ImmPort, toward repurposing of open access immunological assay data for translational and clinical research

Integrative Multi-omics Analysis to Characterize Herpes Virus Infection Increases the Risk of Alzheimer's Disease

Evidence suggests that herpes virus infection is associated with an increased risk of Alzheimer's disease (AD), and innate and adaptive immunity plays an important role in the association. Although there have been many studies, the mechanism of the association is still unclear. This study aims to reveal the underlying molecular and immune regulatory network through multi-omics data and provide support for the study of the mechanism of infection and AD in the future. Here, we found that the herpes virus infection significantly increased the risk of AD. Genes associated with the occurrence and development of AD and genetically regulated by herpes virus infection are mainly enrichment in immune-related pathways. The 22 key regulatory genes identified by machine learning are mainly immune genes. They are also significantly related to the infiltration changes of 3 immune cell in AD. Furthermore, many of these genes have previously been reported to be linked, or potentially linked, to the pathological mechanisms of both herpes virus infection and AD. In conclusion, this study contributes to the study of the mechanisms related to herpes virus infection and AD, and indicates that the regulation of innate and adaptive immunity may be an effective strategy for preventing and treating herpes virus infection and AD. Additionally, the identified key regulatory genes, whether previously studied or newly discovered, may serve as valuable targets for prevention and treatment strategies.

Identification of ferroptosis-related gene signature for tuberculosis diagnosis and therapy efficacy

Diagnosis of tuberculosis remains a challenge when microbiological tests are negative. Immune cell atlas of patients with tuberculosis and healthy controls were established by single-cell transcriptome. Through integrated analysis of scRNA-seq with microarray and bulk RNA sequencing data, a ferroptosis-related gene signature containing ACSL4, CTSB, and TLR4 genes that were associated with tuberculosis disease was identified. Four gene expression datasets from blood samples of patients with tuberculosis, latent tuberculosis infection, and healthy controls were used to assess the diagnostic value of the gene signature. The areas under the ROC curve for the combined gene signature were 1.000, 0.866, 0.912, and 0.786, respectively, in differentiating active tuberculosis from latent infection. During anti-tuberculosis treatment, the expression of the gene signature decreased significantly in cured patients with tuberculosis. In conclusion, the ferroptosis-related gene signature was associated with tuberculosis treatment efficacy and was a promising biomarker for differentiating active tuberculosis from latent infection.

Deciphering the impact of aging on splenic endothelial cell heterogeneity and immunosenescence through single-cell RNA sequencing analysis

BACKGROUND

Aging is associated with significant structural and functional changes in the spleen, leading to immunosenescence, yet the detailed effects on splenic vascular endothelial cells (ECs) and their immunomodulatory roles are not fully understood. In this study, a single-cell RNA (scRNA) atlas of EC transcriptomes from young and aged mouse spleens was constructed to reveal age-related molecular changes, including increased inflammation and reduced vascular development and also the potential interaction between splenic endothelial cells and immune cells.

RESULTS

Ten clusters of splenic endothelial cells were identified. DEGs analysis across different EC clusters revealed the molecular changes with aging, showing the increase in the overall inflammatory microenvironment and the loss in vascular development function of aged ECs. Notably, four EC clusters with immunological functions were identified, suggesting an Endothelial-to-Immune-like Cell Transition (EndICLT) potentially driven by aging. Pseudotime analysis of the Immunology4 cluster further indicated a possible aging-induced transitional state, potentially initiated by Ctss gene activation. Finally, the effects of aging on cell signaling communication between different EC clusters and immune cells were analyzed.

CONCLUSIONS

This comprehensive atlas elucidates the complex interplay between ECs and immune cells in the aging spleen, offering new insights into endothelial heterogeneity, reprogramming, and the mechanisms of immunosenescence.

Harnessing genetic interactions for prediction of immune checkpoint inhibitors response signature in cancer cells

Genetic interactions (GIs) refer to two altered genes having a combined effect that is not seen individually. They play a crucial role in influencing drug efficacy. We utilized CGIdb 2.0 (http://www.medsysbio.org/CGIdb2/), an updated database of comprehensively published GIs information, encompassing synthetic lethality (SL), synthetic viability (SV), and chemical-genetic interactions. CGIdb 2.0 elucidates GIs relationships between or within protein complex models by integrating protein-protein physical interactions. Additionally, we introduced GENIUS (GENetic Interactions mediated drUg Signature) to leverage GIs for identifying the response signature of immune checkpoint inhibitors (ICIs). GENIUS identified high MAP4K4 expression as a resistant signature and high HERC4 expression as a sensitive signature for ICIs treatment. Melanoma patients with high expression of MAP4K4 were associated with decreased efficacy and poorer survival following ICIs treatment. Conversely, overexpression of HERC4 in melanoma patients correlated with a positive response to ICIs. Notably, HERC4 enhances sensitivity to immunotherapy by facilitating antigen presentation. Analyses of immune cell infiltration and single-cell data revealed that B cells expressing MAP4K4 may contribute to resistance to ICIs in melanoma. Overall, CGIdb 2.0, provides integrated GIs data, thus serving as a crucial tool for exploring drug effects.

Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma

The expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation. Integrative multi-omics analysis revealed four subtypes with differing prognoses. These glioma subtypes exhibited distinct immune-related characteristics as well as clinical and genetic features. By construction of a miRNA regulatory network, we identified candidate miRNAs associated with immune evasion and response to immunotherapy. Finally, eight prognosis related miRNAs were validated to promote cell migration, invasion and proliferation through in vitro experiments. Our study reveals the crosstalk among DNA methylation, copy number variation and miRNA expression for immune regulation in glioma, and could have important implications for patient stratification and development of biomarkers for immunotherapy approaches.

Hypoxia and inflammation induce synergistic transcriptome turnover in macrophages

Macrophages are effector immune cells that experience substantial changes to oxygenation when transiting through tissues, especially when entering tumors or infected wounds. How hypoxia alters gene expression and macrophage effector function at the post-transcriptional level remains poorly understood. Here, we use TimeLapse-seq to measure how inflammatory activation modifies the hypoxic response in primary macrophages. Nucleoside recoding sequencing allows the derivation of steady-state transcript levels, degradation rates, and transcriptional synthesis rates from the same dataset. We find that hypoxia produces distinct responses from resting and inflammatory macrophages. Hypoxia induces destabilization of mRNA transcripts, though inflammatory macrophages substantially increase mRNA degradation compared to resting macrophages. Increased RNA turnover results in the upregulation of ribosomal protein genes and downregulation of extracellular matrix components in inflammatory macrophages. Pathways regulated by mRNA decay in vitro are differentially regulated in tumor-associated macrophages implying that mixed stimuli could induce post-transcriptional regulation of macrophage function in solid tumors.

Construction of an immune-related prognostic signature and lncRNA-miRNA-mRNA ceRNA network in acute myeloid leukemia

The progression of acute myeloid leukemia (AML) is influenced by the immune microenvironment in the bone marrow and dysregulated intracellular competing endogenous RNA (ceRNA) networks. Our study utilized data from UCSC Xena, The Cancer Genome Atlas Program, the Gene Expression Omnibus, and the Immunology Database and Analysis Portal. Using Cox regression analysis, we identified an immune-related prognostic signature. Genomic analysis of prognostic messenger RNA (mRNA) was conducted through Gene Set Cancer Analysis (GSCA), and a prognostic ceRNA network was constructed using the Encyclopedia of RNA Interactomes. Correlations between signature mRNAs and immune cell infiltration, checkpoints, and drug sensitivity were assessed using R software, gene expression profiling interactive analysis (GEPIA), and CellMiner, respectively. Adhering to the ceRNA hypothesis, we established a potential long noncoding RNA (lncRNA)/microRNA (miRNA)/mRNA regulatory axis. Our findings pinpointed 9 immune-related prognostic mRNAs (KIR2DL1, CSRP1, APOBEC3G, CKLF, PLXNC1, PNOC, ANGPT1, IL1R2, and IL3RA). GSCA analysis revealed the impact of copy number variations and methylation on AML. The ceRNA network comprised 14 prognostic differentially expressed lncRNAs (DE-lncRNAs), 6 prognostic DE-miRNAs, and 3 prognostic immune-related DE-mRNAs. Correlation analyses linked these mRNAs' expression to 22 immune cell types and 6 immune checkpoints, with potential sensitivity to 27 antitumor drugs. Finally, we identified a potential LINC00963/hsa-miR-431-5p/CSRP1 axis. This study offers innovative insights for AML diagnosis and treatment through a novel immune-related signature and ceRNA axis. Identified novel biomarkers, including 2 mRNAs (CKLF, PNOC), 1 miRNA (hsa-miR-323a-3p), and 10 lncRNAs (SNHG25, LINC01857, AL390728.6, AC127024.5, Z83843.1, AP002884.1, AC007038.1, AC112512, AC020659.1, AC005921.3) present promising candidates as potential targets for precision medicine, contributing to the ongoing advancements in the field.

