Immune-related Signaling Pathways Research Articles

Bispecific CAR-T cells targeting CD19/20 in patients with relapsed or refractory B cell non-Hodgkin lymphoma: a phase I/II trial

Non-Hodgkin lymphoma (NHL) is a common malignancy in the hematologic system, and traditional therapy has limited efficacy for people with recurrent/refractory NHL (R/R NHL), especially for patients with diffuse large B cell lymphoma (DLBCL). Chimeric antigen receptor (CAR) T-cell therapy is a novel and effective immunotherapy strategy for R/R hematopoietic malignancies, but relapses can occur due to the loss of CAR-T cells in vivo or the loss of antigen. One strategy to avoid antigen loss after CAR-T cell therapy is to target one more antigen simultaneously. Tandem CAR targeting CD19 and CD22 has demonstrated the reliability of tandem CAR-T cell therapy for R/R B-ALL. This study explores the therapeutic potential of tandem CD19/20 CAR-T in the treatment of R/R B cell NHL. The efficacy and safety of autologous CD19/20 CAR-T cells in eleven R/R B cell NHL adult patients were evaluated in an open-label, single-arm trial. Most patients achieved complete response, exhibiting the efficacy and safety of tandem CD19/20 CAR-T cells. The TCR repertoire diversity of CAR-T cells decreased after infusion. The expanded TCR clones in vivo were mainly derived from TCR clones that had increased expression of genes associated with immune-related signaling pathways from the infusion product (IP). The kinetics of CAR-T cells in vivo were linked to an increase in the expression of genes related to immune response and cytolysis/cytotoxicity.

Identification of necroptosis-related genes in ankylosing spondylitis by bioinformatics and experimental validation.

The pathogenesis of ankylosing spondylitis (AS) remains unclear, and while recent studies have implicated necroptosis in various autoimmune diseases, an investigation of its relationship with AS has not been reported. In this study, we utilized the Gene Expression Omnibus database to compare gene expressions between AS patients and healthy controls, identifying 18 differentially expressed necroptosis-related genes (DENRGs), with 8 upregulated and 10 downregulated. Through the application of three machine learning algorithms-least absolute shrinkage and selection operation, support vector machine-recursive feature elimination and random forest-two hub genes, FASLG and TARDBP, were pinpointed. These genes demonstrated high specificity and sensitivity for AS diagnosis, as evidenced by receiver operating characteristic curve analysis. These findings were further supported by external datasets and cellular experiments, which confirmed the downregulation of FASLG and upregulation of TARDBP in AS patients. Immune cell infiltration analysis suggested that CD4+ T cells, CD8+ T cells, NK cells and neutrophils may be associated with the development of AS. Notably, in the group with high FASLG expression, there was a significant infiltration of CD8+ T cells, memory-activated CD4+ T cells and resting NK cells, with relatively less infiltration of memory-resting CD4+ T cells and neutrophils. Conversely, in the group with high TARDBP expression, there was enhanced infiltration of naïve CD4+ T cells and M0 macrophages, with a reduced presence of memory-resting CD4+ T cells. In summary, FASLG and TARDBP may contribute to AS pathogenesis by regulating the immune microenvironment and immune-related signalling pathways. These findings offer new insights into the molecular mechanisms of AS and suggest potential new targets for therapeutic strategies.

Prognostic value of GARS in bladder cancer and its role in the tumor microenvironment.

