Immune cell traits and causal relationships with cholecystitis: a mendelian randomization analysis.

Cholecystitis, characterized by inflammation of the gallbladder, is intricately linked to immune cells and the cytokines they produce. Despite this association, the specific contributions of immune cells to the onset and progression of cholecystitis remain to be fully understood. To delineate this relationship, we utilized the Mendelian randomization (MR) method to scrutinize the causal connections between 731 immune cell phenotypes and cholecystitis. By conducting MR analysis on 731 immune cell markers from public datasets, this study seeks to understand their potential impact on the risk of cholecystitis. It aims to elucidate the interactions between immune phenotypes and the disease, aiming to lay the groundwork for advancing precision medicine and developing effective treatment strategies for cholecystitis. Taking immune cell phenotypes as the exposure factor and cholecystitis as the outcome event, this study used single nucleotide polymorphisms (SNPs) closely associated with both immune cell phenotypes and cholecystitis as genetic instrumental variables. We conducted a two-sample MR analysis on genome-wide association studies (GWAS) data. Our research thoroughly examined 731 immune cell markers, to determine potential causal relationships with susceptibility to cholecystitis. Sensitivity analyses were performed to ensure the robustness of our findings, excluding the potential impacts of heterogeneity and pleiotropy. To avoid reverse causality, we conducted reverse MR analyses with cholecystitis as the exposure factor and immune cell phenotypes as the outcome event. Among the 731 immune phenotypes, our study identified 21 phenotypes with a causal relationship to cholecystitis (P < 0.05). Of these, eight immune phenotypes exhibited a protective effect against cholecystitis (odds ratio (OR) < 1), while the other 13 immune phenotypes were associated with an increased risk of developing cholecystitis (OR > 1). Additionally, employing the false discovery rate (FDR) method at a significance level of 0.2, no significant causal relationship was found between cholecystitis and immune phenotypes. Our research has uncovered a significant causal relationship between immune cell phenotypes and cholecystitis. This discovery not only enhances our understanding of the role of immune cells in the onset and progression of cholecystitis but also establishes a foundation for developing more precise biomarkers and targeted therapeutic strategies. It provides a scientific basis for more effective and personalized treatments in the future. These findings are expected to substantially improve the quality of life for patients with cholecystitis and mitigate the impact of the disease on patients and their families.

CIEC: Cross-tissue Immune Cell Type Enrichment and Expression Map Visualization for Cancer.

Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells, revealing their tissue-specific gene expression patterns and functions in cancer immunity. Comprehensive assessments of immune cells within and across tissues will provide us with a deeper understanding of the tumor immune system in general. Here, we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer (CIEC), the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell type or state. CIEC version 1.0 consists of 480 samples covering primary tumor, adjacent normal tissue, lymph node, metastasis tissue, and peripheral blood from 323 cancer patients. By applying integrative analysis, we constructed an immune cell-type/state map for each context and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) algorithm to estimate the enrichment for context-specific immune cell type/state. In addition, CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues, including expression map, correlation, similar genes detection, signature score, and expression comparison. We believe that CIEC will be a valuable resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune biomarker development and immunotherapy strategies. CIEC is freely accessible at http://ciec.gene.ac/.

Bidirectional Mendelian Randomization Reveals Causal Effects of Immune Cell Traits on Endometriosis Risk.

PURPOSE: Previous studies have identified associations between immune cell traits and endometriosis, but the causality of these relationships remains uncertain. In this study, we utilized Mendelian randomization (MR) to investigate the causal relationship between immune cell traits and endometriosis for the first time. METHODS: Seven hundred and thirty-one immune cell signatures associated with single-nucleotide polymorphisms (SNPs) were extracted from a published genome-wide association study (GWAS) involving 472 174 individuals, while endometriosis data, including four stages and seven subtypes, were obtained from the FinnGen consortium. Four methods were used for MR. The causal effect of immune cell traits on endometriosis was explored after Bonferroni correction. RESULTS: Significant causal relationship included 92 immune cell traits distributed among B cell (28 cells), cDC (2 cells), maturation stages of T cell (10 cells), monocyte (12 cells), Myeloid cell (5 cells), TBNK (13 cells), and Treg panels (22 cells). One of the most significant findings is that for every 1-standard deviation (SD) increase in CD8 on Central Memory CD8+ T cell, the risks of developing endometriosis of the fallopian tube increased by 72%. In the reverse MR analysis, a one-unit increase in the log odds of endometriosis of the ovary risk corresponded to a decrease in the absolute count of CD4+ CD8dim T cell by 0.10. CONCLUSION: This study represents the first comprehensive evaluation of the causal effects of immune cell traits on the risk/protection of different stages/subtypes of endometriosis. The findings highlight the complex and significant role of immune-derived factors in the pathogenesis of the disease.

