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Ischemic stroke (IS) is a severe condition regulated by complex molecular alterations. This study aimed to identify potential nicotinamide adenine dinucleotide (NAD+) metabolism-associated diagnostic markers of IS and explore their associations with immune dynamics. Weighted Gene Co-expression Network Analysis and single-sample gene set enrichment analysis (ssGSEA) were employed to identify key gene modules on the GEO dataset (GSE16561). LASSO regression was used to identify diagnostic genes. A diagnostic model was then developed using the training dataset, and its performance was assessed using a validation dataset (GSE22255 dataset). Associations between hub genes and immune cells, immune response genes, and human leukocyte antigen (HLA) genes were assessed by ssGSEA. A regulatory network was constructed using mirBase and TRRUST databases. A total of 20 NAD+ metabolic genes exhibited noteworthy expression variations. Within the module notably associated with NAD+ metabolism, 19 specific genes were included in the diagnostic model, which was validated on the GSE22255 dataset (AUC: 0.733). There were significant disparities in immune cell populations, immune response genes, and HLA gene expression, all of which were associated with the hub genes. A regulatory network composed of 153 edges and 103 nodes was constructed. This study advances our understanding of IS by providing insights into NAD+ metabolism and gene interactions, contributing to potential diagnostic innovations in IS.

Background: The poor prognosis of sepsis warrants the investigation of biomarkers for predicting the outcome. Several studies have indicated that PANoptosis exerts a critical role in tumor initiation and development. Nevertheless, the role of PANoptosis in sepsis has not been fully elucidated. Methods: We obtained Sepsis samples and scRNA-seq data from the GEO database. PANoptosis-related genes were subjected to consensus clustering and functional enrichment analysis, followed by identification of differentially expressed genes and calculation of the PANoptosis score. A PANoptosisbased prognostic model was developed. In vitro experiments were performed to verify distinct PANoptosis-related genes. An external scRNAseq dataset was used to verify cellular localization. Results: Unsupervised clustering analysis using 16 PANoptosis-related genes identified three subtypes of sepsis. Kaplan-Meier analysis showed significant differences in patient survival among the subtypes, with different immune infiltration levels. Differential analysis of the subtypes identified 48 DEGs. Boruta algorithm PCA analysis identified 16 DEGs as PANoptosis-related signature genes. We developed PANscore based on these signature genes, which can distinguish different PANoptosis and clinical characteristics and may serve as a potential biomarker. Single-cell sequencing analysis identified six cell types, with high PANscore clustering relatively in B cells, and low PANscore in CD16+ and CD14+ monocytes and Megakaryocyte progenitors. ZBP1, XAF1, IFI44L, SOCS1, and PARP14 were relatively higher in cells with high PANscore. Conclusion: We developed a machine learning based Boruta algorithm for profiling PANoptosis related subgroups with in predicting survival and clinical features in the sepsis. [ABSTRACT FROM AUTHOR]

Background: Stomach adenocarcinoma (STAD) is one of the most common malignancies. Infection of helicobacter pylori (H. pylori) is a major risk factor that leads to the development of STAD. This study constructed a risk model based on the H. pylori-related macrophages for predicting STAD prognosis. Methods: The single-cell RNA sequencing (scRNA-seq) dataset and the clinic information and RNA-seq datasets of STAD patients were collected for establishing a prognostic model and for validation. The “Seurat” and “harmony” packages were used to process the scRNA-seq data. Key gene modules were sectioned using the “limma” package and the “WGCNA” package. Kaplan-Meier (KM) and Receiver Operating Characteristic Curve (ROC) analyses were performed with “survminer” package. The “GSVA” package was employed for single sample gene set enrichment analysis (ssGSEA). Cell migration and invasion were measured by carrying out wound healing and trans-well assays. Results: A total of 17397 were screened and classified into 8 cell type clusters, among which the macrophage cluster was closely associated with the H. pylori infection. Macrophages were further categorized into four subtypes (including C1, C2, C3, and C4), and highly variable genes of macrophage subtype C4 could serve as an indicator of the prognosis of STAD. Subsequently, we developed a RiskScore model based on six H. pylori -associated genes (TNFRSF1B, CTLA4, ABCA1, IKBIP, AKAP5, and NPC2) and observed that the high-risk patients exhibited poor prognosis, higher suppressive immune infiltration, and were closely associated with cancer activation-related pathways. Furthermore, a nomogram combining the RiskScore was developed to accurately predict the survival of STAD patients. ABCA1 in the RiskScore model significantly affected the migration and invasion of tumor cells. Conclusion: The gene expression profile served as an indicator of the survival for patients with STAD and addressed the clinical significance of using H. pylori-associated genes to treat STAD. The current findings provided novel understandings for the clinical evaluation and management of STAD.

Immune cell infiltration and tumor-related immune molecules play key roles in tumorigenesis and tumor progression. The influence of immune interactions on the molecular characteristics and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. A machine learning algorithm was applied to the transcriptome data from The Cancer Genome Atlas database to determine the immunophenotypic and immunological characteristics of ccRCC patients. These algorithms included single-sample gene set enrichment analyses and cell type identification. Using bioinformatics techniques, we examined the prognostic potential and regulatory networks of immune-related genes (IRGs) involved in ccRCC immune interactions. Fifteen IRGs (CCL7, CHGA, CMA1, CRABP2, IFNE, ISG15, NPR3, PDIA2, PGLYRP2, PLA2G2A, SAA1, TEK, TGFA, TNFSF14, and UCN2) were identified as prognostic IRGs associated with overall survival and were used to construct a prognostic model. The area under the receiver operating characteristic curve at 1 year was 0.927; 3 years, 0.822; and 5 years, 0.717, indicating good predictive accuracy. Molecular regulatory networks were found to govern immune interactions in ccRCC. Additionally, we developed a nomogram containing the model and clinical characteristics with high prognostic potential. By systematically examining the sophisticated regulatory mechanisms, molecular characteristics, and prognostic potential of ccRCC immune interactions, we provided an important framework for understanding the molecular mechanisms of ccRCC and identifying new prognostic markers and therapeutic targets for future research.

Patients with thymoma (THYM)-associated myasthenia gravis (MG) typically have a poor prognosis and recurring illness. This study aimed to discover important biomarkers associated with immune cell infiltration and THYM-associated MG (THYM-MG) development. Gene expression microarray data were downloaded from The Cancer Genome Atlas website (TCGA) and Gene Expression Omnibus (GEO). A total of 102 differentially expressed genes were investigated. According to the immune infiltration data, the distribution of Tfh cells, B cells, and CD4 T cells differed significantly between the THYM-MG and THYM-NMG groups. WGCNA derived 25 coexpression modules; one hub module (the blue module) strongly correlated with Tfh cells. Combining differential genes revealed 21 intersecting genes. LASSO analysis subsequently revealed 16 hub genes as potential THYM-MG biomarkers. ROC curve analysis of the predictive model revealed moderate diagnostic value. The association between the 16 hub genes and infiltrating immune cells was further evaluated in TIMER2.0 and the validation dataset. Draggability analysis identified the therapeutic target genes PTGS2 and ALB, along with significant drugs including Firocoxib, Alclofenac, Pyridostigmine, and Stavudine. This was validated through MD simulation, PCA, and MM-GBSA analyses. The interaction between numerous activated B cells and follicular helper T cells is closely associated with THYM-MG pathogenesis from a bioinformatics perspective. Hub genes (including SP6, SCUBE3, B3GNT7, and MAGEL2) may be downregulated in immune cells in THYM-MG and associated with progression.

