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

Automated and reproducible cell identification in mass cytometry using neural networks

The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples. To address these limitations, we present two novel developments: (1) a pre-trained cell identification model named Immunopred that allows automated identification of immune cells without user-defined prior knowledge of expected cell types and (2) a fully automated cytometry meta-analysis pipeline built around Immunopred. We evaluated this pipeline on six COVID-19 study data sets comprising 270 unique samples and uncovered novel significant phenotypic changes in the wider immune landscape of COVID-19 that were not identified when each study was analyzed individually. Applied widely, our approach will support the discovery of novel findings in research areas where cytometry data sets are available for integration.

Identification of key lipid metabolism-related genes in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) represents profound degenerative conditions of the brain that cause significant deterioration in memory and cognitive function. Despite extensive research on the significant contribution of lipid metabolism to AD progression, the precise mechanisms remain incompletely understood. Hence, this study aimed to identify key differentially expressed lipid metabolism-related genes (DELMRGs) in AD progression.

METHODS

Comprehensive analyses were performed to determine key DELMRGs in AD compared to controls in GSE122063 dataset from Gene Expression Omnibus. Additionally, the ssGSEA algorithm was utilized for estimating immune cell levels. Subsequently, correlations between key DELMRGs and each immune cell were calculated specifically in AD samples. The key DELMRGs expression levels were validated via two external datasets. Furthermore, gene set enrichment analysis (GSEA) was utilized for deriving associated pathways of key DELMRGs. Additionally, miRNA-TF regulatory networks of the key DELMRGs were constructed using the miRDB, NetworkAnalyst 3.0, and Cytoscape software. Finally, based on key DELMRGs, AD samples were further segmented into two subclusters via consensus clustering, and immune cell patterns and pathway differences between the two subclusters were examined.

RESULTS

Seventy up-regulated and 100 down-regulated DELMRGs were identified. Subsequently, three key DELMRGs (DLD, PLPP2, and PLAAT4) were determined utilizing three algorithms [(i) LASSO, (ii) SVM-RFE, and (iii) random forest]. Specifically, PLPP2 and PLAAT4 were up-regulated, while DLD exhibited downregulation in AD cerebral cortex tissue. This was validated in two separate external datasets (GSE132903 and GSE33000). The AD group exhibited significantly altered immune cell composition compared to controls. In addition, GSEA identified various pathways commonly associated with three key DELMRGs. Moreover, the regulatory network of miRNA-TF for key DELMRGs was established. Finally, significant differences in immune cell levels and several pathways were identified between the two subclusters.

CONCLUSION

This study identified DLD, PLPP2, and PLAAT4 as key DELMRGs in AD progression, providing novel insights for AD prevention/treatment.

M6A regulator methylation patterns and characteristics of immunity in acute ST-segment elevation myocardial infarction

M6A methylation is the most prevalent and abundant RNA modification in mammals. Although there are many studies on the regulatory role of m6A methylation in the immune response, the m6A regulators in the pathogenesis of acute ST-segment elevation myocardial infarction (STEMI) remain unclear. We comprehensively analysed the role of m6A regulators in STEMI and built a predictive model, revealing the relationship between m6A methylations and the immune microenvironment. Differential analysis revealed that 18 of 24 m6A regulators were significantly differentially expressed, and there were substantial interactions between the m6A regulator. Then, we established a classifier and nomogram model based on 6 m6A regulators, which can easily distinguish the STEMI and control samples. Finally, two distinct m6A subtypes were obtained and significantly differentially expressed in terms of infiltrating immunocyte abundance, immune reaction activity and human leukocyte antigen genes. Three hub m6A phenotype related genes (RAC2, RELA, and WAS) in the midnightblue module were identified by weighted gene coexpression network analysis, and were associated with immunity. These findings suggest that m6A modification and the immune microenvironment play a key role in the pathogenesis of STEMI.

The XIST lncRNA is a sex-specific reservoir of TLR7 ligands in SLE

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease with a dramatic sex bias, affecting 9 times more women than men. Activation of Toll-like receptor 7 (TLR7) by self-RNA is a central pathogenic process leading to aberrant production of type I interferon (IFN) in SLE, but the specific RNA molecules that serve as TLR7 ligands have not been defined. By leveraging gene expression data and the known sequence specificity of TLR7, we identified the female-specific X-inactive specific transcript (XIST) long noncoding RNA as a uniquely rich source of TLR7 ligands in SLE. XIST RNA stimulated IFN-α production by plasmacytoid DCs in a TLR7-dependent manner, and deletion of XIST diminished the ability of whole cellular RNA to activate TLR7. XIST levels were elevated in blood leukocytes from women with SLE compared with controls, correlated positively with disease activity and the IFN signature, and were enriched in extracellular vesicles released from dying cells in vitro. Importantly, XIST was not IFN inducible, suggesting that XIST is a driver, rather than a consequence, of IFN in SLE. Overall, our work elucidated a role for XIST RNA as a female sex-specific danger signal underlying the sex bias in SLE.

Sex and dose rate effects in automated cytogenetics

Testing and validation of biodosimetry assays is routinely performed using conventional dose rate irradiation platforms, at a dose rate of approximately 1 Gy/min. In contrast, the exposures from an improvised nuclear device will be delivered over a large range of dose rates with a prompt irradiation component, delivered in less than 1 μs, and a protracted component delivered over hours and days. We present preliminary data from a large demographic study we have undertaken for investigation of age, sex and dose rate effects on dicentric and micronucleus yields. Our data demonstrate reduced dicentric and micronucleus yields at very high dose rates. Additionally, we have seen small differences between males and females, with males having slightly fewer micronuclei and slightly more dicentrics than females, at high doses.

Poor Outcome in Postpartum Breast Cancer Patients Is Associated with Distinct Molecular and Immunologic Features

PURPOSE

Patients with postpartum breast cancer diagnosed after cessation of breastfeeding (postweaning, PP-BCPW) have a particularly poor prognosis compared with patients diagnosed during lactation (PP-BCDL), or to pregnant (Pr-BC) and nulliparous (NP-BC) patients, regardless of standard prognostic characteristics. Animal studies point to a role of the involution process in stimulation of tumor growth in the mammary gland. However, in women, the molecular mechanisms that underlie this poor prognosis of patients with PP-BCPW remain vastly underexplored, due to of lack of adequate patient numbers and outcome data.

EXPERIMENTAL DESIGN

We explored whether distinct prognostic features, common to all breast cancer molecular subtypes, exist in postpartum tumor tissue. Using detailed breastfeeding data, we delineated the postweaning period in PP-BC as a surrogate for mammary gland involution and performed whole transcriptome sequencing, immunohistochemical, and (multiplex) immunofluorescent analyses on tumor tissue of patients with PP-BCPW, PP-BCDL, Pr-BC, and NP-BC.

