Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers.

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of the myocardium, representing a significant contributor to mortality. While the involvement of immune inflammation in the development of cardiac ailments is well-documented, its specific impact on HCM pathogenesis remains uncertain. Five distinct machine learning algorithms, namely LASSO, SVM, RF, Boruta and XGBoost, were utilized to discover new biomarkers associated with HCM. A unique nomogram was developed using two newly identified biomarkers and subsequently validated. Furthermore, samples of HCM and normal heart tissues were gathered from our institution to confirm the variance in expression levels and prognostic significance of GATM and MGST1. Five novel biomarkers (DARS2, GATM, MGST1, SDSL and ARG2) associated with HCM were identified. Subsequent validation revealed that GATM and MGST1 exhibited significant diagnostic utility for HCM in both the training and test cohorts, with all AUC values exceeding 0.8. Furthermore, a novel risk assessment model for HCM patients based on the expression levels of GATM and MGST1 demonstrated favourable performance in both the training (AUC = 0.88) and test cohorts (AUC = 0.9). Furthermore, our study revealed that GATM and MGST1 exhibited elevated expression levels in HCM tissues, demonstrating strong discriminatory ability between HCM and normal cardiac tissues (AUC of GATM = 0.79; MGST1 = 0.86). Our findings suggest that two specific cell types, monocytes and multipotent progenitors (MPP), may play crucial roles in the pathogenesis of HCM. Notably, GATM and MGST1 were found to be highly expressed in various tumours and showed significant prognostic implications. Functionally, GATM and MGST1 are likely involved in xenobiotic metabolism and epithelial mesenchymal transition in a wide range of cancer types. GATM and MGST1 have been identified as novel biomarkers implicated in the progression of both HCM and cancer. Additionally, monocytes and MPP may also play a role in facilitating the progression of HCM.

Identification of oxidative stress-related diagnostic markers and immune infiltration features for idiopathic pulmonary fibrosis by bibliometrics and bioinformatics.

Idiopathic pulmonary fibrosis (IPF) garners considerable attention due to its high fatality rate and profound impact on quality of life. Our study conducts a comprehensive literature review on IPF using bibliometric analysis to explore existing hot research topics, and identifies novel diagnostic and therapeutic targets for IPF using bioinformatics analysis. Publications related to IPF from 2013 to 2023 were searched on the Web of Science Core Collection (WoSCC) database. Data analysis and visualization were conducted using CiteSpace and VOSviewer software primarily. The gene expression profiles GSE24206 and GSE53845 were employed as the training dataset. The GSE110147 dataset was employed as the validation dataset. We identified differentially expressed genes (DEGs) and differentially expressed genes related to oxidative stress (DEOSGs) between IPF and normal samples. Then, we conducted Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were screened by protein-protein interaction (PPI) networks and machine learning algorithms. The CIBERSORT was used to analyze the immune infiltration of 22 kinds of immune cells. Finally, we conducted the expression and validation of hub genes. The diagnostic efficacy of hub genes was evaluated by employing Receiver Operating Characteristic (ROC) curves and the associations between hub genes and immune cells were analyzed. A total of 6,500 articles were identified, and the annual number of articles exhibited an upward trend. The United States emerged as the leading contributor in terms of publication count, institutional affiliations, highly cited articles, and prolific authorship. According to co-occurrence analysis, oxidative stress and inflammation are hot topics in IPF research. A total of 1,140 DEGs were identified, and 72 genes were classified as DEOSGs. By employing PPI network analysis and machine learning algorithms, PON2 and TLR4 were identified as hub genes. A total of 10 immune cells exhibited significant differences between IPF and normal samples. PON2 and TLR4, as oxidative stress-related genes, not only exhibit high diagnostic efficacy but also show close associations with immune cells. In summary, our study highlights oxidative stress and inflammation are hot topics in IPF research. Oxidative stress and immune cells play a vital role in the pathogenesis of IPF. Our findings suggest the potential of PON2 and TLR4 as novel diagnostic and therapeutic targets for IPF.

The identification of heterogeneous reactive oxygen subtypes in esophageal squamous cell carcinoma to aid patient prognosis and immunotherapy.