Advances in the field of genomics and transcriptomics have enabled researchers to identify gene signatures related to development and treatment of Small Cell Lung Cancer. In most cases, complex gene expression patterns are identified, comprising of genes with differential behavior. Most tools use single-genes as predictors of drug response, with only limited options for multi-gene use. Here we examine the potential of predicting drug response using these complex gene expression signatures by employing clustering and signal enrichment in Small Cell Lung Cancer. Our results demonstrate clustering genes from complex expression patterns helps identify differential activity of gene groups with alternate function which can then be used to predict drug response. • Clustering genes in expression signatures before enrichment analysis helps find key groups • Most small cell lung cancer cell lines are enriched for epithelial genes • A subset of inflamed signature genes used to predict immunotherapy response are not expressed in cell lines [ABSTRACT FROM AUTHOR]

Introduction: Osteoarthritis (OA) is still an important health problem, and understanding its pathological mechanisms is essential for its diagnosis and treatment. There is evidence that autophagy may play a role in OA progression, but the exact mechanism remains unclear. Methods: In this study, we adopted a multi-prong approach to systematically identify the key autophagy-related genes (ARGs) associated with OA. Through weighted gene co-expression network analysis, we initially identified significant gene modules associated with OA. Subsequent differential gene analysis performed on normal and OA specimens. Further analysis later using the MCC algorithm highlighted hub ARGs. These genes were then incorporated into the prediction model of OA. In addition, the expression patterns of these DEGs were verified by in vitro experiments. Results: A total of 104 differentially expressed genes (DEGs) were identified by differential gene analysis, of which 102 were up-regulated and 2 were down-regulated. These differentially expressed genes were closely related to key signaling pathways, such as PI3K-Akt signaling pathway, IL-17 signaling pathway and osteoclast differentiation. Further MCC analysis highlighted 10 hub ARGs, among which ATF3, CYCS, FOXO3, KLF6, NFKBIA and SOCS3 were particularly significant, which were then included in the prediction model of OA, which showed robust prediction ability with an area under the curve of 0.783. In vitro experiments confirmed that the expression pattern of these DEGs was consistent with our prediction. Conclusion: In summary, our comprehensive analysis not only provides new insights into the molecular basis of OA, but also suggests potential biomarkers for its diagnosis. [ABSTRACT FROM AUTHOR]

Zhe Xiong,1,2 Ying Fang,1,2 Shuangshuang Lu,1 Qiuyue Sun,1,3 Jin Huang1 1Department of Gastroenterology, the Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, People’s Republic of China; 2Graduate School of Dalian Medical University, Dalian, Liaoning Province, People’s Republic of China; 3Graduate School of Nanjing Medical University, Nanjing, Jiangsu Province, People’s Republic of ChinaCorrespondence: Jin Huang, Email hj042153@hotmail.comBackground: Inflammatory bowel disease (IBD) and periodontitis (PD) are correlated, although the pathogenic mechanism behind their correlation has not been clarified. This study aims to explore the common signature genes and potential therapeutic targets of IBD and PD using transcriptomic analysis.Methods: The GEO database was used to download datasets of IBD and PD, and differential expression analysis was used to identify DEGs. We then conducted GO and KEGG enrichment analyses of the shared genes. Next, we applied 4 machine learning (ML) algorithms (GLM, RF, GBM, and SVM) to select the best prediction model for diagnosing the disease and obtained the hub genes of IBD and PD. The diagnostic value of the signature genes was verified by a validation set and qRT‒PCR experiments. Subsequently, immune cell infiltration in IBD samples and PD samples was analyzed by ssGSEA. Finally, we investigated and validated the response of hub genes to infliximab therapy.Results: We identified 43 upregulated genes as shared genes by intersecting the DEGs of IBD and PD. Functional enrichment analysis suggested that the shared genes were closely associated with immunity and inflammation. The ML algorithm and qRT‒PCR results indicated that IGKC and COL4A1 were the hub genes with the most diagnostic value for IBD and PD. Subsequently, through immune infiltration analysis, CD4 T cells, NK cells and neutrophils were identified to play crucial roles in the pathogenesis of IBD and PD. Finally, through in vivo and in vitro experiments, we found that IGKC and COL4A1 were significantly downregulated during the treatment of patients with IBD using infliximab.Conclusion: We investigated the potential association between IBD and PD using transcriptomic analysis. The IGKC and COL4A1 genes were identified as characteristic genes and novel intervention targets for these two diseases. Infliximab may be used to treat or prevent IBD and PD.Keywords: Inflammatory bowel disease, periodontitis, machine learning, ssGSEA, infliximab, IGKC, COL4A1

N1-methyladenosine (m1A) modification is a crucial post-transcriptional regulatory mechanism of messenger RNA (mRNA) in living organisms. Few studies have focused on analysis of m1A regulators in lower-grade gliomas (LGG). We employed the Nonnegative Matrix Factorization (NMF) technique on The Cancer Genome Atlas (TCGA) dataset to categorize LGG patients into 2 groups. These groups exhibited substantial disparities in terms of both overall survival (OS) and levels of infiltrating immune cells. We collected the significantly differentially expressed immune-related genes between the 2 clusters, and performed LASSO regression analysis to obtain m1AScores, and established an m1A-related immune-related gene signature (m1A-RIGS). Next, we categorized all patients with LGG into high- and low-risk subgroups, predictive significance of m1AScore was confirmed by conducting univariate/multivariate Cox regression analyses. Additionally, we confirmed variations in immune-related cells and ssGSEA and among the high-/low-risk subcategories in the TCGA dataset. Finally, our study characterized the effects of MSR1 and BIRC5 on LGG cells utilizing Edu assay and flow cytometry to explore the effects of modulation of these genes on glioma. The results of this study suggested that m1A-RIGS may be an excellent prognostic indicator for patients with LGG, and could also promote development of novel immune-based treatment strategies for LGG. Additionally, BIRC5 and MSR1 may be potential therapeutic targets for LGG.

BackgroundThe poor prognosis of sepsis warrants the investigation of biomarkers for predicting the outcome. Several studies have indicated that PANoptosis exerts a critical role in tumor initiation and development. Nevertheless, the role of PANoptosis in sepsis has not been fully elucidated.MethodsWe obtained Sepsis samples and scRNA-seq data from the GEO database. PANoptosis-related genes were subjected to consensus clustering and functional enrichment analysis, followed by identification of differentially expressed genes and calculation of the PANoptosis score. A PANoptosis-based prognostic model was developed. In vitro experiments were performed to verify distinct PANoptosis-related genes. An external scRNA-seq dataset was used to verify cellular localization.ResultsUnsupervised clustering analysis using 16 PANoptosis-related genes identified three subtypes of sepsis. Kaplan-Meier analysis showed significant differences in patient survival among the subtypes, with different immune infiltration levels. Differential analysis of the subtypes identified 48 DEGs. Boruta algorithm PCA analysis identified 16 DEGs as PANoptosis-related signature genes. We developed PANscore based on these signature genes, which can distinguish different PANoptosis and clinical characteristics and may serve as a potential biomarker. Single-cell sequencing analysis identified six cell types, with high PANscore clustering relatively in B cells, and low PANscore in CD16+ and CD14+ monocytes and Megakaryocyte progenitors. ZBP1, XAF1, IFI44L, SOCS1, and PARP14 were relatively higher in cells with high PANscore.ConclusionWe developed a machine learning based Boruta algorithm for profiling PANoptosis related subgroups with in predicting survival and clinical features in the sepsis.

Background: Multiple modes of cell death occur during the development of sepsis. Among these patterns, cuproptosis has recently been identified as a regulated form of cell death. However, its impact on the onset and progression of sepsis remains unclear. Method: We screened a dataset of gene expression profiles from patients with sepsis using the GEO database. Survival analysis was performed to analyze the relationship between cuproptosis-related genes (CRGs) and prognosis. Hub genes were identified through univariate Cox regression analysis. The diagnostic value of hub genes in sepsis was tested in both training sets (GSE65682) and validation sets (GSE134347). To examine the association between hub genes and immune cells, single-sample gene set enrichment analysis (ssGSEA) and Pearson correlation analysis were employed. Additionally, the CRGs were validated in a septic mouse model using real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC). Results: In sepsis, most CRGs were upregulated, with only DLD and MTF1 downregulated. High expression of three genes (GLE, LIAS, and PDHB) was associated with better prognosis, but only two hub genes (LIAS, PDHB) reached statistical significance. The receiver operating characteristic (ROC) analysis for diagnosing sepsis showed LIAS had a range of 0.793–0.906, while PDHB achieved values of 0.882 and 0.975 in the training and validation sets, respectively. ssGSEA analysis revealed a lower number of immune cells in the sepsis group, and there was a correlation between immune cell population and CRGs (LIAS, PDHB). Analysis in the septic mouse model demonstrated no significant difference in mRNA expression levels and IHC staining between LIAS and PDHB in heart and liver tissues, but up-regulation was observed in lung tissues. Furthermore, the mRNA expression levels and IHC staining of LIAS and PDHB were down-regulated in renal tissues. Conclusions: Cuproptosis is emerging as a significant factor in the development of sepsis. LIAS and PDHB, identified as potential diagnostic biomarkers for cuproptosis-associated sepsis, are believed to play crucial roles in the initiation and progression of cuproptosis-induced sepsis.

