NET-Related Gene as Potential Diagnostic Biomarkers for Diabetic Tubulointerstitial Injury.

Background: Tubulointerstitial injury plays a pivotal role in the progression of diabetic kidney disease (DKD), yet the link between neutrophil extracellular traps (NETs) and diabetic tubulointerstitial injury is still unclear. Methods: We analyzed microarray data (GSE30122) from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with DKD's tubulointerstitial injury. Functional and pathway enrichment analyses were conducted to elucidate the involved biological processes (BP) and pathways. Weighted gene coexpression network analysis (WGCNA) identified modules associated with DKD. LASSO regression and random forest selected NET-related characteristic genes (NRGs) related to DKD tubulointerstitial injury. Results: Eight hundred ninety-eight DEGs were identified from the GSE30122 dataset. A significant module associated with diabetic tubulointerstitial injury overlapped with 15 NRGs. The hub genes, CASP1 and LYZ, were identified as potential biomarkers. Functional enrichment linked these genes with immune cell trafficking, metabolic alterations, and inflammatory responses. NRGs negatively correlated with glomerular filtration rate (GFR) in the Neph v5 database. Immunohistochemistry (IHC) validated increased NRGs in DKD tubulointerstitial injury. Conclusion: Our findings suggest that the CASP1 and LYZ genes may serve as potential diagnostic biomarkers for diabetic tubulointerstitial injury. Furthermore, NRGs involved in diabetic tubulointerstitial injury could emerge as prospective targets for the diagnosis and treatment of DKD.

Machine learning-based identification and immune characterization of ferroptosis-related molecular clusters in osteoarthritis and validation.

Osteoarthritis (OA), a degenerative joint disease, involves synovial inflammation, subchondral bone erosion, and cartilage degeneration. Ferroptosis, a regulated non-apoptotic programmed cell death, is associated with various diseases. This study investigates ferroptosis-related molecular subtypes in OA to comprehend underlying mechanisms. The Gene Expression Omnibus datasets GSE206848, GSE55457, GSE55235, GSE77298 and GSE82107 were used utilized. Unsupervised clustering identified the ferroptosis-related gene (FRG) subtypes, and their immune characteristics were assessed. FRG signatures were derived using LASSO and SVM-RFE algorithms, forming models to evaluate OA's ferroptosis-related immune features. Three FRG clusters were found to be immunologically heterogeneous, with cluster 1 displaying robust immune response. Models identified nine key signature genes via algorithms, demonstrating strong diagnostic and prognostic performance. Finally, qRT-PCR and Western blot validated these genes, offering consistent results. In addition, some of these genes may have implications as new therapeutic targets and can be used to guide clinical applications.

Discovery and validation of molecular patterns and immune characteristics in the peripheral blood of ischemic stroke patients.

Background: Stroke is a disease with high morbidity, disability, and mortality. Immune factors play a crucial role in the occurrence of ischemic stroke (IS), but their exact mechanism is not clear. This study aims to identify possible immunological mechanisms by recognizing immune-related biomarkers and evaluating the infiltration pattern of immune cells. Methods: We downloaded datasets of IS patients from GEO, applied R language to discover differentially expressed genes, and elucidated their biological functions using GO, KEGG analysis, and GSEA analysis. The hub genes were then obtained using two machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)) and the immune cell infiltration pattern was revealed by CIBERSORT. Gene-drug target networks and mRNA-miRNA-lncRNA regulatory networks were constructed using Cytoscape. Finally, we used RT-qPCR to validate the hub genes and applied logistic regression methods to build diagnostic models validated with ROC curves. Results: We screened 188 differentially expressed genes whose functional analysis was enriched to multiple immune-related pathways. Six hub genes (ANTXR2, BAZ2B, C5AR1, PDK4, PPIH, and STK3) were identified using LASSO and SVM-RFE. ANTXR2, BAZ2B, C5AR1, PDK4, and STK3 were positively correlated with neutrophils and gamma delta T cells, and negatively correlated with T follicular helper cells and CD8, while PPIH showed the exact opposite trend. Immune infiltration indicated increased activity of monocytes, macrophages M0, neutrophils, and mast cells, and decreased infiltration of T follicular helper cells and CD8 in the IS group. The ceRNA network consisted of 306 miRNA-mRNA interacting pairs and 285 miRNA-lncRNA interacting pairs. RT-qPCR results indicated that the expression levels of BAZ2B, C5AR1, PDK4, and STK3 were significantly increased in patients with IS. Finally, we developed a diagnostic model based on these four genes. The AUC value of the model was verified to be 0.999 in the training set and 0.940 in the validation set. Conclusion: Our research explored the immune-related gene expression modules and provided a specific basis for further study of immunomodulatory therapy of IS.

