI) network of DE-MRGs was created and the top 10 genes in the degree ranking of Maximal Clique Centrality (MCC) algorithm were defined as feature genes. Hub genes were obtained from the intersection genes from the Least absolute shrinkage and selection operator (LASSO) and EXtreme Gradient Boosting (XGBoost) algorithms. Additionally, the expression validation was conducted, functional enrichment analysis, immune infiltration analysis were finished, and transcription factors (TFs)-miRNA-mRNA regulatory network was constructed. Results A total of 1505 DEGs were obtained from the GSE40888, and 44 DE-MRGs were obtained. A PPI network based on these 44 DE-MRGs was created and revealed strong interactions between ADCK5 and MFN1, BNIP3 and NBR1. Four hub genes (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) were obtained by taking the intersection of genes from the LASSO and XGBoost algorithms based on 10 signature genes which obtained from PPI. In addition, hub genes-based alignment diagram showed good diagnostic performance. The results of Gene Set Enrichment Analysis (GSEA) suggested that hub genes were closely related to mismatch repair. The B cells naive cells were significantly expressed between CAS and control groups, and MTIF3 was most strongly negatively correlated with B cells naive. In addition, the expression of MTIF3 and MRPS26 may have influenced the inflammatory response in CAS patients by affecting mitochondria-related functions. The quantitative real-time polymerase chain reaction (qRT‒PCR) results showed that four hub genes were all down-regulated in the CAS samples. Conclusion NDUFAF7, MTIF3, MRPS26, and NDUFAF1 were identified as an MRGs-related biomarkers in CAS, which provides some reference for further research on CAS.

Abstract Research background and purpose Osteoporosis (OP) is one of the most common bone diseases worldwide, characterized by low bone mineral density and susceptibility to pathological fractures, especially in postmenopausal women and elderly men. Ferroptosis is one of the newly discovered forms of cell death regulated by genes in recent years. Many studies have shown that ferroptosis is closely related to many diseases. However, there are few studies on ferroptosis in osteoporosis, and the mechanism of ferroptosis in osteoporosis is still unclear. This study aims to identify biomarkers related to osteoporosis ferroptosis from the GEO (Gene Expression Omnibus) database through bioinformatics technology, and to mine potential therapeutic small molecule compounds through molecular docking technology, trying to provide a basis for the diagnosis and treatment of osteoporosis in the future. Materials and methods We downloaded the ferroptosis-related gene set from the FerrDb database ( http://www.zhounan.org/ferrdb/index.html ), downloaded the data sets GSE56815 and GSE7429 from the GEO database, and used the R software “limma” package to screen differentially expressed genes (DEGs) from GSE56815, and intersected with the ferroptosis gene set to obtain ferroptosis-related DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by the R software “clusterProfiler” package. The random forest model was further screened to obtain essential ferroptosis genes. R software “corrplot” package was used for correlation analysis of essential ferroptosis genes, and the Wilcox test was used for significance analysis. The lncRNA-miRNA-mRNA-TF regulatory network was constructed using Cytoscape software. The least absolute shrinkage and selection operator (LASSO) was used to construct a disease diagnosis model, and a Receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic performance, and then GSE7429 was used to verify the reliability of the diagnosis model. Molecular docking technology was used to screen potential small molecule compounds from the Drugbank database. Finally, a rat osteoporosis model was constructed, and peripheral blood mononuclear cells were extracted for qRT-PCR detection to verify the mRNA expression levels of crucial ferroptosis genes. Result Six DEGs related to ferroptosis were initially screened out. GO function and KEGG pathway enrichment analysis showed that ferroptosis-related DEGs were mainly enriched in signaling pathways such as maintenance of iron ion homeostasis, copper ion binding function, and ferroptosis. The random forest model identified five key ferroptosis genes, including CP, FLT3, HAMP, HMOX1, and SLC2A3. Gene correlation analysis found a relatively low correlation between these five key ferroptosis genes. The lncRNA-miRNA-mRNA-TF regulatory network shows that BAZ1B and STAT3 may also be potential molecules. The ROC curve of the disease diagnosis model shows that the model has a good diagnostic performance. Molecular docking technology screened out three small molecule compounds, including NADH, Midostaurin, and Nintedanib small molecule compounds. qRT-PCR detection confirmed the differential expression of CP, FLT3, HAMP, HMOX1 and SLC2A3 between OP and normal control group. Conclusion This study identified five key ferroptosis genes (CP, FLT3, HAMP, HMOX1, and SLC2A3), they were most likely related to OP ferroptosis. In addition, we found that the small molecule compounds of NADH, Midostaurin, and Nintedanib had good docking scores with these five key ferroptosis genes. These findings may provide new clues for the early diagnosis and treatment of osteoporosis in the future.

Objectives: Adrenocortical carcinoma (ACC), a rare and aggressive adrenal cortex cancer, poses significant challenges due to high mortality, poor prognosis, and early post-surgery recurrence. Variability in survival across ACC stages emphasizes the need to uncover its molecular underpinnings. Adrenocortical adenoma, a benign tumor, adds to diagnostic challenges, highlighting the necessity for molecular insights. The Non-SMC Associated Condensin Complex (NCAP) gene family, recognized for roles in chromosomal structure and cell cycle control. This study focuses on evaluating NCAP gene functions and importance in ACC through gene expression profiling to identify diagnostic and therapeutic targets. Methods: Microarray datasets from ACC patients, obtained from the Gene Expression Omnibus database, were normalized to eliminate batch effects. Differential expression analysis of NCAP family genes, facilitated by the GEPIA2 database, included survival and pathological stage evaluations. A Protein-Protein Interaction network was constructed using GeneMANIA, and additional insights were gained through Gene Ontology enrichment analysis, correlation analysis, and ROC curve analysis. Results: ACC samples exhibited elevated levels of NCAPG, NCAPG2, and NCAPH compared to normal and adenoma samples. Increased expression of these genes correlated with poor overall survival, particularly in advanced disease stages. The Protein-Protein Interaction network highlighted interactions with related proteins, and Gene Ontology enrichment analysis demonstrated their involvement in chromosomal structure and control. Differentially expressed NCAP genes showed positive associations, and ROC curve analysis indicated their high discriminatory power in identifying ACC from adenoma and normal samples. Conclusion: The study emphasizes the potential importance of NCAPG, NCAPG2, and NCAPH in ACC, suggesting roles in tumor aggressiveness and diagnostic relevance. These genes could serve as therapeutic targets and markers for ACC, but further exploration into their molecular activities and validation studies is imperative to fully harness their diagnostic and therapeutic potential, advancing precision medicine approaches against this rare but lethal malignancy. [ABSTRACT FROM AUTHOR]

Energy metabolism plays a crucial role in supporting cancer cell growth and driving tumor progression. Our objective was to create a unique gene signature based on metabolic genes that could accurately predict the prognosis of patients with ovarian cancer (OC). We accessed microarray data of patients with OC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients from the TCGA dataset were divided into training and internal validation sets, maintaining a ratio of 3:1. Based on Least absolute shrinkage and selection operator Cox regression analysis, twenty-nine metabolism-related genes were identified for the development of the metabolic signature. Patients in the training set were successfully divided into lowand high-risk groups with a significantly different prognosis (Hazard Ratio (HR): 2.76, 95% Confidence Interval (CI): 2.12-3.59, p < 0.001). The prognostic value of this signature was confirmed in the internal (HR: 3.06, 95% CI: 1.80-5.17, p < 0.001) and external validation sets (HR: 2.17, 95% CI: 1.57-2.99, p < 0.001). The timedependent receiver operating characteristic (ROC) at the 5-year interval demonstrated that the prognostic accuracy of this metabolic signature (Area under curve (AUC) = 0.723) was superior to that of any other clinicopathological features, including the Federation of Gynecology and Obstetrics stage (AUC = 0.509), grade (AUC = 0.536), and debulking status (AUC = 0.637). Further immune cell infiltration analysis showed that low-risk patients had a higher enrichment of immune-activating cells. In conclusion, a novel metabolic signature with good performance was established in this study. This prognostic model could aid in the identification of high-risk patients who require aggressive follow-up and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Sex is a crucial biological variable, and influence of biological sex on the change of gene expression in ageing skeletal muscle has not yet been fully revealed. In this study, the mRNA expression profiles were obtained from the Gene Expression Omnibus database. Key genes were identified by differential expression analysis and weighted gene co-expression network analysis. The gene set enrichment analysis software and Molecular Signatures Database were used for functional and enrichment analysis. A protein–protein interaction network was constructed using STRING and visualized in Cytoscape. The results were compared between female and male subgroups. Differentially expressed genes and enriched pathways in different sex subgroups shared only limited similarities. The pathways enriched in the female subgroup were more similar to the pathways enriched in the older groups without taking sex difference into consideration. The pathways enriched in the female subgroup were more similar to the pathways enriched in the older groups without taking sex difference into consideration. The muscle myosin filament pathways were downregulated in the both aged female and male samples whereas transforming growth factor beta pathway and extracellular matrix-related pathways were upregulated. With muscle ageing, the metabolism-related pathways, protein synthesis and degradation pathways, results of predicted immune cell infiltration, and gene cluster associated with slow-type myofibers drastically different between the female and male subgroups. This finding may indicate that changes in muscle type with ageing may differ between the sexes in vastus lateralis muscle. [ABSTRACT FROM AUTHOR]