Introduction: Esophageal cancer is increasingly recognized as a significant global malignancy. The main pathological subtype of this cancer is esophageal squamous cell carcinoma (ESCC), which displays a higher degree of malignancy and a poorer prognosis. Reactive oxygen species (ROS) play a critical role in modulating the immune response to tumors, and understanding the regulation of ROS in ESCC could lead to novel and improved therapeutic strategies for ESCC patients. Methods: A consensus matrix derived from genes involved in the ROS pathway revealed two subtypes of ROS. These subtypes were categorized as ROS-active or ROS-suppressive based on their level of ROS activity. The heterogeneity among the different ROS subtypes was then explored from various perspectives, including gene function, immune response, genomic stability, and immunotherapy. In order to assess the prognosis and the potential benefits of immunotherapy, a ROS activity score (RAS) was developed using the identified ROS subtypes. In vitro experiments were performed to confirm the impact of core RAS genes on the proliferative activity of esophageal cancer cell lines. Results: Two distinctive subtypes of ROS were identified. The first subtype, referred to as ROS-active, exhibited elevated ROS activity, enhanced involvement in cancer-associated immune pathways, and increased infiltration of effector immune cells. The second subtype, named ROS-suppressive, demonstrated weaker ROS activity but displayed more pronounced dysregulation in the cell cycle and a denser extracellular matrix, indicating malignant characteristics. Genomic stability, particularly in terms of copy number variation (CNV) events, differed between the two ROS subtypes. By developing a RAS model, reliable risk assessment for overall survival (OS) in patients with ESCC was achieved, and the model demonstrated strong predictive capabilities in real-world immunotherapy cohorts. Moreover, the core gene LDLRAD1 within the RAS model was found to enhance proliferative activity in esophageal cancer cell lines. Conclusion: Based on the ROS pathway, we successfully identified two distinct subtypes in ESCC: the ROS-active subtype and the ROS-suppressive subtype. These subtypes were utilized to evaluate prognosis and the sensitivity to immunotherapy.

Prognosis poor, immune infiltration of colon adenocarcinoma associated with low expression levels of calcium-activated chloride channel.

The calcium-activated chloride channel (CLCA4) in colon adenocarcinoma (COAD) and immunological infiltration have not been extensively studied. This work thoroughly employed several datasets to assess the expression, prognosis, and association between immune infiltration and clinicopathological characteristics of CLCA4 in cancer, as well as look into potential signalling pathways. The human protein atlas (HPA), TIMER, UALCAN, TISIDB, GSCA, SangerBox, GeneMANIA, and LinkedOmics were among the datasets that were used. The findings demonstrated that, in comparison to normal tissues, COAD tissues had lower levels of CLCA4 expression. The prognosis was worse for those whose levels of CLCA4 expression were lower. For validation, immunohistochemistry (HPA) was used. Positive correlations between CLCA4 mRNA expression and its copy number variation (CNV) were observed, and CLCA4 CNV was linked to immunological infiltration. Subsequent investigation demonstrated the association between immune cell markers, immune checkpoint genes, and immunological infiltration with CLCA4. The overall survival and disease-free survival of M0 patients were considerably better than those of M1 patients, and the groups with tumour stages M0 and M1 had notably different levels of CLCA4 expression. Its substantial enrichment in ion channel activity, transmembrane transporter activity, digestion, and other biological processes was revealed by gene ontology analysis. Oxidative phosphorylation, pancreatic secretion, Parkinson's and Alzheimer's diseases, renin secretion, and other signalling pathways were the primary associations found for CLCA4. It is evident that the immunological microenvironment and functions like ion transport, metabolism, and intestinal digestion are all impacted by CLCA4 expression.

Multiple omics integrative analysis identifies GARS1 as a novel prognostic and immunological biomarker: from pan-cancer to bladder cancer.