Network analysis reveals age- and virus-specific circuits in nasal epithelial cells of extremely premature infants

BACKGROUND AND OBJECTIVES

Viral respiratory infections significantly affect young children, particularly extremely premature infants, resulting in high hospitalization rates and increased health-care burdens. Nasal epithelial cells, the primary defense against respiratory infections, are vital for understanding nasal immune responses and serve as a promising target for uncovering underlying molecular and cellular mechanisms.

METHODS

Using a trans-well pseudostratified nasal epithelial cell system, we examined age-dependent developmental differences and antiviral responses to influenza A and respiratory syncytial virus through systems biology approaches.

RESULTS

Our studies revealed differences in innate-receptor repertoires, distinct developmental pathways, and differentially connected antiviral network circuits between neonatal and adult nasal epithelial cells. Consensus network analysis identified unique and shared cellular-viral networks, emphasizing highly relevant virus-specific pathways, independent of viral replication kinetics.

CONCLUSION

This research highlights the importance of nasal epithelial cells in innate antiviral immune responses and offers crucial insights that allow for a deeper understanding of age-related differences in nasal epithelial cell immunity following respiratory virus infections.

Nicotinamide metabolism face-off between macrophages and fibroblasts manipulates the microenvironment in gastric cancer

Immune checkpoint blockade has led to breakthroughs in the treatment of advanced gastric cancer. However, the prominent heterogeneity in gastric cancer, notably the heterogeneity of the tumor microenvironment, highlights the idea that the antitumor response is a reflection of multifactorial interactions. Through transcriptomic analysis and dynamic plasma sample analysis, we identified a metabolic "face-off" mechanism within the tumor microenvironment, as shown by the dual prognostic significance of nicotinamide metabolism. Specifically, macrophages and fibroblasts expressing the rate-limiting enzymes nicotinamide phosphoribosyltransferase and nicotinamide N-methyltransferase, respectively, regulate the nicotinamide/1-methylnicotinamide ratio and CD8+ T cell function. Mechanistically, nicotinamide N-methyltransferase is transcriptionally activated by the NOTCH pathway transcription factor RBP-J and is further inhibited by macrophage-derived extracellular vesicles containing nicotinamide phosphoribosyltransferase via the SIRT1/NICD axis. Manipulating nicotinamide metabolism through autologous injection of extracellular vesicles restored CD8+ T cell cytotoxicity and the anti-PD-1 response in gastric cancer.

Construction and validation of a novel immunological model to predict prognosis in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with limited treatment options. In this study, we investigated the role of immune cell infiltration in PDAC progression and constructed an immune-related predictive model for patients with PDAC based on the International Cancer Genome Consortium (ICGC) cohort. Related algorithms have been used to assess the immune microenvironment. Least Absolute Shrinkage and Selection Operator (LASSO) Cox analysis was used to construct the model, and receiver operating characteristic and decision curve analysis analyses were conducted to evaluate its diagnostic and prognostic efficacy. The results demonstrated a correlation between high immune infiltration and better prognosis in PDAC. The immune-related prognostic model (IPM) identified four genes through LASSO Cox analysis, with the high IPM group being associated with a worse prognosis. Cox regression analysis confirmed that IPM is an independent risk factor for PDAC. Validation through analysis of The Cancer Genome Atlas cohort and our own individual tumor samples revealed a similar trend to that observed in the ICGC cohort. Finally, a nomogram incorporating age and IPM demonstrated efficacy in the prognostic evaluation of patients with PDAC. In conclusion, we developed a novel immune-related prognosis prediction model for PDAC that offers new possibilities for the measurement of immunotherapy and prognostic assessment of patients.

Biomedical Data Repository Concepts and Management Principles

The demand for open data and open science is on the rise, fueled by expectations from the scientific community, calls to increase transparency and reproducibility in research findings, and developments such as the Final Data Management and Sharing Policy from the U.S. National Institutes of Health and a memorandum on increasing public access to federally funded research, issued by the U.S. Office of Science and Technology Policy. This paper explores the pivotal role of data repositories in biomedical research and open science, emphasizing their importance in managing, preserving, and sharing research data. Our objective is to familiarize readers with the functions of data repositories, set expectations for their services, and provide an overview of methods to evaluate their capabilities. The paper serves to introduce fundamental concepts and community-based guiding principles and aims to equip researchers, repository operators, funders, and policymakers with the knowledge to select appropriate repositories for their data management and sharing needs and foster a foundation for the open sharing and preservation of research data.

Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Background

This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response.

Methods

A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness.

Results

SVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (P <0.0001) and the subsequent validation cohort (P <0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness.

Conclusions

The HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.

Specialized Tfh cell subsets driving type-1 and type-2 humoral responses in lymphoid tissue

Effective antibody responses are essential to generate protective humoral immunity. Different inflammatory signals polarize T cells towards appropriate effector phenotypes during an infection or immunization. Th1 and Th2 cells have been associated with the polarization of humoral responses. However, T follicular helper cells (Tfh) have a unique ability to access the B cell follicle and support the germinal center (GC) responses by providing B cell help. We investigated the specialization of Tfh cells induced under type-1 and type-2 conditions. We first studied homogenous Tfh cell populations generated by adoptively transferred TCR-transgenic T cells in mice immunized with type-1 and type-2 adjuvants. Using a machine learning approach, we established a gene expression signature that discriminates Tfh cells polarized towards type-1 and type-2 response, defined as Tfh1 and Tfh2 cells. The distinct signatures of Tfh1 and Tfh2 cells were validated against datasets of Tfh cells induced following lymphocytic choriomeningitis virus (LCMV) or helminth infection. We generated single-cell and spatial transcriptomics datasets to dissect the heterogeneity of Tfh cells and their localization under the two immunizing conditions. Besides a distinct specialization of GC Tfh cells under the two immunizations and in different regions of the lymph nodes, we found a population of Gzmk+ Tfh cells specific for type-1 conditions. In human individuals, we could equally identify CMV-specific Tfh cells that expressed Gzmk. Our results show that Tfh cells acquire a specialized function under distinct types of immune responses and with particular properties within the B cell follicle and the GC.

Identification of Immune-Related Gene Signature in Schizophrenia

BACKGROUND

Schizophrenia (SCZ) is a type of psychiatric disorder characterized by multiple symptoms. Our aim is to decipher the relevant mechanisms of immune-related gene signatures in SCZ.

METHODS

The SCZ dataset and its associated immunoregulatory genes were retrieved using Gene Expression Omnibus (GEO) and single-sample gene set enrichment analysis (ssGSEA). Co-expressed gene modules were determined through weighted gene correlation network analysis (WGCNA). To elucidate the functional characteristics of these clusters, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used. Additionally, gene set enrichment analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to identify enriched pathways for the immune subgroups. A protein-protein interaction (PPI) network analysis was performed to identify core genes relevant to SCZ.

RESULTS

A significantly higher immune score was observed in SCZ compared to control samples. Seven distinct gene modules were identified, with genes highlighted in green selected for further analysis. Using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method, degrees of immune cell adhesion and accumulation related to 22 different immune cell types were calculated. Significantly enriched bioprocesses concerning the immunoregulatory genes with differential expressions included interferon-beta, IgG binding, and response to interferon-gamma, according to GO and KEGG analyses. Eleven hub genes related to immune infiltration emerged as key players among the three top-ranked GO terms.

CONCLUSIONS

This study underscores the involvement of immunoregulatory reactions in SCZ development. Eleven immune-related genes (IFITM1 (interferon induced transmembrane protein 1), GBP1 (guanylate binding protein 1), BST2 (bone marrow stromal cell antigen 2), IFITM3 (interferon induced transmembrane protein 3), GBP2 (guanylate binding protein 2), CD44 (CD44 molecule), FCER1G (Fc epsilon receptor Ig), HLA-DRA (major histocompatibility complex, class II, DR alpha), FCGR2A (Fc gamma receptor IIa), IFI16 (interferon gamma inducible protein 16), and FCGR3B (Fc gamma receptor IIIb)) were identified as hub genes, representing potential biomarkers and therapeutic targets associated with the immune response in SCZ patients.