Bladder cancer (BC), as a common type of cancer, has a poor prognosis, also some common invasive prognostic or therapeutic markers are difficult to obtain, which makes further treatment of BC difficult. Glycyl-tRNA synthetase (GARS), as one of the aminoacyl-tRNA synthetases that charge tRNAs with their cognate amino acids, has been identified as a target in many diseases, including tumors. Bioassay analysis revealed that GARS was in high expression in most cancer tissues. The expression of GARS gene in BC tissues could assess the prognosis of BC patients, and the expression in urinary extracellular vesicles (uEVs) of patients was positively correlated with the expression in tissues. In addition to this, we analyzed GARS-related differential gene expression, copy number variation (CNV) and mutation profiles, potential biological functions, immune cell infiltration and drug sensitivity. In vivo and vitro tumorigenic experiments were performed to validate the function of GARS. Single-cell data were used to further analyze its role in the microenvironment. In our study, we found that GARS was highly expressed in 30 cancer tissues including BC, and high GARS expression was negatively correlated with the prognosis of BC patients. To address this phenomenon, we analyzed the differential genes between high and low GARS groups by enrichment analysis, and identified the biological signaling pathways that were mainly enriched for their functions, and found that the enrichment was found in immune-related signaling pathways and regulation of cell-cell adhesion. Then we found that GARS was positively associated with immune cell infiltration in BC, and some common immune checkpoints were significantly overexpressed in the GARS-high group. Besides, we found that GARS was enriched in myofibroblasts in the tumor microenvironment, and the enrichment was positively correlated with epithelial-mesenchymal transition (EMT)-related genes. This study also showed a positive correlation between GARS and BC RNA stemness. Patients in the GARS-high group had considerably higher rates of P53 and Titin (TTN) mutations than those in the GARS-low group. Drug Sensitivity analysis screened for drugs that were more sensitive to GARS-high patients. Further, we found that knockdown of GARS significantly inhibited the proliferation, migration and invasion ability both in vivo and in vitro. Finally, we found that in patients with high GARS the expression in uEVs was also at a high level. In summary, this study provided evidence that GARS can be used as a prognostic and therapeutic marker for BC, we can detect GARS in uEVs instead of tissue, to provide a new, simple, noninvasive way to obtain prognostic and therapeutic markers for BC patients.

Identification of coagulation diagnostic biomarkers related to the severity of spinal cord injury

BackgroundBlood always shows coagulation changes after spinal cord injury (SCI), and identifying these blood changes may be helpful for diagnosis and treatment of SCI. Nevertheless, studies to date on blood coagulation changes after SCI in humans are not comprehensive. Therefore, this study aims to identify blood coagulation diagnostic biomarkers and immune changes related to SCI and its severity levels. MethodsHuman blood sequencing datasets were obtained from public databases. Differentially expressed coagulation-related genes were analyzed (DECRGs). Enrichment analysis and assessment of immune changes were conducted. Weighted gene co-expression network analysis, least absolute shrinkage and selection operator logistic regression were used to identify biomarkers. Validation for these biomarkers was performed. The correlation between biomarkers and immune cells was evaluated. Transcription factors, miRNA, lncRNA, and drugs that can regulate biomarkers were analyzed. ResultsDECRGs associated with SCI and its different grades were identified, showing enrichment in altered coagulation and immune-related signaling pathways. ADAM9, CD55, and STAT4 were identified as coagulation diagnostic biomarkers for SCI. IRF4 and PABPC4 were identified as coagulation diagnostic biomarkers for American Spinal Injury Association Impairment Scale (AIS) A grade of SCI. GP9 was designated as a diagnostic biomarker for AIS D grade of SCI. Immune changes in blood of SCI and its different grades were observed. Correlation between diagnostic biomarkers and immune cells were identified. Transcription factors, miRNA, lncRNA, and drugs that can regulate diagnostic biomarker expression were discovered. ConclusionTherefore, detecting the expression of these putative diagnostic biomarkers and related immune changes may be helpful for predicting the severity of SCI. Uncovering potential regulatory mechanisms for biomarkers may be beneficial for further research.