Background: The mechanism of mitochondria-related genes (MRGs) in childhood allergic asthma (CAS) was unclear. The aim of this study was to find new biomarkers related to MRGs in CAS. Methods: This research utilized two CAS-related datasets (GSE40888 and GSE40732) and extracted 40 MRGs from the MitoCarta3.0 Database. Initially, differential expression analysis was performed on CAS and control samples in the GSE40888 dataset to obtain the differentially expressed genes (DEGs). Differentially expressed MRGs (DE-MRGs) were obtained by overlapping the DEGs and MRGs. Protein protein interactions (PPI) network of DE-MRGs was created and the top 10 genes in the degree ranking of Maximal Clique Centrality (MCC) algorithm were defined as feature genes. Hub genes were obtained from the intersection genes from the Least absolute shrinkage and selection operator (LASSO) and EXtreme Gradient Boosting (XGBoost) algorithms. Additionally, the expression validation was conducted, functional enrichment analysis, immune infiltration analysis were finished, and transcription factors (TFs)-miRNA-mRNA regulatory network was constructed. Results: A total of 1505 DEGs were obtained from the GSE40888, and 44 DE-MRGs were obtained. A PPI network based on these 44 DE-MRGs was created and revealed strong interactions between ADCK5 and MFN1, BNIP3 and NBR1. Four hub genes (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) were obtained by taking the intersection of genes from the LASSO and XGBoost algorithms based on 10 signature genes which obtained from PPI. In addition, hub genes-based alignment diagram showed good diagnostic performance. The results of Gene Set Enrichment Analysis (GSEA) suggested that hub genes were closely related to mismatch repair. The B cells naive cells were significantly expressed between CAS and control groups, and MTIF3 was most strongly negatively correlated with B cells naive. In addition, the expression of MTIF3 and MRPS26 may have influenced the inflammatory response in CAS patients by affecting mitochondria-related functions. The quantitative real-time polymerase chain reaction (qRT‒PCR) results showed that four hub genes were all down-regulated in the CAS samples. Conclusion: NDUFAF7, MTIF3, MRPS26, and NDUFAF1 were identified as an MRGs-related biomarkers in CAS, which provides some reference for further research on CAS. [ABSTRACT FROM AUTHOR]

Ferroptosis, a lipid peroxidation-mediated programmed cell death, is closely linked to tumor development, including prostate cancer (PCa). Despite established connections between ferroptosis and PCa, a comprehensive investigation is essential for understanding its impact on patient prognosis. Methods: A risk model incorporating four ferroptosis-related genes was developed and validated. Elevated risk scores correlated with an increased likelihood of biochemical recurrence (BCR), diminished immune infiltration, and adverse clinicopathological characteristics. To corroborate these results, we performed validation analyses utilizing datasets from both the Cancer Genome Atlas Cohort (TCGA) and the Gene Expression Synthesis Cohort (GEO). Moreover, we conducted further investigations into the pivotal gene identified in our model to explore its impact on tumor characteristics through cell proliferation and invasion assays, as well as animal studies conducted in vivo. Additionally, we conducted further experiments involving ferroptosis-related analysis to validate its association with ferroptosis. Results: The risk model demonstrated exceptional predictive capabilities for prognosis and therapeutic outcomes in PCa patients. Mitogen-activated protein kinase 9 (MAPK9) emerged as a crucial gene within the model. In vivo and in vitro experiments explored MAPK9's role in ferroptosis and its influence on tumor migration and proliferation. Conclusion: The findings provide a novel perspective for advancing ferroptosis exploration in PCa, bridging basic research and clinical applications. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune‐associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain–Barré syndrome. Methods: Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour‐MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions. Results: Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players. Conclusion: This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine. [ABSTRACT FROM AUTHOR]

Studies on Oryza sativa (rice) are crucial for improving agricultural productivity and ensuring global sustenance security, especially considering the increasing drought and heat stress caused by extreme climate change. Currently, the genes and mechanisms underlying drought and heat resistance in rice are not fully understood, and the scope for enhancing the development of new strains remains considerable. To accurately identify the key genes related to drought and heat stress responses in rice, multiple datasets from the Gene Expression Omnibus (GEO) database were integrated in this study. A co-expression network was constructed using a Weighted Correlation Network Analysis (WGCNA) algorithm. We further distinguished the core network and intersected it with differentially expressed genes and multiple expression datasets for screening. Differences in gene expression levels were verified using quantitative real-time polymerase chain reaction (PCR). OsDjC53, MBF1C, BAG6, HSP23.2, and HSP21.9 were found to be associated with the heat stress response, and it is also possible that UGT83A1 and OsCPn60a1, although not directly related, are affected by drought stress. This study offers significant insights into the molecular mechanisms underlying stress responses in rice, which could promote the development of stress-tolerant rice breeds. [ABSTRACT FROM AUTHOR]

Background: Early and timely diagnosis benefits the prognosis of patients with colorectal cancer (CRC). The purpose of this study was to explore biomarkers with diagnostic ability in colorectal cancer. Objective: Boruta and multi-cluster feature selection (MCFS) algorithms were employed to analyze the expression data of serum exosome-sourced microRNAs (miRNAs) in CRC patients in the Gene Expression Omnibus (GEO) database to find candidate feature miRNAs that could distinguish between CRC and normal samples. To identify the feature miRNAs with the highest diagnostic ability, different support vector machine (SVM) classifiers were constructed, and the SVM classifier with the highest F1 score was selected based on IFS curve. To validate the clinical application value of the classifier, serum samples from 32 CRC patients and 19 healthy individuals were collected as external validation sets, and serum exosomes were extracted for quantitative real-time polymerase chain reaction (qRT-PCR) analysis. The data were imported into the model to verify the performance of the model. Result: After feature selection by Boruta and MCFS algorithms, the first five candidate miRNAs (miR-21, miR-193b, miR-23a, miR-575, and miR-610) with sufficient ability to distinguish sample types were identified. In a series of classifiers constructed, the SVM classifier composed of the first four feature miRNAs (miR-21, miR-193b, miR-23a, and miR-575) was determined to have the best classification effect. qRT-PCR results of serum exosome miRNAs in clinical samples demonstrated that the expression of miR-21, miR-193b, and miR-23a in serum exosomes from CRC patients was significantly higher than that in normal samples, while that of miR-575 was significantly lower than that in normal samples. Subsequently, the receiver operating characteristics (ROC) curve of the diagnostic model based on four feature miRNAs was plotted. According to the results, the area under concentrations curves (AUC) value of the diagnostic model was 0.854, which suggested that the predictive performance of the model built on miR-21, miR-193b, miR-23a, and miR-575 was effective enough to distinguish healthy subjects from CRC patients. In addition, the expression of miR-21, miR-193b, miR-23a, and miR-575 was closely related to the tumor size, stage, and the presence of distant metastasis in CRC patients. Conclusion: MiR-21, miR-193b, miR-23a, and miR-575 may be a new potential biomarker combination for the diagnosis of CRC. Clinical biopsy combined with these miRNA biomarkers is expected to promote the early diagnosis of CRC, thus optimizing the prognosis of CRC patients. [ABSTRACT FROM AUTHOR]

BackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease characterized by unexplained irreversible pulmonary fibrosis. Although the etiology of IPF is unclear, studies have shown that it is related to telomere length shortening. However, the prognostic value of telomere-related genes in IPF has not been investigated.MethodsWe utilized the GSE10667 and GSE110147 datasets as the training set, employing differential expression analysis and weighted gene co-expression network analysis (WGCNA) to screen for disease candidate genes. Then, we used consensus clustering analysis to identify different telomere patterns. Next, we used summary data-based mendelian randomization (SMR) analysis to screen core genes. We further evaluated the relationship between core genes and overall survival and lung function in IPF patients. Finally, we performed immune infiltration analysis to reveal the changes in the immune microenvironment of IPF.ResultsThrough differential expression analysis and WGCNA, we identified 35 significant telomere regulatory factors. Consensus clustering analysis revealed two distinct telomere patterns, consisting of cluster A (n = 26) and cluster B (n = 19). Immune infiltration analysis revealed that cluster B had a more active immune microenvironment, suggesting its potential association with IPF. Using GTEx eQTL data, our SMR analysis identified two genes with potential causal associations with IPF, including GPA33 (PSMR = 0.0013; PHEIDI = 0.0741) and MICA (PSMR = 0.0112; PHEIDI = 0.9712). We further revealed that the expression of core genes is associated with survival time and lung function in IPF patients. Finally, immune infiltration analysis revealed that NK cells were downregulated and plasma cells and memory B cells were upregulated in IPF. Further correlation analysis showed that GPA33 expression was positively correlated with NK cells and negatively correlated with plasma cells and memory B cells.ConclusionOur study provides a new perspective for the role of telomere dysfunction and immune infiltration in IPF and identifies potential therapeutic targets. Further research may reveal how core genes affect cell function and disease progression, providing new insights into the complex mechanisms of IPF.

BackgroundNeutrophil extracellular traps (NETs) cause delayed wound closed up in type 2 diabetes mellitus (T2DM), but the specific regulatory mechanism of NETs-related genes (NETs-RGs) in T2DM is unclear.MethodsWe acquired GSE21321 and GSE15932 datasets from gene expression omnibus (GEO) database. First, differentially expressed genes (DEGs) between T2DM and control samples of GSE21321 dataset were sifted out by differential expression analysis. NETs scores were calculated for all samples in GSE21321 dataset, and key module genes associated with NETs scores were screened by constructing co-expression network. Then, DEGs and key module genes were intersected to yield intersection genes, and candidate genes were identified by constructing a protein protein interaction (PPI) network. Least absolute shrinkage and selection operator (LASSO) regression analysis was implemented on candidate genes to screen out diagnostic genes, and they were subjected to single sample gene set enrichment analysis (ssGSEA). Finally, immune characteristic analysis was carried out, and we constructed the gene-drug and transcription factor (TF)-miRNA-mRNA networks. Besides, we validated the expression of diagnostic genes by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsIn total, 23 candidate genes were gained by PPI analysis. The 5 diagnostic genes, namely, inter-trypsin inhibitor heavy chain 3 (ITIH3), fibroblast growth factor 1 (FGF1), neuron cell adhesion molecule (NRCAM), advanced glycosylation end-product-specific receptor (AGER), and calcium voltage-gated channel subunit alpha1 C (CACNA1C), were identified via LASSO analysis, and they were involved in carboxylic acid transport, axonogenesis, etc. M2 Macrophage, Monocyte, Natural killer (NK) cell, and Myeloid dendritic cells (DC) were remarkably different between T2DM and control samples. Diagnostic genes had the strongest and the most significant positive correlation with B cells. The gene-drug network included CACNA1C-Isradipine, CACNA1C-Benidipine and other relationship pairs. Totally 76 nodes and 44 edges constituted the TF-miRNA-mRNA network, including signal transducer and activator of transcription 1(STAT1) -hsa-miR-3170-AGER, CCCTC-binding factor (CTCF)-hsa-miR-455-5p-CACNA1C, etc. Moreover, qRT-PCR suggested that the expression trends of FGF1 and AGER were in keeping with the results of bioinformatic analysis. FGF1 and AGER were markedly regulated downwards in the T2DM group.ConclusionThrough bioinformatic analysis, we identified NETs-related diagnostic genes (ITIH3, FGF1, NRCAM, AGER, CACNA1C) in T2DM, and explored their mechanism of action from different aspects, providing new ideas for the studies related to diagnosis and treatment of T2DM.

Background: Ischemic stroke (IS) is a serious health hazard and identified as the second leading cause of mortality around the world. However, the role of pyroptosis in the immune microenvironment regulation in IS is still unclear. Here, our study aims to elucidate the effect of pyroptosis on immune microenvironment in IS. Methods: The regulation mode of pyroptosis in IS was systematically evaluated, and its effects on immune microenvironment were explored, including infiltration of immune cells, immune response gene sets, and human leukocyte antigen (HLA) gene. The genes and drugs related to pyroptosis phenotype were also identified. An MCAO rat model was constructed, and the mRNA expression levels in the classifier model were validated by qRT-PCR. Results: The separator is composed of 11 pyroptosis genes, out of which 10 genes could distinguish between ischemic stroke and control samples. CHMP2A, CHMP4A, and NAIP genes are significantly related to immune infiltrating cells, immune response gene sets, and HLA. However, two different pyroptosis subtypes mediated by 10 pyroptosis genes were identified, which were different in immune cell abundance, HLA genes, and immune response gene sets. Furthermore, 199 genes associated with pyroptosis phenotype was identified along with the analysis of biological functions. Conclusion: These findings reveal the potential mechanism of pyroptosis in the immune microenvironment of IS, indicating that pyroptosis functions as a vital component in the complexity and diversity of the immune microenvironment in patients with IS.

Abstract Esophageal squamous cell carcinoma (ESCC) is one of the most common malignant tumors. However, the mechanisms underlying ESCC tumorigenesis have not been fully elucidated. Thus, we aimed to determine the key genes involved in ESCC tumorigenesis. The following bioinformatics analyses were performed: identification of differentially expressed genes (DEGs); gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis; integrated analysis of the protein–protein interaction network and Gene Expression Profiling Interactive Analysis database for validation of hub genes. Finally, western blotting and qPCR were used to explore the expression of cell division cycle 6 (CDC6) in ESCC cell lines. Immunohistochemistry analysis of ESCC samples from patients and matched clinical characteristics was used to determine the effects of CDC6. A total of 494 DEGs were identified, and functional enrichment was mainly focused on cell cycle and DNA replication. Biological pathway analysis of the hub genes was closely related to the cell cycle. We found that CDC6 was upregulated in ESCC cell lines and patient tissues and was related to the clinicopathological characteristics of ESCC. In conclusion, this study identified hub genes and crucial biological pathways related to ESCC tumorigenesis and integrated analyses indicated that CDC6 may be a novel diagnostic and therapeutic target for ESCC.