Identification and validation of ferroptosis-related biomarkers in intervertebral disc degeneration.

Introduction: Ferroptosis plays a significant role in intervertebral disc degeneration (IDD). Understanding the key genes regulating ferroptosis in IDD could reveal fundamental mechanisms of the disease, potentially leading to new diagnostic and therapeutic targets. Methods: Public datasets (GSE23130 and GSE70362) and the FerrDb database were analyzed to identify ferroptosis-related genes (DE-FRGs) involved in IDD. Single-cell RNA sequencing data (GSE199866) was used to validate the specific roles and expression patterns of these genes. Immunohistochemistry and Western blot analyses were subsequently conducted in both clinical samples and mouse models to assess protein expression levels across different tissues. Results: The analysis identified seven DE-FRGs, including MT1G, CA9, AKR1C1, AKR1C2, DUSP1, CIRBP, and KLHL24, with their expression patterns confirmed by single-cell RNA sequencing. Immunohistochemistry and Western blot analysis further revealed that MT1G, CA9, AKR1C1, AKR1C2, DUSP1, and KLHL24 exhibited differential expression during the progression of IDD. Additionally, the study highlighted the potential immune-modulatory functions of these genes within the IDD microenvironment. Discussion: Our study elucidates the critical role of ferroptosis in IDD and identifies specific genes, such as MT1G and CA9, as potential targets for diagnosis and therapy. These findings offer new insights into the molecular mechanisms underlying IDD and present promising avenues for future research and clinical applications.

Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.

Objective: To identify HBV-related genes (HRGs) implicated in osteoporosis (OP) pathogenesis and develop a diagnostic model for early OP detection in chronic HBV infection (CBI) patients. Methods: Five public sequencing datasets were collected from the GEO database. Gene differential expression and LASSO analyses identified genes linked to OP and CBI. Machine learning algorithms (random forests, support vector machines, and gradient boosting machines) further filtered these genes. The best diagnostic model was chosen based on accuracy and Kappa values. A nomogram model based on HRGs was constructed and assessed for reliability. OP patients were divided into two chronic HBV-related clusters using non-negative matrix factorization. Differential gene expression analysis, Gene Ontology, and KEGG enrichment analyses explored the roles of these genes in OP progression, using ssGSEA and GSVA. Differences in immune cell infiltration between clusters and the correlation between HRGs and immune cells were examined using ssGSEA and the Pearson method. Results: Differential gene expression analysis of CBI and combined OP dataset identified 822 and 776 differentially expressed genes, respectively, with 43 genes intersecting. Following LASSO analysis and various machine learning recursive feature elimination algorithms, 16 HRGs were identified. The support vector machine emerged as the best predictive model based on accuracy and Kappa values, with AUC values of 0.92, 0.83, 0.74, and 0.7 for the training set, validation set, GSE7429, and GSE7158, respectively. The nomogram model exhibited AUC values of 0.91, 0.79, and 0.68 in the training set, GSE7429, and GSE7158, respectively. Non-negative matrix factorization divided OP patients into two clusters, revealing statistically significant differences in 11 types of immune cell infiltration between clusters. Finally, intersecting the HRGs obtained from LASSO analysis with the HRGs identified three genes. Conclusion: This study successfully identified HRGs and developed an efficient diagnostic model based on HRGs, demonstrating high accuracy and strong predictive performance across multiple datasets. This research not only offers new insights into the complex relationship between OP and CBI but also establishes a foundation for the development of early diagnostic and personalized treatment strategies for chronic HBV-related OP.

Gut microbiota and autoimmune thyroid disease: a bidirectional Mendelian randomization study and mediation analysis.