Transcriptome-based protein-protein interaction analysis reveals immune gene network elucidating white body immunity mechanisms in response to LPS stimulation in Amphioctopus fangsiao

One of the major challenges in the aquaculture of marine organisms is the susceptibility to pathogens. Immune cells defend bacterial invasion by recognizing pathogen-associated molecular patterns (PAMPs) such as Lipopolysaccharides (LPS) in Gram-negative bacteria. The white body are key organs for immune response and hematopoiesis, and play an important role in understanding the antimicrobial biological response in gold-ringed octopus (Amphioctopus fangsiao). In the present research, transcriptome sequencing and bioinformatics analysis were conducted on A. fangsiao white body tissue after 6 h and 24 h of LPS stimulation. The results showed 2,029 and 73 differentially expressed genes (DEGs) at the 6- and 24-hour time points, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were beneficial in determining immune-related terms and signaling pathways. Subsequently, a protein-protein interaction (PPI) network was constructed and 20 hub genes, such as AKT3, MAPK14, and PIK3CA, were identified. These genes are involved in a variety of signaling pathways, including the Hippo signaling pathway, chemokine signaling pathway, and leukocyte trans endothelial migration. Using qRT-PCR, the accuracy of the 20 hub genes was verified. The present study provides a substantial theoretical foundation for the research on A. fangsiao white body immunity, enhancing the current understanding of cephalopods' innate immunity.

Based on scRNA-seq and bulk RNA-seq to establish tumor immune microenvironment-associated signature of skin melanoma and predict immunotherapy response.

Skin cutaneous melanoma (SKCM), a form of skin cancer, ranks among the most formidable and lethal malignancies. Exploring tumor microenvironment (TME)-based prognostic indicators would help improve the efficacy of immunotherapy for SKCM patients. This study analyzed SKCM scRNA-seq data to cluster non-malignant cells that could be used to explore the TME into nine immune/stromal cell types, including B cells, CD4 T cells, CD8 T cells, dendritic cells, endothelial cells, Fibroblasts, macrophages, neurons, and natural killer (NK) cells. Using data from The Cancer Genome Atlas (TCGA), we employed SKCM expression profiling to identify differentially expressed immune-associated genes (DEIAGs), which were then incorporated into weighted gene co-expression network analysis (WGCNA) to investigate TME-associated hub genes. Discover candidate small molecule drugs based on pivotal genes. Tumor immune microenvironment-associated genes (TIMAGs) for constructing TIMAS were identified and validated. Finally, the characteristics of TIAMS subgroups and the ability of TIMAS to predict immunotherapy outcomes were analyzed. We identified five TIMAGs (CD86, CD80, SEMA4D, C1QA, and IRF1) and used them to construct TIMAS. In addition, five potential SKCM drugs were identified. The results showed that TIMAS-low patients were associated with immune-related signaling pathways, high MUC16 mutation frequency, high T cell infiltration, and M1 macrophages, and were more favorable for immunotherapy. Collectively, TIMAS constructed by comprehensive analysis of scRNA-seq and bulk RNA-seq data is a promising marker for predicting ICI treatment outcomes and improving individualized therapy for SKCM patients.

IFI30 as a key regulator of PDL1 immunotherapy prognosis in breast cancer

BackgroundIFI30 is a lysosomal thiol reductase involved in antigen presentation and immune regulation in various cancers, including breast cancer. Despite its known involvement, the precise mechanism, function, and relationship with the PD-L1 axis and immune response remain unclear. MethodsWe conducted an extensive investigation into IFI30 mRNA expression in breast cancer utilizing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Furthermore, we characterized IFI30 mRNA expression across various cell types using publicly available single-cell RNA sequencing datasets, and assessed protein expression through immunohistochemistry using an in-house breast cancer tissue microarray. Functional experiments were performed to elucidate the effects of IFI30 overexpression on PD-L1 expression and inhibitory efficacy in both macrophages and breast tumor cells. ResultsOur study unveiled a marked upregulation of IFI30 expression in breast cancer tissues compared to their normal counterparts, with notable associations identified with tumor stage and prognosis. Additionally, IFI30 expression demonstrated significant correlations with various immune-related signaling pathways, encompassing peptide antigen binding, cytokine binding, and MHC class II presentation. Notably, breast cancer samples exhibiting high IFI30 expression in tumor cells displayed high PD-L1 expression on corresponding cells, alongside a diminished ratio of CD8 + T cell infiltration within the tumor microenvironment. Furthermore, ectopic knockdown of IFI30 in both tumor cells and macrophages resulted in a reduction of PD-L1 expression, while conversely, overexpression of IFI30 led to an increase in PD-L1 expression. ConclusionsThis study offers new insights into the involvement of IFI30 in breast cancer, elucidating its interplay with the PD-L1 axis and immune response dynamics. Our findings suggest that modulation of the IFI30-PD-L1 axis could serve as a promising strategy for regulating T cells infiltration in breast cancer thus treating breast cancer.