Integrated analysis of m6A regulator-mediated RNA methylation modification patterns and immune characteristics in Sjögren's syndrome.

The epigenetic modifier N6-methyladenosine (m6A), recognized as the most prevalent internal modification in messenger RNA (mRNA), has recently emerged as a pivotal player in immune regulation. Its dysregulation has been implicated in the pathogenesis of various autoimmune conditions. However, the implications of m6A modification within the immune microenvironment of Sjögren's syndrome (SS), a chronic autoimmune disorder characterized by exocrine gland dysfunction, remain unexplored. Herein, we leverage an integrative analysis combining public database resources and novel sequencing data to investigate the expression profiles of m6A regulatory genes in SS. Our cohort comprised 220 patients diagnosed with SS and 62 healthy individuals, enabling a comprehensive evaluation of peripheral blood at the transcriptomic level. We report a significant association between SS and altered expression of key m6A regulators, with these changes closely tied to the activation of CD4+ T cells. Employing a random forest (RF) algorithm, we identified crucial genes contributing to the disease phenotype, which facilitated the development of a robust diagnostic model via multivariate logistic regression analysis. Further, unsupervised clustering revealed two distinct m6A modification patterns, which were significantly associated with variations in immunocyte infiltration, immune response activity, and biological function enrichment in SS. Subsequently, we proceeded with a screening process aimed at identifying genes that were differentially expressed (DEGs) between the two groups distinguished by m6A modification. Leveraging these DEGs, we employed weight gene co-expression network analysis (WGCNA) to uncover sets of genes that exhibited strong co-variance and hub genes that were closely linked to m6A modification. Through rigorous analysis, we identified three critical m6A regulators - METTL3, ALKBH5, and YTHDF1 - alongside two m6A-related hub genes, COMMD8 and SRP9. These elements collectively underscore a complex but discernible pattern of m6A modification that appears to be integrally linked with SS's pathogenesis. Our findings not only illuminate the significant correlation between m6A modification and the immune microenvironment in SS but also lay the groundwork for a deeper understanding of m6A regulatory mechanisms. More importantly, the identification of these key regulators and hub genes opens new avenues for the diagnosis and treatment of SS, presenting potential targets for therapeutic intervention.

RNA methylation patterns, immune characteristics, and autophagy-related mechanisms mediated by N6-methyladenosine (m6A) regulatory factors in venous thromboembolism.

Recent studies have found a link between deep vein thrombosis and inflammatory reactions. N6-methyladenosine (m6A), a crucial element in immunological regulation, is believed to contribute to the pathophysiology of venous thromboembolism (VTE). However, how the m6A-modified immune microenvironment is involved in VTE remains unclear. In the present study, we identified a relationship between VTE and the expression of several m6A regulatory elements by analyzing peripheral blood samples from 177 patients with VTE and 88 healthy controls from public GEO databases GSE19151 and GSE48000. We used machine learning to identify essential genes and constructed a diagnostic model for VTE using multivariate logistic regression. Unsupervised cluster analysis revealed a marked difference between m6A modification patterns in terms of immune cell infiltration, inflammatory reactivity, and autophagy. We identified two m6A-related autophagy genes (i.e., CHMP2B and SIRT1) and the crucial m6A regulator YTHDF3 using bioinformatics. We also examined two potential mechanisms through which YTHDF3 may affect VTE. m6A modification, immunity, and autophagy are closely linked in VTE, offering novel mechanistic and therapeutic insights.