Background: The gut microbiota (GM) plays a pivotal role in influencing various health outcomes, including immune-mediated conditions, but its potential association with autoimmune thyroid disease (AITD) remains underexplored. We aimed to investigate the potentially pathogenic or protective causal impacts of specific GM on two types of AITD, namely Graves' disease and Hashimoto's thyroiditis, and analyzed the mediating effect of 731 immune cell phenotypes. Methods: Leveraging pooled genome-wide association study (GWAS) data of 211 gut microbiota traits, 731 immune cell phenotypes, and two types of AITD (Hashimoto's thyroiditis and Graves' disease), we performed bidirectional Mendelian randomization (MR) analyses to explore the causal relationships between the GM and AITD. Subsequently, we employed a multivariable MR analysis to discover potential mediating immune cell traits. Additionally, sensitivity analyses were utilized to ensure the reliability of the outcomes. Results: Our analysis revealed that a total of 7 GM taxa were positively associated with AITD, and other 14 taxa showed a negative correlation with AITD. Furthermore, we identified several immune cell traits that mediated the effects of GM on AITD. Most notably, Actinobacteria (p) presented protective effects on Hashimoto's thyroiditis via CCR2 on myeloid Dendritic Cell (5.0%), and Bifidobacterium (g) showed facilitating effects on Graves' disease through CD39+ CD4+ T cell %CD4+ T cell (5.0%) and CD14 on CD33+ HLA DR+ CD14dim (12.2%). Conclusion: The current MR study provides evidence supporting the causal relationships between several specific GM taxa and AITD, and further identified potential mediating immunophenotypes.

Impact of Hashimoto's thyroiditis on the tumor microenvironment in papillary thyroid cancer: insights from single-cell analysis.

This study investigates the impact of Hashimoto's thyroiditis (HT), an autoimmune disorder, on the papillary thyroid cancer (PTC) microenvironment using a dataset of 140,456 cells from 11 patients. By comparing PTC cases with and without HT, we identify HT-specific cell populations (HASCs) and their role in creating a TSH-suppressive environment via mTE3, nTE0, and nTE2 thyroid cells. These cells facilitate intricate immune-stromal communication through the MIF-(CD74+CXCR4) axis, emphasizing immune regulation in the TSH context. In the realm of personalized medicine, our HASC-focused analysis within the TCGA-THCA dataset validates the utility of HASC profiling for guiding tailored therapies. Moreover, we introduce a novel, objective method to determine K-means clustering coefficients in copy number variation inference from bulk RNA-seq data, mitigating the arbitrariness in conventional coefficient selection. Collectively, our research presents a detailed single-cell atlas illustrating HT-PTC interactions, deepening our understanding of HT's modulatory effects on PTC microenvironments. It contributes to our understanding of autoimmunity-carcinogenesis dynamics and charts a course for discovering new therapeutic targets in PTC, advancing cancer genomics and immunotherapy research.

Predicting gene signature in breast cancer patients with multiple machine learning models.

AIMS: The aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models. METHODS: In this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO). RESULTS: We identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K-Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples. CONCLUSIONS: Our findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers.

Clinical prognosticators and targets in the immune microenvironment of intrahepatic cholangiocarcinoma.

Background: Intrahepatic cholangiocarcinoma (ICC) is a disease with poor prognosis and limited therapeutic options. We investigated the tumor immune microenvironment (TIME) to identify predictors of disease outcome and to explore targets for therapeutic modulation. Methods: Liver tissue samples were collected during 2008-2019 from patients (n = 139) diagnosed with ICC who underwent curative intent surgery without neoadjuvant chemotherapy. Samples from the discovery cohort (n = 86) were immunohistochemically analyzed on tissue microarrays (TMAs) for the expression of CD68, CD3, CD4, CD8, Foxp3, PD-L1, STAT1, and p-STAT1 in tumor core and stroma areas. Results were digitally analyzed using QuPath software and correlated with clinicopathological characteristics. For validation of TIME-related biomarkers, we performed multiplex imaging mass cytometry (IMC) in a validation cohort (n = 53). Results: CD68+ cells were the predominant immune cell type in the TIME of ICC. CD4+high T cell density correlated with better overall survival (OS). Prediction modeling together with validation cohort confirmed relevance of CD4+ cells, PD-L1 expression by immune cells in the stroma and N-stage on overall disease outcome. In turn, IMC analyses revealed that silent CD3+CD4+ clusters inversely impacted survival. Among annotated immune cell clusters, PD-L1 was most relevantly expressed by CD4+FoxP3+ cells. A subset of tumors with high density of immune cells ("hot" cluster) correlated with PD-L1 expression and could identify a group of candidates for immune checkpoint inhibition (ICI). Ultimately, higher levels of STAT1 expression were associated with higher lymphocyte infiltration and PD-L1 expression. Conclusions: These results highlight the importance of CD4+ T cells in immune response against ICC. Secondly, a subset of tumors with "hot" TIME represents potential candidates for ICI, while stimulation of STAT1 pathway could be a potential target to turn "cold" into "hot" TIME in ICC.