Construction of a pathological model of skin lesions in acute herpes zoster virus infection and its molecular mechanism.

Varicella-zoster virus (VZV), a common pathogen with humans as the sole host, causes primary infection and undergoes a latent period in sensory ganglia. The recurrence of VZV is often accompanied by severe neuralgia in skin tissue, which has a serious impact on the life of patients. During the acute infection of VZV, there are few related studies on the pathophysiological mechanism of skin tissue. In this study, transcriptome sequencing data from the acute response period within 2 days of VZV antigen stimulation of the skin were used to explore a model of the trajectory of skin tissue changes during VZV infection. It was found that early VZV antigen stimulation caused activation of mainly natural immune-related signaling pathways, while in the late phase activation of mainly active immune-related signaling pathways. JAK-STAT, NFκB, and TNFα signaling pathways are gradually activated with the progression of infection, while Hypoxia is progressively inhibited. In addition, we found that dendritic cell-mediated immune responses play a dominant role in the lesion damage caused by VZV antigen stimulation of the skin. This study provides a theoretical basis for the study of the molecular mechanisms of skin lesions during acute VZV infection.

Global Publication Trends and Research Hotspots of the Immune System and Osteoporosis: A Bibliometric and Visualization Analysis from 2012 to 2022.

Osteoporosis (OP) is a systemic bone metabolism disorder in which the immune system and bone metabolism interact. The purpose of this study was to explore the research status, hot spots and trends regarding the influence of the immune system on OP and to provide a basis for research directions and applications in this field. We searched and collected literature about the immune system and OP published from 2012 to 2022 in the Web of Science Core Collection database. All the included studies were subjected to bibliometrics analysis using Hiplot Pro, VOSviewer and CiteSpace software to produce statistics and visual analyses of the literature output, countries, institutions, authors, keywords and journals. A total of 1201 papers were included, and the number of citations of these articles reached 31,776. The number of publications and citations on the immune system and OP has increased year by year. The top three countries with the greatest number of papers published were China, the United States of America (USA) and Italy. The two institutions with the largest number of papers published were Sichuan University and Soochow University, both located in China. De Martinis Massimo (Italy) and Ginaldi Lia (Italy) are prolific authors in this field. The representative academic journals are Osteoporosis International, Frontiers in Immunology, Journal of Bone and Mineral Research, PloS One and Bone. The results of the keyword cooccurrence analysis showed that the research topics in this field mainly focused on T cells, cytokines, signaling pathways, vitamin D, postmenopausal OP and immune diseases. The keyword burst results showed that zoledronic acid, chain fatty acids and gut microbiota are the frontiers and trends of future research on this topic. The influence of the immune system on OP has been widely studied, and the current research in this field focuses on the effect or mechanism of immune-related cytokines, signaling pathways and vitamin D on OP. Future research trends in this field should focus on the immune regulation mechanism and clinical transformation of zoledronic acid, chain fatty acids and the gut microbiota in OP.

Identification of osteogenic differentiation-related miRNA-mRNA regulatory network of human periodontal ligament stem cells.