A unique human cord blood CD8+CD45RA+CD27+CD161+ T-cell subset identified by flow cytometric data analysis using Seurat

Advances in single-cell level analytical techniques, especially cytometric approaches, have led to profound innovation in biomedical research, particularly in the field of clinical immunology. This has resulted in an expansion of high-dimensional data, posing great challenges for comprehensive and unbiased analysis. Conventional manual analysis is thus becoming untenable to handle these challenges. Furthermore, most newly developed computational methods lack flexibility and interoperability, hampering their accessibility and usability. Here, we adapted Seurat, an R package originally developed for single-cell RNA sequencing (scRNA-seq) analysis, for high-dimensional flow cytometric data analysis. Based on a 20-marker antibody panel and analyses of T-cell profiles in both adult blood and cord blood (CB), we showcased the robust capacity of Seurat in flow cytometric data analysis, which was further validated by Spectre, another high-dimensional cytometric data analysis package, and conventional manual analysis. Importantly, we identified a unique CD8+ T-cell population defined as CD8+CD45RA+CD27+CD161+ T cell that was predominantly present in CB. We characterised its IFN-γ-producing and potential cytotoxic properties using flow cytometry experiments and scRNA-seq analysis from a published dataset. Collectively, we identified a unique human CB CD8+CD45RA+CD27+CD161+ T-cell subset and demonstrated that Seurat, a widely used package for scRNA-seq analysis, possesses great potential to be repurposed for cytometric data analysis. This facilitates an unbiased and thorough interpretation of complicated high-dimensional data using a single analytical pipeline and opens a novel avenue for data-driven investigation in clinical immunology.

New Tools for Data Harmonization and Their Potential Applications in Organ Transplantation

In organ transplantation, accurate analysis of clinical outcomes requires large, high-quality data sets. Not only are outcomes influenced by a multitude of factors such as donor, recipient, and transplant characteristics and posttransplant events but they may also change over time. Although large data sets already exist and are continually expanding in transplant registries and health institutions, these data are rarely combined for analysis because of a lack of harmonization. Promoted by the digitalization of the healthcare sector, effective data harmonization tools became available, with potential applications also for organ transplantation. We discuss herein the present problems in the harmonization of organ transplant data and offer solutions to enhance its accuracy through the use of emerging new tools. To overcome the problem of inadequate representation of transplantation-specific terms, ontologies and common data models particular to this field could be created and supported by a consortium of related stakeholders to ensure their broad acceptance. Adopting clear data-sharing policies can diminish administrative barriers that impede collaboration between organizations. Secure multiparty computation frameworks and the artificial intelligence (AI) approach federated learning can facilitate decentralized and harmonized analysis of data sets, without sharing sensitive data and compromising patient privacy. A common image data model built upon a standardized format would be beneficial to AI-based analysis of pathology images. Implementation of these promising new tools and measures, ideally with the involvement and support of transplant societies, is expected to produce improved integration and harmonization of transplant data and greater accuracy in clinical decision-making, enabling improved patient outcomes.

Identifying the miRNA-gene networks contributes to exploring paravertebral muscle degeneration's underlying pathogenesis and therapy strategy

Low back pain (LBP) is a worldwide problem with public health. Paravertebral muscle degeneration (PMD) is believed to be associated with LBP. Increasing evidence has demonstrated that microRNA (miRNA)-mRNA signaling networks have been implicated in the pathophysiology of diseases. Research suggests that cell death, oxidative stress, inflammatory and immune response, and extracellular matrix (ECM) metabolism are the pathogenesis of PMD; however, the miRNA-mRNA mediated the pathological process of PMD remains elusive. RNA sequencing (RNA-seq) and single cell RNA-seq (scRNA-seq) are invaluable tools for uncovering the functional biology underlying these miRNA and gene expression changes. Using scRNA-seq, we show that multiple immunocytes are presented during PMD, revealing that they may have been implicated with PMD. Additionally, using RNA-seq, we identified 76 differentially expressed genes (DEGs) and 106 differentially expressed miRNAs (DEMs), among which IL-24 and CCDC63 were the top upregulated and downregulated genes in PMD. Comprehensive bioinformatics analyses, including Venn diagrams, differential expression, functional enrichment, and protein-protein interaction analysis, were then conducted to identify six ferroptosis-related DEGs, two oxidative stress-related DEGs, eleven immunity-related DEGs, five ECM-related DEGs, among which AKR1C2/AKR1C3/SIRT1/ALB/IL-24 belong to inflammatory genes. Furthermore, 67 DEMs were predicted to be upstream miRNAs of 25 key DEGs by merging RNA-seq, TargetScan, and mirDIP databases. Finally, a miRNA-gene network was constructed using Cytoscape software and an alluvial plot. ROC curve analysis unveiled multiple key DEGs with the high clinical diagnostic value, providing novel approaches for diagnosing and treating PMD diseases.

Investigating gene signatures associated with immunity in colon adenocarcinoma to predict the immunotherapy effectiveness using NFM and WGCNA algorithms

Colon adenocarcinoma (COAD), a frequently encountered and highly lethal malignancy of the digestive system, has been the focus of intensive research regarding its prognosis. The intricate immune microenvironment plays a pivotal role in the pathological progression of COAD; nevertheless, the underlying molecular mechanisms remain incompletely understood. This study aims to explore the immune gene expression patterns in COAD, construct a robust prognostic model, and delve into the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby providing critical support for individualized treatment strategies and prognostic evaluation. Initially, we curated a comprehensive dataset by screening 2600 immune-related genes (IRGs) from the ImmPort and InnateDB databases, successfully obtaining a rich data resource. Subsequently, the COAD patient cohort was classified using the non-negative matrix factorization (NMF) algorithm, enabling accurate categorization. Continuing on, utilizing the weighted gene co-expression network analysis (WGCNA) method, we analyzed the top 5000 genes with the smallest p-values among the differentially expressed genes (DEGs) between immune subtypes. Through this rigorous screening process, we identified the gene modules with the strongest correlation to the COAD subpopulation, and the intersection of genes in these modules with DEGs (COAD vs COAD vs Normal colon tissue) is referred to as Differentially Expressed Immune Genes Associated with COAD (DEIGRC). Employing diverse bioinformatics methodologies, we successfully developed a prognostic model (DPM) consisting of six genes derived from the DEIGRC, which was further validated across multiple independent datasets. Not only does this predictive model accurately forecast the prognosis of COAD patients, but it also provides valuable insights for formulating personalized treatment regimens. Within the constructed DPM, we observed a downregulation of CALB2 expression levels in COAD tissues, whereas NOXA1, KDF1, LARS2, GSR, and TIMP1 exhibited upregulated expression levels. These genes likely play indispensable roles in the initiation and progression of COAD and thus represent potential therapeutic targets for patient management. Furthermore, our investigation into the molecular mechanisms and therapeutic targets for COAD liver metastasis revealed associations with relevant processes such as fat digestion and absorption, cancer gene protein polysaccharides, and nitrogen metabolism. Consequently, genes including CAV1, ANXA1, CPS1, EDNRA, and GC emerge as promising candidates as therapeutic targets for COAD liver metastasis, thereby providing crucial insights for future clinical practices and drug development. In summary, this study uncovers the immune gene expression patterns in COAD, establishes a robust prognostic model, and elucidates the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby possessing significant theoretical and clinical implications. These findings are anticipated to offer substantial support for both the treatment and prognosis management of COAD patients.

The role of the immune system in early-onset schizophrenia: identifying immune characteristic genes and cells from peripheral blood

BACKGROUND

Early-onset schizophrenia (EOS) is a type of schizophrenia (SCZ) with an age of onset of < 18 years. An abnormal inflammatory immune system may be involved in the occurrence and development of SCZ. We aimed to identify the immune characteristic genes and cells involved in EOS and to further explore the pathogenesis of EOS from the perspective of immunology.

METHODS

We obtained microarray data from a whole-genome mRNA expression in peripheral blood mononuclear cells (PBMCs); 19 patients with EOS (age range: 14.79 ± 1.90) and 18 healthy controls (HC) (age range: 15.67 ± 2.40) were involved. We screened for differentially expressed genes (DEGs) using the Limma software package and modular genes using weighted gene co-expression network analysis (WGCNA). In addition, to identify immune characteristic genes and cells, we performed enrichment analysis, immune infiltration analysis, and receiver operating characteristic (ROC) curve analysis; we also used a random forest (RF), a support vector machine (SVM), and the LASSO-Cox algorithm.