RESULTS

We found that patients with PP-BCPW having a low expression level of an immunoglobulin gene signature, but high infiltration of plasma B cells, have an increased risk for metastasis and death. Although PP-BCPW tumor tissue was also characterized by an increase in CD8+ cytotoxic T cells and reduced distance among these cell types, these parameters were not associated with differential clinical outcomes among groups.

CONCLUSIONS

These data point to the importance of plasma B cells in the postweaning mammary tumor microenvironment regarding the poor prognosis of PP-BCPW patients. Future prospective and in-depth research needs to further explore the role of B-cell immunobiology in this specific group of young patients with breast cancer.

Integrated analysis reveals Atf3 promotes neuropathic pain via orchestrating JunB mediated release of inflammatory cytokines in DRG macrophage

The dorsal root ganglion (DRG) is actively involved in the development of neuropathic pain (NP), serving as an intermediate station for pain signals from the peripheral nervous system to the central nervous system. The mechanism by which DRG is involved in NP regulation is not fully understood. The immune system plays a pivotal role in the physiological and pathological states of the human body. In recent years, the immune system has been thought to play an increasingly important role in the pathogenesis of NP. The immune system plays a key role in pain through specific immune cells and their immune-related genes (IRGs). However, the mechanism by which IRGs of DRG regulate NP action has not been fully elucidated. Here, we performed Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of IRGs in DRG bulk-RNA sequencing data from spared nerve injury (SNI) model mice and found that their IRGs were enriched in many pathways, especially in the immune response pathway. Subsequently, we analyzed single-cell RNA sequencing (scRNA-seq) data from DRGs extracted from the SNI model and identified eight cell populations. Among them, the highest IRG activity was presented in macrophages. Next, we analyzed the scRNA and bulk-sequencing data and deduced five common transcription factors (TFs) from differentially expressed genes (DEGs). The protein-protein interaction (PPI) network suggested that Atf3 and JunB are closely related. In vitro experiments, we verified that the protein and mRNA expressions of Atf3 and JunB were up-regulated in macrophages after lipopolysaccharide (LPS) stimulation. Moreover, the down-regulation of Atf3 reduced the release of inflammatory cytokines and decreased the protein and mRNA expression levels of JunB. The down-regulation of JunB also reduced the release of inflammatory cytokines. Furthermore, overexpression of JunB attenuated the effect of Atf3 down-regulation in reducing the release of inflammatory cytokines. Therefore, we speculated that Atf3 might promote NP through JunB-mediated release of inflammatory factors in DRG macrophages.

Immune landscape and regulatory mechanisms in human atherosclerotic coronary plaques: Evidence from single-cell and bulk transcriptomics

Atherosclerosis is a chronic immuno-inflammatory disease, however, the immune landscape and regulatory mechanisms have not been clear. We detected seven principal immune cell clusters with distinct phenotypic and spatial characteristics using single-cell RNA-sequencing of aortic immune cells from patients with acute coronary syndrome and stable angina pectoris. Then we acquired 265 differentially expressed immune-related genes and the high scores were mainly found in T cells and monocytes, which were differentially regulated in atherosclerotic coronary plaques. The CCL signaling pathway was the most relevant pattern in the T cells and CCL5-CCR1 and CCL5-CCR5 ligand-receptor pairs played a vital role in the CCL signaling pathway. Further comparative analysis indicated MCH-I signaling was the most relevant pattern in the T cells and HLA ligand-related ligand-receptor pairs played a vital role. Functional analysis of the single-cell and bulk transcriptomics pointed to multiple pathways, such as antigen presentation and immune response. Nineteen common differentially expressed immune-related genes were found in both immune cells and the human peripheral blood mononuclear cells. Nine common differentially expressed transcription factors were differentially expressed in both T cell and monocyte clusters from the coronary plaques and human peripheral blood mononuclear cells and the network demonstrated that CEBPB might play an essential role in the transcriptional regulation of atherosclerosis as a hub transcription factor. The definition of immune cell diversity and heterogeneity by single-cell level analysis of aortic immune cell subsets not only unveils cell-type-specific pathways and new immune mechanisms but also discovers the functional correlation of immune cells in human atherosclerosis. Our findings provide great promise for the discovery of novel molecular mechanisms and precise therapeutic targets for atherosclerosis.

Identification of a novel pyroptosis-related gene signature in human spermatogenic dysfunction

PURPOSE

To reveal the underlying roles that pyroptosis-related genes (PRGs) played in human spermatogenic dysfunction.

METHODS

One discovery set and three validation sets were employed to inspect the previously reported 33 PRGs in the human testis with different status of spermatogenesis. PRGs that differentially expressed in all sets were considered as key differentially expressed pyroptosis-related genes (PR-DEGs). The relationships between key PR-DEGs and samples' clinicopathological, therapeutic, and immune patterns were respectively studied. Single-cell RNA sequencing (scRNS-seq) analyses were conducted to show the expression changes and related mechanisms of key PR-DEGs at a single-cell resolution.

RESULTS

CASP4 and GPX4 were identified as two key PR-DEGs. These two genes were significantly dysregulated in spermatogenic dysfunctional samples, but with opposite tendency. CASP4 was negatively correlated with Johnsen scores but positively correlated with follicle-stimulating hormone (FSH) levels (all p < 0.05), while GPX4 exhibited significant positive correlations with Johnsen scores and negative relevance with FSH. For treatments, both molecules showed a prospective value of being predictors for sperm retrieval surgeries. Moreover, CASP4 and GPX4 were potential immunoregulators in the testicular immune microenvironment and showed significant correlations to testicular macrophages and mast cell infiltration. In scRNA-seq analyses, GPX4 was highly expressed in germ cells, which therefore suffered a sharp reduction with the loss of germ cells in spermatogenic dysfunction. On the other hand, CASP4 were basically somatic cell-derived, and the proportion of CASP4-positive Leydig cells significantly increased in disease testes (p = 0.0001).

CONCLUSION

In all, we revealed two key PRGs of human testes that might be functional in spermatogenic dysfunction.

Targeting PPAR-gamma counteracts tumour adaptation to immune-checkpoint blockade in hepatocellular carcinoma

OBJECTIVE

Therapy-induced tumour microenvironment (TME) remodelling poses a major hurdle for cancer cure. As the majority of patients with hepatocellular carcinoma (HCC) exhibits primary or acquired resistance to antiprogrammed cell death (ligand)-1 (anti-PD-[L]1) therapies, we aimed to investigate the mechanisms underlying tumour adaptation to immune-checkpoint targeting.

DESIGN

Two immunotherapy-resistant HCC models were generated by serial orthotopic implantation of HCC cells through anti-PD-L1-treated syngeneic, immunocompetent mice and interrogated by single-cell RNA sequencing (scRNA-seq), genomic and immune profiling. Key signalling pathway was investigated by lentiviral-mediated knockdown and pharmacological inhibition, and further verified by scRNA-seq analysis of HCC tumour biopsies from a phase II trial of pembrolizumab (NCT03419481).