This study aimed to investigate the hub genes and miRNA-mRNA regulatory network around periodontal ligament stem cells (PDLSC) for osteogenic differentiation through bioinformatic analysis. The dataset with osteogenic differentiation of human PDLSC was downloaded from the GEO database. The Weighted gene coexpression network analysis (WGCNA) was performed to identify key modules and hub genes. In addition, differentially expressed genes (DEGs) analysis was conducted with limma. The functional enrichment of differentially expressed hub genes was implemented with KEGG and GSEA analysis. The targeted genes of differentially expressed miRNA were predicted based on miRWalk database. The miRNA-mRNA interaction network of osteogenic differentiation of PDLSC was constructed and visualized. The WGNCA results showed that the light-cyan module was positively correlated with osteogenic differentiation (r=0.98, P<0.05). A total of 3125 hub genes and 1426 differentially expressed hub genes were detected in OG group. Innate immune-related signaling pathways and metabolic pathways were involved in the osteogenic differentiation. In addition, total of 2 upregulated miRNAs with 63 targeted DEGs and 6 downregulated miRNAs with 214 targeted DEGs were detected, which contributed to osteogenic differentiation by regulating amino acid metabolism signaling pathway. We identified hub genes and miRNA-mRNA regulatory network contributing to osteogenic differentiation of human PDLSC, which will provide novel strategy for periodontal disease therapy.

Transcriptomics-based identification of TYROBP and TLR8 as novel macrophage-related biomarkers for the diagnosis of acute rejection after kidney transplantation

Macrophages play an important role in the development and progression of acute rejection after kidney transplantation. The study aims to investigate the biological role and significance of macrophage-associated genes (MAG) in acute rejection after kidney transplantation. We utilized transcriptome sequencing results from public databases related to acute rejection of kidney transplantation for comprehensive analysis and validation in animal experiments. We found that a large number of immune-related signaling pathways are activated in acute rejection. PPI protein interaction networks and machine learning were used to establish a Hub gene consisting of TYROBP and TLR8 for the diagnosis of acute rejection. The single-gene GSEA enrichment analysis and immune cell correlation analysis revealed a close correlation between the expression of Hub genes and immune-related biological pathways as well as the expression of multiple immune cells. In addition, the study of TF, miRNAs, and drugs provided a theoretical basis for regulating and treating the Hub genes in acute rejection. Finally, the animal experiments demonstrated once again that acute rejection can aggravate kidney tissue damage, apoptosis level, and increase the release of inflammatory factors. We established and validated a macrophage-associated diagnostic model for acute rejection after kidney transplantation, which can accurately diagnose the biological alterations in acute rejection after kidney transplantation.

Transcriptomic profiling revealed immune-related signaling pathways in response to experimental infection of Leishmania donovani in two desert lizards from Northwest China

Little is known about the immune response of lizards to Leishmania parasties. In this study, we conducted the first liver transcriptome analysis of two lizards (Phrynocephalus przewalskii and Eremias multiocellata) challenged with L. donovani, endemic to the steppe desert region of northwestern China. Our results revealed that multiple biological processes and immune-related signaling pathways are closely associated with the immune response to experimental L. donovani infection in the two lizards, and that both lizards show similar changes to mammals in terms of immunity to Leishmania. However, the interspecific divergence of the two lizards leads to different transcriptomic changes. In particular, in contrast to P. przewalskii, the challenged E. mutltiocellata was characterized by the induction of down-regulation of most DEGs. These findings will contribute to the scarce resources on lizard immunity and provide a reference for further research on immune mechanisms in reptiles.

Identification of the Effects of 5-Azacytidine on Porcine Circovirus Type 2 Replication in Porcine Kidney Cells.