Glycyl-tRNA synthetase (GARS1) is differentially expressed across cancers. In this study, the value of GARS1 in the diagnosis and prognosis of various cancers was comprehensively evaluated by multiple omics integrative pan-cancer analysis and experimental verification. Through Kaplan-Meier, ROC and multiple databases, we explored GARS1 expression and prognostic and diagnostic patterns across cancers. The GARS1 relative reaction network was identified in PPI, GO, KEGG, methylation models and the genetic mutation atlas. Further research on the GARS1 value in bladder urothelial carcinoma (BLCA) was conducted by regression and nomogram models. We further analyzed the correlation between GARS1 and immune markers and cells in BLCA. Finally, in vitro experiments were used to validate GARS1 the oncogenic function of GARS1 in BLCA. We found that GARS1 was highly expressed across cancers, especially in BLCA. GARS1 expression was correlated with poor survival and had high diagnostic value in most tumor types. GARS1 is significantly associated with tRNA-related pathways whose mutation sites are mainly located on tRNA synthetase. In addition, Upregulation of GARS1 was connected with immune cell infiltration and five key MMR genes. M2 macrophages, TAMs, Th1 and T-cell exhaustion, and marker sets associated with GARS1 expression indicated specific immune infiltration in BLCA. Finally, in vitro experiments validated that GARS1 expression promotes BLCA cell proliferation and metastasis and inhibits apoptosis. Overall, GARS1 can be a novel prognostic and immunological biomarker through multiple omics integrative pan-cancer analysis. The expression of GARS1 in BLCA was positively correlated with specific immune infiltration, indicating that GARS1 might be related to the tumor immune microenvironment.

Identification of diagnostic genes and drug prediction in metabolic syndrome-associated rheumatoid arthritis by integrated bioinformatics analysis, machine learning, and molecular docking.

Background: Interactions between the immune and metabolic systems may play a crucial role in the pathogenesis of metabolic syndrome-associated rheumatoid arthritis (MetS-RA). The purpose of this study was to discover candidate biomarkers for the diagnosis of RA patients who also had MetS. Methods: Three RA datasets and one MetS dataset were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) were employed to identify hub genes in MetS-RA. Enrichment analysis was used to explore underlying common pathways between MetS and RA. Receiver operating characteristic curves were applied to assess the diagnostic performance of nomogram constructed based on hub genes. Protein-protein interaction, Connectivity Map (CMap) analyses, and molecular docking were utilized to predict the potential small molecule compounds for MetS-RA treatment. qRT-PCR was used to verify the expression of hub genes in fibroblast-like synoviocytes (FLS) of MetS-RA. The effects of small molecule compounds on the function of RA-FLS were evaluated by wound-healing assays and angiogenesis experiments. The CIBERSORT algorithm was used to explore immune cell infiltration in MetS and RA. Results: MetS-RA key genes were mainly enriched in immune cell-related signaling pathways and immune-related processes. Two hub genes (TYK2 and TRAF2) were selected as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning and proved to have a high diagnostic value (area under the curve, TYK2, 0.92; TRAF2, 0.90). qRT-PCR results showed that the expression of TYK2 and TRAF2 in MetS-RA-FLS was significantly higher than that in non-MetS-RA-FLS (nMetS-RA-FLS). The combination of CMap analysis and molecular docking predicted camptothecin (CPT) as a potential drug for MetS-RA treatment. In vitro validation, CPT was observed to suppress the cell migration capacity and angiogenesis capacity of MetS-RA-FLS. Immune cell infiltration results revealed immune dysregulation in MetS and RA. Conclusion: Two hub genes were identified in MetS-RA, a nomogram for the diagnosis of RA and MetS was established based on them, and a potential therapeutic small molecule compound for MetS-RA was predicted, which offered a novel research perspective for future serum-based diagnosis and therapeutic intervention of MetS-RA.

Nc-RNA-mediated low expression of AZIN1 correlated with unfavorable prognosis in kidney renal clear cell carcinoma.