RESULTS

Anti-PD-L1-resistant tumours grew >10-fold larger than parental tumours in immunocompetent but not immunocompromised mice without overt genetic changes, which were accompanied by intratumoral accumulation of myeloid-derived suppressor cells (MDSC), cytotoxic to exhausted CD8+ T cell conversion and exclusion. Mechanistically, tumour cell-intrinsic upregulation of peroxisome proliferator-activated receptor-gamma (PPARγ) transcriptionally activated vascular endothelial growth factor-A (VEGF-A) production to drive MDSC expansion and CD8+ T cell dysfunction. A selective PPARγ antagonist triggered an immune suppressive-to-stimulatory TME conversion and resensitised tumours to anti-PD-L1 therapy in orthotopic and spontaneous HCC models. Importantly, 40% (6/15) of patients with HCC resistant to pembrolizumab exhibited tumorous PPARγ induction. Moreover, higher baseline PPARγ expression was associated with poorer survival of anti-PD-(L)1-treated patients in multiple cancer types.

CONCLUSION

We uncover an adaptive transcriptional programme by which tumour cells evade immune-checkpoint targeting via PPARγ/VEGF-A-mediated TME immunosuppression, thus providing a strategy for counteracting immunotherapeutic resistance in HCC.

A pairwise immune gene model for predicting overall survival and stratifying subtypes of colon adenocarcinoma

OBJECTIVES

There is increasing evidence for a close correlation between risk stratification, prognosis and the immune environment in colon adenocarcinoma (COAD). However, the efficacy of immunotherapy is different among different patients with COAD. Therefore, the current work tends to use immune-related gene to develop a gene-pair model to evaluate the COAD prognosis, and to develop a new method for risk stratification of COAD, which is conducive to better predict the immunotherapy effect of patients.

METHODS

Specifically, from the TCGA and GEO (GSE14333 and GSE39582) databases, we first collected gene expression profiles, associated survival follow-up information of COAD patients. Through systematic bioinformatics analysis, we established a prognosis-related model of colon cancer with three pairs of "immune gene pairs", with uni- and multivariate and lasso cox regression analyses verifying the model stability. Most immune cells showed markedly different levels of infiltration between the two risk subgroups calculated by the model. More, single-cell RNA-seq analyses were also performed to validate the selected genes in the immune gene-pair model.

RESULTS

A prognosis-related model of colon cancer with three pairs of "immune gene pairs" were built and validated by several datasets. The analysis of immune landscape of COAD revealed that low-risk subgroup obtained by the prognosis-related model for COAD can be further divided into three subclusters with different prognosis. Then, we applied the Tumor online Prognostic analyses Platform (ToPP) to construct a prognostic model using these five genes. Results show that APOD, ISG20 and STC2 are risk factors, while CXCL9 and IL7R are protection factors. We also found that only the five-gene model could also predict the prognosis of COAD patients, indicating the robustness of the gene-pair model. Among the five genes, including CXCL9, APOD, STC2, ISG20, and IL7R, in the gene-pair model, single-cell RNA sequencing reveals the high expression of CXCL9 and IL7R in inflammatory macrophages. Using cell-cell interaction and trajectory analysis, data indicate that CXCL9+/IL7R+ pro-inflammatory macrophages were capable of secreting and activating more anti-tumor pathways than CXCL9-/IL7R- pro-inflammatory macrophages.

CONCLUSIONS

In short, we have successfully developed an "immune gene pair" related model that can judge the prognostic status of patients with COAD and may contribute to risk stratification and evaluate potential beneficiaries of immunotherapy, providing new ideas for the anti-COAD management and therapy.

Identification of Tumor Antigens and Immune Subtypes of High-grade Serous Ovarian Cancer for mRNA Vaccine Development

High-grade serous ovarian cancer (HGSC) is the most common pathology of ovarian cancer and has aggressive characteristics and poor prognosis. mRNA vaccines are a novel tool for cancer immune treatment and may play an important role in HGSC therapy. Our study aimed to explore tumour antigens for vaccine development and identify potential populations amenable to vaccine treatment. Based on transcription data from The Cancer Genome Atlas (TCGA), we identified four tumour-specific antigens for vaccine production: ARPC1B, ELF3, VSTM2L, and IL27RA. In addition to being associated with HGSC patient prognosis, the expression of these antigens was positively correlated with the abundances of antigen-presenting cells (APCs). Furthermore, we stratified HGSC samples into three immune subtypes (IS1-IS3) with different immune characteristics. A corhort from ICGC (International Cancer Genome Consortium) was used to validate. Patients of IS3 had the best prognosis, while patients of IS1 were most likely to benefit from vaccination. There was substantial heterogeneity in immune signatures and immune-associated molecule expression in HGSC. Finally, weighted gene coexpression network analysis (WGCNA) was employed to cluster immune-related genes and explore potential biomarkers related to vaccination. In conclusion, we identified four potential tumour antigens for mRNA vaccine production for HGSC treatment, and the immune subtype could be an important indicator to select suitable HGSC patients to receive vaccination.

Immune-related risk prognostic model for clear cell renal cell carcinoma: Implications for immunotherapy

Clear cell renal cell carcinoma (ccRCC) is associated with complex immune interactions. We conducted a comprehensive analysis of immune-related differentially expressed genes in patients with ccRCC using data from The Cancer Genome Atlas and ImmPort databases. The immune-related differentially expressed genes underwent functional and pathway enrichment analysis, followed by COX regression combined with LASSO regression to construct an immune-related risk prognostic model. The model comprised 4 IRGs: CLDN4, SEMA3G, CAT, and UCN. Patients were stratified into high-risk and low-risk groups based on the median risk score, and the overall survival rate of the high-risk group was significantly lower than that of the low-risk group, confirming the reliability of the model from various perspectives. Further comparison of immune infiltration, tumor mutation load, and immunophenoscore (IPS) comparison between the 2 groups indicates that the high-risk group could potentially demonstrate a heightened sensitivity towards immunotherapy checkpoints PD-1, CTLA-4, IL-6, and LAG3 in ccRCC patients. The proposed model not only applies to ccRCC but also shows potential in developing into a prognostic model for renal cancer, thus introducing a novel approach for personalized immunotherapy in ccRCC.

Prediction of prognosis and immunotherapy response in lung adenocarcinoma based on CD79A, DKK1 and VEGFC

Background

Tumor immune microenvironment (TIME) is crucial for tumor initiation, progression, and metastasis; however, its relationship with lung adenocarcinoma (LUAD) is unknown. Traditional predictive models screen for biomarkers that are too general and infrequently associated with immune genes.

Methods

RNA sequencing data of LUAD patients and immune-related gene sets were retrieved from public databases. Using the common genes shared by The Cancer Genome Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort), differential gene expression analysis, survival analysis, Lasso regression analysis, and univariate and multivariate Cox regression analyses were performed to generate a novel risk score model. LUAD cohort in International Cancer Genome Consortium (ICGC), GSE68465 cohort in Gene Expression Omnibus (GEO) and an immunohistochemical assay were used to validate the key genes constructed risk score. The LUAD-related prognosis, clinical indicators, immune infiltrate characteristics, response to immunotherapy, and response to chemotherapeutic agents in different risk groups were evaluated by CIBERSORT, ImmuCellAI, pRRophetic and other tools.