The role of HLA-DR on plasmacytoid dendritic cells in mediating the effects of Butyrivibrio gut microbiota on Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is viewed as a progressively deteriorating neurodegenerative disorder, the exact etiology of which remains not fully deciphered to this date. The gut microbiota could play a crucial role in PD development by modulating the human immune system. OBJECTIVE: This study aims to explore the relationship between gut microbiota and PD, focusing on how immune characteristics may both directly and indirectly influence their interaction. METHODS: Utilizing cumulative data from genome-wide association studies (GWAS), our research conducted a two-sample Mendelian randomization (MR) analysis to clarify the association between the gut microbiome and PD. Additionally, by employing a two-step MR approach, we assessed the impact of gut microbiota on PD development via immune characteristics and quantified HLA-DR mediation effect on plasmacytoid dendritic cells (pDCs). RESULTS: We discovered significant associations between PD and microbiota, comprising one class, one order, two families, and two genera. Furthermore, we explored the extent to which HLA-DR on pDCs mediates the effect of Butyrivibrio gut microbiota on PD. CONCLUSION: Our study emphasizes the complex interactions between the gut microbiota, immune characteristics, and PD. The relationships and intermediary roles identified in our research provide important insights for developing potential therapies that target the gut microbiome to alleviate symptoms in PD patients.

Hyperactivation and enhanced cytotoxicity of reduced CD8+ gamma delta T cells in the intestine of patients with Crohn's disease correlates with disease activity.

BACKGROUND AND AIMS: We aimed to investigate the immune characteristics of intestinal CD8+ gamma delta T (CD8+ γδ T) cells in Crohn's disease (CD) and their correlation with disease activity. METHODS: The study cohorts included 21 CD patients and 21 healthy individuals. CD8+ γδ T cells were isolated from human ileal mucosa for detection by flow cytometry. The activation or inhibition status of cells was detected by detecting the expression of activation marker HLA-DR and the immunosuppressive molecule PD-1 on cells. The cytotoxicity of cells was assessed by detecting the expression of cytotoxic molecules (Perforin, Granzyme B, and TRAIL) in cells. Ratios of investigated cells were calculated as prediction factors by receiver operating characteristic curve (ROC) analysis. RESULTS: The study revealed a reduction in intestinal CD8+ γδT cells among active CD patients, with a more pronounced reduction observed in moderately active patients compared to mildly active patients. Moreover, active CD patients exhibited heightened activation levels in their intestinal CD8+ γδT cells, whereas the activation was comparatively weakened in moderately active patients compared with mildly active patients. Additionally, the cytotoxicity of intestinal CD8+ γδT cells was enhanced solely in mildly active patients, while it was impaired in moderately active patients compared with mildly active patients. Furthermore, HLA-DR+ CD8+ γδT cell ratio, CD8+ γδT ratio, and CD8+ γδT count were identified as indicators in the diagnosis of active CD. Meanwhile, the ratios of Granzyme B+ CD8+ γδT cell and Perforin+ CD8+ γδT cell were identified as indicators that distinguish mildly moderately active CD cases. CONCLUSIONS: Intestinal CD8+ γδT was reduced in active CD patients, but their activation and cytotoxicity were enhanced. However, with increased disease activity, intestinal CD8+ γδ T cells became dysfunctional. CD-specific perturbations observed in various phenotypic markers in CD8+ γδ T cells can be used as indicators to assist in diagnosing CD patients.

Immune characteristics of dedifferentiated retroperitoneal liposarcomas and the reliability of regional samples in evaluating their tumor immune microenvironments.

BACKGROUND: Tumor immunotherapy is a new treatment breakthrough for retroperitoneal liposarcoma (RPLS), which is highly invasive and has few effective treatment options other than tumor resection. However, the heterogeneity of the tumor immune microenvironment (TIME) leads to missed clinical diagnosis and inappropriate treatment. Therefore, it is crucial to evaluate whether the TIME of a certain part of the tumor reliably represents the whole tumor, particularly for very large tumors, such as RPLS. METHODS: We conducted a prospective study to evaluate the TIME in different regions of dedifferentiated RPLS (DDRPLS) by detecting the expressions of markers such as CD4+, CD8+, Foxp3+, CD20+, CD68+, LAMP3+, PD-1+ tumor-infiltrating lymphocytes (TILs), and PD-L1 in tumors and corresponding paratumor tissues via immunohistochemistry and RNA sequencing. RESULTS: In DDRPLS, very few TILs were observed. Differentially expressed genes were significantly enriched in cell part and cell functions, as well as the metabolic pathway and PI3K-Akt signaling pathway. In addition, for most tumors (70-80%), the TIME was similar in different tumor regions. CONCLUSIONS: For most tumors (70-80%), the TIME in any region of the tumor reliably represents the whole tumor. DDRPLS may regulate cell functions by modulating the metabolic and PI3K-Akt signaling pathways to promote its malignant behavior.