The tumor immune microenvironment (TIME) plays a critical role in the immune response In many cancers, including intrahepatic cholangiocarcinoma (ICC). Molecular subtyping of the ICC microenvironment already revealed inter-tumoral heterogeneity with variant profiles of immune cell infiltrates. A recent study created an in-depth immune cell atlas of the TIME in biliary tract cancers and could demonstrate the relevance of specific immune cell subpopulations on patient outcome. We are able to provide a distinctive characterization of TIME, separating tumor epithelial- and stroma areas, in a large and representative ICC cohort using digitalized image analysis on tissue microarrays (TMA) as well as multiplex imaging mass cytometry (IMC). The study was designed for identification of immune cell prognosticators allocating institutional ICC patients into a discovery (2008­15) and a validation (2010­19) cohort. Immune cell subpopulations were correlated with clinicopathological characteristics and patient outcome. Our results highlight: i. The important role of CD4+ T cell infiltration in ICC patients; ii. ICC tumors with high density of immune cells associated with PD-L1 expression identifies a subset of patients with variant tumor biology; iii. Stimulation of STAT1 pathway may be a relevant target to turn "cold" into "hot" tumors.

Research progress on ferroptosis in colorectal cancer.

Ferroptosis is a new form of cell death that differs from traditional forms of death. It is ferroptosis-dependent lipid peroxidation death. Colorectal cancer(CRC) is the most common tumor in the gastrointestinal tract with a long occultation period and a poor five-year prognosis. Exploring effective systemic treatments for CRC remains a great challenge worldwide. Numerous studies have demonstrated that ferroptosis can participate in the biological malignant process of various tumor, including CRC, so understanding the role and regulatory mechanisms of ferroptosis in CRC plays a crucial role in the treatment of CRC. In this paper, we reviews the mechanisms of ferroptosis in CRC, the associated regulatory factors and their interactions with various immune cells in the immune microenvironment. In addition, targeting ferroptosis has emerged as an encouraging strategy for CRC treatment. Finally, to inform subsequent research and clinical diagnosis and treatment, we review therapeutic approaches to CRC radiotherapy, immunotherapy, and herbal therapy targeting ferroptosis.

Transcriptional responses in a mouse model of silicone wire embolization induced acute retinal artery ischemia and reperfusion.

Acute retinal ischemia and ischemia-reperfusion injury are the primary causes of retinal neural cell death and vision loss in retinal artery occlusion (RAO). The absence of an accurate mouse model for simulating the retinal ischemic process has hindered progress in developing neuroprotective agents for RAO. We developed a unilateral pterygopalatine ophthalmic artery occlusion (UPOAO) mouse model using silicone wire embolization combined with carotid artery ligation. The survival of retinal ganglion cells and visual function were evaluated to determine the duration of ischemia. Immunofluorescence staining, optical coherence tomography, and haematoxylin and eosin staining were utilized to assess changes in major neural cell classes and retinal structure degeneration at two reperfusion durations. Transcriptomics was employed to investigate alterations in the pathological process of UPOAO following ischemia and reperfusion, highlighting transcriptomic differences between UPOAO and other retinal ischemia-reperfusion models. The UPOAO model successfully replicated the acute interruption of retinal blood supply observed in RAO. 60 min of Ischemia led to significant loss of major retinal neural cells and visual function impairment. Notable thinning of the inner retinal layer, especially the ganglion cell layer, was evident post-UPOAO. Temporal transcriptome analysis revealed various pathophysiological processes related to immune cell migration, oxidative stress, and immune inflammation during the non-reperfusion and reperfusion periods. A pronounced increase in microglia within the retina and peripheral leukocytes accessing the retina was observed during reperfusion periods. Comparison of differentially expressed genes (DEGs) between the UPOAO and high intraocular pressure models revealed specific enrichments in lipid and steroid metabolism-related genes in the UPOAO model. The UPOAO model emerges as a novel tool for screening pathogenic genes and promoting further therapeutic research in RAO.