Results

The risk score model was constructed using CD79a molecule (CD79A), Dickkopf WNT signaling pathway inhibitor 1 (DKK1), and vascular endothelial growth factor C (VEGFC). High risk score was identified as a negative predictor for overall survival (OS) in subgroup analyses with tumor stage, TNM classification, therapy outcome, and ESTIMATE scores (P < 0.05). Low risk score was positively associated with plasma cells, memory B cells, CD8 T cells, regulatory T cells and γδT cells (P < 0.05). In low-risk group, programmed cell death 1 receptor (PD1), cytotoxic T-lymphocyte associated protein 4 (CTLA4), and lymphocyte activating 3 (LAG3) and indoleamine 2,3-dioxygenase (IDO) were more robustly expressed (P < 0.05). The treatment responses of immune checkpoint blockade (ICB) therapy and chemotherapy were likewise superior in low-risk group (P < 0.05). In immunohistochemical analysis, the tumor group had significantly higher levels of CD79A, DKK1, and VEGFC than the adjacent normal group (P < 0.01).

Conclusions

CD79A, DKK1 and VEGFC are important differential genes related to LUAD, risk score could reliably predict prognosis, composition of TIME and immunotherapy responses in LUAD patients. The excellent performance of the risk model shows its strong and broad application potential.

Identification and validation of Birc5 as a novel activated cell cycle program biomarker associated with infiltration of immunosuppressive myeloid-derived suppressor cells in hepatocellular carcinoma

BACKGROUND

Preclinical studies and clinical trials have demonstrated that tumor-intrinsic activation of the cell cycle program impedes anticancer immunotherapy. Identification of cell cycle-related biomarkers may provide novel therapeutic targets to augment the efficacy of immunotherapy in hepatocellular carcinoma (HCC).

METHOD AND RESULTS

Based on the genes related to cell cycle program, two clusters (Cluster 1 and Cluster 2) were detected in HCC patients via non-negative matrix factorization algorithm. Multivariable-adjusted Cox regression analysis indicated that the cell cycle gene-based classification was a significant prognostic factor for predicting the clinical outcome of HCC patients. Cluster 1 showed shorter overall survival time and progression-free interval time was associated with activated cell cycle program, higher infiltration of myeloid-derived suppressor cells (MDSCs) and less sensitivity to immunotherapy. A three-gene prognostic model, including BIRC5, C8G, and SPP1, was constructed to characterize the cell cycle-based classification of HCC, which had strong robustness and a stable predictive performance. Notably, Birc5 was positively correlated with CD11b expression (a MDSC marker) in HCC tissue. Concordant high expression of Birc5 and intratumor infiltration level of MDSCs were correlated with worse prognosis of HCC patients. In vitro, hepatocellular Birc5 overexpression promoted immunosuppressive CD11b+ CD33+ HLA-DR- MDSC expansion from human peripheral blood mononuclear cells. Genetically modified animal model of liver cancer revealed that Birc5 depletion upregulated the genes related to lymphocyte-mediated immunity, natural killer cell-mediated immunity, interferon-gamma production, T-cell activation, and T-cell-mediated cytotoxicity. These results suggest an immunosuppressive function of Birc5 in HCC.

CONCLUSION

Birc5 was a potential biomarker and inducer of intratumor infiltration of MDSCs, which led to T cell exclusion or dysfunction in tumor immune microenvironment, consequently resulting in reduced response to ICIs in HCC.

Luteolin and triptolide: Potential therapeutic compounds for post-stroke depression via protein STAT

Post stroke depression (PSD) is a common neuropsychiatric complication following stroke closely associated with the immune system. The development of medications for PSD remains to be a considerable challenge due to the unclear mechanism of PSD. Multiple researches agree that the functions of gene ontology (GO) are efficient for the investigation of disease mechanisms, and DeepPurpose (DP) is extremely valuable for the mining of new drugs. However, GO terms and DP have not yet been applied to explore the pathogenesis and drug treatment of PSD. This study aimed to interpret the mechanism of PSD and discover important drug candidates targeting risk proteins, based on immune-related risk GO functions and informatics algorithms. According to the risk genes of PSD, we identified 335 immune-related risk GO functions and 37 compounds. Based on the construction of the GO function network, we found that STAT protein may be a pivot protein in underlying the mechanism of PSD. Additionally, we also established networks of Protein-Protein Interaction as well as Gene-GO function to facilitate the evaluation of key genes. Based on DP, a total of 37 candidate compounds targeting 7 key proteins were identified with a potential for the therapy of PSD. Furthermore, we noted that the mechanisms by which luteolin and triptolide acting on STAT-related GO function might involve three crucial pathways, including specifically hsa04010 (MAPK signaling pathway), hsa04151 (PI3K-Akt signaling pathway) and hsa04060 (Cytokine-cytokine receptor interaction). Thus, this study provided fresh and powerful information for the mechanism and therapeutic strategies of PSD.

Cuproptosis and Immune-Related Gene Signature Predicts Immunotherapy Response and Prognosis in Lung Adenocarcinoma

Cuproptosis and associated immune-related genes (IRG) have been implicated in tumorigenesis and tumor progression. However, their effects on lung adenocarcinoma (LUAD) have not been elucidated. Therefore, we investigated the impact of cuproptosis-associated IRGs on the immunotherapy response and prognosis of LUAD using a bioinformatical approach and in vitro experiments analyzing clinical samples. Using the cuproptosis-associated IRG signature, we classified LUAD into two subtypes, cluster 1 and cluster 2, and identified three key cuproptosis-associated IRGs, NRAS, TRAV38-2DV8, and SORT1. These three genes were employed to establish a risk model and nomogram, and to classify the LUAD cohort into low- and high-risk subgroups. Biofunctional annotation revealed that cluster 2, remarkably downregulating epigenetic, stemness, and proliferation pathways activity, had a higher overall survival (OS) and immunoinfiltration abundance compared to cluster 1. Real-time quantitative PCR (RT-qPCR) validated the differential expression of these three genes in both subgroups. scRNA-seq demonstrated elevated expression of NRAS and SORT1 in macrophages. Immunity and oncogenic and stromal activation pathways were dramatically enriched in the low-risk subgroup, and patients in this subgroup responded better to immunotherapy. Our data suggest that the cuproptosis-associated IRG signature can be used to effectively predict the immunotherapy response and prognosis in LUAD. Our work provides enlightenment for immunotherapy response assessment, prognosis prediction, and the development of potential prognostic biomarkers for LUAD patients.