Background: Dilated cardiomyopathy (DCM), a specific form of cardiomyopathy, frequently presents clinically with either left ventricular or biventricular enlargement, often leading to progressive heart failure. In recent years, the application of bioinformatics technology to scrutinize the onset, progression, and prognosis of DCM has emerged as a fervent area of interest among scholars globally. Methods: In this study, core genes closely related to DCM were identified through bioinformatics analysis, including weighted gene co expression network analysis (WGCNA) and single sample gene set enrichment analysis (ssGSEA) and so on. The correlation was verified through experiments on DCM patients, DCM rat models, and core gene knockout mice. Subsequently, the effects of glucocorticoids on DCM and the regulation of core genes were observed. Result: In the present study, natriuretic peptide receptor 1 (NPR1) was identified as a core gene associated with DCM through WGCNA and ssGSEA. Significant impairment of cardiac and renal function was observed in both DCM patients and rats, concomitant with a notable reduction in NPR1 expression. NPR1 KO mice displayed symptomatic manifestations of DCM, underscoring the pivotal role of NPR1 in its pathogenesis. Notably, glucocorticoid treatment led to substantial improvements in cardiac and renal function, accompanied by an upregulation of NPR1 expression. Discussion: These findings highlight the critical involvement of NPR1 in the pathophysiology of DCM and its potential as a key target for glucocorticoidbased DCM therapy. The study provides a robust theoretical and experimental foundation for further investigations into DCM etiology and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is an infectious disease with high mortality, and mining key genes for TB diagnosis is vital to raise the survival rate of patients. The whole microarray datasets GSE83456 (training set) and GSE19444 (validation set) of TB patients were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression was conducted on genes between TB and normal samples (unconfirmed TB) in GSE83456 to yield TB-related differentially expressed genes (DEGs). DEGs were subjected to weighted gene co-expression network analysis (WGCNA) and clustered to form distinct gene modules. The immune scores of 25 kinds of immune cells were obtained by single-sample gene set enrichment analysis (ssGSEA) of TB samples, and Pearson correlation analysis was carried out between the 25 immune scores and diverse gene modules. The gene modules significantly associated with immune cells were retained as Target modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the genes in the modules (p-value <0.05). The protein-protein interaction (PPI) network was established utilizing the STRING database for genes in the Target module, and the selected key genes were intersected with immune-related genes in the ImmPort database. The obtained immune-related module genes were used for subsequent least absolute shrinkage and selection operator (LASSO) regression analysis and diagnostic models were constructed. Finally, the receiver operating characteristic (ROC) curve was utilized to validate the diagnostic model. The turquoise and yellow modules had a high correlation with macrophages. LASSO regression analysis of immune-related genes in TB was carried on to finally construct a 5-gene diagnostic model composed of C5, GRN, IL1B, IL23A, and TYMP. As demonstrated by the ROC curves, the diagnostic efficiency of this diagnostic model was 0.957 and 0.944 in the training and validation sets, respectively. Therefore, the immune-related 5-gene model had a good diagnostic function for TB. We identified 5 immune-related diagnostic markers that may play an important role in TB, and verified that this immune-related key gene model had a good diagnostic performance. [ABSTRACT FROM AUTHOR]

Abstract Objective This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. Methods In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment. Results The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value

Objective: To facilitate immunotherapy and prognostic assessment of non-small cell lung cancer (NSCLC), we established a novel immunogenomic classification to provide valid identification criteria. Methods: The immune enrichment scores were calculated by single sample gene set enrichment analysis (ssGSEA) and clustered into Immunity_L and Immunity_H, and the reliability of this classification was demonstrated. Immune microenvironment score and immune cell infiltration analysis of NSCLC were also performed. Randomly divided into training group and test group, a prognosis-related immune profile was developed using least absolute shrinkage and selection operator (LASSO) and stepwise COX proportional hazards model to construct a prognostic mode. Results: The risk score for this immune profile was identified as an independent prognostic factor and can be used as a powerful prognostic tool to refine tumor immunotherapy. Our study identified two NSCLC classifications based on immunomic profiling, Immunity_H and Immunity_L. Conclusion: In conclusion, Immunogenomic classification can distinguish the immune status of different types of NSCLC patients and contribute to the immunotherapy of NSCLC patients. [ABSTRACT FROM AUTHOR]

Background: Crohn's disease (CD) has an increasing incidence and prevalence worldwide. It is currently believed that both the onset and progression of the disease are closely related to immune system imbalance and the infiltration of immune cells. The aim of this study was to investigate the molecular immune mechanisms associated with CD and its fibrosis through bioinformatics analysis. Methods: Three datasets from the Gene Expression Omnibus data base (GEO) were downloaded for data analysis and validation. Single sample gene enrichment analysis (ssGSEA) was used to evaluate the infiltration of immune cells in CD samples. Immune cell types with significant differences were identified by Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Differentially expressed genes (DEGs) were screened and then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional correlation analysis, as well as protein-protein interaction (PPI) network analysis. The cytoHubba program and the GSE75214 dataset were used to screen for hub genes and plot Receiver operating characteristic (ROC)curves to screen for possible biomarkers of CD based on diagnostic efficacy. The hub genes of CD were correlated with five significantly different immune cells. In addition, validation was performed by real time quantitative PCR (RT-qPCR) experiments in colonic tissue of CD intestinal fibrosis rats to further identify hub genes that are more related to CD intestinal fibrosis. Results: The DEGs were analyzed separately by 10 algorithms and narrowed down to 9 DEGs after taking the intersection. 4 hub genes were further screened by the GSE75214 validation set, namely COL1A1, CXCL10, MMP2 and FGF2. COL1A1 has the highest specificity and sensitivity for the diagnosis of CD and is considered to have the potential to diagnose CD. Five immune cells with significant differences were screened between CD and health controls (HC). Through the correlation analysis between five kinds of immune cells and four biomarkers, it was found that CXCL10 was positively correlated with activated dendritic cells, effector memory CD8+ T cells. MMP2 was positively correlated with activated dendritic cells, gamma delta T cells (γδ T) and mast cells. MMP2 and COL1A1 were significantly increased in colon tissue of CD fibrosis rats. Conclusion: MMP2, COL1A1, CXCL10 and FGF2 can be used as hub genes for CD. Among them, COL1A1 can be used as a biomarker for the diagnosis of CD. MMP2 and CXCL10 may be involved in the development and progression of CD by regulating activated dendritic cell, effector memory CD8+ T cell, γδ T cell and mast cell. In addition, MMP2 and COL1A1 may be more closely related to CD intestinal fibrosis. [ABSTRACT FROM AUTHOR]

Accurately identifying immune cell types in single-cell RNA-sequencing (scRNASeq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions. [ABSTRACT FROM AUTHOR]

Abstract The most common type of lung cancer tissue is lung adenocarcinoma. The TCGA‐LUAD cohort retrieved from the TCGA dataset was considered the internal training cohort, while GSE68465 and GSE13213 datasets from the GEO database were used as the external test cohort. The TCGA‐LUAD cohort was classified into two immune subtypes using single‐sample gene set enrichment analysis of the immune gene set and unsupervised clustering analysis. The ESTIMATE algorithm, the CIBERSORT algorithm, and HLA family expression levels again validated the reliability of this typing. We performed Venn analysis using immune‐related genes from the immport dataset and differentially expressed genes from the subtypes to retrieve differentially expressed immune genes (DEIGs). In addition, DEIGs were used to construct a prognostic model with the least absolute shrinkage and selection operator regression analysis. A reliable risk model consisting of 11 DEIGs, including S100P, INHA, SEMA7A, INSL4, CD40LG, AGER, SERPIND1, CD1D, CX3CR1, SFTPD, and CD79A, was then built, and its reliability was further confirmed by ROC curve and calibration plot analysis. The high‐risk score subgroup had a poor prognosis and a lower tumour immune dysfunction and exclusion score, indicating a greater likelihood of anti‐PD‐1/cytotoxic T lymphocyte antigen 4 benefit.