Porcine circovirus type 2 (PCV2) is the main pathogen causing post-weaning multisystemic wasting syndrome (PMWS), which mainly targets the body's immune system and poses a serious threat to the global pig industry. 5-Azacytidine is a potent inhibitor of DNA methylation, which can participate in many important physiological and pathological processes, including virus-related processes, by inhibiting gene expression. However, the impact of 5-Aza on PCV2 replication in cells is not yet clear. We explored the impact of 5-Aza on PCV2 infection utilizing PK15 cells as a cellular model. Our objective was to gain insights that could potentially offer novel therapeutic strategies for PCV2. Our results showed that 5-Aza significantly enhanced the infectivity of PCV2 in PK15 cells. Transcriptome analysis revealed that PCV2 infection activated various immune-related signaling pathways. 5-Aza may activate the MAPK signaling pathway to exacerbate PCV2 infection and upregulate the expression of inflammatory and apoptotic factors.

Correlation of caecal microbiome endotoxins genes and intestinal immune cells in Eimeria tenella infection based on bioinformatics.

The infection with Eimeria tenella (ET) can elicit expression of various intestinal immune cells, incite inflammation, disrupt intestinal homeostasis, and facilitate co-infection with diverse bacteria. However, the reciprocal interaction between intestinal immune cells and intestinal flora in the progression of ET-infection remains unclear. The aim of this study was to investigate the correlation between cecal microbial endotoxin (CME)-related genes and intestinal immunity in ET-infection, with subsequent identification of hub potential biomarker and immunotherapy target. Differential expression genes (DEGs) within ET-infection and hub genes related to CME were identified through GSE39602 dataset based on bioinformatic methods and Protein-protein interaction (PPI) network analysis. Moreover, immune infiltration was analyzed by CIBERSORT method. Subsequently, comprehensive functional enrichment analyses employing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis along with Gene Ontology (GO), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were performed. A total of 1089 DEGs and 25 hub genes were identified and CXCR4 was ultimately identified as a essential CME related potential biomarker and immunotherapy target in the ET-infection. Furthermore, activated natural killer cells, M0 macrophages, M2 macrophages, and T regulatory cells were identified as expressed intestinal immune cells. The functional enrichment analysis revealed that both DEGs and hub genes were significantly enriched in immune-related signaling pathways. CXCR4 was identified as a pivotal CME-related potential biomarker and immunotherapy target for expression of intestinal immune cells during ET-infection. These findings have significant implications in elucidating the intricate interplay among ET-infection, CME, and intestinal immunity.

Tertiary Lymphoid Structures Gene Signature Predicts Prognosis and Immune Infiltration Analysis in Head and Neck Squamous Cell Carcinoma.

This study aims to assess the prognostic implications of gene signature of the tertiary lymphoid structures (TLSs) in head and neck squamous cell carcinoma (HNSCC) and scrutinize the influence of TLS on immune infiltration. Patients with HNSCC from the Cancer Genome Atlas were categorized into high/low TLS signature groups based on the predetermined TLS signature threshold. The association of the TLS signature with the immune microenvironment, driver gene mutation status, and tumor mutational load was systematically analyzed. Validation was conducted using independent datasets (GSE41613 and GSE102349). Patients with a high TLS signature score exhibited better prognosis compared to those with a low TLS signature score. The group with a high TLS signature score had significantly higher immune cell subpopulations compared to the group with a low TLS signature score. Moreover, the major immune cell subpopulations and immune circulation characteristics in the tumor immune microenvironment were positively correlated with the TLS signature. Mutational differences in driver genes were observed between the TLS signature high/low groups, primarily in the cell cycle and NRF2 signaling pathways. Patients with TP53 mutations and high TLS signature scores demonstrated a better prognosis compared to those with TP53 wild-type. In the independent cohort, the relationship between TLS signatures and patient prognosis and immune infiltration was also confirmed. Additionally, immune-related biological processes and signaling pathways were activated with elevated TLS signature. High TLS signature is a promising independent prognostic factor for HNSCC patients. Immunological analysis indicated a correlation between TLS and immune cell infiltration in HNSCC. These findings provide a theoretical basis for future applications of TLS signature in HNSCC prognosis and immunotherapy.