Prediction of immune and targeted drug efficacy in pain-related risk subtypes for bladder cancer patients

Bladder cancer is a complex disease with high morbidity and mortality rates. At least 430,000 cases are diagnosed annually worldwide. Cancer pain is the most common and distressing symptom in cancer patients. Studies have reported depression, anxiety, and decreased quality of life in survivors of various cancers. The study of pain-related genes in cancer patients may provide a basis for developing targeted drugs for cancer therapy, which could reduce pain and improve quality of life of cancer patients. In this study, the mRNA expression and clinical data of bladder cancer patients were downloaded from public databases. A total of 103 pain-related genes were also downloaded from the public databases. Univariate Cox regression analysis identified 17 pain-related genes that were significantly associated with overall survival. We calculated a pain-related risk score for each patient, constructed a bladder cancer pain risk model, and categorized bladder cancer patients into two risk subtypes. Differences in prognosis, differential gene expression, immune cell signatures, hallmarks, metabolic pathways, and somatic mutations between the different risk subtypes were systematically investigated. Eight drugs associated with bladder cancer risk subtypes were identified. Their differences in the high- and low-risk subtypes of bladder cancer were examined. In addition, the response to immunotherapy was analyzed in patients with different pain-related subtypes. Results revealed significant differences in these characteristics. Finally, a predictive model for pain-related risk subtypes in patients with bladder cancer was established. The study findings provide a reference for prognostication and personalized medical treatment of bladder cancer patients.

Identification of highly reliable risk genes for Alzheimer's disease through joint-tissue integrative analysis

Numerous genetic variants associated with Alzheimer's disease (AD) have been identified through genome-wide association studies (GWAS), but their interpretation is hindered by the strong linkage disequilibrium (LD) among the variants, making it difficult to identify the causal variants directly. To address this issue, the transcriptome-wide association study (TWAS) was employed to infer the association between gene expression and a trait at the genetic level using expression quantitative trait locus (eQTL) cohorts. In this study, we applied the TWAS theory and utilized the improved Joint-Tissue Imputation (JTI) approach and Mendelian Randomization (MR) framework (MR-JTI) to identify potential AD-associated genes. By integrating LD score, GTEx eQTL data, and GWAS summary statistic data from a large cohort using MR-JTI, a total of 415 AD-associated genes were identified. Then, 2873 differentially expressed genes from 11 AD-related datasets were used for the Fisher test of these AD-associated genes. We finally obtained 36 highly reliable AD-associated genes, including APOC1, CR1, ERBB2, and RIN3. Moreover, the GO and KEGG enrichment analysis revealed that these genes are primarily involved in antigen processing and presentation, amyloid-beta formation, tau protein binding, and response to oxidative stress. The identification of these potential AD-associated genes not only provides insights into the pathogenesis of AD but also offers biomarkers for early diagnosis of the disease.

Identification and Validation of Hub Immune-Related Genes in Non-Alcoholic Fatty Liver Disease

Background

Nonalcoholic fatty liver disease (NAFLD) is the most common progressive liver disease worldwide. It can cause liver cancer and possibly death. Abnormal immune infiltration is involved in the progression of NAFLD. The aim of this study was to identify and validate the hub immune-related genes in NAFLD.

Methods

Microarray data were downloaded from Gene Expression Omnibus, and immune-related differentially expressed genes (IRDEGs) were obtained. A protein-protein interaction network was used to further screen. The diagnostic value of the IRDEGs was evaluated by receiver operating characteristic curves. Differences in immune infiltration levels were analyzed using single-sample gene set enrichment analysis. Hub IRDEGs were identified by correlation analysis with immune infiltration levels. Finally, molecular experiments were used to confirm the expression of the hub IRDEGs and explore their roles in NAFLD.

Results

We obtained 18 IRDEGs. Five hub genes were further identified by protein-protein interaction network, receiver operating characteristic curves and correlation analysis: AQP9, BACH2, CD4, IL17RE and S100A9. Based on functional enrichment analysis, the hub genes were enriched primarily in many immune-related pathways. In NAFLD, AQP9, CD4, and IL17RE expression was significantly reduced, whereas BACH2 and S100A9 expression was elevated. PCR, oil red O staining and triglyceride detection revealed that the knock-down of BACH2 and S100A9 reduced lipid accumulation in NAFLD cells.

Conclusion

This study provided insight into the profile of immune infiltration underlying NAFLD and identified AQP9, BACH2, CD4, IL17RE and S100A9 as ancillary diagnostic indicators of NAFLD. And BACH2 and S100A9 might be therapeutic targets for NAFLD.

Understanding the evolution of immune genes in jawed vertebrates

Driven by co-evolution with pathogens, host immunity continuously adapts to optimize defence against pathogens within a given environment. Recent advances in genetics, genomics and transcriptomics have enabled a more detailed investigation into how immunogenetic variation shapes the diversity of immune responses seen across domestic and wild animal species. However, a deeper understanding of the diverse molecular mechanisms that shape immunity within and among species is still needed to gain insight into-and generate evolutionary hypotheses on-the ultimate drivers of immunological differences. Here, we discuss current advances in our understanding of molecular evolution underpinning jawed vertebrate immunity. First, we introduce the immunome concept, a framework for characterizing genes involved in immune defence from a comparative perspective, then we outline how immune genes of interest can be identified. Second, we focus on how different selection modes are observed acting across groups of immune genes and propose hypotheses to explain these differences. We then provide an overview of the approaches used so far to study the evolutionary heterogeneity of immune genes on macro and microevolutionary scales. Finally, we discuss some of the current evidence as to how specific pathogens affect the evolution of different groups of immune genes. This review results from the collective discussion on the current key challenges in evolutionary immunology conducted at the ESEB 2021 Online Satellite Symposium: Molecular evolution of the vertebrate immune system, from the lab to natural populations.

Identification of key signaling pathways and hub genes related to immune infiltration in Kawasaki disease with resistance to intravenous immunoglobulin based on weighted gene co-expression network analysis

Background: Kawasaki disease (KD) is an acute vasculitis, that is, the leading cause of acquired heart disease in children, with approximately 10%-20% of patients with KD suffering intravenous immunoglobulin (IVIG) resistance. Although the underlying mechanism of this phenomenon remains unclear, recent studies have revealed that immune cell infiltration may associate with its occurrence. Methods: In this study, we downloaded the expression profiles from the GSE48498 and GSE16797 datasets in the Gene Expression Omnibus database, analyzed differentially expressed genes (DEGs), and intersected the DEGs with the immune-related genes downloaded from the ImmPort database to obtain differentially expressed immune-related genes (DEIGs). Then CIBERSORT algorithm was used to calculate the immune cell compositions, followed by the WGCNA analysis to identify the module genes associated with immune cell infiltration. Next, we took the intersection of the selected module genes and DEIGs, then performed GO and KEGG enrichment analysis. Moreover, ROC curve validation, Spearman analysis with immune cells, TF, and miRNA regulation network, and potential drug prediction were implemented for the finally obtained hub genes. Results: The CIBERSORT algorithm showed that neutrophil expression was significantly higher in IVIG-resistant patients compared to IVIG-responsive patients. Next, we got differentially expressed neutrophil-related genes by intersecting DEIGs with neutrophil-related module genes obtained by WGCNA, for further analysis. Enrichment analysis revealed that these genes were associated with immune pathways, such as cytokine-cytokine receptor interaction and neutrophil extracellular trap formation. Then we combined the PPI network in the STRING database with the MCODE plugin in Cytoscape and identified 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2), which had good diagnostic performance in IVIG resistance according to ROC analysis. Furthermore, Spearman's correlation analysis confirmed that these genes were closely related to neutrophils. Finally, TFs, miRNAs, and potential drugs targeting the hub genes were predicted, and TF-, miRNA-, and drug-gene networks were constructed. Conclusion: This study found that the 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2) were significantly associated with neutrophil cell infiltration, which played an important role in IVIG resistance. In a word, this work rendered potential diagnostic biomarkers and prospective therapeutic targets for IVIG-resistant patients.