OBJECTIVE: Kidney renal clear cell carcinoma (KIRC, ccRCC) is the most common type of renal cancer with high recurrence and mortality. It has long been recognized that Antizyme inhibitor 1 (AZIN1) serves as a pro-oncogenic molecule in multiple cancers. However, the clinicopathological features of AZIN1 in KIRC remain unexplored. MATERIALS AND METHODS: The Cancer Genome Atlas (TCGA, TIMER, and GEPIA) were employed for pan-cancer expression and survival analysis of AZIN1, indicating the unique anti-tumor role of AZIN1 in KIRC. The expression and clinical characteristics of AZIN1 in KIRC were further proven via Human Protein Atlas and TCGA. single-sample GSEA was employed to investigate the immune infiltration of AZIN1. Then the downstream pathways were illustrated via the LinkedOmics, Metascape, and Cytoscape databases. The possible upper regulating noncoding RNAs (ncRNAs) were analyzed from five programs-TargetScan, StarBase, miRanda, PITA, and miRmap. RESULTS: AZIN1 is downregulated in KIRC patients. Lower levels of AZIN1 were linked with unfavorable outcomes in KIRC patients. The AZIN1 expression was positively related to immune cell infiltration in KIRC. We also elucidated a possible upstream regulatory ncRNA of AZIN1 in KIRC namely STK4-AS1/AC068338.2-miR-106b-5p-AZIN1 axis as well as the downstream signaling pathways. CONCLUSION: This study illustrated the unique anti-tumor role of AZIN1 in KIRC and provided potential value for guiding immunotherapy and targeted therapy.

Common ground on immune infiltration landscape and diagnostic biomarkers in diabetes-complicated atherosclerosis: an integrated bioinformatics analysis.

Introduction: Type 2 diabetes mellitus (T2DM) is a major cause of atherosclerosis (AS). However, definitive evidence regarding the common molecular mechanisms underlying these two diseases are lacking. This study aimed to investigate the mechanisms underlying the association between T2DM and AS. Methods: The gene expression profiles of T2DM (GSE159984) and AS (GSE100927) were obtained from the Gene Expression Omnibus, after which overlapping differentially expressed gene identification, bioinformatics enrichment analyses, protein-protein interaction network construction, and core genes identification were performed. We confirmed the discriminatory capacity of core genes using receiver operating curve analysis. We further identified transcription factors using TRRUST database to build a transcription factor-mRNA regulatory network. Finally, the immune infiltration and the correlation between core genes and differential infiltrating immune cells were analyzed. Results: A total of 27 overlapping differentially expressed genes were identified under the two-stress conditions. Functional analyses revealed that immune responses and transcriptional regulation may be involved in the potential pathogenesis. After protein-protein interaction network deconstruction, external datasets, and qRT-PCR experimental validation, four core genes (IL1B, C1QA, CCR5, and MSR1) were identified. ROC analysis further showed the reliable value of these core genes. Four common differential infiltrating immune cells (B cells, CD4+ T cells, regulatory T cells, and M2 macrophages) between T2DM and AS datasets were selected based on immune cell infiltration. A significant correlation between core genes and common differential immune cells. Additionally, five transcription factors (RELA, NFκB1, JUN, YY1, and SPI1) regulating the transcription of core genes were mined using upstream gene regulator analysis. Discussion: In this study, common target genes and co-immune infiltration landscapes were identified between T2DM and AS. The relationship among five transcription factors, four core genes, and four immune cells profiles may be crucial to understanding T2DM complicated with AS pathogenesis and therapeutic direction.

Identification of HTRA1, DPT and MXRA5 as potential biomarkers associated with osteoarthritis progression and immune infiltration.

BACKGROUND: Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration. METHODS: We utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results. RESULTS: WGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings. CONCLUSIONS: HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

Authentication and validation of key genes in the treatment of atopic dermatitis with Runfuzhiyang powder: combined RNA-seq, bioinformatics analysis, and experimental research.