Abstract Alzheimer's disease (AD) is the leading cause of dementia worldwide, with recent studies highlighting the potential role of immunogenic cell death (ICD) in the pathogenesis of this neurodegenerative disorder. A total of 52 healthy controls and 64 patients with AD were included. Compared to the controls, the patients with AD exhibited 2392 differentially expressed genes (DEGs), of which 1015 and 1377 were upregulated and downregulated genes, respectively. Among them, nine common genes were identified by intersecting the AD-related module genes with the DEGs and ICD-associated genes. Gene ontology (GO)analysis further revealed “positive regulation of cytokine production” as the most significant term. Moreover, the enriched molecular functions were primarily related to the inflammatory body complex, while the overlapping genes were significantly enriched in lipopolysaccharide binding. Kyoto encyclopedia of genes and genomes (KEGG) analysis also indicated that these overlapping genes were mainly enriched in immunity, inflammation, and lipid metabolism pathways. Furthermore, the following four hub genes were detected using machine learning algorithms: P2RX7, HSP90AA1, NT5E, and NLRP3. These genes demonstrated significant differences in expression between the AD and healthy control groups (P 0.7, indicating their potential diagnostic value for AD. We further validated the protein levels of these four genes in the hippocampus of 3xTg-AD and C57BL/6J mice, showing P2RX7 and HSP90AA1 expression levels consistent with the previously analyzed trends. Finally, the single-sample gene set enrichment analysis (ssGSEA) algorithm provided additional evidence by demonstrating the crucial role of immune cell infiltration and its link with the hub genes in AD progression. Our study results suggest that ICD-mediated elevation of HSP90AA1 and P2RX7 levels and the resulting induction of tau hyperphosphorylation and neuroinflammation are vital in the AD pathogenic mechanism.

Introduction Accumulating studies have shown that copper has a detrimental effect in cells, and the cuproptosis-related gene signatures have been constructed as clinical tools to predict prognosis in tumors. However, the heterogeneity of cuproptosis has not been fully investigated in ischemic stroke. Material and methods Here, we combined the bulk RNA-seq and single cell-RNA-seq data for stroke to investigate the role of cuproptosis in stroke. Results We identified the cuproptosis-related differentially expressed genes (CuDEGs) in ischemic stroke. Then, we tried to find the hub genes with the machine learning method and WGCNA. We highlighted four genes identified by these methods and proposed a potential diagnostic model in ischemic stroke. Conclusions Our findings revealed cuproptosis-related hub genes, which could provide useful biomarkers in ischemic stroke.

Objective·To establish a prognostic model for the overall survival (OS) of hepatocellular carcinoma (HCC) based on mitochondrial genes and clinical information.Methods·The gene expression and the clinical data of 369 HCC patients and 50 controls with normal liver were downloaded from The Cancer Genome Atlas (TCGA) database. The nuclear-encoded mitochondrial genes (NEMGs) were obtained from the MitoCarta3.0 database. The "DESeq2" R package and univariate Cox analysis were used to select NEMGs [ubiquinol cytochrome C reductase hinge protein (UQCRH), ATP citrate lyase (ACLY), phosphoenolpyruvate carboxykinase 2 (PCK2), Bcl-2 homologous antagonist/killer1 (BAK1), Bcl-2-associated X protein (BAX) and Bcl-2/adenovirus E1B interacting protein 3-like (BNIP3L)] in HCC that were associated with OS of HCC and participated in dysregulation of oxidative phosphorylation, tricarboxylic acid cycle and cell apoptosis. Multivariate Cox analysis was applied to select independent risk factors for OS of HCC. A comprehensive prognostic model and a prognostic nomogram with 6-NEMG risk characteristics and TNM staging were established. By using the median of prognostic scores as a cut-off, HCC patients were classified into low-risk and high-risk group. Kaplan-Meier survival curve analysis was conducted and log-rank test was performed to evaluate the survival rates between the low-risk and high-risk group. The area under the curve (AUC) values of receiver operating characteristic (ROC) curve were calculated via using the "timeROC" package. The prognostic model for HCC was validated by using the GEO HCC cohort (GSE14520) for 1, 3 and 5 years. Finally, the relative expression level of 6-NEMG was validated in 34 clinical samples of HCC from Xinhua Hospital, Shanghai Jiao Tong University School of Medicine by using real-time quantitative polymerase chain reaction (qPCR) method.Results·Compared to 6-NEMG risk signature only (AUCs for 1, 3 and 5 years were 0.77, 0.66 and 0.65, respectively) or TNM stage only (AUCs for 1, 3 and 5 years were 0.66, 0.67 and 0.63, respectively), ROC curve analysis showed that this integrated prognostic model displayed better predictive performance for 1-year (AUC, 0.78), 3-year (AUC, 0.73) and 5-year (AUC, 0.69) OS of HCC. The Kaplan-Meier survival curve analysis showed that the OS of HCC patients in the high-risk group was significantly worse than that in the low-risk group (P=0.001). In addition, predictive performance of the prognostic model (AUC for 1, 3 and 5 years is 0.67, 0.66 and 0.74, respectively) and prognostic differences between the high-risk and low-risk group (P=0.001) were further validated in GEO (GSE14520) external cohort, and these results were consistent with the TCGA data. In addition to BNIP3L, dysregulation of five other NEMGs in the clinical HCC cohort was validated. The correlation analysis in GSE14520 and HCC clinical cohort showed a positive correlation between prognosis score and the size and number of tumors.Conclusion·A new prognostic model that combines 6-NEMG risk characteristics with TNM staging for predicting OS in HCC patients was constructed and validated. This model may help improve the prognosis prediction of HCC patients.

Abstract Background Systemic juvenile idiopathic arthritis (SJIA) is a form of childhood arthritis with clinical features such as fever, lymphadenopathy, arthritis, rash, and serositis. It seriously affects the growth and development of children and has a high rate of disability and mortality. SJIA may result from genetic, infectious, or autoimmune factors since the precise source of the disease is unknown. Our study aims to develop a genetic-based diagnostic model to explore the identification of SJIA at the genetic level. Methods The gene expression dataset of peripheral blood mononuclear cell (PBMC) samples from SJIA was collected from the Gene Expression Omnibus (GEO) database. Then, three GEO datasets (GSE11907-GPL96, GSE8650-GPL96 and GSE13501) were merged and used as a training dataset, which included 125 SJIA samples and 92 health samples. GSE7753 was used as a validation dataset. The limma method was used to screen differentially expressed genes (DEGs). Feature selection was performed using Lasso, random forest (RF)-recursive feature elimination (RFE) and RF classifier. Results We finally identified 4 key genes (ALDH1A1, CEACAM1, YBX3 and SLC6A8) that were essential to distinguish SJIA from healthy samples. And we combined the 4 key genes and performed a grid search as well as 10-fold cross-validation with 5 repetitions to finally identify the RF model with optimal mtry. The mean area under the curve (AUC) value for 5-fold cross-validation was greater than 0.95. The model’s performance was then assessed once more using the validation dataset, and an AUC value of 0.990 was obtained. All of the above AUC values demonstrated the strong robustness of the SJIA diagnostic model. Conclusions We successfully developed a new SJIA diagnostic model that can be used for a novel aid in the identification of SJIA. In addition, the identification of 4 key genes that may serve as potential biomarkers for SJIA provides new insights to further understand the mechanisms of SJIA.

Prostate cancer (PCa) represents a substantial global health concern and a prominent contributor to male cancer-related mortality. The aim of this study is to explore the role of B-type endothelin receptor (EDNRB) in PCa and evaluate its therapeutic potential. The investigation employed predictive methodologies encompassing data acquisition from the GEO and TCGA databases, gene screening, enrichment analysis, in vitro experiments involving PCR, Western blotting, wound healing, and Transwell assays, as well as animal experiments. Analysis revealed a significant downregulation of EDNRB expression in PCa cells. Overexpression of EDNRB demonstrated inhibitory effects on tumor cell growth, migration, and invasion, likely mediated through activation of the cGMP-Protein Kinase G pathway. In vivo experiments further confirmed the tumor-suppressive properties of EDNRB overexpression. These findings underscore the prospect of EDNRB as a therapeutic target for PCa, offering novel avenues for PCa treatment strategies.