Heterogeneity in clinical prognosis, immune infiltration and molecular characteristics of three glycolytic subtypes in lower-grade gliomas

Background and purpose

Lower-grade gliomas (LGG) exhibit a wide range of metabolic pathway changes, and metabolic reprogramming can be largely seen as a result of oncogenic driving events. Glycolysis, an important pathway of tumor energy source, has been poorly studied in gliomas. The aim of this article is to analyze the relationship between glycolysis and lower-grade glioma development and prognosis in order to explore the heterogeneous relevance of glycolysis in lower-grade gliomas.

Methods and results

Our study searched the TCGA database and identified three glycolytic subtypes with significant prognostic differences by unsupervised clustering analysis of core glycolytic genes, named C1, C2, and C3. By analysis of clinical prognosis, somatic cell variation, and immune infiltration, we found that C3 had the best prognosis with molecular features of IDHmut-codel, followed by C1 with major molecular features of IDHmut-non-codel, G -CIMP high subtype, while C2 had the worst prognosis, mainly exhibiting IDHwt, G-CIMP low and mesenchymal-like subtypes with seven important CNV features, including CDKN2A/B deletion, chr7 gain and chr10 deletion, chr19/20 co-gain, EGFR amplification and PDGFRA/B deletion phenotypes were significantly increased, with the highest level of stemness and significant T-cell depletion features. Finally, to quantify the level of abnormal glycolysis and its impact on prognosis, we developed GlySig to reflect the glycolytic activity of LGG and integrated molecular features to construct nomogram that can be independently assessed to predict prognosis.

Conclusions

Our study analyzed the tumor characteristics of different glycolytic states, and our findings explain and describe the heterogeneity of glycolytic metabolism within diffuse LGGs.

SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data

Introduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions. Methods: In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN), a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features. Results: Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluated their association with genomic, phenotypic, and clinical outcomes. We found that CTIP was significantly (negatively) associated with deconvolved immune cell prevalence scores including CD8+ T-Cells and Natural Killer cells. Furthermore, average CTIP scores differed significantly across previously established transcriptomic classes and significantly associated with survival outcomes. Discussion: SPARTIN provides a general framework for investigating spatial cellular interactions in high-resolution digital histopathology imaging data and its associations with patient level characteristics. The results of our analysis have potential implications relevant to both treatment and prognosis in the context of Skin Cutaneous Melanoma. The R-package for SPARTIN is available at https://github.com/bayesrx/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN.

Integrative multi-omics deciphers the spatial characteristics of host-gut microbiota interactions in Crohn's disease

Dysregulated host-microbial interactions play critical roles in initiation and perpetuation of gut inflammation in Crohn's disease (CD). However, the spatial distribution and interaction network across the intestine and its accessory tissues are still elusive. Here, we profile the host proteins and tissue microbes in 540 samples from the intestinal mucosa, submucosa-muscularis-serosa, mesenteric adipose tissues, mesentery, and mesenteric lymph nodes of 30 CD patients and spatially decipher the host-microbial interactions. We observe aberrant antimicrobial immunity and metabolic processes across multi-tissues during CD and determine bacterial transmission along with altered microbial communities and ecological patterns. Moreover, we identify several candidate interaction pairs between host proteins and microbes associated with perpetuation of gut inflammation and bacterial transmigration across multi-tissues in CD. Signature alterations in host proteins (e.g., SAA2 and GOLM1) and microbes (e.g., Alistipes and Streptococcus) are further imprinted in serum and fecal samples as potential diagnostic biomarkers, thus providing a rationale for precision diagnosis.

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.

Identification and validation of immune-related biomarkers and potential regulators and therapeutic targets for diabetic kidney disease

BACKGROUND

Diabetic kidney disease (DKD) is a major complication of diabetes and the leading cause of end-stage renal disease worldwide. Renal inflammation and infiltration of immune cells contribute to the development and progression of DKD. Thus, the aim of the present study was to identify and validate immune-related biomarkers and analyze potential regulators including transcription factors (TFs), microRNAs (miRNAs), and drugs for DKD.

METHODS

Immune-related genes from the ImmPort database and glomeruli samples from GSE1009 and GSE30528 were used to identify differentially expressed immune-related genes (DEIRGs) of DKD. The expression level and clinical correlation analyses of DEIRGs were verified in the Nephroseq database. Murine podocytes were cultured to construct the high glucose-induced podocyte injury model. The reliability of the bioinformatics analysis was experimentally validated by RT-qPCR in podocytes. Networks among DEIRGs, regulators, and drugs were constructed to predict potential regulatory mechanisms for DKD.

RESULTS

DKD-associated DEIRGs were identified. CCL19 and IL7R were significantly upregulated in the DKD group and negatively correlated with glomerular filtration rate (GFR). GHR, FGF1, FYN, VEGFA, F2R, TGFBR3, PTGDS, FGF9, and SEMA5A were significantly decreased in the DKD group and positively correlated with GFR. RT-qPCR showed that the relative mRNA expression levels of GHR, FGF1, FYN, TGFBR3, PTGDS, FGF9, and SEMA5A were significantly down-regulated in the high glucose-induced podocyte injury group. The enriched regulators for DEIRGs included 110 miRNAs and 8 TFs. The abnormal expression of DEIRGs could be regulated by 16 established drugs.

CONCLUSIONS

This study identified immune-related biomarkers, regulators, and drugs of DKD. The findings of the present study provide novel insights into immune-related diagnosis and treatment of DKD.

A Potential Immune-Related miRNAs Regulatory Network and Corresponding Diagnostic Efficacy in Schizophrenia

PURPOSE

Immune-related pathways actively participate in the progression of schizophrenia (SCZ), however, roles of immune-related miRNAs in SCZ are still unclear.