Abstract Background Both immune-reaction and lncRNAs play significant roles in the proliferation, invasion, and metastasis of ovarian cancer (OC). In this study, we aimed to construct an immune-related lncRNA risk model for patients with OC. Method Single sample GSEA (ssGSEA) algorithm was used to analyze the proportion of immune cells in The Cancer Genome Atlas (TCGA) and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells for OC patients. The stromal and immune scores were computed utilizing the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analyses were utilized to detect immune cluster-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression was conducted for lncRNA selection. The selected lncRNAs were used to construct a prognosis-related risk model, which was then validated in Gene Expression Omnibus (GEO) database and in vitro validation. Results We identify two subtypes based on the ssGSEA analysis, high immunity cluster (immunity_H) and low immunity cluster (immunity_L). The proportion of patients in immunity_H cluster was significantly higher than that in immunity_L cluster. The ESTIMATE related scores are relative high in immunity_H group. Through WGCNA and LASSO analyses, we identified 141 immune cluster-related lncRNAs and found that these genes were mainly enriched in autophagy. A signature consisting of 7 lncRNAs, including AL391832.3, LINC00892, LINC02207, LINC02416, PSMB8.AS1, AC078788.1 and AC104971.3, were selected as the basis for classifying patients into high- and low-risk groups. Survival analysis and area under the ROC curve (AUC) of the signature pointed out that this risk model had high accuracy in predicting the prognosis of patients with OC. We also conducted the drug sensitive prediction and found that rapamycin outperformed in patient with high risk score. In vitro experiments also confirmed our prediction. Conclusions We identified 7 immune-related prognostic lncRNAs that effectively predicted survival in OC patients. These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for these patients.

Objective: This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. Methods: In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment. Results: The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value < 0.05). They revealed high diagnostic and predictive values and were closely related to the expression of multiple immune cells (p-value < 0.05). It was inferred that the expression of PSMB9, STAT 1, and TAP1 could be regulated by different medicinal chemicals. Conclusion: PSMB9, STAT1, and TAP1, might play a key role in the pathogenesis of TB, including spinal TB, and the protein product of the genes can be served as diagnostic markers and potential therapeutic target for TB. [ABSTRACT FROM AUTHOR]

Objective: We aimed to explore immune-related prognosis genes of lung adenocarcinoma (LUAD). Materials and methods: TCGA-LUAD and GSE31210 data sets were accessed from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) respectively. By using "WGCNA" R package, we established a gene co-expression network and clustered genes into various modules. The correlation between immune scores and module eigengenes by using Pearson analysis. Screened hub genes and constructed prognostic model by using LASSO and Cox regression analysis. Evaluated model by survival analysis and receiver operating characteristic (ROC) curves. Hub genes expression in clinical tissues of LUAD patients by qRT-PCR analysis. ssGSEA and TIMER (a website tool for examination of different immune cells in different cancers) analyzed immune correlation of hub genes. Gene set variation analysis (GSVA) uncovered difference of signal pathway between high- and low-risk score group. Results: We found that brown module significantly correlated with the immune scores of immune cells. Therefore, we constructed a 7-gene prognostic model based on brown module genes, and indicated that this model possessed good predictive performance. Patients in training and validation sets were stratified into the high- and low-risk group using this model. Also, hub genes CDCP1, PLSCR1 and CD79A were highly expressed in clinical tissues of LUAD patients, while ID1, CLEC7A, KIAA1324 and CMTM7 were lowly expressed. Both ssGSEA and TIMER revealed a significant negative correlation between risk score and B cell infiltration. Additionally, some signal pathways were suppressed in the high-risk group. Conclusion: We identified 7 immune-associated prognostic markers, which may play vital roles in LUAD and could be used as hopeful targets for immunotherapy of LUAD. [ABSTRACT FROM AUTHOR]

Abstract Background This study developed a gene signature associated with a malignant and common tumor of the urinary system, the Bladder Urothelial Carcinoma (BLCA). Methods The Cancer Genome Atlas (TCGA) database was searched to obtain 414 BLCA samples and the expression spectra of 19 normal samples. Single-sample Gene Set Enrichment Analysis (ssGSEA) was conducted to determine the enrichment levels in the BLCA samples of the 29 immune genes. Unsupervised hierarchical clustering, gene set enrichment analysis (GSEA), single-factor Cox analysis, least absolute shrinkage and selection operator (LASSO) regression models, and GEO queues were used to determine the BLCA immune gene subtype, analyze the biological pathway differences between immune gene subtypes, determine the characteristic genes of BLCA associated with prognosis, identify the BLCA-related genes, and verify the gene signature, respectively. Results We identified two immune gene subtypes (immunity_L and immunity_H). The latter was significantly related to receptors, JAK STAT signaling pathways, leukocyte interleukin 6 generation, and cell membrane signal receptor complexes. Four characteristic genes (RBP1, OAS1, LRP1, and AGER) were identified and constituted the gene signature. Significant survival advantages, higher mutation frequency, and superior immunotherapy were observed in the low-risk group patients. The gene signature had good predictive ability. The results of the validation group were consistent with TCGA queue results. Conclusions We constructed a 4-gene signature that helps monitor BLCA occurrence and prognosis, providing an important basis for developing personalized BLCA immunotherapy.

The current database has no information on the infiltration of glioma samples. Here, we assessed the glioma samples’ infiltration in The Cancer Gene Atlas (TCGA) through the single-sample Gene Set Enrichment Analysis (ssGSEA) with migration and invasion gene sets. The Weighted Gene Co-expression Network Analysis (WGCNA) and the differentially expressed genes (DEGs) were used to identify the genes most associated with infiltration. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the major biological processes and pathways. Protein–protein interaction (PPI) network analysis and the least absolute shrinkage and selection operator (LASSO) were used to screen the key genes. Furthermore, the nomograms and receiver operating characteristic (ROC) curve were used to evaluate the prognostic and predictive accuracy of this clinical model in patients in TCGA and the Chinese Glioma Genome Atlas (CGGA). The results showed that turquoise was selected as the hub module, and with the intersection of DEGs, we screened 104 common genes. Through LASSO regression, TIMP1, EMP3, IGFBP2, and the other nine genes were screened mostly in correlation with infiltration and prognosis. EMP3 was selected to be verified in vitro. These findings could help researchers better understand the infiltration of gliomas and provide novel therapeutic targets for the treatment of gliomas.

Background: Gamma-aminobutyric acid (GABA) participates in the migration, differentiation, and proliferation of tumor cells. However, the GABA-related risk signature has never been investigated. Hence, we aimed to develop a reliable gene signature based on GABA pathways-related genes (GRGs) to predict the survival prognosis of breast cancer patients. Methods: GABA-related gene sets were acquired from the MSigDB database, while mRNA gene expression profiles and corresponding clinical data of breast cancer patients were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Univariate Cox regression analysis was used to identify prognostic-associated GRGs. Subsequently, LASSO analysis was applied to establish a risk score model. We also constructed a clinical nomogram to perform the survival evaluation. Besides, ESTIMATE and ssGSEA algorithms were used to assess the immune cell infiltration among the risk score subgroups. Results: A GRGs-related risk score model was constructed in the TCGA cohort, and validated in the GSE21653 cohort. The risk score was significantly related to the overall survival of breast cancer patients, which could predict the survival prognosis of breast cancer patients independently of other clinical features. Breast cancer patients in the low-risk score group exhibited higher immune cell infiltration levels. Conclusion: A novel prognostic model containing five GRGs could accurately predict the survival prognosis and immune infiltration of breast cancer patients. Our findings provided a novel insight into investigating the immunoregulation roles of GRGs. [ABSTRACT FROM AUTHOR]