Background Circular RNAs (circRNAs) have been shown to play critical roles in the initiation and progression of chronic glomerulonephritis (CGN), while their role from mesangial cells in contributing to the pathogenesis of CGN is rarely understood. Our study aims to explore the potential functions of mesangial cell-derived circRNAs using RNA sequencing (RNA-seq) and bioinformatics analysis.Methods Mouse mesangial cells (MMCs) were stimulated by lipopolysaccharide (LPS) to establish an in vitro model of CGN. Pro-inflammatory cytokines and cell cycle stages were detected by Enzyme-linked immunosorbent assay (ELISA) and Flow Cytometry experiment, respectively. Subsequently, differentially expressed circRNAs (DE-circRNAs) were identified by RNA-seq. GEO microarrays were used to identify differentially expressed mRNAs (DE-mRNAs) between CGN and healthy populations. Weighted co-expression network analysis (WGCNA) was utilized to explore clinically significant modules of CGN. CircRNA-associated CeRNA networks were constructed by bioinformatics analysis. The hub mRNAs from CeRNA network were identified using LASSO algorithms. Furthermore, utilizing protein–protein interaction (PPI), gene ontology (GO), pathway enrichment (KEGG), and GSEA analyses to explore the potential biological function of target genes from CeRNA network. In addition, we investigated the relationships between immune cells and hub mRNAs from CeRNA network using CIBERSORT.Results The expression of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α was drastically increased in LPS-induced MMCs. The number of cells decreased significantly in the G1 phase but increased significantly in the S/G2 phase. A total of 6 DE-mRNAs were determined by RNA-seq, including 4 up-regulated circRNAs and 2 down-regulated circRNAs. WGCNA analysis identified 1747 DE-mRNAs of the turquoise module from CGN people in the GEO database. Then, the CeRNA networks, including 6 circRNAs, 38 miRNAs, and 80 mRNAs, were successfully constructed. The results of GO and KEGG analyses revealed that the target mRNAs were mainly enriched in immune, infection, and inflammation-related pathways. Furthermore, three hub mRNAs (BOC, MLST8, and HMGCS2) from the CeRNA network were screened using LASSO algorithms. GSEA analysis revealed that hub mRNAs were implicated in a great deal of immune system responses and inflammatory pathways, including IL-5 production, MAPK signaling pathway, and JAK-STAT signaling pathway. Moreover, according to an evaluation of immune infiltration, hub mRNAs have statistical correlations with neutrophils, plasma cells, monocytes, and follicular helper T cells.Conclusions Our findings provide fundamental and novel insights for further investigations into the role of mesangial cell-derived circRNAs in CGN pathogenesis.

AbstractSome heat shock proteins (HSPs) have been shown to influence tumor prognosis, but their prognostic significance in colorectal cancer (CRC) remains unclear. This study explored the prognostic significance of HSP-related genes in CRC. Transcriptional data and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database, and a literature search was conducted to identify HSP-related genes. Using Least Absolute Selection and Shrinkage Operator (LASSO) regression and univariate/multivariate Cox regression analyses, 12 HSP-related genes demonstrating significant associations with CRC survival were successfully identified and employed to formulate a predictive risk score model. The efficacy and precision of this model were validated utilizing TCGA and Gene Expression Omnibus (GEO) datasets, demonstrating its reliability in CRC prognosis prediction. gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed significant disparities between high- and low-risk groups in chromatin remodeling biological functions and neutrophil extracellular trap formation pathways. Single sample gene set enrichment analysis (ssGSEA) further revealed differences in immune cell types and immune functional status between the two risk groups. Differential analysis showed higher expression of immune checkpoints within the low-risk group, while the high-risk group exhibited notably higher Tumor Immune Dysfunction and Exclusion (TIDE) scores. Additionally, we predicted the sensitivity of different prognosis risk patients to various drugs, providing potential drug choices for tailored treatment. Combined, our study successfully crafted a novel CRC prognostic model that can effectively predict patient survival, immune landscape, and treatment response, providing important support and guidance for CRC patient prognosis.

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignant tumors. However, the mechanisms underlying ESCC tumorigenesis have not been fully elucidated. Thus, we aimed to determine the key genes involved in ESCC tumorigenesis. The following bioinformatics analyses were performed: identification of differentially expressed genes (DEGs); gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis; integrated analysis of the protein–protein interaction network and Gene Expression Profiling Interactive Analysis database for validation of hub genes. Finally, western blotting and qPCR were used to explore the expression of cell division cycle 6 (CDC6) in ESCC cell lines. Immunohistochemistry analysis of ESCC samples from patients and matched clinical characteristics was used to determine the effects of CDC6. A total of 494 DEGs were identified, and functional enrichment was mainly focused on cell cycle and DNA replication. Biological pathway analysis of the hub genes was closely related to the cell cycle. We found that CDC6 was upregulated in ESCC cell lines and patient tissues and was related to the clinicopathological characteristics of ESCC. In conclusion, this study identified hub genes and crucial biological pathways related to ESCC tumorigenesis and integrated analyses indicated that CDC6 may be a novel diagnostic and therapeutic target for ESCC. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Breast cancer (BC), the most prevalent malignancy in women globally, still lacks comprehensive research on its molecular targets and necessitates further investigation into the underlying molecular mechanisms driving its initiation and progression.The GSE20685 Series Matrix File downloaded from the Gene Expression Omnibus database was divided into a high‐risk group (n = 49) and a low‐risk group (n = 278) to construct the co‐expression network.Four hub genes were identified based on the Weighted Gene Co‐expression Network Analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses were performed. Hub gene immune infiltration was investigated using the Tumor Immune Estimation Resource database, and CD4+ T cell expression levels were substantially correlated with hub gene expression. Based on the CancerRxGene database (Genomics of Drug Sensitivity in Cancer database), it was found that the hub genes were highly sensitive to common chemotherapy drugs such as AKT inhibitor VIII and Erlotinib. The expression of Secreted Frizzled‐Related Protein 1, melanoma‐inhibiting activity (MIA), and Keratin 14 was related to tumor mutation burden, and the expression of MIA also affected the microsatellite instability of the tumor. This study employs multi‐omics analysis to investigate the molecular network associated with the prognosis of BC, highlighting its intricate connection with the immune microenvironment.These findings pinpoint four crucial genes in BC progression, offering targets for further research and therapy. Their connections to immune infiltration and chemotherapy sensitivity underscore complex interactions in the tumor microenvironment. [ABSTRACT FROM AUTHOR]

Objectives: The most frequent cause of kidney damage in systemic lupus erythematosus (SLE) is lupus nephritis (LN), which is also a significant risk factor for morbidity and mortality. Lactate metabolism and protein lactylation might be related to the development of LN. However, there is still a lack of relative research to prove the hypothesis. Hence, this study was conducted to screen the lactate- related biomarkers for LN and analyze the underlying mechanism. Methods: To identify differentially expressed genes (DEGs) in the training set (GSE32591, GSE127797), we conducted a differential expression analysis (LN samples versus normal samples). Then, module genes were mined using WGCNA concerning LN. The overlapping of DEGs, critical module genes, and lactate-related genes (LRGs) was used to create the lactate-related differentially expressed genes (LR-DEGs). By using a machine-learning algorithm, ROC, and expression levels, biomarkers were discovered. We also carried out an immune infiltration study based on biomarkers and GSEA. Results: A sum of 1259 DEGs was obtained between LN and normal groups. Then, 3800 module genes in reference to LN were procured. 19 LR-DEGs were screened out by the intersection of DEGs, key module genes, and LRGs. Moreover, 8 pivotal genes were acquired via two machine-learning algorithms. Subsequently, 3 biomarkers related to lactate metabolism were obtained, including COQ2, COQ4, and NDUFV1. And these three biomarkers were enriched in pathways 'antigen processing and presentation' and 'NOD-like receptor signaling pathway'. We found that Macrophages M0 and T cells regulatory (Tregs) were associated with these three biomarkers as well. Conclusion: Overall, the results indicated that lactate-related biomarkers COQ2, COQ4, and NDUFV1 were associated with LN, which laid a theoretical foundation for the diagnosis and treatment of LN. [ABSTRACT FROM AUTHOR]

Background: Immune microenvironment is involved in tumor initiation and progression, and its effect on glioblastoma (GBM) is still unknown. Object: We sought to investigate the association between immune status and GBM. Methods: Transcriptome data and the relevant clinical data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases, and we identified two immune subtypes based on 29 immune-associated gene sets. Results: Through single-sample gene set enrichment analysis (ssGSEA), we found that the high-immunity subtype had the most tumor-infiltrating immune cells and immune checkpoint molecules in GBM patients. Furthermore, we could more effectively identify immune signature pathways in GBM. Conclusion: After validation with the GEO dataset, we conclude that the identified GBM high-immune subtypes may be amenable to the application of novel immune therapy for GBM. [ABSTRACT FROM AUTHOR]