Novel molecular insights into pyroptosis in triple-negative breast cancer prognosis and immunotherapy.

BACKGROUND: Patients with triple-negative breast cancer (TNBC) often have a poor prognostic outcome. Current treatment strategies cannot benefit all TNBC patients. Previous findings suggested pyroptosis as a novel target for suppressing cancer development, although the relationship between TNBC and pyroptosis-related genes (PRGs) was still unclear. METHODS: Gene expression data and clinical follow-up of TNBC patients were collected from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). PRGs were screened using weighted gene co-expression network analysis. Cox regression analysis and the least absolute shrinkage and selection operator (i.e. LASSO) technique were applied to construct a pyroptosis-related prognostic risk score (PPRS) model, which was further combined with the clinicopathological characteristics of TNBC patients to develop a survival decision tree and a nomogram. The model was used to calculate the PPRS, and then the overall survival, immune infiltration, immunotherapy response and drug sensitivity of TNBC patients were analyzed based on the PPRS. RESULTS: The PPRS model was closely related to clinicopathological features and can independently and accurately predict the prognosis of TNBC. According to normalized PPRS, patients in different cohorts were divided into two groups. Compared with the high-PPRS group, the low-PPRS group had significantly higher ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) score, immune score and stromal score, and it also had overexpressed immune checkpoints and significantly reduced Tumor Immune Dysfunction and Exclusion (TIDE) score, as well as higher sensitivity to paclitaxel, veliparib, olaparib and talazoparib. A decision tree and nomogram based on PPRS and clinical characteristics can improve the prognosis stratification and survival prediction for TNBC patients. CONCLUSIONS: A PPRS model was developed to predict TNBC patients' immune characteristics and response to immunotherapy, chemotherapy and targeted therapy, as well as their survival outcomes.

Comprehensive transcriptomic analysis and machine learning reveal unique gene expression profiles in patients with immune-mediated necrotizing myopathy.

BACKGROUND: Immune-mediated necrotizing myopathy (IMNM) is an autoimmune myopathy characterized by severe proximal weakness and muscle fiber necrosis, yet its pathogenesis remains unclear. So far, there are few bioinformatics studies on underlying pathogenic genes and infiltrating immune cell profiles of IMNM. Therefore, we aimed to characterize differentially expressed genes (DEGs) and infiltrating cells in IMNM muscle biopsy specimens, which may be useful for elucidating the pathogenesis of IMNM. METHODS: Three datasets (GSE39454, GSE48280 and GSE128470) of gene expression profiling related to IMNM were obtained from the Gene Expression Omnibus database. Data were normalized, and DEG analysis was performed using the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed using clusterProfiler. The CIBERSORT algorithm was performed to identify infiltrating cells. Machine learning algorithm and gene set enrichment analysis (GSEA) were performed to find distinctive gene signatures and the underlying signaling pathways of IMNM. RESULTS: DEG analysis identified upregulated and downregulated in IMNM muscle compared to the gene expression levels of other groups. GO and KEGG analysis showed that the pathogenesis of IMNM was notable for the under-representation of pathways that were important in dermatomyositis and inclusion body myositis. Three immune cells (M2 macrophages, resting dendritic cells and resting natural killer cells) with differential infiltration and five key genes (NDUFAF7, POLR2J, CD99, ARF5 and SKAP2) in patients with IMNM were identified through the CIBERSORT and machine learning algorithm. The GSEA results revealed that the key genes were remarkably enriched in diverse immunological and muscle metabolism-related pathways. CONCLUSIONS: We comprehensively explored immunological landscape of IMNM, which is indicative for the research of IMNM pathogenesis.

Characteristics of T Cells in Single-Cell Datasets of Peripheral Blood and Cerebrospinal Fluid in Alzheimer's Disease Patients.

BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia, causing a huge socioeconomic burden. In parallel with the widespread uptake of single-cell RNA sequencing (scRNA-seq) technology, there has been a rapid accumulation of data produced by researching AD at single-cell resolution, which is more conductive to explore the neuroimmune-related mechanism of AD. OBJECTIVE: To explore the potential features of T cells in the peripheral blood and cerebrospinal fluid of AD patients. METHODS: Two datasets, GSE181279 and GSE134578, were integrated from GEO database. Seurat, Monocle, CellChat, scRepertoire, and singleR packages were mainly employed for data analysis. RESULTS: Our analysis demonstrated that in peripheral blood, T cells were significantly expanded, and these expanded T cells were possessed effector function, such as CD8+TEMRA, CD4+TEMRA, and CD8+TEM. Interestingly, CD8+TEMRA and CD4+TEMRA cells positioned adjacently after dimensions reduction and clustering. Notably, we identified that the expanded T cells were developed from Naïve T cells and TCM cells, and TEM cells was in the intermediate state of this developing process. Additionally, in cerebrospinal fluid of AD patients, the amplified T cells were mainly CD8+TEMRA cells, and the number and strength of communication between CD4+TEM, CD8+TEM, and CD8+TEMRA were decreased in AD patients. CONCLUSIONS: Our comprehensive analyses identified the cells in cerebrospinal fluid from AD patients are expanded TEMRA or TEM cells and the TEMRA cells communicating with other immune cells is weakened, which may be an important immune feature that leads to AD.

Immunological characteristics of immunogenic cell death genes and malignant progression driving roles of TLR4 in anaplastic thyroid carcinoma.

Anaplastic thyroid carcinoma (ATC) was a rare malignancy featured with the weak immunotherapeutic response. So far, disorders of immunogenic cell death genes (ICDGs) were identified as the driving factors in cancer progression, while their roles in ATC remained poorly clear. Datasets analysis identified that most ICDGs were high expressed in ATC, while DE-ICDGs were located in module c1_112, which was mainly enriched in Toll-like receptor signalings. Subsequently, the ICD score was established to classify ATC samples into the high and low ICD score groups, and function analysis indicated that high ICD score was associated with the immune characteristics. The high ICD score group had higher proportions of specific immune and stromal cells, as well as increased expression of immune checkpoints. Additionally, TLR4, ENTPD1, LY96, CASP1 and PDIA3 were identified as the dynamic signature in the malignant progression of ATC. Notably, TLR4 was significantly upregulated in ATC tissues, associated with poor prognosis. Silence of TLR4 inhibited the proliferation, metastasis and clone formation of ATC cells. Eventually, silence of TLR4 synergistically enhanced paclitaxel-induced proliferation inhibition, apoptosis, CALR exposure and release of ATP. Our findings highlighted that the aberrant expression of TLR4 drove the malignant progression of ATC, which contributed to our understanding of the roles of ICDGs in ATC.

Hot and cold tumors: Immunological features and the therapeutic strategies.

The "hotness" or "coldness" of the tumors are determined by the information of the cancer cells themselves, tumor immune characteristics, tumor microenvironment, and signaling mechanisms, which are key factors affecting cancer patients' clinical efficacy. The switch mechanism of "hotness" and "coldness" and its corresponding pathological characteristics and treatment strategies are the frontier and hot spot of tumor treatment. How to distinguish the "hotness" or "coldness" effectively and clarify the causes, microenvironment state, and characteristics are very important for the tumor response and efficacy treatments. Starting from the concept of hot and cold tumor, this review systematically summarized the molecular characteristics, influencing factors, and therapeutic strategies of "hot and cold tumors," and analyzed the immunophenotypes, the tumor microenvironment, the signaling pathways, and the molecular markers that contribute to "hot and cold tumors" in details. Different therapeutic strategies for "cold and hot tumors" based on clinical efficacy were analyzed with drug targets and proteins for "cold and hot tumors." Furthermore, this review combines the therapeutic strategies of different "hot and cold tumors" with traditional medicine and modern medicine, to provide a basis and guidance for clinical decision-making of cancer treatment.

Tumor-derived exosomes RNA expression profiling identifies the prognosis, immune characteristics, and treatment in HR+/HER2-breast cancer.