RESULTS

We selected the following immune characteristic genes: CCL8, PSMD1, AVPR1B and SEMG1. We employed a RF, a SVM, and the LASSO-Cox algorithm. We identified the following immune characteristic cells: activated mast cells, CD4+ memory resting T cells, resting mast cells, neutrophils and CD4+ memory activated T cells. In addition, the AUC values of the immune characteristic genes and cells were all > 0.7.

CONCLUSION

Our results indicate that immune system function is altered in SCZ. In addition, CCL8, PSMD1, AVPR1B and SEMG1 may regulate peripheral immune cells in EOS. Further, immune characteristic genes and cells are expected to be diagnostic markers and therapeutic targets of SCZ.

Whole transcriptome sequencing identifies a competitive endogenous RNA network that regulates the immunity of bladder cancer

Several types of non-coding RNAs such as circRNAs, lncRNAs, and miRNAs have been identified to regulate mRNAs through the mechanism known as the competitive endogenous RNA (ceRNA) network. To explore the role of the ceRNA regulatory network in the immune microenvironment of bladder cancer, whole-transcriptome sequencing of bladder tumor and its peritumoral tissues from 38 bladder cancer patients, with a total of 63 samples, was performed to screen differentially expressed circ-, lnc-, mi-, and mRNAs to construct a circ/lnc-mi-mRNA regulatory network with pruning algorithms. We excavated a key immune-related gene BDNF to build the final ceRNA network as hsa-miR-107 sponged by hsa-circ-000211, AC108488.1, and LINC00163. Finally, a meta-analysis of 7 public datasets demonstrated that low expression of BDNF and high expression of hsa-miR-107 were associated with longer survival. Our study identified a ceRNA regulatory network as a potentially new prognostic marker and molecular therapeutic target of bladder cancer.

Novel insights into immune-related genes associated with type 2 diabetes mellitus-related cognitive impairment

BACKGROUND

The cognitive impairment in type 2 diabetes mellitus (T2DM) is a multifaceted and advancing state that requires further exploration to fully comprehend. Neuroinflammation is considered to be one of the main mechanisms and the immune system has played a vital role in the progression of the disease.

AIM

To identify and validate the immune-related genes in the hippocampus associated with T2DM-related cognitive impairment.

METHODS

To identify differentially expressed genes (DEGs) between T2DM and controls, we used data from the Gene Expression Omnibus database GSE125387. To identify T2DM module genes, we used Weighted Gene Co-Expression Network Analysis. All the genes were subject to Gene Set Enrichment Analysis. Protein-protein interaction network construction and machine learning were utilized to identify three hub genes. Immune cell infiltration analysis was performed. The three hub genes were validated in GSE152539 via receiver operating characteristic curve analysis. Validation experiments including reverse transcription quantitative real-time PCR, Western blotting and immunohistochemistry were conducted both in vivo and in vitro. To identify potential drugs associated with hub genes, we used the Comparative Toxicogenomics Database (CTD).

RESULTS

A total of 576 DEGs were identified using GSE125387. By taking the intersection of DEGs, T2DM module genes, and immune-related genes, a total of 59 genes associated with the immune system were identified. Afterward, machine learning was utilized to identify three hub genes (H2-T24, Rac3, and Tfrc). The hub genes were associated with a variety of immune cells. The three hub genes were validated in GSE152539. Validation experiments were conducted at the mRNA and protein levels both in vivo and in vitro, consistent with the bioinformatics analysis. Additionally, 11 potential drugs associated with RAC3 and TFRC were identified based on the CTD.

CONCLUSION

Immune-related genes that differ in expression in the hippocampus are closely linked to microglia. We validated the expression of three hub genes both in vivo and in vitro, consistent with our bioinformatics results. We discovered 11 compounds associated with RAC3 and TFRC. These findings suggest that they are co-regulatory molecules of immunometabolism in diabetic cognitive impairment.

Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC

Background

High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice.

Methods

We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments.

Results

We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment.

Conclusions

We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.

Mendelian randomization and transcriptome analysis identified immune-related biomarkers for osteoarthritis

Background

The immune microenvironment assumes a significant role in the pathogenesis of osteoarthritis (OA). However, the current biomarkers for the diagnosis and treatment of OA are not satisfactory. Our study aims to identify new OA immune-related biomarkers to direct the prevention and treatment of OA using multi-omics data.

Methods

The discovery dataset integrated the GSE89408 and GSE143514 datasets to identify biomarkers that were significantly associated with the OA immune microenvironment through multiple machine learning methods and weighted gene co-expression network analysis (WGCNA). The identified signature genes were confirmed using two independent validation datasets. We also performed a two-sample mendelian randomization (MR) study to generate causal relationships between biomarkers and OA using OA genome-wide association study (GWAS) summary data (cases n = 24,955, controls n = 378,169). Inverse-variance weighting (IVW) method was used as the main method of causal estimates. Sensitivity analyses were performed to assess the robustness and reliability of the IVW results.

Results

Three signature genes (FCER1G, HLA-DMB, and HHLA-DPA1) associated with the OA immune microenvironment were identified as having good diagnostic performances, which can be used as biomarkers. MR results showed increased levels of FCER1G (OR = 1.118, 95% CI 1.031-1.212, P = 0.041), HLA-DMB (OR = 1.057, 95% CI 1.045 -1.069, P = 1.11E-21) and HLA-DPA1 (OR = 1.030, 95% CI 1.005-1.056, P = 0.017) were causally and positively associated with the risk of developing OA.

Conclusion

The present study identified the 3 potential immune-related biomarkers for OA, providing new perspectives for the prevention and treatment of OA. The MR study provides genetic support for the causal effects of the 3 biomarkers with OA and may provide new insights into the molecular mechanisms leading to the development of OA.

Decitabine induces IRF7-mediated immune responses in p53-mutated triple-negative breast cancer: a clinical and translational study

p53 is mutated in half of cancer cases. However, no p53-targeting drugs have been approved. Here, we reposition decitabine for triple-negative breast cancer (TNBC), a subtype with frequent p53 mutations and extremely poor prognosis. In a retrospective study on tissue microarrays with 132 TNBC cases, DNMT1 overexpression was associated with p53 mutations (P = 0.037) and poor overall survival (OS) (P = 0.010). In a prospective DEciTabinE and Carboplatin in TNBC (DETECT) trial (NCT03295552), decitabine with carboplatin produced an objective response rate (ORR) of 42% in 12 patients with stage IV TNBC. Among the 9 trialed patients with available TP53 sequencing results, the 6 patients with p53 mutations had higher ORR (3/6 vs. 0/3) and better OS (16.0 vs. 4.0 months) than the patients with wild-type p53. In a mechanistic study, isogenic TNBC cell lines harboring DETECT-derived p53 mutations exhibited higher DNMT1 expression and decitabine sensitivity than the cell line with wild-type p53. In the DETECT trial, decitabine induced strong immune responses featuring the striking upregulation of the innate immune player IRF7 in the p53-mutated TNBC cell line (upregulation by 16-fold) and the most responsive patient with TNBC. Our integrative studies reveal the potential of repurposing decitabine for the treatment of p53-mutated TNBC and suggest IRF7 as a potential biomarker for decitabine-based treatments.

Development of an immune-related diagnostic predictive model for oral lichen planus

Oral lichen planus (OLP) was a chronic inflammatory disease of unknown etiology with a 1.4% chance of progressing to malignancy. However, it has been suggested in several studies that immune system disorders played a dominant role in the onset and progression of OLP. Therefore, this experiment aimed to develop a diagnostic prediction model for OLP based on immunopathogenesis to achieve early diagnosis and treatment and prevent cancer. In this study, 2 publicly available OLP datasets from the gene expression omnibus database were filtered. In the experimental group (GSE52130), the level of immune cell infiltration was assessed using MCPcounter and ssGSEA algorithms. Subsequently, differential expression analysis and gene set enrichment analysis were performed between the OLP and control groups. The resulting differentially expressed genes were intersected with immunologically relevant genes provided on the immunology database and analysis portal database (ImmPort) website to obtain differentially expressed immunologically relevant genes (DEIRGs). Furthermore, the gene ontology and kyoto encyclopedia of genes and genomes analyses were carried out. Finally, protein-protein interaction network and least absolute shrinkage and selection operator regression analyses constructed a model for OLP. Receiver operating characteristic curves for the experimental and validation datasets (GSE38616) were plotted separately to validate the model's credibility. In addition, real-time quantitative PCR experiment was performed to verify the expression level of the diagnostic genes. Immune cell infiltration analysis revealed a more significant degree of inflammatory infiltration in the OLP group compared to the control group. In addition, the gene set enrichment analysis results were mainly associated with keratinization, antibacterial and immune responses, etc. A total of 774 differentially expressed genes was obtained according to the screening criteria, of which 65 were differentially expressed immunologically relevant genes. Ultimately, an immune-related diagnostic prediction model for OLP, which was composed of 5 hub genes (BST2, RNASEL, PI3, DEFB4A, CX3CL1), was identified. The verification results showed that the model has good diagnostic ability. There was a significant correlation between the 5 hub diagnostic biomarkers and immune infiltrating cells. The development of this model gave a novel insight into the early diagnosis of OLP.