Background: The relationship between inflammation-related genes (IRGs) and keloid disease (KD) is currently unclear. The aim of this study was to identify a new set of inflammation-related biomarkers in KD. Methods: GSE145725 and GSE7890 datasets were used in this study. A list of 3026 IRGs was obtained from the Molecular Signatures Database. Differentially expressed inflammation-related genes (DEGs) were obtained by taking the intersection of DEGs between KD and control samples and the list of IRGs. Candidate genes were selected using least absolute shrinkage and selection operator (LASSO) regression analysis. Candidate genes with consistent expression differences between KD and control in both GSE145725 and GSE7890 datasets were screened as biomarkers. An alignment diagram was constructed and validated, and in silico immune infiltration analysis and drug prediction were performed. Finally, RT-qPCR was performed on KD samples to analyze the expression of the identified biomarkers. Results: A total of 889 DEGs were identified from the GSE145725 dataset, 169 of which were IRGs. Three candidate genes (TRIM32, LPAR1 and FOXF1) were identified by the LASSO regression analysis, and expression validation analysis suggested that FOXF1 and LPAR1 were down-regulated in KD samples and TRIM32 was up-regulated. All three candidate genes had consistent changes in expression in both the GSE145725 and GSE7890 datasets. An alignment diagram was constructed to predict KD. Effector memory CD4 T cells, T follicular helper cell, Myeloid derived suppressor cell, activated dendritic cell, Immature dendritic cell and Monocyte were differentially expressed between the KD and control group. Sixty-seven compounds that may act on FOXF1,108 compounds that may act on LPAR1 and 56 compounds that may act on TRIM32 were predicted. Finally, RT-qPCR showed that the expression of LPAR1 was significantly lower in KD samples compared to normal samples whereas TRIM32 was significantly higher, while there was no difference in the expression of FOXF1. Conclusion: This study provides a new perspective to study the relationship between IRGs and KD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers. [ABSTRACT FROM AUTHOR]

Hypertrophic scar (HS) is a chronic inflammatory skin disorder characterized by excessive deposition of extracellular matrix, and the mechanisms underlying their formation remain poorly understood. We analysed scRNA‐seq data from samples of normal skin and HS. Using the hdWGCNA method, key gene modules of fibroblasts in HS were identified. Non‐negative matrix factorization was employed to perform subtype analysis of HS patients using these gene modules. Multiple machine learning algorithms were applied to screen and validate accurate gene signatures for identifying and predicting HS, and a convolutional neural network (CNN) based on deep learning was established and validated. Quantitative reverse transcription‐polymerase chain reaction and western blotting were performed to measure mRNA and protein expression. Immunofluorescence was used for gene localization analysis, and biological features were assessed through CCK8 and wound healing assay. Single‐cell sequencing revealed distinct subpopulations of fibroblasts in HS. HdWGCNA identified key gene characteristics of this population, and pseudotime analysis was conducted to investigate gene variation during fibroblast differentiation. By employing various machine learning algorithms, the gene range was narrowed down to three key genes. A CNN was trained using the expression of these key genes and immune cell infiltration, enabling diagnosis and prediction of HS. Functional experiments demonstrated that THBS2 is associated with fibroblast proliferation and migration in HS and affects the formation and development of HS through the TGFβ1/P‐Smad2/3 pathway. Our study identifies unique fibroblast subpopulations closely associated with HS and provides biomarkers for the diagnosis and treatment of HS. [ABSTRACT FROM AUTHOR]

Peroxisome proliferator‐activated receptors (PPARs) are essential for cellular physiological processes. However, there is less research on the PPAR‐related genes in lung adenocarcinoma (LUAD). Open‐access data were get from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. All the analysis were conducted in the R software based on different R packages. In this study, we gauged the PPAR score employing a set of 72 PPAR‐associated genes and probed the biological impact of this score on lung adenocarcinoma (LUAD). Subsequently, we established a unique signature composed of eight PPAR‐related genes (ANGPTL4, ACSL3, ADIPOQ, FABP1, SLC27A1, ACOX2, PPARD and OLR1) to forecast the prognosis of LUAD. The signature's effectiveness in predicting survival was validated through the receiver operating characteristic curve in the TCGA‐LUAD cohort. As per the pathway enrichment analysis, several crucial oncogenic pathways and metabolic processes were enriched in high‐risk individuals. Further, we observed that these high‐risk patients exhibited heightened genomic instability. Additionally, compared to the low‐risk cohort, high‐risk patients demonstrated diminished immune components and function. Intriguingly, high‐risk patients exhibited a potential heightened sensitivity to immunotherapy and certain drugs, including Gefitinib, Afatinib, Erlotinib, IAP_5620, Sapitinib, LCL161, Lapatinib and AZD3759. The prognosis model based on eight PPAR‐related genes has satisfactory prognosis prediction efficiency. Meanwhile, our results can provide direction for future studies in the relevant aspects. [ABSTRACT FROM AUTHOR]

Background: Intervertebral disc degeneration (IVDD) is a common chronic disease in orthopedics, and its molecular mechanisms are still not well explained. Aim: This study's objective was to bioinformatics‐based discovery of IVDD biomarkers and immune‐inflammatory infiltrates. Materials and Methods: The IVDD illness gene collection was gathered from GeneCards, DisGeNet, and gene expression profiles were chosen from the extensive Gene Expression Omnibus database (GSE124272, GSE150408, and GSE153761). The STRING database was used to create a network of protein–protein interactions, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases were used for functional enrichment analysis. Using hub genes, the immune cell infiltration between IVDD patient samples and control tissues was examined. Finally, quantitative polymerase chain reaction and Western blot experiments were used to verify the expression of hub genes. Results: A total of 27 differentially expressed hub genes were identified by bioinformatics. According to GO and KEGG analyses, hub genes were prominent in immunological responses, chemokine‐mediated signaling pathways, and inflammatory responses, with the key signaling pathways engaged in cellular senescence, apoptosis, Th1 and Th2 cell differentiation, and Th17 cell differentiation. Immune cell infiltration research revealed that T cells, lymphocytes, B cells, and NK cells were decreased in IVDD patients while monocytes, neutrophils, and CD8 T cells were increased. The expression levels of the senescence hub genes SP1, VEGFA, IL‐6, and the apoptosis key gene CASP3 were considerably greater in the IVDD model group than in the control group, according to in vitro validation. Conclusion: In conclusion, the cellular senescence signaling pathway, the apoptosis signaling pathway, and associated hub genes play significant roles in the development and progression of IVDD, this finding may help direct future research on the senescence signaling route in IVDD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the leading cause of dementia worldwide, with recent studies highlighting the potential role of immunogenic cell death (ICD) in the pathogenesis of this neurodegenerative disorder. A total of 52 healthy controls and 64 patients with AD were included. Compared to the controls, the patients with AD exhibited 2392 differentially expressed genes (DEGs), of which 1015 and 1377 were upregulated and downregulated genes, respectively. Among them, nine common genes were identified by intersecting the AD-related module genes with the DEGs and ICD-associated genes. Gene ontology (GO)analysis further revealed "positive regulation of cytokine production" as the most significant term. Moreover, the enriched molecular functions were primarily related to the inflammatory body complex, while the overlapping genes were significantly enriched in lipopolysaccharide binding. Kyoto encyclopedia of genes and genomes (KEGG) analysis also indicated that these overlapping genes were mainly enriched in immunity, inflammation, and lipid metabolism pathways. Furthermore, the following four hub genes were detected using machine learning algorithms: P2RX7, HSP90AA1, NT5E, and NLRP3. These genes demonstrated significant differences in expression between the AD and healthy control groups (P < 0.05). Additionally, the area under the curve values of these four genes were all > 0.7, indicating their potential diagnostic value for AD. We further validated the protein levels of these four genes in the hippocampus of 3xTg-AD and C57BL/6J mice, showing P2RX7 and HSP90AA1 expression levels consistent with the previously analyzed trends. Finally, the single-sample gene set enrichment analysis (ssGSEA) algorithm provided additional evidence by demonstrating the crucial role of immune cell infiltration and its link with the hub genes in AD progression. Our study results suggest that ICD-mediated elevation of HSP90AA1 and P2RX7 levels and the resulting induction of tau hyperphosphorylation and neuroinflammation are vital in the AD pathogenic mechanism. [ABSTRACT FROM AUTHOR]