Background: Atopic dermatitis (AD) is inflammatory disease. So far, therapeutic mechanism of Runfuzhiyang powder on AD remains to be studied. This study aimed to mine key biomarkers to explore potential molecular mechanism for AD incidence and Runfuzhiyang powder treatment. Methods: The control group, AD group, treat group (AD mice treated with Runfuzhiyang powder were utilized for studying. Differentially expressed AD-related genes were acquired by intersecting of key module genes related to control group, AD group and treatment group which were screened by WGCNA and AD-related differentially expressed genes (DEGs). KEGG and GO analyses were further carried out. Next, LASSO regression analysis was utilized to screen feature genes. The ROC curves were applied to validate the diagnostic ability of feature genes to obtain AD-related biomarkers. Then protein-protein interaction (PPI) network, immune infiltration analysis and single-gene gene set enrichment analysis (GSEA) were presented. Finally, TF-mRNA-lncRNA and drug-gene networks of biomarkers were constructed. Results: 4 AD-related biomarkers (Ddit4, Sbf2, Senp8 and Zfp777) were identified in AD groups compared with control group and treat group by LASSO regression analysis. The ROC curves revealed that four biomarkers had good distinguishing ability between AD group and control group, as well as AD group and treatment group. Next, GSEA revealed that pathways of E2F targets, KRAS signaling up and inflammatory response were associated with 4 biomarkers. Then, we found that Ddit4, Sbf2 and Zfp777 were significantly positively correlated with M0 Macrophage, and were significantly negatively relevant to Resting NK. Senp8 was the opposite. Finally, a TF-mRNA-lncRNA network including 200 nodes and 592 edges was generated, and 20 drugs targeting SENP8 were predicted. Conclusion: 4 AD-related and Runfuzhiyang powder treatment-related biomarkers (Ddit4, Sbf2, Senp8 and Zfp777) were identified, which could provide a new idea for targeted treatment and diagnosis of AD.

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

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

Comprehensively prognostic and immunological analyses of GLP-1 signaling-related genes in pan-cancer and validation in colorectal cancer.

Background: Glucagon-like peptide-1 (GLP-1) has crucial impact on glycemic control and weight loss physiologically. GLP-1 receptor agonists have been approved for treatment of diabetes and obesity. Emerging evidence suggests that GLP-1 receptor agonists exert anticancer effect in tumorigenesis and development. However, the role and mechanism of GLP-1 signaling-related genes in pan-cancer still need further study. Methods: We comprehensively investigated the aberrant expression and genetic alterations of GLP-1 signaling-related genes in 33 cancer types. Next, GLP-1 signaling score of each patient in The Cancer Genome Atlas were established by the single-sample gene set enrichment analysis. In addition, we explored the association of GLP-1 signaling score with prognostic significance and immune characteristics. Furthermore, qRT-PCR and immunohistochemistry staining were applied to verify the expression profiling of GLP-1 signaling-related genes in colorectal cancer (CRC) tissues. Wound-healing assays and migration assays were carried out to validate the role of GLP-1 receptor agonist in CRC cell lines. Results: The expression profiling of GLP-1 signaling-related genes is commonly altered in pan-cancer. The score was decreased in cancer tissues compared with normal tissues and the lower expression score was associated with worse survival in most of cancer types. Notably, GLP-1 signaling score was strongly correlated with immune cell infiltration, including T cells, neutrophils, dendritic cells and macrophages. In addition, GLP-1 signaling score exhibited close association with tumor mutation burden, microsatellite instability and immunotherapy response in patients with cancer. Moreover, we found that the expression of GLP-1 signaling-related genes ITPR1 and ADCY5 were significantly reduced in CRC tissues, and GLP-1 receptor agonist semaglutide impaired the migration capacity of CRC cells, indicating its protective role. Conclusion: This study provided a preliminary understanding of the GLP-1 signaling-related genes in pan-cancer, showing the prognosis significance and potential immunotherapeutic values in most cancer types, and verified the potential anticancer effect of GLP-1 receptor agonist in CRC.

Identification of signature genes and immune infiltration analysis in thyroid cancer based on PANoptosis related genes.

Background: Thyroid cancer is the most common malignancy of the endocrine system. PANoptosis is a specific form of inflammatory cell death. It mainly includes pyroptosis, apoptosis and necrotic apoptosis. There is increasing evidence that PANoptosis plays a crucial role in tumour development. However, no pathogenic mechanism associated with PANoptosis in thyroid cancer has been identified. Methods: Based on the currently identified PANoptosis genes, a dataset of thyroid cancer patients from the GEO database was analysed. To screen the common differentially expressed genes of thyroid cancer and PANoptosis. To analyse the functional characteristics of PANoptosis-related genes (PRGs) and screen key expression pathways. The prognostic model was established by LASSO regression and key genes were identified. The association between hub genes and immune cells was evaluated based on the CIBERSORT algorithm. Predictive models were validated by validation datasets, immunohistochemistry as well as drug-gene interactions were explored. Results: The results showed that eight key genes (NUAK2, TNFRSF10B, TNFRSF10C, TNFRSF12A, UNC5B, and PMAIP1) exhibited good diagnostic performance in differentiating between thyroid cancer patients and controls. These key genes were associated with macrophages, CD4+ T cells and neutrophils. In addition, PRGs were mainly enriched in the immunomodulatory pathway and TNF signalling pathway. The predictive performance of the model was confirmed in the validation dataset. The DGIdb database reveals 36 potential therapeutic target drugs for thyroid cancer. Conclusion: Our study suggests that PANoptosis may be involved in immune dysregulation in thyroid cancer by regulating macrophages, CD4+ T cells and activated T and B cells and TNF signalling pathways. This study suggests potential targets and mechanisms for thyroid cancer development.