METHODS

A microarray expression study was conducted to explored roles of immune-related genes in SCZ. Functional enrichment analysis by using "clusterProfiler" was used to identify molecular alterations of SCZ. Protein-protein interaction (PPI) network was constructed and helped core molecular factors identification. Based on The Cancer Genome Atlas (TCGA) database, clinical significances of hub immune-related genes in cancers were also been explored. Then, correlation analyses were used to determine immune-related miRNAs. We further validated that hsa-miR-1299 could be an effective diagnostic biomarker for SCZ via analyzing multi-cohorts' data and quantitative real-time PCR (qRT-PCR).

RESULTS

A total of 455 mRNAs and 70 miRNAs that were differentially expressed between SCZ and control samples. Functional enrichment analysis based on differentially expressed genes (DEGs) hinted that immune-related pathways were significantly correlated with SCZ. Furthermore, a total of 35 immune-related genes that involved in disease onset and showed significant co-expressed relationships. Hub immune-related gene CCL4 and CCL22 are valuable in tumor diagnosis and survival prediction. Furthermore, we also identified 22 immune-related miRNAs that play important roles in this disease. An immune-related miRNAs-mRNAs regulatory network was constructed to provide miRNAs regulatory roles in SCZ. Core miRNAs expression status of hsa-miR-1299 were also validated in another cohort, which suggested its diagnostic performance for SCZ.

CONCLUSIONS

Our study reports the downregulation of some miRNAs in the process of SCZ are important. Shared genomics characteristics between SCZ and cancers also provide novel insights for cancers. A significant alteration of hsa-miR-1299 expression is effective as biomarker for the diagnosis of SCZ, suggesting that this miRNA could be a specific biomarker.

Aggravated pneumonia and diabetes in SARS-CoV-2 infected diabetic mice

Multiple clinical and epidemiological studies have shown an interconnection between coronavirus disease 2019 (COVID-19) and diabetes, but experimental evidence is still lacking. Understanding the interplay between them is important because of the global health burden of COVID-19 and diabetes. We found that C57BL/6J mice were susceptible to the alpha strain of SARS-CoV-2. Moreover, diabetic C57BL/6J mice with leptin receptor gene deficiency (db/db mice) showed a higher viral load in the throat and lung and slower virus clearance in the throat after infection than C57BL/6J mice. Histological and multifactor analysis revealed more advanced pulmonary injury and serum inflammation in SARS-CoV-2 infected diabetic mice. Moreover, SARS-CoV-2 infected diabetic mice exhibited more severe insulin resistance and islet cell loss than uninfected diabetic mice. By RNA sequencing analysis, we found that diabetes may reduce the collagen level, suppress the immune response and aggravate inflammation in the lung after infection, which may account for the greater susceptibility of diabetic mice and their more severe lung damage after infection. In summary, we successfully established a SARS-CoV-2 infected diabetic mice model and demonstrated that diabetes and COVID-19 were risk factors for one another.

Comprehensive analysis of cuproptosis-related immune biomarker signature to enhance prognostic accuracy in gastric cancer

BACKGROUND

Gastric cancer (GC) is a malignant tumor with high prevalence and fatality. Cuproptosis is a recently identified copper-dependent programmed cell death mechanism. Multiple studies have demonstrated the profound impact of the immune microenvironment on tumor development. Hence, we decided to excavate the potential functional roles of cuproptosis-related immune genes (CRIGs) in GC and their values as biomarkers.

METHODS

Cuproptosis- and immune-related genes were curated from top published studies on cell cuproptosis and cellular immunity. Transcriptome data and clinical information were obtained from TCGA, GTEx, and GEO databases. Cox and LASSO analyses were used to establish a prognostic signature for GC. Long-term prognosis, immune infiltration, immune checkpoint, and drug response were compared between signature groups. CRIG expression in GC scRNA-seq was analyzed. Immunohistochemistry was used to evaluate CRIG and cuproptosis regulator FDX1 in GC tissues.

RESULTS

Seven CRIGs (ANOS1, CTLA4, ITGAV, CXCR4, NRP1, FABP3, and LGR6) were selected to establish a potent signature to forecast the long-term prognosis of patients. GC patients had worse prognosis and poor responses to chemotherapeutic drugs (5-Fluorouracil and paclitaxel) in the high-risk group. scRNA-seq revealed that CTLA4, ITGAV, CXCR4, and NRP1 enrichment in specific cell types regulated the progression of GC. Moreover, NRP1, CXCR4, LGR6, CTLA4, and FDX1 were elevated in GC tissues, with a positive correlation between their expression and FDX1.

CONCLUSIONS

To conclude, this study first provides insights into the functions of CRIGs in GC. Furthermore, a robust cuproptosis-related immune biomarker signature was established to forecast the long-term survival of GC patients accurately.

Identification of Breast Cancer Subtypes by Integrating Genomic Analysis with the Immune Microenvironment

Objectives: We aim to identify the breast cancer (BC) subtype clusters and the crucial gene classifier prognostic signatures by integrating genomic analysis with the tumor immune microenvironment (TME). Methods: Data sets of BC were derived from the Cancer Genome Atlas (TCGA), METABRIC, and Gene Expression Omnibus (GEO) databases. Unsupervised consensus clustering was carried out to obtain the subtype clusters of BC patients. Weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and univariate and multivariate regression analysis were employed to obtain the gene classifier signatures and their biological functions, which were validated by the BC dataset from the METABRIC database. Additionally, to evaluate the overall survival rates of BC patients, Kaplan-Meier survival analysis was carried out. Moreover, to assess how BC subtype clusters are related to the TME, single-cell analysis was performed. Finally, the drug sensitivity and the immune cell infiltration for different phenotypes of BC patients were also calculated by the CIBERSORT and ESTIMATE algorithms. Results : TCGA-BC samples were divided into three subtype clusters, S1, S2, and S3, among which the prognosis of S2 was poor and that of S1 and S3 were better. Three key pathways and 10 crucial prognostic-related gene signatures are screened. Finally, single-cell analysis suggests that S1 samples have the most types of immune cells, S2 with more sensitivity to tumor treatment drugs are enriched with more neutrophils, and more multilymphoid progenitor cells are involved in subtype cluster S3. Conclusions: Our novelty was to identify the BC subtype clusters and the gene classifier signatures employing a large-amount dataset combined with multiple bioinformatics methods. All of the results provide a basis for clinical precision treatment of BC.

Construction of an STK11 Mutation and Immune-Related Prognostic Prediction Model in Lung Adenocarcinoma

Background

STK11 mutation in LUAD affects immune cell infiltration in tumor tissue, and is associated with tumor prognosis.

Objective

This study aimed to construct a STK11 mutation and immune-related LUAD prognostic model.

Materials and Methods

The mutation frequency of STK11 in LUAD was queried via cBioPortal in TCGA and PanCancer Atlas databases. The degree of immune infiltration was analyzed by CIBERSORT analysis. DEGs in STK11mut and STK11wt samples were analyzed. Metascape, GO and KEGG methods were adopted for functional and signaling pathway enrichment analysis of DEGs. Genes related to immune were overlapped with DEGs to acquire immune-related DEGs, whose Cox regression and LASSO analyses were employed to construct prognostic model. Univariate and multivariate Cox regression analyses verified the independence of riskscore and clinical features. A nomogram was established to predict the OS of patients. Additionally, TIMER was introduced to analyze relationship between infiltration abundance of 6 immune cells and expression of feature genes in LUAD.