Background: Diabetic Nephropathy (DN) is one of the microvascular complications of diabetes. The potential targets of renin-angiotensinaldosterone system (RAAS) inhibitors for the treatment of DN need to be explored. Methods: The GSE96804 and GSE1009 datasets, 729 RAAS inhibitors-related targets and 6,039 DN-related genes were derived from the public database and overlapped with the differentially expressed genes (DN vs. normal) in GSE96804 to obtain the candidate targets. Next, key targets were screened via the Mendelian randomization analysis and expression analysis. The diagnostic nomogram was constructed and assessed in GSE96804. Additionally, enrichment analysis was conducted and a 'core active ingredient-key targetdisease pathway' network was established. Finally, molecular docking was performed. Results: In total, 60 candidate targets were derived, in which CTSC and PDE5A were screened as the key targets and had a causal association with DN as the protective factors (P < 0.05, OR < 1). Further, a nomogram exhibited pretty prediction efficiency. It is indicated that Benadryl hydrochloride might play a role in the DN by affecting the pathways of 'cytokine cytokine receptor interaction', etc. targeting the CTSC. Moreover, PDE5A might be involved in 'ECM receptor interaction', etc. for the effect of NSAID, captopril, chlordiazepoxide on DN. Molecular docking analysis showed a good binding ability of benadryl hydrochloride and CTSC, NSAID and PDE5A. PTGS2, ITGA4, and ANPEP are causally associated with acute kidney injury. Conclusion: CTSC and PDE5A were identified as key targets for RAAS inhibitors in the treatment of DN, which might provide some clinical significance in helping to diagnose and treat DN. Among the targets of RAAS inhibitors, PTGS2, ITGA4 and ANPEP have a causal relationship with acute kidney injury, which is worthy of further clinical research. [ABSTRACT FROM AUTHOR]

Objective To analyze the prognosis of patients with medulloblastoma at different ages, and to explore the influence of immune cell infiltration on prognosis and the correlation with age, and screen age-related immune regulatory molecules. Methods In the study, cohorts from public database were studied through informatic analysis, Kaplan-meier curves were used for survival analysis, and MCPcounter was used to quantify the absolute abundance of immune cells in different ages. Meanwhile, age-related different expression genes were screened, and the effects of different expression genes on immune cell infiltration were analyzed. Results The survival rate of patients aged 0-3 years was the lowest among all patients in the cohort. The number of CD8+T cells, cytotoxic lymphocytes, neutrophils and B lineage in the tumor microenvironment of infants were significantly lower than other age groups, while the number of tumor-associated fibroblasts, monocyte lineage and endothelial cells were significantly higher than other age groups. The expression of CXCR4 was negatively correlated with the infiltration of effector immune cells and positively correlated with tumor-associated cells, which were closely related to the prognosis of medulloblastoma. Conclusion The poor prognosis of infants medulloblastoma is closely related to the remodeling of tumor microenvironment mediated by CXCR4 expression. [ABSTRACT FROM AUTHOR]

In recent years, the matrisome, a set of proteins that make up the extracellular matrix (ECM) or are closely involved in ECM behavior, has been shown to have great importance for characterizing and understanding disease pathogenesis and progression. The matrisome is especially critical for examining diseases characterized by extensive tissue remodeling. Endometriosis is characterized by the extrauterine growth of endometrial tissue, making it an ideal condition to study through the lens of matrisome gene expression. While large gene expression datasets have become more available and gene dysregulation in endometriosis has been the target of several studies, the gene expression profile of the matrisome specifically in endometriosis has not been well characterized. In our study, we explored four Gene Expression Omnibus (GEO) DNA microarray datasets containing eutopic endometrium of people with and without endometriosis. After batch correction, menstrual cycle phase accounted for 53% of variance and disease accounted for 23%; thus, the data were separated by menstrual cycle phase before performing differential expression analysis, statistical and machine learning modeling, and enrichment analysis. We established that matrisome gene expression alone can effectively differentiate endometriosis samples from healthy ones, demonstrating the potential of matrisome gene expression for diagnostic applications. Furthermore, we identified specific matrisome genes and gene networks whose expression can distinguish endometriosis stages I/II from III/IV. Taken together, these findings may aid in developing future in vitro models of disease, offer insights into novel treatment strategies, and advance diagnostic tools for this underserved patient population. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)‐related gene signature in CRC. Methods: We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co‐expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg‐related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored. Results: Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg‐related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients' survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients' survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples. Conclusion: We established a useful riskscore model in light of seven Treg‐related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients. [ABSTRACT FROM AUTHOR]

Background: Systemic juvenile idiopathic arthritis (SJIA) is a form of childhood arthritis with clinical features such as fever, lymphadenopathy, arthritis, rash, and serositis. It seriously affects the growth and development of children and has a high rate of disability and mortality. SJIA may result from genetic, infectious, or autoimmune factors since the precise source of the disease is unknown. Our study aims to develop a genetic-based diagnostic model to explore the identification of SJIA at the genetic level. Methods: The gene expression dataset of peripheral blood mononuclear cell (PBMC) samples from SJIA was collected from the Gene Expression Omnibus (GEO) database. Then, three GEO datasets (GSE11907-GPL96, GSE8650-GPL96 and GSE13501) were merged and used as a training dataset, which included 125 SJIA samples and 92 health samples. GSE7753 was used as a validation dataset. The limma method was used to screen differentially expressed genes (DEGs). Feature selection was performed using Lasso, random forest (RF)-recursive feature elimination (RFE) and RF classifier. Results: We finally identified 4 key genes (ALDH1A1, CEACAM1, YBX3 and SLC6A8) that were essential to distinguish SJIA from healthy samples. And we combined the 4 key genes and performed a grid search as well as 10-fold cross-validation with 5 repetitions to finally identify the RF model with optimal mtry. The mean area under the curve (AUC) value for 5-fold cross-validation was greater than 0.95. The model's performance was then assessed once more using the validation dataset, and an AUC value of 0.990 was obtained. All of the above AUC values demonstrated the strong robustness of the SJIA diagnostic model. Conclusions: We successfully developed a new SJIA diagnostic model that can be used for a novel aid in the identification of SJIA. In addition, the identification of 4 key genes that may serve as potential biomarkers for SJIA provides new insights to further understand the mechanisms of SJIA. [ABSTRACT FROM AUTHOR]

Introduction: Accumulating studies have shown that copper has a detrimental effect in cells, and the cuproptosis-related gene signatures have been constructed as clinical tools to predict prognosis in tumors. However, the heterogeneity of cuproptosis has not been fully investigated in ischemic stroke. Methods: Here, we combined the bulk RNA-seq and single cell-RNA-seq data for stroke to investigate the role of cuproptosis in stroke. Results: We identified the cuproptosis-related differentially expressed genes (CuDEGs) in ischemic stroke. Then, we tried to find the hub genes with the machine learning method and WGCNA. We highlighted four genes identified by these methods and proposed a potential diagnostic model in ischemic stroke. Conclusions: Our findings revealed cuproptosis-related hub genes, which could provide useful biomarkers in ischemic stroke. [ABSTRACT FROM AUTHOR]