Genetically predicted Caspase 8 levels mediates the causal association between CD4+ T cell and breast cancer.

Background: Breast cancer (BC) remains a significant contributor to female mortality globally, with inflammation and the immune system implicated in its pathogenesis. To elucidate potential causal relationships, we evaluated the relationship among 731 immune cell phenotypes and BC be at risk by using Mendelian randomization (MR), while also exploring inflammatory proteins as mediators in this association. Methods: We obtained immune cell genome-wide association study (GWAS) summary data and 91 inflammatory factors from the GWAS Catalog. BC GWAS data was obtained from the IEU Open GWAS project (ukb-b-16890 for discovery and GCST004988 for validation). We investigated the causal link between immune cells and BC risk by employing a two-sample MR method. Furthermore, we use a two-step MR to quantify the percentage of mediation of immune cell-BC causal effects mediated by inflammatory proteins. To make sure the causal findings were robust, a sensitivity analysis was done. Results: In both discovery and validation GWAS, a critical inverse correlation between CD4+ T cells and BC risk was found using MR analysis (Discovery: OR, 0.996; P = 0.030. Validation: OR, 0.843; P = 4.09E-07) with Caspase 8 levels mediating 18.9% of the reduced BC risk associated with immune cells(Mediation proportion=a×b/c, Discovery:0.151×-0.005/-0.004 = 18.9%; Validation:0.151×-0.214/-0.171 = 18.9%). Conclusion: Our study establishes a causal connection linking CD4+ T cells and BC, with Caspase 8 levels partially mediating this relationship. These findings enhance our genetic and molecular comprehension of BC, suggesting potential pathways for future BC immunotherapy drug development.

Novel prognostic model of complement and coagulation cascade-related genes correlates with immune environment and drug sensitivity in hepatocellular carcinoma.

Introduction: Hepatocellular carcinoma (HCC) is an immunogenic cancer characterized by high morbidity and mortality rates. The complement and coagulation systems are traditionally associated with the incidence of thrombotic complications and complement activation in cancer. However, the prognostic value of complement and coagulation-related factors (CCCR) in HCC remains undetermined. This study aims to construct a prognostic model based on the complement and coagulation cascades to evaluate its potential for immunotherapy and its relationship with drug sensitivity. Materials and methods: We comprehensively investigated the expression profiles of CCCR genes using the TCGA, ICGC, and GTEx databases. Cox proportional hazards regression models were employed to assess prognostic value. Results: This study presents a novel prognostic model derived from the comprehensive analysis of nine CCCR genes (C1S, C6, C7, F11, F13B, F7, SERPINE1, SERPINF2, and SERPING1) to elucidate their correlation with the tumor immune environment and drug sensitivity in patients with HCC. Our model stratified patients into high- and low-risk groups based on distinct survival outcomes. The area under the curve (AUC) values of the risk score for one-, two-, and three-year survival rates were all greater than 0.660. Additionally, we analyzed immune cell infiltration patterns, revealing a strong correlation between CCCR gene expression and the immune microenvironment, including T cell and macrophage activity. Our findings also identified potential therapeutic targets, demonstrating differential drug sensitivity profiles between the risk groups. JAK1_8709_1718 was found to be more suitable for patients with low-risk HCC. Conclusion: Our findings provide promising insights into the clinical relevance of CCCR genes as prognostic markers and therapeutic targets. This study underscores the significance of CCCR in HCC and paves the way for improved therapeutic strategies.

Germinal center B-cell subgroups in the tumor microenvironment cannot be overlooked: Their involvement in prognosis, immunotherapy response, and treatment resistance in head and neck squamous carcinoma.