An Immune Gene Expression Risk Score for Distant Metastases after Radiotherapy for Cervical Cancer

PURPOSE

To develop an immune-based gene expression risk score to identify patients with cervical cancer at increased risk of distant metastases (DM).

EXPERIMENTAL DESIGN

Tumor biopsies were obtained from 81 patients prior to chemoradiotherapy. Whole-transcriptome RNA sequencing was performed (Illumina NextSeq500). Beginning with 4,723 immune-related genes, a 55-gene risk score for DM was derived using Cox modeling and principal component analysis. It was validated in independent cohorts of 274 patients treated at the Norwegian Radium Hospital (NRH) and 206 patients from The Cancer Genome Atlas (TCGA).

RESULTS

The risk score was predictive of DM (HR, 2.7; P < 0.0001) and lower cause-specific survival (CSS) by univariate analysis (HR, 2.0; P = 0.0003) and multivariate analysis adjusted for clinical factors (DM HR, 3.0; P < 0.0001; CSS HR, 2.2; P = 0.0004). The risk score predicted DM (HR, 1.4; P = 0.05) and CSS (HR, 1.48; P = 0.013) in the NRH cohort and CSS (HR, 1.4; P = 0.03) in TCGA cohort. Higher risk scores were associated with lower CIBERSORT estimates of tumor-infiltrating immune cells, including CD8 T cells and M1 and M2 macrophages (all P < 0.001). Higher risk scores were associated with lower expression (all P < 0.001) of important chemokines (CXCL12, CXCR4), IFN-regulated genes (IRF1, STAT1, IDO1), and immune checkpoint regulators (PD-1, PD-L1, CTLA-4).

CONCLUSIONS

The immune metastatic risk score addresses important challenges in the treatment of cervical cancer-identifying patients at high risk of DM after radiotherapy. The findings of this study indicate that high tumor mutational burden and a "cold," immune-excluded tumor microenvironment influence distant metastatic recurrence. Further validation of the risk score is needed.

EPRIM: An approach of identifying cancer immune-related epigenetic regulators

Epigenetic regulation contributes to the dysregulation of gene expression involved in cancer biology. Nevertheless, the roles of epigenetic regulators (ERs) in tumor immunity and immune response remain basically unclear. Here, we developed the epigenetic regulator in immunology (EPRIM) approach to identify immune-related ERs and comprehensively dissected the ER regulation in tumor immune response across 33 cancers. The identified immune-related ERs were related to immune infiltration and could stratify cancer patients into two risk groups in multiple independent datasets. These patient groups were characterized by distinct immune functions, immune infiltrates, driver gene mutations, and prognoses. Furthermore, we constructed an immune ER-based signature and highlighted its potential utility in predicting clinical benefit from immunotherapy and selecting therapeutic agents. Taken together, our identification and evaluation of immune-related ERs highlight the usefulness of EPRIM for the understanding of ERs in immune regulation and the clinical relevance in evaluation of cancer patient prognosis and response to immune checkpoint blockade therapy.

Expression of immune related genes and possible regulatory mechanisms in different stages of non-alcoholic fatty liver disease

Background

Non-alcoholic fatty liver disease (NAFLD), which includes simple steatosis (SS) and non-alcoholic steatohepatitis (NASH), is a significant contributor to liver disease on a global scale. The change of immunity-related genes (IRGs) expression level leads to different immune infiltrations. However, the expression of IRGs and possible regulatory mechanisms involved in NAFLD remain unclear. The objective of our research is to investigate crucial genes linked to the development of NAFLD and the transition from SS to NASH.

Methods

Dataset GSE89632, which includes healthy controls, SS patients, and NASH patients, was obtained using the GEO database. To examine the correlation between sets of genes and clinical characteristics, we employed weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Hub genes were extracted using a network of protein-protein interactions (PPI) and three different machine learning algorithms. To validate the findings, another dataset that is publicly accessible and mice that were subjected to a high-fat diet (HFD) or MCD diet were utilized. Furthermore, the ESTIMATE algorithm and ssGSEA were employed to investigate the immune landscape in the normal versus SS group and SS versus NASH group, additionally, the relationship between immune infiltration and the expression of hub genes was also examined.

Results

A total of 28 immune related key genes were selected. Most of these genes expressed reverse patterns in the initial and progressive stages of NAFLD. GO and KEGG analyses showed that they were focused on the cytokine related pathways and immune cell activation and chemotaxis. After screening by various algorithms, we obtained two hub genes, including JUN and CCL20. Validation of these findings was confirmed by analyzing gene expression patterns in both the validation dataset and the mouse model. Ultimately, two hub genes were discovered to have a significant correlation with the infiltration of immune cells.

Conclusion

We proposed that there were dynamic changes in the expression levels of IRGs in different stages of NAFLD disease, which led to different immune landscapes in SS and NASH. The findings of our research could serve as a guide for the accurate management of various phases of NAFLD.

The Importance, Challenges, and Possible Solutions for Sharing Proteomics Data While Safeguarding Individuals' Privacy

Proteomics data sharing has profound benefits at the individual level as well as at the community level. While data sharing has increased over the years, mostly due to journal and funding agency requirements, the reluctance of researchers with regard to data sharing is evident as many shares only the bare minimum dataset required to publish an article. In many cases, proper metadata is missing, essentially making the dataset useless. This behavior can be explained by a lack of incentives, insufficient awareness, or a lack of clarity surrounding ethical issues. Through adequate training at research institutes, researchers can realize the benefits associated with data sharing and can accelerate the norm of data sharing for the field of proteomics, as has been the standard in genomics for decades. In this article, we have put together various repository options available for proteomics data. We have also added pros and cons of those repositories to facilitate researchers in selecting the repository most suitable for their data submission. It is also important to note that a few types of proteomics data have the potential to re-identify an individual in certain scenarios. In such cases, extra caution should be taken to remove any personal identifiers before sharing on public repositories. Data sets that will be useless without personal identifiers need to be shared in a controlled access repository so that only authorized researchers can access the data and personal identifiers are kept safe.

Construction of circ_0071922-miR-15a-5p-mRNA network in intervertebral disc degeneration by RNA-sequencing

Background

Low back pain (LBP) is the main factor of global disease burden. Intervertebral disc degeneration (IVDD) has long been known as the leading reason of LBP. Increasing studies have verified that circular RNAs (circRNAs)-microRNAs (miRNAs)-mRNAs network is widely involved in the pathological processes of IVDD. However, no study was made to demonstrate the circRNAs-mediated ferroptosis, oxidative stress, extracellular matrix metabolism, and immune response in IVDD.

Methods

We collected 3 normal and 3 degenerative nucleus pulposus tissues to conduct RNA-sequencing to identify the key circRNAs and miRNAs in IVDD. Bioinformatics analysis was then conducted to construct circRNAs-miRNAs-mRNAs interaction network associated with ferroptosis, oxidative stress, extracellular matrix metabolism, and immune response. We also performed animal experiments to validate the therapeutic effects of key circRNAs in IVDD.

Results

We found that circ_0015435 was most obviously upregulated and circ_0071922 was most obviously downregulated in IVDD using RNA-sequencing. Then we observed that hsa-miR-15a-5p was the key downstream of circ_0071922, and hsa-miR-15a-5p was the top upregulated miRNA in IVDD. Bioinformatics analysis was conducted to predict that 56 immunity-related genes, 29 ferroptosis-related genes, 23 oxidative stress-related genes and 8 ECM-related genes are the targets mRNAs of hsa-miR-15a-5p. Then we constructed a ceRNA network encompassing 24 circRNAs, 6 miRNAs, and 101 mRNAs. Additionally, we demonstrated that overexpression of circ_0071922 can alleviate IVDD progression in a rat model.

Conclusions

The findings of this study suggested that circ_0071922-miR-15a-5p-mRNA signaling network might affect IVDD by modulating the nucleus pulposus cells ferroptosis, oxidative stress, ECM metabolism, and immune response, which is an effective therapeutic targets of IVDD.