Background: Uveal melanoma (UM) is an aggressive intraocular malignant tumor. The present study aimed to identify the key genes associated with UM metastasis and established a gene signature to analyze the relationship between the signature and prognosis and immune cell infiltration. Later, a predictive model combined with clinical variables was developed and validated. Methods: Two UM gene expression profile chip datasets were downloaded from TCGA and GEO databases. Immune-related genes (IRGs) were obtained from IMPORT database. First, these mRNAs were intersected with IRGs, and weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate Cox regression analysis screened the genes related to prognosis. LASSO-Cox established a risk score to distinguish high-risk group and low-risk group. Then the GSEA enrichment pathway and immune cell infiltration of the two groups were compared. And combined with clinical variables, a predictive model was constructed. The time-dependent receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to verify the stability and accuracy of the final predictive model, and a nomogram was then drawn. Results: The MEblack, MEpurple, and MEblue modules were significantly associated with the metastasis of UM patients (P value < 0.001, = 0.001, = 0.022, respectively). Four genes (UBXN2B, OTUD3, KAT8, LAMTOR2) were obtained by Pearson correlation analysis, weighted gene correlation network analysis (WGCNA), univariate Cox, and LASSO-Cox. And a novel prognostic risk score was established. Immune-related prognostic signature can well classify UM patients into high-risk and low-risk groups. Kaplan–Meier curve showed that the OS of high-risk patients was worse than that of low-risk patients. In addition, the risk score played an important role in evaluating the signaling pathway and immune cell infiltration of UM patients in high-risk and low-risk groups. Both the training set and validation set of the model showed good predictive accuracy in the degree of differentiation and calibration (e.g., 1-year overall survival: AUC = 0.930 (0.857–1.003)). Finally, a nomogram was established to serve in clinical practice. Significance: UM key gene signature and prognosis predictive model might provide insights for further investigation of the pathogenesis and development of UM at the molecular level, and provide theoretical basis for determining new prognostic markers of UM and immunotherapy. [ABSTRACT FROM AUTHOR]

Objective·To establish a prognostic model for the overall survival (OS) of hepatocellular carcinoma (HCC) based on mitochondrial genes and clinical information. Methods·The gene expression and the clinical data of 369 HCC patients and 50 controls with normal liver were downloaded from The Cancer Genome Atlas (TCGA) database. The nuclear-encoded mitochondrial genes (NEMGs) were obtained from the MitoCarta3.0 database. The "DESeq2" R package and univariate Cox analysis were used to select NEMGs [ubiquinol cytochrome C reductase hinge protein (UQCRH), ATP citrate lyase (ACLY), phosphoenolpyruvate carboxykinase 2 (PCK2), Bcl-2 homologous antagonist/killer1 (BAK1), Bcl-2-associated X protein (BAX) and Bcl-2/adenovirus E1B interacting protein 3-like (BNIP3L)] in HCC that were associated with OS of HCC and participated in dysregulation of oxidative phosphorylation, tricarboxylic acid cycle and cell apoptosis. Multivariate Cox analysis was applied to select independent risk factors for OS of HCC. A comprehensive prognostic model and a prognostic nomogram with 6-NEMG risk characteristics and TNM staging were established. By using the median of prognostic scores as a cut-off, HCC patients were classified into low-risk and high-risk group. Kaplan-Meier survival curve analysis was conducted and log-rank test was performed to evaluate the survival rates between the lowrisk and high-risk group. The area under the curve (AUC) values of receiver operating characteristic (ROC) curve were calculated via using the "timeROC" package. The prognostic model for HCC was validated by using the GEO HCC cohort (GSE14520) for 1, 3 and 5 years. Finally, the relative expression level of 6-NEMG was validated in 34 clinical samples of HCC from Xinhua Hospital, Shanghai Jiao Tong University School of Medicine by using real-time quantitative polymerase chain reaction (qPCR) method. Results·Compared to 6-NEMG risk signature only (AUCs for 1, 3 and 5 years were 0.77, 0.66 and 0.65, respectively) or TNM stage only (AUCs for 1, 3 and 5 years were 0.66, 0.67 and 0.63, respectively), ROC curve analysis showed that this integrated prognostic model displayed better predictive performance for 1-year (AUC, 0.78), 3-year (AUC, 0.73) and 5-year (AUC, 0.69) OS of HCC. The Kaplan-Meier survival curve analysis showed that the OS of HCC patients in the high-risk group was significantly worse than that in the low-risk group (P=0.001). In addition, predictive performance of the prognostic model (AUC for 1, 3 and 5 years is 0.67, 0.66 and 0.74, respectively) and prognostic differences between the high-risk and low-risk group (P=0.001) were further validated in GEO (GSE14520) external cohort, and these results were consistent with the TCGA data. In addition to BNIP3L, dysregulation of five other NEMGs in the clinical HCC cohort was validated. The correlation analysis in GSE14520 and HCC clinical cohort showed a positive correlation between prognosis score and the size and number of tumors. Conclusion·A new prognostic model that combines 6-NEMG risk characteristics with TNM staging for predicting OS in HCC patients was constructed and validated. This model may help improve the prognosis prediction of HCC patients. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to identify and verify potential biomarkers in the development of myopia associated with immunogenic cell death (ICD). Methods: We download high myopia (HM) dataset GSE136701 from Gene Expression Omnibus. Differentially expressed genes in HM were identified to overlapped with ICD-related genes. Least absolute shrinkage and selection operator were used to select the Hub genes. Furthermore, the correlation between the hub genes and immune infiltration, immune response activities, and hub genes Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis was investigated using Spearman's rank correlation. Prediction of the miRNAs upstream of the Hub genes was based on the TargetScan database. We used guinea pig lens-induced myopia model’s scleral tissues performed quantitative real-time polymerase chain reaction. Results: We identified overlapped with ICD-related genes (LY96, IL1A, IL33, and AGER) and two genes (LY96 and AGER) as hub genes. Single sample gene set enrichment analysis and Spearman’s rank correlation revealed that hub gene expression levels in HM were significantly correlated with the infiltration percentages of CD56dim natural killer cells, macrophages, immature B cells, and the immune response activities of APC co-stimulation and Kyoto Encyclopedia of Genes and Genomes pathways, such as terpenoid backbone biosynthesis, aminoacyl-trna biosynthesis, Huntington’s disease, oxidative phosphorylation; there were a few additional signaling pathways compared to normal samples. Additionally, several miRNA were predicted as upstream regulators of LY96 and AGER. LY96 was identified as a significantly differentially expressed biomarker in myopia guinea pig’s scleral tissues, as verified by qPCR. Conclusion: LY96 was identified and verified as a ICD-related potential myopia biomarker. Molecular mechanisms or pathways involved in myopia development by LY96 requires further research. [ABSTRACT FROM AUTHOR]

Background: Lung adenocarcinoma (LUAD) is the commonest subtype of primary lung cancer. A comprehensive analysis of the association of immunity with amino acid metabolism in LUAD is critical for understanding the disease. Methods: The present study examined LUAD and noncancerous cases from the TCGA database. Differentially expressed genes (DEGs) between LUAD and noncancerous tissues were detected by analyzing processed expression profiles. We cross-referenced the up-regulated DEGs with Immune and Amino Acid Metabolism-related genes (I&AAMGs), resulting in Immune and Amino Acid Metabolism related differentially expressed genes (IAAAMRDEGs). The STRING database was employed to analyze PPI on IAAAMRDEGs, obtaining excavated hub genes, whose biological processes, molecular functions and cellular components were examined with GO/KEGG. Potential mechanisms related to LUAD were investigated by GSEA and GSVA. A prognostic model was built by LASSO-COX analysis, taking into consideration risk scores and prognostic factors to determine biomarkers affecting LUAD occurrence and prognosis. Results: Totally 377 genes were detected at the intersection of upregulated DEGs and I&AAMGs. Analysis of PPI on these 377 IAAAMRDEGs yielded 17 hub genes. A LASSO regression analysis was utilized to assess the prognostic values of the 17 hub genes. Validation using the combined dataset confirmed 4 genes, e.g., polo-like kinase (PLK1), Ribonucleotide Reductase Subunit M2 (RRM2), Thyroid Hormone Receptor Interactor 13 (TRIP13), and Hyaluronan-Mediated Motility Receptor (HHMR). The model's accuracy was further assessed by ROC curve analysis and the COX model. In addition, immunohistochemical staining obtained from the HPA database, revealed enhanced PLK1 expression in LUAD samples. Conclusion: LUAD pathogenesis is highly associated with immunity and amino acid metabolism. The PLK1, RRM2, TRIP13, and HMMR genes have prognostic values for LUAD. PLK1 upregulation in LUAD might be involved in tumorigenesis by modulating the cell cycle and represents a potential prognostic factor in clinic.