Screening of Diagnostic Biomarkers and Immune Infiltration Characteristics Linking Rheumatoid Arthritis and Rosacea Based on Bioinformatics Analysis.

Introduction: Both rheumatoid arthritis (RA) and rosacea represent common chronic systemic autoimmune conditions. Recent research indicates a heightened RA risk among individuals with rosacea. However, the molecular mechanisms linking these diseases remain largely unknown. This study aims to uncover shared molecular regulatory networks and immune cell infiltration patterns in both rosacea and RA. Methods: The gene expression profiles of RA (GSE12021, GSE55457), and the rosacea gene expression profile (GSE6591), were downloaded from Gene Expression Omnibus (GEO) databases, and obtained to screen differentially expressed genes (DEGs) by using "limma" package in R software. Various analyses including GO, KEGG, protein-protein interaction (PPI) network, and weighted gene co-expression network analyses (WGCNA) were conducted to explore potential biological functions and signaling pathways. CIBERSORT was used to assess the abundance of immune cells. Pearson coefficients were used to calculate the correlations between overlapped genes and the leukocyte gene signature matrix. Flow cytometry (FCM) analysis confirmed the most abundant immune cells detected in rheumatoid arthritis and rosacea. Receiver operator characteristic (ROC) analysis, enzyme-linked immunosorbent assay (ELISA), and qRT-PCR were used to confirm biomarkers and functions. Results: Two hundred seventy-seven co-expressed DEGs were identified from these datasets. Functional enrichment analysis indicated that these DEGs were associated with immune processes and chemokine-mediated signaling pathways. Fourteen and 17 hub genes overlapped between cytoHubba and WGCNA were identified in RA and rosacea, respectively. Macrophages and dendritic cells were RA and rosacea's most abundant immune cells, respectively. The ROC curves demonstrated potential diagnostic values of CXCL10 and CCL27, showing higher levels in the serum of patients with RA or rosacea, and suggesting possible regulation in the densities and functions of macrophages and dendritic cells from RA and rosacea, which were validated by FCM and qRT-PCR. Conclusion: Importantly, our findings may contribute to the scientific basis for biomarkers and therapeutic targets for patients with RA and rosacea in the future.

Identification and verification of immune-related genes for diagnosing the progression of atherosclerosis and metabolic syndrome.

BACKGROUND: Atherosclerosis and metabolic syndrome are the main causes of cardiovascular events, but their underlying mechanisms are not clear. In this study, we focused on identifying genes associated with diagnostic biomarkers and effective therapeutic targets associated with these two diseases. METHODS: Transcriptional data sets of atherosclerosis and metabolic syndrome were obtained from GEO database. The differentially expressed genes were analyzed by RStudio software, and the function-rich and protein-protein interactions of the common differentially expressed genes were analyzed.Furthermore, the hub gene was screened by Cytoscape software, and the immune infiltration of hub gens was analyzed. Finally, relevant clinical blood samples were collected for qRT-PCR verification of the three most important hub genes. RESULTS: A total of 1242 differential genes (778 up-regulated genes and 464 down-regulated genes) were screened from GSE28829 data set. A total of 1021 differential genes (492 up-regulated genes and 529 down-regulated genes) were screened from the data set GSE98895. Then 23 up-regulated genes and 11 down-regulated genes were screened by venn diagram. Functional enrichment analysis showed that cytokines and immune activation were involved in the occurrence and development of these two diseases. Through the construction of the Protein-Protein Interaction(PPI) network and Cytoscape software analysis, we finally screened 10 hub genes. The immune infiltration analysis was further improved. The results showed that the infiltration scores of 7 kinds of immune cells in GSE28829 were significantly different among groups (Wilcoxon Test < 0.05), while in GSE98895, the infiltration scores of 4 kinds of immune cells were significantly different between groups (Wilcoxon Test < 0.05). Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets. The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05). There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05). Finally, our results identified 10 small molecules with the highest absolute enrichment value, and the three most significant key genes (CX3CR1, TLR5, IL32) were further verified in the data expression matrix and clinical blood samples. CONCLUSION: We have established a co-expression network between atherosclerotic progression and metabolic syndrome, and identified key genes between the two diseases. Through the method of bioinformatics, we finally obtained 10 hub genes in As and MS, and selected 3 of the most significant genes (CX3CR1, IL32, TLR5) for blood PCR verification. This may be helpful to provide new research ideas for the diagnosis and treatment of AS complicated with MS.