Optimizing and integrating depletion and precipitation methods for plasma proteomics through data-independent acquisition-mass spectrometry

The application of plasma proteomics is a reliable approach for the discovery of biomarkers. However, the utilization of mass spectrometry-based proteomics in plasma encounters limitations due to the presence of high-abundant proteins (HAPs) and the vast dynamic range. To address this issue, we conducted an optimization and integration of depletion and precipitation strategies eliminating interference from HAPs. The optimized procedure involved utilizing 40 µL of beads for the removal of 1 µL of plasma, and maintaining a ratio of 1:1:1 between plasma, urea, and trichloroacetic acid for the precipitation of 50 µL of plasma. To facilitate high-throughput processing, experimental procedures were carried out utilizing 96-well plates. The depletion method identified a total of 1510 proteins, whereas the precipitated method yielded a total of 802 proteins. The integration of these methods yielded a total of 1794 proteins, including a wide concentration range spanning over 8 orders of magnitude. Furthermore, these approaches exhibited a commendable level of reproducibility, as indicated by median coefficients of variation of 14.7 % and 21.1 % for protein intensities, respectively. The integrative method was found to be effective in precisely quantifying yeast proteins that were intentionally spiked in plasma at predetermined rations of 5, 2, 0.5, and 0.2 with a high genuine positive recovery with a range of 71 % to 91 % of all yeast proteins. The use of a complementary and finely tuned approach involving depletion and precipitation demonstrates tremendous potential in the field of discovering protein biomarkers from large-scale cohort studies.

Predictive role of ctDNA in esophageal squamous cell carcinoma receiving definitive chemoradiotherapy combined with toripalimab

The combination of toripalimab (an anti-PD-1 antibody) with definitive chemoradiotherapy (CRT) demonstrated encouraging efficacy against locally advanced esophageal squamous cell carcinoma (ESCC) in the EC-CRT-001 phase II trial (NCT04005170). The primary endpoint of this trial was the clinical complete response rate (cCR), and the secondary endpoints included overall survival (OS), progression-free survival (PFS), duration of response, and quality of life. The exploratory analyses of EC-CRT-001 include exploring the role of circulating tumor DNA (ctDNA) and blood-based tumor mutational burden (bTMB) in predicting the response and survival. In total, 118 blood and 35 tissue samples from 42 enrolled patients were included in the analyses. We found that ctDNA-negative patients achieved a higher cCR compared to those with detectable ctDNA during CRT (83%, 19/23 vs. 39%, 7/18; p = 0.008) or post-CRT (78%, 21/27 vs. 30%, 3/10; p = 0.017). Patients with detectable ctDNA during CRT had shorter PFS (p = 0.014). Similarly, patients with post-CRT detectable ctDNA had a significantly shorter PFS (p = 0.012) and worse OS (p = 0.004). Moreover, patients with high bTMB levels during CRT had prolonged OS (p = 0.027). In conclusion, ctDNA and bTMB have the potential to predict treatment efficacy and survival in ESCC treated with CRT and immunotherapy.

The role of tertiary lymphoid structure and B cells in nasopharyngeal carcinoma: Based on bioinformatics and experimental verification

OBJECTIVE

Transcriptomic characteristics and prognosis of tertiary lymphoid structures (TLS) and infiltrating B cells in nasopharyngeal carcinoma (NPC) remain unclear. Here, NPC transcriptomic data and clinical samples were used to investigate the role of infiltrating B cells and TLS in NPC.

METHODS

We investigated the gene expression and infiltrating immune cells of NPC patients and further investigated the clinical relevance of B cell and TLS signatures. Transcriptional features of infiltrating B cell subsets were revealed by single-cell RNA sequencing (scRNA-seq) analysis. Immunohistochemical (IHC) and HE staining were performed to validate the clinical relevance of infiltrating B cells and TLS in NPC samples.

RESULTS

27 differentially expressed immune-related genes (IRGs) associated with prognosis were identified, including B cell marker genes CD19 and CD79B. The higher B cells and TLS signature scores were associated with better outcomes and early pathological staging in 88 NPC patients. ScRNA-seq identified five distinct B cell subsets in NPC, including the BC-4 cluster associated with poor outcomes and the BC-0 cluster associated with better outcomes. EBV infection was positively associated with the formation of TLS. Furthermore, experimental results showed that the infiltration of B cells in NPC tissues was higher than that of normal tissues, and the density of TLS in an early stage of NPC was higher than that in advanced-stage TLS.

CONCLUSION

Our findings demonstrate the functional importance of distinct B cell subsets in the prognosis of NPC. Additionally, we confirmed that B cells and TLS may serve as prognostic biomarkers of survival for NPC patients.

Risk subgroups and intervention effects among infants at high risk for peanut allergy: A model for clinical decision making

BACKGROUND

The Learning Early About Peanut Allergy (LEAP) trial showed that early dietary introduction of peanut reduced the risk of developing peanut allergy by age 60 months in infants at high risk for peanut allergy. In this secondary analysis of LEAP data, we aimed to determine risk subgroups within these infants and estimate their respective intervention effects of early peanut introduction.

METHODS

LEAP raw data were retrieved from ITNTrialShare.org. Conditional random forest was applied to participants in the peanut avoidance arm to select statistically important features for the classification and regression tree (CART) analysis to group infants based on their risk of peanut allergy at 60 months of age. Intervention effects were estimated for each derived risk subgroup using data from both arms. Our main model was generated based on baseline data when the participants were 4-11 months old. Specific IgE measurements were truncated to account for the limit of detection commonly used by laboratories in clinical practice.

RESULTS

The model found infants with higher predicted probability of peanut allergy at 60 months of age had a similar relative risk reduction, but a greater absolute risk reduction in peanut allergy with early introduction of peanut, than those with lower probability. The intervention effects were significant across all risk subgroups. Participants with baseline peanut sIgE ≥0.22 kU/L (n = 78) had an absolute risk reduction of 40.4% (95% CI 27.3, 51.9) whereas participants with baseline peanut sIgE<0.22 kU/L and baseline Ara h 2 sIgE <0.10 kU/L (n = 226) had an absolute risk reduction of 6.5% (95% CI 2.6, 11.0). These findings were consistent in sensitivity analyses using alternative models.

CONCLUSION

In this study, risk subgroups were determined among infants from the LEAP trial based on the probability of developing peanut allergy and the intervention effects of early peanut introduction were estimated. This may be relevant for further risk assessment and personalized clinical decision-making.

Identification of immune-associated genes in vascular dementia by integrated bioinformatics and inflammatory infiltrates

Objective

Dysregulation of the immune system plays a vital role in the pathological process of vascular dementia, and this study aims to spot critical biomarkers and immune infiltrations in vascular dementia employing a bioinformatics approach.

Methods

We acquired gene expression profiles from the Gene Expression Database. The gene expression data were analyzed using the bioinformatics method to identify candidate immune-related central genes for the diagnosis of vascular dementia. and the diagnostic value of nomograms and Receiver Operating Characteristic (ROC) curves were evaluated. We also examined the role of the VaD hub genes. Using the database and potential therapeutic drugs, we predicted the miRNA and lncRNA controlling the Hub genes. Immune cell infiltration was initiated to examine immune cell dysregulation in vascular dementia.

Results

1321 immune genes were included in the combined immune dataset, and 2816 DEGs were examined in GSE122063. Twenty potential genes were found using differential gene analysis and co-expression network analysis. PPI network design and functional enrichment analysis were also done using the immune system as the main subject. To create the nomogram for evaluating the diagnostic value, four potential core genes were chosen by machine learning. All four putative center genes and nomograms have a solid diagnostic value (AUC ranged from 0.81 to 0.92). Their high confidence level became unquestionable by validating each of the four biomarkers using a different dataset. According to GeneMANIA and GSEA enrichment investigations, the pathophysiology of VaD is strongly related to inflammatory responses, drug reactions, and central nervous system degeneration. The data and Hub genes were used to construct a ceRNA network that includes three miRNAs, 90 lncRNA, and potential VaD therapeutics. Immune cells with varying dysregulation were also found.

Conclusion

Using bioinformatic techniques, our research identified four immune-related candidate core genes (HMOX1, EBI3, CYBB, and CCR5). Our study confirms the role of these Hub genes in the onset and progression of VaD at the level of immune infiltration. It predicts potential RNA regulatory pathways control VaD progression, which may provide ideas for treating clinical disease.