Exosomes play crucial roles in intercellular communication and are involved in the onset and progression of various types of cancers, including breast cancer. However, the RNA composition of breast cancer-derived exosomes has not been comprehensively explored. We conducted microarray assays on exosomes isolated from breast cancer and healthy breast epithelial cells from three patients with hormone receptor (HR) +/ human epidermal growth factor receptor (HER2) - breast cancer and identified 817 differentially expressed genes (DEGs). Among these, 315 upregulated tumor-derived exosome genes (UTEGs) were used to classify HR+/HER2- breast cancers into two categories, revealing a difference in survival rates between the groups. We developed and validated a novel prognostic exosome score (ES) model consisting of four UTEGs that provides a refined prognosis prediction in HR+/HER2-breast cancer. ES reflects various immune-related features, including somatic variation, immunogenicity, and tumor immune infiltrate composition. Our findings indicate a considerable positive correlation between the ES and drug sensitivity values for vincristine, paclitaxel, and docetaxel. However, ES was remarkably higher in the endocrine therapy non-responder group than in the responder group. Immunohistochemistry confirmed the remarkable expression of the four model genes in tumor tissues, and their expression in MCF-7 cell exosomes was higher than that in MCF10A cells, as verified via qPCR. In summary, tumor-derived exosome genes provide novel insights into the subtyping, prognosis, and treatment of HR+/HER2-breast cancer.

PANoptosis-based molecular subtyping and HPAN-index predicts therapeutic response and survival in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a highly prevalent and fatal cancer. The role of PANoptosis, a novel form of programmed cell death, in HCC is yet to be fully understood. This study focuses on identifying and analyzing PANoptosis-associated differentially expressed genes in HCC (HPAN_DEGs), aiming to enhance our understanding of HCC pathogenesis and potential treatment strategies. Methods: We analyzed HCC differentially expressed genes from TCGA and IGCG databases and mapped them to the PANoptosis gene set, identifying 69 HPAN_DEGs. These genes underwent enrichment analyses, and consensus clustering analysis was used to determine three distinct HCC subgroups based on their expression profiles. The immune characteristics and mutation landscape of these subgroups were evaluated, and drug sensitivity was predicted using the HPAN-index and relevant databases. Results: The HPAN_DEGs were mainly enriched in pathways associated with the cell cycle, DNA damage, Drug metabolism, Cytokines, and Immune receptors. We identified three HCC subtypes (Cluster_1, SFN+PDK4-; Cluster_2, SFN-PDK4+; Cluster_3, SFN/PDK4 intermediate expression) based on the expression profiles of the 69 HPAN_DEGs. These subtypes exhibited distinct clinical outcomes, immune characteristics, and mutation landscapes. The HPAN-index, generated by machine learning using the expression levels of 69 HPAN_DEGs, was identified as an independent prognostic factor for HCC. Moreover, the high HPAN-index group exhibited a high response to immunotherapy, while the low HPAN-index group showed sensitivity to small molecule targeted drugs. Notably, we observed that the YWHAB gene plays a significant role in Sorafenib resistance. Conclusion: This study identified 69 HPAN_DEGs crucial to tumor growth, immune infiltration, and drug resistance in HCC. Additionally, we discovered three distinct HCC subtypes and constructed an HPAN-index to predict immunotherapeutic response and drug sensitivity. Our findings underscore the role of YWHAB in Sorafenib resistance, presenting valuable insights for personalized therapeutic strategy development in HCC.

PRKAA1 predicts prognosis and is associated with immune characteristics in gastric cancer.

PRKAA1 is the α-subunit of 5-AMP-activated protein kinase. This study aimed to investigate the role of PRKAA1 expression with multiple clinical parameters, the overall survival rate, blood indexes, and immune infiltration in gastric cancer (GC) patients. We investigated PRKAA1 expression data in GC patients using ELISA, protein atlas, UALCAN, and GEPIA. PRKAA1 expression was associated with immune cell infiltration, and immune cell types were analyzed with the TIMER, DICE, and protein atlas databases. We compared the level of PRKAA1 expression based on the clinical features of GC patients (n = 345). GC patients were divided into two groups based on PRKAA1 expression, and the lymphocyte subsets, overall survival rate, and clinical parameters were compared with peripheral blood mononuclear cell and biochemical indexes. PRKAA1 was highly expressed in the serum of GC patients compared with that of healthy individuals. GC patients with distant metastases, a later TNM stage, and stage IV in UICC exhibited higher PRKAA1 expression. PRKAA1 expression was significantly correlated with circulating T cells. The protein atlas and DICE database results confirmed that PRKAA1 was closely associated with T cells in a single-cell cluster. Furthermore, GC patients with low PRKAA1 expression had better OS rates. PRKAA1 may serve as a potential prognostic biomarker for GC and have an association with immune infiltrates.