Construction and validation of a prognostic model for overall survival time of patients with ovarian cancer by metabolism-related genes.

BACKGROUND: Ovarian cancer is a female-specific malignancy with high morbidity and mortality. The metabolic reprogramming of tumor cells is closely related to the biological behavior of tumors. METHODS: The prognostic signature of the metabolism-related gene (MRGs) was established by LASSO-Cox regression analysis. The prognostic signature of MRGs was also prognosticated in each clinical subgroup. These genes were subjected to functional enrichment analysis and tissue expression exploration. Analysis of the MRG prognostic signature in terms of immune cell infiltration and antitumor drug susceptibility was also performed. RESULTS: A MRG prognostic signature including 21 genes was established and validated. Most of the 21 MRGs were expressed at different levels in ovarian cancer than in normal ovarian tissue. The enrichment analysis suggested that MRGs were involved in lipid metabolism, membrane organization, and molecular binding. The MRG prognostic signature demonstrated the predictive value of overall survival time in various clinical subgroups. The monocyte, NKT, Tgd and Tex cell scores showed differences between the groups with high- and low-risk score. The antineoplastic drug analysis we performed provided information on ovarian cancer drug therapy and drug resistance. In vitro experiments verified that PLCH1 in 21 MRGs can regulate the apoptosis and proliferation of ovarian cancer cells. CONCLUSION: This metabolism-related prognostic signature was a potential prognostic factor in patients with ovarian cancer, demonstrating high stability and accuracy.

HIGD1B, as a novel prognostic biomarker, is involved in regulating the tumor microenvironment and immune cell infiltration; its overexpression leads to poor prognosis in gastric cancer patients.

Background: HIGD1B (HIG1 Hypoxia Inducible Domain Family Member 1B) is a protein-coding gene linked to the occurrence and progression of various illnesses. However, its precise function in gastric cancer (GC) remains unclear. Methods: The expression of HIGD1B is determined through the TCGA and GEO databases and verified using experiments. The association between HIGD1B and GC patients' prognosis was analyzed via the Kaplan-Meier (K-M) curve. Subsequently, the researchers utilized ROC curves to assess the diagnostic capacity of HIGD1B and employed COX analysis to investigate risk factors for GC. The differentially expressed genes (DEGs) were then subjected to functional enrichment analysis, and a nomogram was generated to forecast the survival outcome and probability of GC patients. Additionally, we evaluated the interaction between HIGD1B and the immune cell infiltration and predicted the susceptibility of GC patients to therapy. Results: HIGD1B is markedly elevated in GC tissue and cell lines, and patients with high HIGD1B expression have a poorer outcome. In addition, HIGD1B is related to distinct grades, stages, and T stages. The survival ROC curves of HIGD1B and nomogram for five years were 0.741 and 0.735, suggesting appropriate levels of diagnostic efficacy. According to Cox regression analysis, HIGD1B represents a separate risk factor for the prognosis of gastric cancer (p<0.01). GSEA analysis demonstrated that the HIGD1B is closely related to cancer formation and advanced pathways. Moreover, patients with high HIGD1B expression exhibited a higher level of Tumor-infiltration immune cells (TIICs) and were more likely to experience immune escape and drug resistance after chemotherapy and immunotherapy. Conclusion: This study explored the potential mechanisms and diagnostic and prognostic utility of HIGD1B in GC, as well as identified HIGD1B as a valuable biomarker and possible therapeutic target for GC.