Integrated profiling identifies ferredoxin 1 as an immune-related biomarker of malignant phenotype in glioma

BackgroundGlioma, a highly resistant and recurrent type of central nervous system tumor, poses a significant challenge in terms of effective drug treatments and its associated mortality rates. Despite the discovery of Ferredoxin 1 (FDX1) as a crucial participant in cuproptosis, an innovative mechanism of cellular demise, its precise implications for glioma prognosis and tumor immune infiltration remain inadequately elucidated. MethodsTo analyze pan-cancer data, we employed multiple public databases. Gene expression evaluation was performed using tissue microarray (TMA) and single-cell sequencing data. Furthermore, four different approaches were employed to assess the prognostic importance of FDX1 in glioma. We conducted the analysis of differential expression genes (DEGs) and Gene Set Enrichment Analysis (GSEA) to identify immune-related predictive signaling pathways. Somatic mutations were assessed using Tumor Mutation Burden (TMB) and waterfall plots. Immune cell infiltration was evaluated with five different algorithms. Furthermore, we performed in vitro investigations to evaluate the biological roles of FDX1 in glioma. ResultsGlioma samples exhibited upregulation of FDX1, which in turn predicted poor prognosis and was positively associated with unfavorable clinicopathological characteristics. Notably, the top four enriched signaling pathways were immune-related, and the discovery revealed a connection between the expression of FDX1 and the frequency of mutations or the TMB. The FDX1_high group exhibited heightened infiltration of immune cells, and there existed a direct association between the expression of FDX1 and the regulation of immune checkpoint. In vitro experiments demonstrated that FDX1 knockdown reduced proliferation, migration, invasion and transition from G2 to M phase in glioma cells. ConclusionIn glioma, FDX1 demonstrated a positive association with the advancement of malignancy and changes in the infiltration of immune cells.

5 signature genes revealed by single-cell profiling identified unique immune subtypes affecting the prognosis of ovarian cancer.

Ovarian cancer (OC) ranks among the most prevalent gynecological malignancies, with surgery, chemotherapy, and immunotherapy constituting primary treatment modalities. However, despite advancements, immunotherapy, particularly immune checkpoint inhibitors, has yielded suboptimal outcomes. The pressing need to identify biomarkers predictive of clinical prognosis underscores our objective. We aim to discern gene signatures and establish prognostic subgroups, specifically in the context of immunotherapy and chemotherapy, guiding clinical decision-making. We used the Tumor Immunotherapy Gene Expression Resource (TIGER) and The Cancer Genome Atlas (TCGA) databases to extract signature genes of prognostic significance. Unsupervised consensus clustering was employed to classify patients based on these signature genes. The Tumor Immune Estimation Resource (TIMER) database, along with the R packages "maftools" and "ESTIMATE" facilitated immune infiltration estimation. Gene set variation analysis (GSVA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were implemented to probe immune-related cell signaling pathways among distinct subtypes. The Tumor Immune Dysfunction and Exclusion (TIDE) database was used to assess immunotherapy effects, while the R package "OncoPredict" evaluated drug sensitivity differences among subtypes. We identified five prognostically influential genes in ovarian cancer: IGFBP7, JCHAIN, CCDC80, VSIG4, and MS4A1. Utilizing these signature genes, we categorized TCGA-OV patients into five clusters, each associated with varying clinical prognoses. Notably, 2 clusters exhibited superior prognoses, accompanied by enhanced immune cell infiltration. KEGG enrichment analysis revealed their heightened enrichment in cellular immunity and immune cell interaction pathways. Given the elevated expression levels of multiple immune checkpoint molecules, these clusters may substantially benefit from immune checkpoint inhibitor therapy. Additionally, chemotherapy sensitivity analysis indicated their favorable responses to first or second-line chemotherapy regimens. We subclustered ovarian cancer patients by 5 signature genes obtained from the Single-cell RNA sequencing (scRNA-seq) dataset, which demonstrated a good typing effect. Patients in the two molecular subtypes showed better survival, higher immune cell infiltration, and higher drug sensitivity. This meticulous typing may help clinicians to quickly assess the prognosis of patients and the response to immunotherapy and chemotherapy.