Background: More than 60 % of patients with head and neck squamous carcinoma (HNSCC) are diagnosed at advanced stages and miss radical treatment. This has prompted the need to find new biomarkers to achieve early diagnosis and predict early recurrence and metastasis of tumors. Methods: Single-cell RNA sequencing (scRNA-seq) data from HNSCC tissues and peripheral blood samples were obtained through the Gene Expression Omnibus (GEO) database (GSE164690) to characterize the B-cell subgroups, differentiation trajectories, and intercellular communication networks in HNSCC and to construct a prognostic model of the associated risks. In addition, this study analyzed the differences in clinical features, immune cell infiltration, functional enrichment, tumor mutational burden (TMB), and drug sensitivity between the high- and low-risk groups. Results: Using scRNA-seq of HNSCC, we classified B and plasma cells into a total of four subgroups: naive B cells (NBs), germinal center B cells (GCBs), memory B cells (MBs), and plasma cells (PCs). Pseudotemporal trajectory analysis revealed that NBs and GCBs were at the early stage of B cell differentiation, while MBs and PCs were at the end. Cellular communication revealed that GCBs acted on tumor cells through the CD99 and SEMA4 signaling pathways. The independent prognostic value, immune cell infiltration, TMB and drug sensitivity assays were validated for the MEF2B+ GCB score groups. Conclusions: We identified GCBs as B cell-specific prognostic biomarkers for the first time. The MEF2B+ GCB score fills the research gap in the genetic prognostic prediction model of HNSCC and is expected to provide a theoretical basis for finding new therapeutic targets for HNSCC.

Identification and validation of SHC1 and FGFR1 as novel immune-related oxidative stress biomarkers of non-obstructive azoospermia.

Background: Non-obstructive azoospermia (NOA) is a major contributor of male infertility. Herein, we used existing datasets to identify novel biomarkers for the diagnosis and prognosis of NOA, which could have great significance in the field of male infertility. Methods: NOA datasets were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT was utilized to analyze the distributions of 22 immune cell populations. Hub genes were identified by applying weighted gene co-expression network analysis (WGCNA), machine learning methods, and protein-protein interaction (PPI) network analysis. The expression of hub genes was verified in external datasets and was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) was applied to explore the important functions and pathways of hub genes. The mRNA-microRNA (miRNA)-transcription factors (TFs) regulatory network and potential drugs were predicted based on hub genes. Single-cell RNA sequencing data from the testes of patients with NOA were applied for analyzing the distribution of hub genes in single-cell clusters. Furthermore, testis tissue samples were obtained from patients with NOA and obstructive azoospermia (OA) who underwent testicular biopsy. RT-PCR and Western blot were used to validate hub gene expression. Results: Two immune-related oxidative stress hub genes (SHC1 and FGFR1) were identified. Both hub genes were highly expressed in NOA samples compared to control samples. ROC curve analysis showed a remarkable prediction ability (AUCs > 0.8). GSEA revealed that hub genes were predominantly enriched in toll-like receptor and Wnt signaling pathways. A total of 24 TFs, 82 miRNAs, and 111 potential drugs were predicted based on two hub genes. Single-cell RNA sequencing data in NOA patients indicated that SHC1 and FGFR1 were highly expressed in endothelial cells and Leydig cells, respectively. RT-PCR and Western blot results showed that mRNA and protein levels of both hub genes were significantly upregulated in NOA testis tissue samples, which agree with the findings from analysis of the microarray data. Conclusion: It appears that SHC1 and FGFR1 could be significant immune-related oxidative stress biomarkers for detecting and managing patients with NOA. Our findings provide a novel viewpoint for illustrating potential pathogenesis in men suffering from infertility.

A microphysiological assay for studying T-cell chemotaxis, trafficking and tumor killing.

Tumors in patients non-responsive to immunotherapy harbor a series of barriers that impede the efficacy of effector T-cells. Consequently, therapeutically modulating the chemotaxis machinery to enable effector T cell infiltration and function in the tumor could result in more successful therapeutic outcomes. Complex in-vitro models allow re-creation of in-vivo tumor complexities in an in-vitro setting, allowing improved translatability to patient biology at the laboratory scale. We identified a gap in available industrial scale microphysiological (MPS) assays for faster validation of targets and strategies that enable T-cell chemotaxis and effector function within tumor microenvironments. Using a commercially available, 96 -chip 2-lane microfluidic assay system, we present a novel, scalable, complex in vitro microphysiological assay to study 3D T-cell chemotaxis and function within native, extracellular matrix (ECM)-rich multicellular tumor environments. Activated or naïve CD3+ T-cells stained with far-red nuclear stain responded to the chemokine gradients generated within the matrigel-collagen ECM by migrating into the microfluidic channel (~5 mm horizontal window), in a concentration- and cell type-dependent manner. Furthermore, we observed and tracked chemotaxis and cancer cell killing function of antigen-specific CD4.CD8.CAR-T cells (chimeric antigen receptor (CAR)-T cells) that responded to CXCR3 agonist gradient built through the expansive 5 mm of cancer cell colony containing stroma. The 2-lane assay system yielded useful information regarding donor and dose-dependent differences in CAR-T cell chemotaxis and tumor killing. The scalable assay system allows a granular window into immune cell migration and function in tissue spaces beyond endothelium, addressing a missing gap in studying tissue-specific immune cell chemotaxis and function to bring forward advancements in cancer immunotherapy. .