Analysis of machine learning based integration to identify the crosslink between inflammation and immune response in non-alcoholic fatty liver disease through bioinformatic analysis.

Background: The prevalence of nonalcoholic fatty liver disease (NAFLD) is a major form of chronic liver disease. This study aimed to scrutinize the diagnostic biomarkers of NAFLD and their correlation with the immune microenvironment through bioinformatic analysis. Methods: To identify genes associated with nonalcoholic fatty liver disease (NAFLD), we obtained microarray datasets (GSE63067 and GSE89632) from the Gene Expression Omnibus (GEO) database. Machine learning techniques such as Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) were used to identify key genes. We performed gene ontology analysis to identify the driver pathways of NAFLD. External datasets (merging GSE48452, GSE66676 and GSE135251) were used to validate the identified genes and confirm protein levels by Western blotting. The CIBERSORT algorithm and immune-related techniques, such as ssGSEA, were used to assess the level of infiltration of different immune cell types and their functions. Finally, Spearman's analysis confirmed the relationship between pivotal genes and immune cells. Results: Hub genes (BBOX1, FOSB, NR4A2, RAB26 and SOCS2) were identified as potential biomarkers. This study demonstrates that these hub genes are significantly dysregulated in NAFLD, suggesting that they may be useful as diagnostic indicators and possible targets for treatment. Also covered are their possible effects on inflammation, immune cell activation, and liver damage in NAFLD. A better understanding of the intricate relationship between metabolic inefficiency, immunological response, and liver pathology in NAFLD may be gained from this work, which can lead to the development of new diagnostic tools and clinical treatments. Conclusion: The current study identified BBOX1, FOSB, NR4A2, RAB26 and SOCS2 as important diagnostic biomarkers for NAFLD. The study highlights the important function of immune cell infiltration in developing NAFLD. Their findings provide valuable molecular biological insights into the development of NAFLD and may lead to novel therapeutic strategies for treating this disease.

Weighted gene coexpression network analysis and machine learning for the determination of tfh cell and B cell infiltrating biomarkers in thymoma-associated myasthenia gravis.

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.

CD74 facilitates immunotherapy response by shaping the tumor microenvironment of hepatocellular carcinoma.

BACKGROUND: CD74 is ectopically expressed in many tumors and can regulate tumor immunity. However, there are many gaps in the study of the prognostic value of CD74 expression and immune infiltration in hepatocellular carcinoma (HCC). METHODS: An online tumor database was searched to obtain data on gene/protein expression. Immune infiltration analysis was performed using the Tumor Immune Estimation Resource and Comprehensive Analysis on Multi-Omics of Immunotherapy in Pan-cancer databases. Single-cell data were obtained from the Tissue-specific Gene Expression and Regulation, Single-cell Transcriptomes of Tumor Immune Microenvironment and Tumor Immune Single-cell Hub 2 databases. RESULTS: CD74 was highly expressed in HCC patients. HCC patients with high CD74 expression who consumed alcohol or were negative for hepatitis virus had a better prognosis than patients with low CD74 expression. CD74 was mainly enriched in immune response regulation pathways. Both copy number variations in CD74 and CD74 expression patterns affected the infiltration levels of immune cells. Interestingly, CD74 regulated the differentiation of myeloid cells. CD74 in macrophages and dendritic cells (DCs) forms complex networks with malignant cells and hepatic progenitor cell (HPC)-like cells, respectively. High CD74 expression in HPC-like cells and malignant cells significantly decreased the fraction of C-type lectin domain family 9 A (CLEC9A)-cDC1+ DCs and IL-1B+ macrophages, respectively. Their crosstalk subsequently shaped the tumor microenvironment of HCC, possibly through the CD74-MIF axis. Importantly, patients with high CD74 expression presented higher immune scores and achieved good outcomes after receiving immunotherapy. CONCLUSION: High CD74 expression is associated with the abundance of a variety of immune cell types, mediating interactions among tumor and immune cells and shaping the malignant behavior of HCC. In summary, CD74 may be a hallmark for determining the prognosis and immune cell infiltration levels of HCC patients.