Background: Both immune-reaction and lncRNAs play significant roles in the proliferation, invasion, and metastasis of ovarian cancer (OC). In this study, we aimed to construct an immune-related lncRNA risk model for patients with OC. Method: Single sample GSEA (ssGSEA) algorithm was used to analyze the proportion of immune cells in The Cancer Genome Atlas (TCGA) and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells for OC patients. The stromal and immune scores were computed utilizing the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analyses were utilized to detect immune cluster-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression was conducted for lncRNA selection. The selected lncRNAs were used to construct a prognosis-related risk model, which was then validated in Gene Expression Omnibus (GEO) database and in vitro validation. Results: We identify two subtypes based on the ssGSEA analysis, high immunity cluster (immunity_H) and low immunity cluster (immunity_L). The proportion of patients in immunity_H cluster was significantly higher than that in immunity_L cluster. The ESTIMATE related scores are relative high in immunity_H group. Through WGCNA and LASSO analyses, we identified 141 immune cluster-related lncRNAs and found that these genes were mainly enriched in autophagy. A signature consisting of 7 lncRNAs, including AL391832.3, LINC00892, LINC02207, LINC02416, PSMB8.AS1, AC078788.1 and AC104971.3, were selected as the basis for classifying patients into high- and low-risk groups. Survival analysis and area under the ROC curve (AUC) of the signature pointed out that this risk model had high accuracy in predicting the prognosis of patients with OC. We also conducted the drug sensitive prediction and found that rapamycin outperformed in patient with high risk score. In vitro experiments also confirmed our prediction. Conclusions: We identified 7 immune-related prognostic lncRNAs that effectively predicted survival in OC patients. These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for these patients. [ABSTRACT FROM AUTHOR]

The most common type of lung cancer tissue is lung adenocarcinoma. The TCGA‐LUAD cohort retrieved from the TCGA dataset was considered the internal training cohort, while GSE68465 and GSE13213 datasets from the GEO database were used as the external test cohort. The TCGA‐LUAD cohort was classified into two immune subtypes using single‐sample gene set enrichment analysis of the immune gene set and unsupervised clustering analysis. The ESTIMATE algorithm, the CIBERSORT algorithm, and HLA family expression levels again validated the reliability of this typing. We performed Venn analysis using immune‐related genes from the immport dataset and differentially expressed genes from the subtypes to retrieve differentially expressed immune genes (DEIGs). In addition, DEIGs were used to construct a prognostic model with the least absolute shrinkage and selection operator regression analysis. A reliable risk model consisting of 11 DEIGs, including S100P, INHA, SEMA7A, INSL4, CD40LG, AGER, SERPIND1, CD1D, CX3CR1, SFTPD, and CD79A, was then built, and its reliability was further confirmed by ROC curve and calibration plot analysis. The high‐risk score subgroup had a poor prognosis and a lower tumour immune dysfunction and exclusion score, indicating a greater likelihood of anti‐PD‐1/cytotoxic T lymphocyte antigen 4 benefit. [ABSTRACT FROM AUTHOR]

This study aimed to construct an immune-related prognostic model and a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) of breast cancer patients. We applied single-sample gene set enrichment analysis to classify 1,053 breast cancer samples from The Cancer Genome Atlas (TCGA) database into high and low immune cell infiltration clusters. In cluster construction and validation, the R packages “GSVA,” “hclust,” “ESTIMATE,” and “CIBERSORT” and GSEA software were utilized. ImmPort, univariate Cox regression analysis, and Venn analysis were then used to identify 42 prognostic immune-related genes. Eventually, the genes TAPBPL, RAC2, IL27RA, ULBP2, PSMB8, SOCS3, NFKBIE, IGLV6- 57, CXCL1, IGHD, AIMP1, and CXCL13 were chosen for model construction utilizing least absolute shrinkage and selection operator regression analysis. The Kaplan–Meier curves of both the training and validation sets indicated that the overall survival of patients in the low-risk group was superior to that of patients in the high-risk group (p < .05). The areas under curves (AUCs) of the model at 1, 3, and 5 years were, respectively, .697, .710, and .675 for the training set and .930, .688, and .712 for the validation set. Regarding clinicopathologic characteristics, breast cancer-related genes, and tumor mutational burden, effective differentiation was achieved between high-risk and low-risk groups. A nomogram integrating the risk model and clinicopathologic factors was constructed using the “rms” R software package. The nomogram’s 1-, 3-, and 5-year AUCs were .828, .783, and .751, respectively. Overall, our study developed an immune-related model and a nomogram that could reliably predict OS for breast cancer patients, and offered insights into tumor immune and pathological mechanisms. [ABSTRACT FROM AUTHOR]

Ischemic stroke (IS) is a severe condition regulated by complex molecular alterations. This study aimed to identify potential nicotinamide adenine dinucleotide (NAD+) metabolism-associated diagnostic markers of IS and explore their associations with immune dynamics. Weighted Gene Co-expression Network Analysis and single-sample gene set enrichment analysis (ssGSEA) were employed to identify key gene modules on the GEO dataset (GSE16561). LASSO regression was used to identify diagnostic genes. A diagnostic model was then developed using the training dataset, and its performance was assessed using a validation dataset (GSE22255 dataset). Associations between hub genes and immune cells, immune response genes, and human leukocyte antigen (HLA) genes were assessed by ssGSEA. A regulatory network was constructed using mirBase and TRRUST databases. A total of 20 NAD+ metabolic genes exhibited noteworthy expression variations. Within the module notably associated with NAD+ metabolism, 19 specific genes were included in the diagnostic model, which was validated on the GSE22255 dataset (AUC: 0.733). There were significant disparities in immune cell populations, immune response genes, and HLA gene expression, all of which were associated with the hub genes. A regulatory network composed of 153 edges and 103 nodes was constructed. This study advances our understanding of IS by providing insights into NAD+ metabolism and gene interactions, contributing to potential diagnostic innovations in IS. • Identified NAD+ metabolism-associated markers for ischemic stroke (IS). • Used WGCNA and ssGSEA for key gene modules on GSE16561 dataset. • Constructed a diagnostic model using LASSO regression on GSE22255 dataset. • Explored associations between hub genes, immune dynamics, and HLA genes. • Created a regulatory network using mirBase and TRRUST databases. [ABSTRACT FROM AUTHOR]

Kaisheng Yuan, Ruiqi Zeng, Pengteng Deng, Aiping Zhang, Huiqian Liu, Ning Wang, Yongxi Tang, Zhikang Yin, Hang Liu Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of ChinaCorrespondence: Hang Liu, Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China, Tel +86-185-8030-9681, Email liuhang723@163.comPurpose: Adrenocortical carcinoma (ACC) is an endocrine malignant tumor with poor prognosis. The study aimed to construct ACC immune-related gene prognostic signature and verify the efficacy of prognostic signature.Methods: ACC RNA-seq data and clinical information are downloaded from TCGA databases and GEO databases. We used single sample gene set enrichment analysis (ssGSEA) to assess immune cell infiltration in ACC patients and ACC patients were divided into high- and low-immune cell infiltration clusters. The validity of ssGSEA grouping was verified using the ESTIMATE algorithm. A total of 275 differentially expressed immune-related genes (IRGs) were obtained from the intersection of IRGs and differentially expressed genes (DEGs) in high and low immune cell infiltration clusters. LASSO analysis was used to identify 13 IRGs that regulate the prognosis of ACC patients through immune infiltration. Kaplan–Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that these 13 immune-related gene signatures were innovative and significant prognostic factors, which were independent of clinical features. Finally, ACC prognostic nomogram was constructed, ROC curve and calibration curve were drawn to evaluate the accuracy of the prognostic nomogram.Results: LASSO regression analysis was used to screen out ACC survival-related genes. Univariate and multivariate Cox proportional risk regression models were used to analyze and construct the ACC prognosis nomogram. The AUC for predicting 1-, 3- and 5-year overall survival rate of ACC patients was 0.799, 0.966 and 0.969, suggesting good prediction accuracy. The calibration curve shows that the predicted results of the prognostic nomogram are in good agreement with the actual situation.Conclusion: ssGSEA technique plays an important role in the construction of ACC prognostic model. Based on IRGs associated with survival independently predicted ACC prognosis, we identified thirteen immune-related genes as prognostic signature for ACC.Keywords: immune, prognostic, adrenocortical carcinoma, ACC, single sample gene set enrichment analysis, ssGSEA, risk score

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Costimulatory molecules have been proven to be the foundation of immunotherapy. However, the potential roles of costimulatory molecule genes (CMGs) in HCC remain unclear. Our study is aimed to develop a costimulatory molecule-related gene signature that could evaluate the prognosis of HCC patients. Methods Based on The Cancer Gene Atlas (TCGA) database, univariate Cox regression analysis was applied in CMGs to identify prognosis-related CMGs. Consensus clustering analysis was performed to stratify HCC patients into different subtypes and compared them in OS. Subsequently, the LASSO Cox regression analysis was performed to construct the CMGs-related prognostic signature and Kaplan–Meier survival curves as well as ROC curve were used to validate the predictive capability. Then we explored the correlations of the risk signature with tumor-infiltrating immune cells, tumor mutation burden (TMB) and response to immunotherapy. The expression levels of prognosis-related CMGs were validated based on qRT-PCR and Human Protein Atlas (HPA) databases. Results All HCC patients were classified into two clusters based on 11 CMGs with prognosis values and cluster 2 correlated with a poorer prognosis. Next, a prognostic signature of six CMGs was constructed, which was an independent risk factor for HCC patients. Patients with low-risk score were associated with better prognosis. The correlation analysis showed that the risk signature could predict the infiltration of immune cells and immune status of the immune microenvironment in HCC. The qRT-PCR and immunohistochemical results indicated six CMGs with differential expression in HCC tissues and normal tissues. Conclusion In conclusion, our CMGs-related risk signature could be used as a prediction tool in survival assessment and immunotherapy for HCC patients.