Identification of immune infiltration and immune-related biomarkers of periprosthetic joint infection

Background

The immune response associated with periprosthetic joint infection (PJI) is an emerging but relatively unexplored topic. The aim of this study was to investigate immune cell infiltration in periprosthetic tissues and identify potential immune-related biomarkers.

Methods

The GSE7103 dataset from the GEO database was selected as the data source. Differentially expressed genes (DEGs) and significant modular genes in weighted correlation network analysis (WGCNA) were identified. Functional enrichment analysis and transcription factor prediction were performed on the overlapping genes. Next, immune-related genes from the ImmPort database were matched. The protein-protein interaction (PPI) analysis was performed to identify hub genes. CIBERSORTx was used to evaluate the immune cell infiltration pattern. Spearman correlation analysis was used to evaluate the relationship between hub genes and immune cells.

Results

A total of 667 DEGs were identified between PJI and control samples, and 1847 PJI-related module genes were obtained in WGCNA. Enrichment analysis revealed that the common genes were mainly enriched in immune and host defense-related terms. TFEC, SPI1, and TWIST2 were the top three transcription factors. Three hub genes, SDC1, MMP9, and IGF1, were identified in the immune-related PPI network. Higher levels of plasma cells, CD4+ memory resting T cells, follicular helper T cells, resting mast cells, and neutrophils were found in the PJI group, while levels of M0 macrophages were lower. Notably, the expression of all three hub genes correlated with the infiltration levels of seven types of immune cells.

Conclusion

The present study revealed immune infiltration signatures in the periprosthetic tissues of PJI patients. SDC1, MMP9, and IGF1 were potential immune-related biomarkers for PJI.

Neuroimmune mechanisms in autism etiology - untangling a complex problem using human cellular models

Autism spectrum disorder (ASD) affects 1 in 36 people and is more often diagnosed in males than in females. Core features of ASD are impaired social interactions, repetitive behaviors and deficits in verbal communication. ASD is a highly heterogeneous and heritable disorder, yet its underlying genetic causes account only for up to 80% of the cases. Hence, a subset of ASD cases could be influenced by environmental risk factors. Maternal immune activation (MIA) is a response to inflammation during pregnancy, which can lead to increased inflammatory signals to the fetus. Inflammatory signals can cross the placenta and blood brain barriers affecting fetal brain development. Epidemiological and animal studies suggest that MIA could contribute to ASD etiology. However, human mechanistic studies have been hindered by a lack of experimental systems that could replicate the impact of MIA during fetal development. Therefore, mechanisms altered by inflammation during human pre-natal brain development, and that could underlie ASD pathogenesis have been largely understudied. The advent of human cellular models with induced pluripotent stem cell (iPSC) and organoid technology is closing this gap in knowledge by providing both access to molecular manipulations and culturing capability of tissue that would be otherwise inaccessible. We present an overview of multiple levels of evidence from clinical, epidemiological, and cellular studies that provide a potential link between higher ASD risk and inflammation. More importantly, we discuss how stem cell-derived models may constitute an ideal experimental system to mechanistically interrogate the effect of inflammation during the early stages of brain development.

Bronchial epithelial transcriptomics and experimental validation reveal asthma severity-related neutrophilc signatures and potential treatments

Airway epithelial transcriptome analysis of asthma patients with different severity was used to disentangle the immune infiltration mechanisms affecting asthma exacerbation, which may be advantageous to asthma treatment. Here we introduce various bioinformatics methods and develop two models: an OVA/CFA-induced neutrophil asthma mouse model and an LPS-induced human bronchial epithelial cell damage model. Our objective is to investigate the molecular mechanisms, potential targets, and therapeutic strategies associated with asthma severity. Multiple bioinformatics methods identify meaningful differences in the degree of neutrophil infiltration in asthma patients with different severity. Then, PTPRC, TLR2, MMP9, FCGR3B, TYROBP, CXCR1, S100A12, FPR1, CCR1 and CXCR2 are identified as the hub genes. Furthermore, the mRNA expression of 10 hub genes is determined in vivo and in vitro models. Reperixin is identified as a pivotal drug targeting CXCR1, CXCR2 and MMP9. We further test the potential efficiency of Reperixin in 16HBE cells, and conclude that Reperixin can attenuate LPS-induced cellular damage and inhibit the expression of them. In this study, we successfully identify and validate several neutrophilic signatures and targets associated with asthma severity. Notably, Reperixin displays the ability to target CXCR1, CXCR2, and MMP9, suggesting its potential therapeutic value for managing deteriorating asthma.

Construction and evaluation of immune-related diagnostic model in patients with heart failure caused by idiopathic dilated cardiomyopathy

OBJECTIVE

The purpose of the study was to construct the potential diagnostic model of immune-related genes during the development of heart failure caused by idiopathic dilated cardiomyopathy.

METHOD

GSE5406 and GSE57338 were downloaded from the GEO website ( https://www.ncbi.nlm.nih.gov/geo/ ). CIBERSORT was used for the evaluation of immune infiltration in idiopathic dilated cardiomyopathy (DCM) of GSE5406. Differently expressed genes were calculated by the limma R package and visualized by the volcano plot. The immune-related genes were downloaded from Immport, TISIDB, and InnateDB. Then the immune-related differential genes (IRDGs) were acquired from the intersection. Protein-protein interaction network (PPI) and Cytoscape were used to visualize the hub genes. Three machine learning methods such as random forest, logical regression, and elastic network regression model were adopted to construct the prediction model. The diagnostic value was also validated in GSE57338.

RESULTS

Our study demonstrated the obvious different ratio of T cell CD4 memory activated, T cell regulatory Tregs, and neutrophils between DCM and control donors. As many as 2139 differential genes and 274 immune-related different genes were identified. These genes were mainly enriched in lipid and atherosclerosis, human cytomegalovirus infection, and cytokine-cytokine receptor interaction. At the same time, as many as fifteen hub genes were identified as the IRDGs (IFITM3, IFITM2, IFITM1, IFIT3, IFIT1, HLA-A, HLA-B, HLA-C, ADAR, STAT1, SAMHD1, RSAD2, MX1, ISG20, IRF2). Moreover, we also discovered that the elastic network and logistic regression models had a higher diagnostic value than that of random forest models based on these hub genes.

CONCLUSION

Our study demonstrated the pivotal role of immune function during the development of heart failure caused by DCM. This study may offer new opportunities for the detection and intervention of immune-related DCM.

Genetic evidence for causal effects of immune dysfunction in psychiatric disorders: where are we?

The question of whether immune dysfunction contributes to risk of psychiatric disorders has long been a subject of interest. To assert this hypothesis a plethora of correlative evidence has been accumulated from the past decades; however, a variety of technical and practical obstacles impeded on a cause-effect interpretation of these data. With the advent of large-scale omics technology and advanced statistical models, particularly Mendelian randomization, new studies testing this old hypothesis are accruing. Here we synthesize these new findings from genomics and genetic causal inference studies on the role of immune dysfunction in major psychiatric disorders and reconcile these new data with pre-omics findings. By reconciling these evidences, we aim to identify key gaps and propose directions for future studies in the field.

SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

SingPro: a knowledge base providing single-cell proteomic data

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.

Effects of methionine deficiency on B7H3-DAP12-CAR-T cells in the treatment of lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is a subtype of lung cancer for which precision therapy is lacking. Chimeric antigen receptor T-cells (CAR-T) have the potential to eliminate cancer cells by targeting specific antigens. However, the tumor microenvironment (TME), characterized by abnormal metabolism could inhibit CAR-T function. Therefore, the aim of this study was to improve CAR-T efficacy in solid TME by investigating the effects of amino acid metabolism. We found that B7H3 was highly expressed in LUSC and developed DAP12-CAR-T targeting B7H3 based on our previous findings. When co-cultured with B7H3-overexpressing LUSC cells, B7H3-DAP12-CAR-T showed significant cell killing effects and released cytokines including IFN-γ and IL-2. However, LUSC cells consumed methionine (Met) in a competitive manner to induce a Met deficiency. CAR-T showed suppressed cell killing capacity, reduced cytokine release and less central memory T phenotype in medium with lower Met, while the exhaustion markers were up-regulated. Furthermore, the gene NKG7, responsible for T cell cytotoxicity, was downregulated in CAR-T cells at low Met concentration due to a decrease in m5C modification. NKG7 overexpression could partially restore the cytotoxicity of CAR-T in low Met. In addition, the anti-tumor efficacy of CAR-T was significantly enhanced when co-cultured with SLC7A5 knockdown LUSC cells at low Met concentration. In conclusion, B7H3 is a prospective target for LUSC, and B7H3-DAP12-CAR-T cells are promising for LUSC treatment. Maintaining Met levels in CAR-T may help overcome TME suppression and improve its clinical application potential.