Identification and validation of immune-associated NETosis subtypes and biomarkers in anti-neutrophil cytoplasmic antibody associated glomerulonephritis.

Background: NETosis is a new form of cell death, marked by DNA chromatin release from dead neutrophils. While it aids in microbe defense, it may worsen inflammation in autoimmune diseases, causing tissue harm. The impact of NETosis on Anti-neutrophil Cytoplasmic Antibody-associated Glomerulonephritis (ANCA-GN) remains unexplored and requires investigation. Methods: First, a weighted gene co-expression network analysis (WGCNA) was conducted to uncover differential expression of neutrophil extranuclear trap-associated genes (DE-NETs) in ANCA-GN. The NETosisScore model was established through the single sample gene set enrichment analysis (ssGSEA), which categorized all patients into high-risk and low-risk groups. The accuracy of model was assessed by ROC curve. The biological function of various subgroups was explored through Gene Set Variation Analysis (GSVA), while the abundance of immune cell infiltration was measured with CIBERSORT. Furthermore, the key NETosis-related genes (NRGs) were identified using three machine learning algorithms, and their relationship with renal function was analyzed through the NephroseqV5 database. Through the application of qPCR and immunohistochemical staining techniques, the mRNA and protein expression levels of NRGs were determined in patients with ANCA-GN and control. Results: A NETosisScore model was developed from 18 DE-NETs using the ssGSEA algorithm. The model's ability to predict ANCA-GN patients with a ROC AUC of 0.921. The high-risk group in ANCA-GN showed enrichment of immune-related pathways and greater infiltration of immune cells, as revealed by KEGG enrichment analysis and CIBERSORT. Using three machine learning algorithms, we identified six NRGs. Significant positive correlations were found between NRGs and CCR, macrophages, T-cell co-inhibition, and TIL. Further KEGG analysis revealed that the functions of NRGs may be closely related to the toll-like receptor signaling pathway. The levels of NRGs increased as kidney function declined and were positively correlated with Scr (serum creatinine) and negatively correlated with GFR (glomerular filtration rate), qPCR analysis showed increased expression of most NRGs in ANCA-GN patients. Furthermore, immunohistochemical staining confirmed higher expression of all NRGs in ANCA-GN patients. Conclusion: NETosisScore model accurately predicts high-risk patients in ANCA-GN with enriched immune pathways, 6 NRGs identified as potential biomarkers.

Identification and validation of a 7-genes prognostic signature for adult acute myeloid leukemia based on aging-related genes.

To explore effects of aging-related genes (ARGs) on the prognosis of Acute Myeloid Leukemia (AML), a seven-ARGs signature was developed and validated in AML patients. The numbers of seven-ARG sequences were selected to construct the survival prognostic signature in TCGA-LAML cohort, and two GEO datasets were used independently to verify the prognostic values of signature. According to seven-ARGs signature, patients were categorized into two subgroups. Patients with high-risk prognostic score were defined as HRPS-group/high-risk group, while others were set as LRPS-group/low-risk group. HRPS-group presented adverse overall survival (OS) than LRPS-group in TCGA-AML cohort (HR=3.39, P<0.001). In validation, the results emphasized a satisfactory discrimination in different time points, and confirmed the poor OS of HRPS-group both in GSE37642 (HR=1.96, P=0.001) and GSE106291 (HR=1.88, P<0.001). Many signal pathways, including immune- and tumor-related processes, especially NF-κB signaling, were highly enriched in HRPS-group. Coupled with high immune-inflamed infiltration, the HRPS-group was highly associated with the driver gene and oncogenic signaling pathway of TP53. Prediction of blockade therapy targeting immune checkpoint indicated varied benefits base on the different ARGs signature score, and the results of predicted drug response suggested that Pevonedistat, an inhibitor of NEDD8-activating enzyme, targeting NF-κB signaling, may have potential therapeutic value for HRPS-group. Compared with clinical factors alone, the signature had an independent value and more predictive power of AML prognosis. The 7-ARGs signature may help to guide clinical-decision making to predict drug response, and survival in AML patients.