BackgroundSARS-CoV-2 infection is a respiratory infectious disease similar to influenza virus infection. Numerous studies have reported similarities and differences in the clinical manifestations, laboratory tests, and mortality between these two infections. However, the genetic effects of coronavirus and influenza viruses on the host that lead to these characteristics have rarely been reported.MethodsCOVID-19 (GSE157103) and influenza (GSE111368, GSE101702) datasets were downloaded from the Gene Expression Ominbus (GEO) database. Differential gene, gene set enrichment, protein-protein interaction (PPI) network, gene regulatory network, and immune cell infiltration analyses were performed to identify the critical impact of COVID-19 and influenza viruses on the regulation of host gene expression.ResultsThe number of differentially expressed genes in the COVID-19 patients was significantly higher than in the influenza patients. 22 common differentially expressed genes (DEGs) were identified between the COVID-19 and influenza datasets. The effects of the viruses on the regulation of host gene expression were determined using gene set enrichment and PPI network analyses. Five HUB genes were finally identified: IFI27, OASL, RSAD2, IFI6, and IFI44L.ConclusionWe identified five HUB genes between COVID-19 and influenza virus infection, which might be helpful in the diagnosis and treatment of COVID-19 and influenza. This knowledge may also guide future mechanistic studies that aim to identify pathogen-specific interventions.

Osteoarthritis (OA) is a major cause of pain, disability, and social burden in the elderly throughout the world. Although many studies focused on the molecular mechanism of OA, its etiology remains unclear. Therefore, more biomarkers need to be explored to help early diagnosis, clinical outcome measurement, and new therapeutic target development. Our study aimed to retrieve the potential hub genes of osteoarthritis (OA) by weighted gene co-expression network analysis (WGCNA) and assess their clinical utility for predicting OA. Here, we integrated WGCNA to identify novel OA susceptibility modules and hub genes. In this study, we first selected 477 and 834 DEGs in the GSE1919 and the GSE55235 databases, respectively, from the Gene Expression Omnibus (GEO) website. Genes with p-value<0.05 and | log2FC | > 1 were included in our analysis. Then, WGCNA was conducted to build a gene co-expression network, which filtered out the most relevant modules and screened out 23 overlapping WGCNA-derived hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated that these hub genes were associated with cell adhesion molecules pathway, leukocyte activation, and inflammatory response. In addition, we conducted the protein-protein interaction (PPI) network in 23 hub genes, and the top four upregulated hub genes were sorted out (CD4, SELL, ITGB2, and CD52). Moreover, our nomogram model showed good performance in predicting the risk of OA (C-index = 0.76), and this model proved to be efficient in diagnosis by ROC curves (AUC = 0.789). After that, a single-sample gene set enrichment (ssGSEA) analysis was performed to discover immune cell infiltration in OA. Finally, human primary synoviocytes and immunohistochemistry study of synovial tissues confirmed that those candidate genes were significantly upregulated in the OA groups compared with normal groups. We successfully constructed a co-expression network based on WGCNA and found out that OA-associated susceptibility modules and hub genes, which may provide further insight into the development of presymptomatic diagnosis, may contribute to understanding the molecular mechanism study of OA risk genes. [ABSTRACT FROM AUTHOR]

Background: Late-onset depression (LOD) is often associated with more severe cognitive impairment and a higher risk of disability and suicide. Emerging evidence suggests that immune system problems may be involved. This study aims to systematically characterize the genetic signature of LOD based on the immune landscape., Methods: The expression profile of GSE76826 was obtained from the Gene Expression Omnibus (GEO) database to gather gene expression data for 10 LOD patients and 12 healthy controls (HC). Various analyses, such as Single-Sample Gene Set Enrichment Analysis (ssGSEA) and Weighted Gene Co-expression Network Analysis (WGCNA), were used to mine key genes closely related to LOD. ImmuCellAI helped us understand differences in the immune environment between LOD patients and controls, and we used an LOD animal model to validate the relevant immune characteristics., Results: We found enriched immune pathways linked to LOD and adaptive immune responses. Using advanced bioinformatics techniques, we identified two key genes: apelin (APLN) and leptin (LEP) , which have good diagnostic efficacy (AUC=0.925, 95% CI=1.00-0.83) for LOD. Neutrophil infiltration increased significantly in LOD, while CD8+ T lymphocytes (CD8&#95;T) decreased. We finally constructed an animal model of LOD, validated two key genes and microglia marker genes in blood and hippocampus, and detected elevated pro-inflammatory factors such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α)., Conclusion: We identified and validated the presence of aberrant expression of APLN and LEP in LOD and described a possible immune mechanism involving increased release of IL-6 and TNF-α, leading to decreased CD8&#95;T infiltration and increased neutrophil infiltration. Meanwhile, peripheral inflammation across the blood-brain barrier further promotes microglia activation, leading to neuronal damage., Competing Interests: The authors report no conflicts of interest in this work., (© 2024 Wang et al.)

Abstract Background Tongue squamous cell carcinoma (TSCC) is characterized by aggressive invasion and poor prognosis. Currently, immune checkpoint inhibitors may prolong overall survival compared with conventional treatments. However, PD1/PDL1 remain inapplicable in predicting the prognosis of TSCC; thus, it is urgent to explore the genetic characteristics of TSCC. Materials and methods We utilized single‐sample gene set enrichment analysis (ssGSEA) to classify TSCC patients from the TCGA database into clusters with different immune cell infiltrations. ESTIMATE (immune‐related scores) and CIBERSORT (immune cell distribution) analyses were used to evaluate the immune landscape among clusters. GO, KEGG, and GSEA analyses were performed to analyze the different underlying molecular mechanisms in the clusters. Based on the immune characteristics, we applied the LASSO Cox regression to select hub genes and construct a prognostic risk model. Finally, we established an interactive network among these hub genes by using Cytoscape, and a pan‐cancer analysis to further verify and decipher the innate function of these genes. Results Using ssGSEA, we constructed three functional clusters with different overall survival and immune‐cell infiltration. ESTIMATE and CIBERSORT analyses revealed the different distributions of immune cells (T cells, B cells, and macrophages) with diverse immune‐related scores (ESTIMATE, immune, stromal, and tumor purity scores). Moreover, pathways including those of the interferon‐gamma response, hypoxia, and glycolysis of the different subtypes were investigated to elucidate their involvement in mediating the heterogeneous immune characteristics. Subsequently, after LASSO Cox regression, a signature of 15 immune‐related genes was established that is more prognostically effective than the TNM stage. Furthermore, three hub genes—PGK1, GPI, and RPE—were selected using Cytoscape evaluation and verified by immunohistochemistry. PGK1, the foremost regulator, was a comprehensively profiled pan‐cancer, and a PGK1‐based interactive network was established. Conclusion Our results suggest that immune‐related genes and clusters in TSCC have the potential to guide individualized treatments.