Patient-Specific Nanoparticle Targeting in Human Leukemia Blood.

Antibody-directed targeting of chemotherapeutic nanoparticles to primary human cancers holds promise for improving efficacy and reducing off-target toxicity. However, clinical responses to targeted nanomedicines are highly variable. Herein, we prepared and examined a matrix of 9 particles (organic and inorganic particles of three surface chemistries with and without antibody functionalization) and developed an ex vivo model to study the person-specific targeting of nanoparticles in whole blood of 15 patients with chronic lymphocytic leukemia (CLL). Generally, anti-CD20-functionalized poly(ethylene glycol) (PEG) nanoparticles efficiently targeted CLL cells, leading to low off-target phagocytosis by granulocytes and monocytes in the blood. However, there was up to 164-fold patient-to-patient variability in the CLL targeting. This was further exemplified through using clinically relevant PEGylated doxorubicin-encapsulated liposomes, which showed high interpersonal differences in CLL targeting (up to 234-fold differences) and off-target phagocytosis (up to 65- and 112-fold differences in granulocytes and monocytes, respectively). Off-target phagocytosis led to almost all monocytes being killed within 24 h of treatment. Variance of the off-target association of PEGylated liposomes with granulocytes and monocytes significantly correlated to anti-PEG immunoglobulin G levels in the blood of CLL patients. A negative correlation between CLL targeting of PEG particles and anti-PEG immunoglobulin M levels was found in the blood. Taken together, our study identifies anti-PEG antibodies as key proteins in modulating patient-specific targeting of PEGylated nanoparticles in human leukemia blood. Other factors, such as the antigen expression of targeted cells and fouling properties of nanoparticles, also play an important role in patient-specific targeting. The human leukemia blood assay we developed provides an ex vivo model to evaluate interpersonal variances in response to targeted nanomedicines.

Reduction of myeloid-derived suppressor cells in prostate cancer murine models and patients following white button mushroom treatment.

BACKGROUND: In a previously reported Phase I trial, we observed therapy-associated declines in circulating myeloid-derived suppressor cells (MDSCs) with the administration of white button mushroom (WBM) tablets in prostate cancer (PCa) patients. These observations led us to hypothesise that WBM could mitigate PCa progression by suppressing MDSCs. METHODS: We performed bidirectional translational research to examine the immunomodulatory effects of WBM consumption in both syngeneic murine PCa models and patients with PCa participating in an ongoing randomised Phase II trial (NCT04519879). RESULTS: In murine models, WBM treatment significantly suppressed tumour growth with a reduction in both the number and function of MDSCs, which in turn promoted antitumour immune responses mediated by T cells and natural killer (NK) cells. In patients, after consumption of WBM tablets for 3 months, we observed a decline in circulating polymorphonuclear MDSCs (PMN-MDSCs), along with an increase in cytotoxic CD8+ T and NK cells. Furthermore, single immune cell profiling of peripheral blood from WBM-treated patients showed suppressed STAT3/IRF1 and TGFß signalling in circulating PMN-MDSCs. Subclusters of PMN-MDSCs presented transcriptional profiles associated with responsiveness to fungi, neutrophil chemotaxis, leukocyte aggregation, and regulation of inflammatory response. Finally, in mouse models of PCa, we found that WBM consumption enhanced the anticancer activity of anti-PD-1 antibodies, indicating that WBM may be used as an adjuvant therapy with immune checkpoint inhibitors. CONCLUSION: Our results from PCa murine models and patients provide mechanistic insights into the immunomodulatory effects of WBM and provide a scientific foundation for WBM as a nutraceutical intervention to delay or prevent PCa progression. HIGHLIGHTS: White button mushroom (WBM) treatment resulted in a reduction in pro-tumoural MDSCs, notably polymorphonuclear MDSCs (PMN-MDSCs), along with activation of anti-tumoural T and NK cells. Human single immune cell gene expression profiling shed light on the molecular alterations induced by WBM, specifically on PMN-MDSCs. A proof-of-concept study combining WBM with PD-1 blockade in murine models revealed an additive effect on tumour regression and survival outcomes, highlighting the clinical relevance of WBM in cancer management.