Landscape analysis of m6A modification regulators related biological functions and immune characteristics in myasthenia gravis.

BACKGROUND: N6-methyladenosine (m6A) modification has been recognized to play fundamental roles in the development of autoimmune diseases. However, the implication of m6A modification in myasthenia gravis (MG) remains largely unknown. Thus, we aimed to systematically explore the potential functions and related immune characteristics of m6A regulators in MG. METHODS: The GSE85452 dataset with MG and healthy samples was downloaded from Gene Expression Omnibus (GEO) database. m6A modification regulators were manually curated. The targets of m6A regulators were obtained from m6A2Target database. The differential expressed m6A regulators in GSE85452 dataset were identified by "limma" package and were validated by RT-PCR. Function enrichment analysis of dysregulated m6A regulators was performed using "clusterProfiler" package. Correlation analysis was applied for analyzing the relationships between m6A regulators and immune characteristics. Unsupervised clustering analysis was used to identify distinct m6A modification subtypes. The differences between subtypes were analyzed, including the expression level of all genes and the enrichment degree of immune characteristics. Weighted gene co-expression network analysis (WGCNA) was conducted to obtain modules associated with m6A modification subtypes. RESULTS: We found that CBLL1, RBM15 and YTHDF1 were upregulated in MG samples of GSE85452 dataset, and the results were verified by RT-PCR in blood samples from19 MG patients and 19 controls. The targeted genes common modified by CBLL1, RBM15, and YTHDF1 were mainly enriched in histone modification and Wnt signaling pathway. Correlation analysis showed that three dysregulated m6A regulators were closely associated with immune characteristics. Among them, RBM15 possessed the strongest correlation with immune characteristics, including CD56dim natural killer cell (r = 0.77, P = 0.0023), T follicular helper cell (r = - 0.86, P = 0.0002), Interferon Receptor (r = 0.78, P = 0.0017), and HLA-DOA (r = 0.64, P = 0.0200). Further two distinct m6A modification patterns mediated by three dysregulated m6A regulators was identified. Bioinformatics analysis found that there were 3029 differentially expressed genes and different immune characteristics between two m6A modification patterns. Finally, WGCNA analysis obtained a total of 12 modules and yellow module was the most positively correlated to subtype-2. CONCLUSION: Our findings suggested that m6A RNA modification had an important effect on immunity molecular mechanism of MG and provided a new perspective into understanding the pathogenesis of MG.

Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer's disease.

Background: To date, the pathogenesis of Alzheimer's disease is still not fully elucidated. Much evidence suggests that Ferroptosis plays a crucial role in the pathogenesis of AD, but little is known about its molecular immunological mechanisms. Therefore, this study aims to comprehensively analyse and explore the molecular mechanisms and immunological features of Ferroptosis-related genes in the pathogenesis of AD. Materials and methods: We obtained the brain tissue dataset for AD from the GEO database and downloaded the Ferroptosis-related gene set from FerrDb for analysis. The most relevant Hub genes for AD were obtained using two machine learning algorithms (Least absolute shrinkage and selection operator (LASSO) and multiple support vector machine recursive feature elimination (mSVM-RFE)). The study of the Hub gene was divided into two parts. In the first part, AD patients were genotyped by unsupervised cluster analysis, and the different clusters' immune characteristics were analysed. A PCA approach was used to quantify the FRGscore. In the second part: we elucidate the biological functions involved in the Hub genes and their role in the immune microenvironment by integrating algorithms (GSEA, GSVA and CIBERSORT). Analysis of Hub gene-based drug regulatory networks and mRNA-miRNA-lncRNA regulatory networks using Cytoscape. Hub genes were further analysed using logistic regression models. Results: Based on two machine learning algorithms, we obtained a total of 10 Hub genes. Unsupervised clustering successfully identified two different clusters, and immune infiltration analysis showed a significantly higher degree of immune infiltration in type A than in type B, indicating that type A may be at the peak of AD neuroinflammation. Secondly, a Hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully constructed. Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed. Conclusion: Our study provides new insights into the role of Ferroptosis-related molecular patterns and immune mechanisms in AD, as well as providing a theoretical basis for the addition of diagnostic markers for AD.