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease.

Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited. Methods: We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed. Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset. Conclusion: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.

A hypoxia-related genes prognostic risk model, and mechanisms of hypoxia contributing to poor prognosis through immune microenvironment and drug resistance in acute myeloid leukemia.

Objective: Acute myeloid leukemia (AML) is a malignant hematologic cancer with poor prognosis. Emerging evidence suggests a close association between AML progression and hypoxia. The purpose of this study was to establish a new risk prognostic model for AML based on hypoxia-related genes, and to explore the mechanisms by which hypoxia-related genes affect the prognosis of AML based on tumor immune microenvironment (TIME) and drug resistance. Methods: The AML patient samples obtained from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were classified into C1 and C2 based on hypoxia-related genes, followed by analysis utilizing Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA). Through univariate and LASSO Cox regression analysis, the hypoxia-related hub genes 26S proteasome non-ATPase regulatory subunit 11 (PSMD11) and 26S proteasome non-ATPase regulatory subunit 14 (PSMD14) were identified to construct the model. AML patient samples were obtained from the TARGET and The Cancer Genome Atlas (TCGA) databases, serving as the training and the validation sets, and were stratified into high-risk and low-risk group according to the median risk score. The correlations between the model and TIME and anti-tumor drugs were analysed using CIBERSORT and Genomics of Drug Sensitivity in Cancer (GDSC) databases. The expressions of PSMD11/PSMD14 in clinical samples and AML sensitive and drug-resistant cell lines were detected by Western blot and real-time PCR. Results: The C1 group with high expression of hypoxia-related genes had lower overall survival (OS). Immune-related signaling pathways were different between C1/C2, and hypoxia was positively correlated with the activation of mammalian target of rapamycin (mTOR) signaling pathway. The model had good accuracy in both the training and the validation sets. The high-risk group exhibited lower OS and TIME activity, and was more sensitive to several anti-tumor drugs. PSMD11/PSMD14 were highly expressed in relapsed patients and AML drug-resistant cell lines. Conclusion: The established novel risk prognostic model and experiment results offer valuable insights for predicting AML prognosis and guiding drug selection. It also provides a fundamental framework for the mechanisms through which hypoxia impacts AML prognosis by modulating TIME and drug resistance.

Screening of key immune-related gene in Parkinson's disease based on WGCNA and machine learning.

Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand how certain immune-related genes contribute to the disease's development and progression. This study aims to screen key immune-related gene in Parkinson's disease based on weighted gene co-expression network analysis (WGCNA) and machine learning. This study downloaded the gene chip data from the Gene Expression Omnibus (GEO) database, and used WGCNA to screen out important gene modules related to Parkinson's disease. Genes from important modules were exported and a Venn diagram of important Parkinson's disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson's disease. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the the functions of immune-related genes and signaling pathways involved. Immune cell infiltration analysis was performed using the CIBERSORT package of R language. Using bioinformatics method and 3 machine learning methods [least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine (SVM)], the immune-related genes of Parkinson's disease were further screened. A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes (hub) gene. The downstream proteins of the Parkinson's disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn. A total of 218 immune genes related to Parkinson's disease were identified, including 45 upregulated genes and 50 downregulated genes. Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways. The results of immune infiltration analysis showed that the infiltration percentages of CD4+ T cells, NK cells, CD8+ T cells, and B cells were higher in the samples of Parkinson's disease patients, while resting NK cells and resting CD4+ T cells were significantly infiltrated in the samples of Parkinson's disease patients. ANK1 was screened out as the hub gene. The analysis of the protein-protein interaction network showed that the ANK1 translated and expressed 11 proteins which mainly participated in functions such as signal transduction, iron homeostasis regulation, and immune system activation. This study identifies the Parkinson's disease immune-related key gene ANK1 via WGCNA and machine learning methods, suggesting its potential as a candidate therapeutic target for Parkinson's disease.