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

BackgroundThe role of inflammation in the formation of idiopathic pulmonary fibrosis (IPF) has gained a lot of attention recently. However, the involvement of genes related to inflammation and immune exchange environment status in the prognosis of IPF remains to be further clarified. The objective of this research is to establish a new model for the prediction of the overall survival (OS) rate of inflammation-related IPF.MethodsGene Expression Omnibus (GEO) was employed to obtain the three expression microarrays of IPF, including two from alveolar lavage fluid cells and one from peripheral blood mononuclear cells. To construct the risk assessment model of inflammation-linked genes, least absolute shrinkage and selection operator (lasso), univariate cox and multivariate stepwise regression, and random forest method were used. The proportion of immune cell infiltration was evaluated by single sample Gene Set Enrichment Analysis (ssGSEA) algorithm.ResultsThe value of genes linked with inflammation in the prognosis of IPF was analyzed, and a four-genes risk model was constructed, including tpbg, Myc, ffar2, and CCL2. It was highlighted by Kaplan Meier (K-M) survival analysis that patients with high-risk scores had worse overall survival time in all training and validation sets, and univariate and multivariate analysis highlighted that it has the potential to act as an independent risk indicator for poor prognosis. ROC analysis showed that the prediction efficiency of 1-, 3-, and 5-year OS time in the training set reached 0.784, 0.835, and 0.921, respectively. Immune infiltration analysis showed that Myeloid-Derived Suppressor Cells (MDSC), macrophages, regulatory T cells, cd4+ t cells, neutrophils, and dendritic cells were more infiltrated in the high-risk group than in the low-risk group.ConclusionInflammation-related genes can be well used to evaluate the IPF prognosis and impart a new idea for the treatment and follow-up management of IPF patients.

This document is a corrigendum published in the journal Frontiers in Immunology. It corrects an error in a previously published article titled "Integrated analysis of single-cell RNA-seq and chipset data unravels PANoptosis-related genes in sepsis." The error was the omission of a reference for a section of the article. The authors apologize for the error and state that it does not change the scientific conclusions of the article. The original article has been updated. [Extracted from the article]

Background: This study developed a gene signature associated with a malignant and common tumor of the urinary system, the Bladder Urothelial Carcinoma (BLCA).Methods: The Cancer Genome Atlas (TCGA) database was searched to obtain 414 BLCA samples and the expression spectra of 19 normal samples. Single-sample Gene Set Enrichment Analysis (ssGSEA) was conducted to determine the enrichment levels in the BLCA samples of the 29 immune genes. Unsupervised hierarchical clustering, gene set enrichment analysis (GSEA), single-factor Cox analysis, least absolute shrinkage and selection operator (LASSO) regression models, and GEO queues were used to determine the BLCA immune gene subtype, analyze the biological pathway differences between immune gene subtypes, determine the characteristic genes of BLCA associated with prognosis, identify the BLCA-related genes, and verify the gene signature, respectively.Results: We identified two immune gene subtypes (immunity_L and immunity_H). The latter was significantly related to receptors, JAK STAT signaling pathways, leukocyte interleukin 6 generation, and cell membrane signal receptor complexes. Four characteristic genes (RBP1, OAS1, LRP1, and AGER) were identified and constituted the gene signature. Significant survival advantages, higher mutation frequency, and superior immunotherapy were observed in the low-risk group patients. The gene signature had good predictive ability. The results of the validation group were consistent with TCGA queue results.Conclusions: We constructed a 4-gene signature that helps monitor BLCA occurrence and prognosis, providing an important basis for developing personalized BLCA immunotherapy. [ABSTRACT FROM AUTHOR]

Aberrant deposits of neurofibrillary tangles (NFT), the main characteristic of Alzheimer's disease (AD), are highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. This study explored dierences in gene expression patterns in multiple brain regions [entorhinal, temporal, and frontal cortex (EC, TC, FC)] with distinct Braak stages (0-VI), and identified the hub genes via weighted gene co-expression network analysis (WGCNA) and machine learning. For WGCNA, consensus modules were detected and correlated with the single sample gene set enrichment analysis (ssGSEA) scores. Overlapping the dierentially expressed genes (DEGs, Braak stages 0 vs. I-VI) with that in the interestmodule, metascape analysis, and Random Forest were conducted to explore the function of overlapping genes and obtain the most significant genes. We found that the three brain regions have high similarities in the gene expression pattern and that oxidative damage plays a vital role in NFT formation via machine learning. Through further filtering of genes from interested modules by Random Forest, we screened out key genes, such as LYN, LAPTM5, and IFI30. These key genes, including LYN, LAPTM5, and ARHGDIB, may play an important role in the development of AD through the inflammatory response pathway mediated by microglia. [ABSTRACT FROM AUTHOR]

Shaomei Tang,1 Xiaoliang Huang,2 Haixing Jiang,1 Shanyu Qin1 1Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 2Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, People’s Republic of ChinaCorrespondence: Shanyu QinDepartment of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of ChinaEmail qsy0511@163.comBackground: Pancreatic adenocarcinoma (PAAD) is an extremely malignant cancer. Immunotherapy is a promising avenue to increase the survival time of patients with PAAD.Methods: RNA sequencing and clinical data for PAAD were downloaded from the TCGA database. The ssGSEA method and weighted gene co-expression network analysis were used to calculate the relative abundance of tumor-infiltrating immune cells and identify the related modules. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were used to construct a prognostic model. MCPcounter and EPIC were also used to assess immune cell components using gene expression profiles.Results: The B cells closely related module was identified, and five genes, including ARID5A, CLEC2B, MICAL1, MZB1, and RAPGEF1, were ultimately selected to establish a prognostic signature to calculate the risk scores of PAAD patients. Kaplan–Meier curves showed worse survival in the high-risk patients (p < 0.05), and the area under the receiver operating characteristic (ROC) curves of risk score for 1-year and 3-year survival were 0.78 and 0.80, respectively, based on the training set. Similar results were verified using the validated and combined sets. Interestingly, the low-risk group presented significantly elevated immune and stromal scores, proportion of B cells, and associations between these five genes and B cells were identified using multiple methods including ssGSEA, MCPcounter, and EPIC.Conclusion: This is the first attempt to study a B cells-related prognostic signature, which is instrumental in the exploration of novel prognostic biomarkers in PAAD.Keywords: B cells, prognostic signature, WGCNA, ssGSEA, pancreatic adenocarcinoma

Immune genes play an important role in the development and progression of acute myeloid leukemia (AML). However, the role of immune genes in the prognosis and microenvironment of AML remains unclear. In this study, we analyzed 151 AML patients in the TCGA database for relevant immune cell infiltration. AML patients were divided into high and low immune cell infiltration clusters based on ssGSEA results. Immune-related pathways, AML pathways and glucose metabolism pathways were enriched in the high immune cell infiltration cluster. Then we screened the differential immune genes between the two immune cell infiltration clusters. Nine prognostic immune genes were finally identified in the train set by LASSO-Cox regression. We constructed a model in the train set based on the nine prognostic immune genes and validated the predictive capability in the test set. The areas under the ROC curve of the train set and the test set for ROC at 1, 3, 5 years were 0.807, 0.813, 0.815, and 0.731, 0.745, 0.830, respectively. The areas under ROC curve of external validation set in 1, 3, and 5 years were 0.564, 0.619, and 0.614, respectively. People with high risk scores accompanied by high TMB had been detected with the worst prognosis. Single-cell sequencing analysis revealed the expression of prognostic genes in AML cell subsets and pseudo-time analysis described the differentiation trajectory of cell subsets. In conclusion, our results reveal the characteristics of immune microenvironment and cell subsets of AML, while it still needs to be confirmed in larger samples studies. The prognosis model constructed with nine key immune genes can provide a new method to assess the prognosis of AML patients.