Deciphering MOSPD1's impact on breast cancer progression and therapeutic response.

BACKGROUND: Motile Sperm Domain-Containing Protein 1 (MOSPD1) has been implicated in breast cancer (BC) pathophysiology, but its exact role remains unclear. This study aimed to assess MOSPD1 expression levels in BC versus normal tissues and investigate its diagnostic potential. METHODS: MOSPD1 expression was analyzed in BC and normal tissues, with Receiver Operating Characteristic analysis for diagnostic evaluation. Validation was performed using immunohistochemistry. Functional studies included tumor growth assays, MOSPD1 suppression and overexpression experiments, and testing BC cell responses to anti-PD-L1 therapy. RESULTS: MOSPD1 expression was significantly higher in BC samples than normal tissues, correlating with poor clinical outcomes in BC patients. MOSPD1 suppression inhibited tumor growth, while overexpression accelerated it. Silencing MOSPD1 enhanced BC cell sensitivity to anti-PD-L1 therapy and decreased Th2 cell activity. In vivo experiments supported these findings, showing the impact of MOSPD1 on tumor growth and response to therapy. CONCLUSIONS: Elevated MOSPD1 levels in BC suggest its potential as a biomarker for adverse outcomes. Targeting MOSPD1, particularly with anti-PD-L1 therapy, may effectively inhibit BC tumor growth and modulate immune responses. This study emphasizes the significance of MOSPD1 in BC pathophysiology and highlights its promise as a therapeutic target.

Exploring the significance and potential mechanisms of hippo pathway-associated genes in prognosis of glioma patients.

PURPOSE: Despite the efforts of countless researchers to develop glioma treatment strategies, the current therapeutic effect of glioma is still not ideal, and it is necessary to further explore the mechanism to guide treatment. Thus, this study aims to introduce a novel approach for predicting patient prognosis and guiding further treatment interventions. METHODS: Initially, we conducted a differential gene expression analysis to identify Hippo pathway-associated genes overexpressed in tumors and determined genes correlated with prognosis. Subsequently, employing cluster analysis, we categorized samples into two groups and performed further analyses including prediction, immune cell infiltration abundance, and drug response rates. We utilized weighted gene co-expression analysis to reveal gene sets with high co-variation, delineate inter-sample gene correlation patterns, and conduct enrichment analysis. Prognostic models were built using ten machine learning algorithms combined in 101 different combinations, followed by evaluation and validation. Immune infiltration analysis, differential expression analysis of depleted T cell-related markers, drug sensitivity analysis, and exploration of pathway dysregulation were performed for different risk groups. Quality control and batch integration were performed, and single-cell data were analyzed using dimensionality reduction clustering algorithms and annotation tools to evaluate the activity of the prognostic model in malignant cells. RESULTS: We conducted data filtering to identify genes overexpressed in tumors, intersecting these genes with Hippo pathway-related genes, identifying 62 genes correlated with prognosis, and performing cluster analysis to divide tumor tissues into two groups. Cluster 2 exhibited a poorer prognosis and demonstrated differences in immune cell infiltration. Utilizing weighted gene co-expression analysis on Cluster 2, we identified gene modules, conducted functional enrichment analysis, and delineated pathways. Employing a combined model based on ten machine learning algorithm combinations, we selected the optimal prognostic model system and validated the model's predictive ability within the dataset. Through immune-related analysis and drug sensitivity analysis, we uncovered differences in immune infiltration and varying sensitivities to chemotherapy drugs. Additionally, the enrichment analysis of gene set revealed discrepancies in upregulation within relevant pathways between the high and low-risk groups. Finally, annotation and evaluation of malignant cells via single-cell analysis showed increased activity of the prognostic model and variations in distribution across different prognostic levels in malignant cells. CONCLUSION: This study introduces a novel approach utilizing the Hippo pathway and associated genes for glioma prognosis research, demonstrating the potential and significance of this method in evaluating the outcome for patients with glioma. These findings hold substantial clinical significance in guiding therapy and predicting outcomes for individuals diagnosed with glioma, offering significant clinical utility.