LncSEA 2.0: an updated platform for long non-coding RNA related sets and enrichment analysis

Long non-coding RNAs (lncRNAs) possess a wide range of biological functions, and research has demonstrated their significance in regulating major biological processes such as development, differentiation, and immune response. The accelerating accumulation of lncRNA research has greatly expanded our understanding of lncRNA functions. Here, we introduce LncSEA 2.0 (http://bio.liclab.net/LncSEA/index.php), aiming to provide a more comprehensive set of functional lncRNAs and enhanced enrichment analysis capabilities. Compared with LncSEA 1.0, we have made the following improvements: (i) We updated the lncRNA sets for 11 categories and extremely expanded the lncRNA scopes for each set. (ii) We newly introduced 15 functional lncRNA categories from multiple resources. This update not only included a significant amount of downstream regulatory data for lncRNAs, but also covered numerous epigenetic regulatory data sets, including lncRNA-related transcription co-factor binding, chromatin regulator binding, and chromatin interaction data. (iii) We incorporated two new lncRNA set enrichment analysis functions based on GSEA and GSVA. (iv) We adopted the snakemake analysis pipeline to track data processing and analysis. In summary, LncSEA 2.0 offers a more comprehensive collection of lncRNA sets and a greater variety of enrichment analysis modules, assisting researchers in a more comprehensive study of the functional mechanisms of lncRNAs.

Exploring the biomarkers and potential therapeutic drugs for sepsis via integrated bioinformatic analysis

BACKGROUND

Sepsis is a life-threatening condition caused by an excessive inflammatory response to an infection, associated with high mortality. However, the regulatory mechanism of sepsis remains unclear.

RESULTS

In this study, bioinformatics analysis revealed the novel key biomarkers associated with sepsis and potential regulators. Three public datasets (GSE28750, GSE57065 and GSE95233) were employed to recognize the differentially expressed genes (DEGs). Taking the intersection of DEGs from these three datasets, GO and KEGG pathway enrichment analysis revealed 537 shared DEGs and their biological functions and pathways. These genes were mainly enriched in T cell activation, differentiation, lymphocyte differentiation, mononuclear cell differentiation, and regulation of T cell activation based on GO analysis. Further, pathway enrichment analysis revealed that these DEGs were significantly enriched in Th1, Th2 and Th17 cell differentiation. Additionally, five hub immune-related genes (CD3E, HLA-DRA, IL2RB, ITK and LAT) were identified from the protein-protein interaction network, and sepsis patients with higher expression of hub genes had a better prognosis. Besides, 14 drugs targeting these five hub related genes were revealed on the basis of the DrugBank database, which proved advantageous for treating immune-related diseases.

CONCLUSIONS

These results strengthen the new understanding of sepsis development and provide a fresh perspective into discriminating the candidate biomarkers for predicting sepsis as well as identifying new drugs for treating sepsis.

Identification of TNFRSF1A as a potential biomarker for osteosarcoma

BACKGROUND

Osteosarcoma (OS) is a relatively rare malignant bone tumor in teenagers; however, its molecular mechanisms are not yet understood comprehensively.

OBJECTIVE

The study aimed to use necroptosis-related genes (NRGs) and their relationships with immune-related genes to construct a prognostic signature for OS.

METHODS

TARGET-OS was used as the training dataset, and GSE 16091 and GSE 21257 were used as the validation datasets. Univariate regression, survival analysis, and Kaplan-Meier curves were used to screen for hub genes. The immune-related targets were screened using immune infiltration assays and immune checkpoints. The results were validated using nomogram and decision curve analyses (DCA).

RESULTS

Using univariate Cox regression analysis, TNFRSF1A was screened from 14 NRGs as an OS prognostic signature. Functional enrichment was analyzed based on the median expression of TNFRSF1A. The prognosis of the TNFRSF1A low-expression group in the Kaplan-Meier curve was notably worse. Immunohistochemistry analysis showed that the number of activated T cells and tumor purity increased considerably. Furthermore, the immune checkpoint lymphocyte activation gene 3 (LAG-3) is a possible target for intervention. The nomogram accurately predicted 1-, 3-, and 5-year survival rates. DCA validated the model (C = 0.669).

Conclusion

TNFRSF1A can be used to elucidate the potential relationship between the immune microenvironment and NRGs in OS pathogenesis.

Role of immune related genes in predicting prognosis and immune response in patients with hepatocellular carcinoma

Immunotherapy has developed rapidly in recent years. This study aimed to establish a prognostic signature for immune-related genes (IRGs) and explore related potential immunotherapies. The RNA-seq transcriptome profiles and clinicopathological information of patients were obtained from The Cancer Genome Atlas. Differentially expressed IRGs in tumors and normal tissues were screened and a risk score signature was constructed to predict the prognosis in patients with hepatocellular carcinoma (HCC). Receiver operating characteristic curves, survival analyses, and correlation analyses were used to explore the clinical application of this model. We further analyzed the differences in clinical characteristics, immune infiltration, somatic mutations, and treatment sensitivity between the high- and low-risk populations characterized by the prognostic models. The immune cell infiltration score and immune-related pathway activity were calculated using the single sample gene set enrichment analysis (ssGSEA) set enrichment analysis. Gene ontology (GO), Kyoto encyclopedia of genes and genomes, and GSEA were used to explore the underlying mechanisms. We constructed a nine-IRG formula to predict the prognosis in HCC patients. The higher the risk score, the higher the malignancy of the tumor and the worse the prognosis. There were significant differences in immune related processes between the high- and low-risk groups. TP53 and CTNNB1 mutations were significantly different between different risk groups. The expression of model gene was closely related to the sensitivity of tumor cells to chemotherapeutic drugs. This risk score model, which is helpful for the individualized treatment of patients with different risk factors, could be a reliable prognostic tool for HCC patients.

Artificial Intelligence, Big Data, and Regulation of Immunity: Challenges and Opportunities

The immune system is regulated by a complex set of genetic, molecular, and cellular interactions. Rapid advances in the study of immunity and its network of interactions have been boosted by a spectrum of "omics" technologies that have generated huge amounts of data that have reached the status of big data (BD). With recent developments in artificial intelligence (AI), theoretical and clinical breakthroughs could emerge. Analyses of large data sets with AI tools will allow the formulation of new testable hypotheses open new research avenues and provide innovative strategies for regulating immunity and treating immunological diseases. This includes diagnosis and identification of rare diseases, prevention and treatment of autoimmune diseases, allergic disorders, infectious diseases, metabolomic disorders, cancer, and organ transplantation. However, ethical and regulatory challenges remain as to how these studies will be used to advance our understanding of basic immunology and how immunity might be regulated in health and disease. This will be particularly important for entities in which the complexity of interactions occurring at the same time and multiple cellular pathways have eluded conventional approaches to understanding and treatment. The analyses of BD by AI are likely to be complicated as both positive and negative outcomes of regulating immunity may have important ethical ramifications that need to be considered. We suggest there is an immediate need to develop guidelines as to how the analyses of immunological BD by AI tools should guide immune-based interventions to treat various diseases, prevent infections, and maintain health within an ethical framework.

Identification and functional analysis of circulating small extracellular vesicle lncRNA signatures in children with fulminant myocarditis

Fulminant myocarditis (FM) is the most serious type of myocarditis. However, the molecular mechanism underlying the pathogenesis of FM has not been fully elucidated. Small extracellular vesicles (sEVs) play important roles in many diseases, but any potential role in paediatric FM has not been reported. Here, the differential signatures of lncRNAs in plasma sEVs were studied in FM children and healthy children using transcriptome sequencing followed by functional analysis. Then immune-related lncRNAs were screened to study their role in immune mechanisms, the levels and clinical relevance of core immune-related lncRNAs were verified by qRT-PCR in a large sample size. Sixty-eight lncRNAs had increased levels of plasma sEVs in children with FM and 11 had decreased levels. Functional analysis showed that the sEVs-lncRNAs with different levels were mainly related to immunity, apoptosis and protein efflux. Seventeen core immune-related sEVs-lncRNAs were screened, functional enrichment analysis showed that these lncRNAs were closely related to immune activation, immune cell migration and cytokine pathway signal transduction. The results of the study show that sEVs-lncRNAs may play an important role in the pathogenesis of fulminant myocarditis in children, especially in the mechanism of immune regulation.