Background: The prognosis of cervical cancer (CC) is poor and not accurately reflected by the primary tumor node metastasis staging system. Our study aimed to develop a novel survival-prediction model. Methods: Hallmarks of CC were quantified using single-sample gene set enrichment analysis and univariate Cox proportional hazards analysis. We linked gene expression, hypoxia, and angiogenesis using weighted gene co-expression network analysis (WGCNA). Univariate and multivariate Cox regression was combined with the random forest algorithm to construct a prognostic model. We further evaluated the survival predictive power of the gene signature using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. Results: Hypoxia and angiogenesis were the leading risk factors contributing to poor overall survival (OS) of patients with CC. We identified 109 candidate genes using WGCNA and univariate Cox regression. Our established prognostic model contained six genes (MOCSI, PPP1R14A, ESM1, DES, ITGA5, and SERPINF1). Kaplan-Meier analysis indicated that high-risk patients had worse OS (hazard ratio = 4.63, p < 0.001). Our model had high predictive power according to the ROC curve. The C-index indicated that the risk score was a better predictor of survival than other clinicopathological variables. Additionally, univariate and multivariate Cox regressions indicated that the risk score was the only independent risk factor for poor OS. The risk score was also an independent predictor in the validation set (GSE52903). Bivariate survival prediction suggested that patients exhibited poor prognosis if they had high z-scores for hypoxia or angiogenesis and high risk scores. Conclusions: We established a six-gene survival prediction model associated with hypoxia and angiogenesis. This novel model accurately predicts survival and also provides potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Though patients with hepatocellular carcinoma (HCC) benefit from the treatment of immune checkpoint inhibitor (ICB), it is still of vital significance to develop more effective drugs and predict patients' response to ICB therapy. Herein, we utilized single sample gene set enrichment analysis (ssGSEA) to score the downloaded tumor samples from TCGA-LIHC based on 29 immune gene sets, thus reflecting the immunologic competence of samples. Then samples were classified into high, moderate, and low immunity groups. Additionally, we utilized survival analysis and ESTIMATE score to verify the reliability of the immunity grouping. We then performed differential expression analysis on the samples in these two groups and obtained 716 differentially expressed genes (DEGs). Next, the DEGs mentioned above were subjected to GO and KEGG analyses. The outcomes demonstrated that these DEGs were mostly correlated with the immune-related biological functions. To further verify biological processes in which DEGs might be involved, we constructed a protein–protein interaction network. Afterward, we used MCODE plugin to conduct subnetwork analysis. Thereafter, KEGG enrichment analysis was performed on two genes with the highest score in the subnetwork. The results exhibited that these genes were gathered in pathways such as Th1 and Th2 cell differentiation and NF-κB. Finally, we utilized Connectivity Map to find possible drugs for the treatment of HCC and obtained complex methyl-angolensate. The above results may contribute to distinguishing HCC patients who are eligible for immunotherapy and providing the foundations for the development of therapeutic drugs for HCC. [ABSTRACT FROM AUTHOR]

Abstract Background Papillary thyroid cancer (PTC) is the most common type of thyroid cancer. However, due to the lack of reliable prognostic biomarkers for PTC, overtreatment has been on the rise. Therefore, our research aims to identify new and promising prognostic biomarkers and provide fresh perspectives for clinical decision making. Methods The RNA‐seq data and clinical data of PTC samples were obtained from The Cancer Genome Atlas data portal. GSE64912 and GSE83520 datasets were downloaded through the GEOquery R package. The difference in the expression of oxoglutarate dehydrogenase like (OGDHL) between PTC and normal tissues was explored by the Wilcoxon test. Kaplan–Meier (KM) and Cox regression analyses were used to further explore the prognostic value of OGDHL. The tumor microenvironments of PTC patients were explored based on ssGSEA and Tumor Immune Estimation Resource online database. Gene Set Enrichment Analysis (GSEA) was performed to explore the biological processes associated with OGDHL. Results The expression level of OGDHL in PTC was significantly altered compared to that in normal tissues (p

Background: The prognosis of cervical cancer (CC) is poor and not accurately reflected by the primary tumor node metastasis staging system. Our study aimed to develop a novel survival-prediction model.Methods: Hallmarks of CC were quantified using single-sample gene set enrichment analysis and univariate Cox proportional hazards analysis. We linked gene expression, hypoxia, and angiogenesis using weighted gene co-expression network analysis (WGCNA). Univariate and multivariate Cox regression was combined with the random forest algorithm to construct a prognostic model. We further evaluated the survival predictive power of the gene signature using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves.Results: Hypoxia and angiogenesis were the leading risk factors contributing to poor overall survival (OS) of patients with CC. We identified 109 candidate genes using WGCNA and univariate Cox regression. Our established prognostic model contained six genes (MOCSI, PPP1R14A, ESM1, DES, ITGA5, and SERPINF1). Kaplan-Meier analysis indicated that high-risk patients had worse OS (hazard ratio = 4.63, p < 0.001). Our model had high predictive power according to the ROC curve. The C-index indicated that the risk score was a better predictor of survival than other clinicopathological variables. Additionally, univariate and multivariate Cox regressions indicated that the risk score was the only independent risk factor for poor OS. The risk score was also an independent predictor in the validation set (GSE52903). Bivariate survival prediction suggested that patients exhibited poor prognosis if they had high z-scores for hypoxia or angiogenesis and high risk scores.Conclusions: We established a six-gene survival prediction model associated with hypoxia and angiogenesis. This novel model accurately predicts survival and also provides potential therapeutic targets.

There are many experimental methods for characterizing immune profiles of tumors, such as flow and mass cytometry. However, these approaches are time and resource intensive. Thus, several “digital cytometry” methods have been developed to extract cell frequencies from RNA-seq data. Here, we introduce TumorDecon, named for its potential to deconvolve the distribution of cells from the gene expression levels of a bulk of cells, such as a tumor. The Python package provides an accessible way of applying these methods. It includes four deconvolution methods as well as several gene sets, signature matrices, and functions for generating custom signature matrices.

Background: Keloid disorder is a recurrent fibroproliferative cutaneous tumor. Due to the lack of early identification of keloid patients before the formation of keloids, it is impossible to carry out pre-traumatic intervention and prevention for these patients. This led us to identify and determine signatures with diagnostic significance for keloids.Methods: Public series of matrix files were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were calculated from expression profiling data, and the diagnostic signature was identified by constructing a protein-protein interaction (PPI) network. The diagnostic efficacy of the screened signature was assessed by employing receiver operating characteristic (ROC) curves. Furthermore, we calculated the proportion of different immune cells in the gene expression matrix microenvironment by the “ssGSEA” algorithm, and assessed the difference in immune cell abundance between keloids and control groups and the relationship between the signature and immune cell infiltration. Clinical keloid and normal skin tissues were collected, and the expression of the screened diagnostic signature was validated by RT-qPCR and immunohistochemical assay.Results: By screening the key genes in PPI, TGM2 was recognized and validated as a diagnostic signature and the infiltrating abundance of 10 immune cells was significantly correlated with TGM2 expression. Gene ontology enrichment analysis demonstrated that TGM2 and molecules interacting with it were mainly enriched in processes involving wound healing and collagen fiber organization. TGM2 correlated positively with HIF-1A (R = 0.82, p-value = 1.4e-05), IL6 (R = 0.62, p-value = 0.0053), and FN1 (R = 0.66, p-value = 0.0019). Besides, TGM2 was significantly upregulated in clinical keloid samples compared to normal skin tissues.Conclusion: TGM2 may serve as an auxiliary diagnostic indicator for keloids. However, the role of TGM2 in keloids has not been adequately reported in the current literature, which may provide a new direction for molecular studies of keloids.

Currently, the aetiology and pathogenesis of idiopathic pulmonary fibrosis (IPF) are still largely unclear. Moreover, patients with IPF exhibit a considerable difference in clinical presentation, treatment, and prognosis. Optimal biomarkers or models for IPF prognosis are lacking. Therefore, this study quantified the levels of various hallmarks using a single-sample gene set enrichment analysis algorithm. The hazard ration was calculated using Univariate Cox regression analysis based on the transcriptomic profile of bronchoalveolar lavage cells and clinical survival information. Afterwards, weighted Gene Co-expression Network Analysis was performed to construct a network between gene expression, inflammation response, and hypoxia. Subsequently, univariate Cox, random forest, and multivariate Cox regressions were applied to develop a robust inflammation and hypoxia-related gene signature for predicting clinical outcomes in patients with IPF. Furthermore, a nomogram was constructed to calculate risk assessment. The inflammation response and hypoxia were identified as latent risk factors for patients with IPF. Five genes, including HS3ST1, WFDC2, SPP1, TFPI, and CDC42EP2, were identified that formed the inflammation-hypoxia-related gene signature. Kaplan-Meier plotter showed that the patients with high-risk scores had a worse prognosis than those with low-risk scores in training and validation cohorts. The time-dependent concordance index and the receiver operating characteristic analysis revealed that the risk model could accurately predict the clinical outcome of patients with IPF. Therefore, this study contributes to elucidating the role of inflammation and hypoxia in IPF, which can aid in assessing individual prognosis and personalised treatment decisions. [ABSTRACT FROM AUTHOR]