Results

The mutation frequency of STK11 in LUAD was 16%, and the degrees of immune cell infiltration were different between the wild-type and mutant STK11. DEGs of STK11 mutated and unmutated LUAD samples were mainly enriched in immune-related biological functions and signaling pathways. Finally, 6 feature genes were obtained, and a prognostic model was established. Riskscore was an independent immuno-related prognostic factor for LUAD. The nomogram diagram was reliable.

Conclusion

Collectively, genes related to STK11 mutation and immunity were mined from the public database, and a 6-gene prognostic prediction signature was generated.

RNA2Immune: A Database of Experimentally Supported Data Linking Non-coding RNA Regulation to The Immune System

Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as important regulators of the immune system and are involved in the control of immune cell biology, disease pathogenesis, as well as vaccine responses. A repository of ncRNA-immune associations will facilitate our understanding of ncRNA-dependent mechanisms in the immune system and advance the development of therapeutics and prevention for immune disorders. Here, we describe a comprehensive database, RNA2Immune, which aims to provide a high-quality resource of experimentally supported database linking ncRNA regulatory mechanisms to immune cell function, immune disease, cancer immunology, and vaccines. The current version of RNA2Immune documents 50,433 immune-ncRNA associations in 42 host species, including (1) 6690 ncRNA associations with immune functions involving 31 immune cell types; (2) 38,672 ncRNA associations with 348 immune diseases; (3) 4833 ncRNA associations with cancer immunology; and (4) 238 ncRNA associations with vaccine responses involving 26 vaccine types targeting 22 diseases. RNA2Immune provides a user-friendly interface for browsing, searching, and downloading ncRNA-immune system associations. Collectively, RNA2Immune provides important information about how ncRNAs influence immune cell function, how dysregulation of these ncRNAs leads to pathological consequences (immune diseases and cancers), and how ncRNAs affect immune responses to vaccines. RNA2Immune is available at http://bio-bigdata.hrbmu.edu.cn/rna2immune/home.jsp.

Immune infiltration and diagnostic value of immune-related genes in periodontitis using bioinformatics analysis

BACKGROUND AND OBJECTIVES

Periodontitis, which is a chronic inflammatory periodontal disease resulting in destroyed periodontal tissue, is the leading cause of tooth loss in adults. Many studies have found that inflammatory immune responses are involved in the risk of periodontal tissue damage. Therefore, we analyzed the association between immunity and periodontitis using bioinformatics methods to further understand this disease.

MATERIALS AND METHODS

First, the expression profiles of periodontitis and healthy samples were downloaded from the GEO database, including a training dataset GSE16134 and an external validation dataset GSE10334. Then, differentially expressed genes were identified using the limma package. Subsequently, immune cell infiltration was calculated by using the CIBERSORT algorithm. We further identified genes linking periodontitis and immunity from the ImmPort and DisGeNet databases. In addition, some of them were selected to construct a diagnostic model via a logistic stepwise regression analysis.

RESULTS AND CONCLUSIONS

Two hundred sixty differentially expressed genes were identified and found to be involved in responses to bacterial and immune-related processes. Subsequently, immune cell infiltration analysis demonstrates significant differences in the abundance of most immune cells between periodontitis and healthy samples, especially in plasma cells. These results suggested that immunity doses play a non-negligible role in periodontitis. Twenty-one genes linking periodontitis and immunity were further identified. And nine hub genes of them were identified that may be key genes involved in the development of periodontitis. Gene ontology analyses showed that these genes are involved in response to molecules of bacterial origin, cell chemotaxis, and response to chemokines. In addition, three genes of them were selected to construct a diagnostic model. And its good diagnostic performance was demonstrated by the receiver operating characteristic curves, with an area under the curve of 0.9424 for the training dataset and 0.9244 for the external validation dataset.

Identification through machine learning of potential immune- related gene biomarkers associated with immune cell infiltration in myocardial infarction

BACKGROUND

To investigate the potential role of immune-related genes (IRGs) and immune cells in myocardial infarction (MI) and establish a nomogram model for diagnosing myocardial infarction.

METHODS

Raw and processed gene expression profiling datasets were archived from the Gene Expression Omnibus (GEO) database. Differentially expressed immune-related genes (DIRGs), which were screened out by four machine learning algorithms-partial least squares (PLS), random forest model (RF), k-nearest neighbor (KNN), and support vector machine model (SVM) were used in the diagnosis of MI.

RESULTS

The six key DIRGs (PTGER2, LGR6, IL17B, IL13RA1, CCL4, and ADM) were identified by the intersection of the minimal root mean square error (RMSE) of four machine learning algorithms, which were screened out to establish the nomogram model to predict the incidence of MI by using the rms package. The nomogram model exhibited the highest predictive accuracy and better potential clinical utility. The relative distribution of 22 types of immune cells was evaluated using cell type identification, which was done by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. The distribution of four types of immune cells, such as plasma cells, T cells follicular helper, Mast cells resting, and neutrophils, was significantly upregulated in MI, while five types of immune cell dispersion, T cells CD4 naive, macrophages M1, macrophages M2, dendritic cells resting, and mast cells activated in MI patients, were significantly downregulated in MI.

CONCLUSION

This study demonstrated that IRGs were correlated with MI, suggesting that immune cells may be potential therapeutic targets of immunotherapy in MI.

Comparison of cell subsets in chronic obstructive pulmonary disease and controls based on single-cell transcriptome sequencing

BACKGROUND

Currently, chronic obstructive pulmonary disease (COPD) significantly impacts patients' quality of life and survival as it has a high morbidity and mortality rate. COPD progression is associated with infiltration of adaptive inflammatory immune cells that form lymphatic follicles into the lung.

OBJECTIVE

The rapid development of single-cell RNA sequencing technology (scRNA-seq) provided us with powerful tools for studying the classification of cell subtypes. Additionally, it is known that COPD is closely related to the abnormal function of long-chain non-coding RNAs (lncRNAs), and scRNA-seq can help to study the expression of lncRNA from a single cell level.

METHODS

We reanalyzed the scRNA-seq data of peripheral blood mononuclear cells of COPD patients downloaded from Gene Expression Omnibus (GEO) database, and performed the mRNA-based and lncRNA-based single cell clustering to compare the cell subsets in COPD and controls without COPD. Furthermore, we performed Gene Ontology (GO) enrichment analysis for the top ranked differentially expressed genes and target genes of differentially expressed lncRNAs in different cell subtypes for COPD and controls respectively.

RESULTS

Differences in cell subtypes were found between COPD and controls.

CONCLUSION

This study may help us to further understand the mechanism of the human adaptive immune cell response of COPD.

Study of the shared gene signatures of polyarticular juvenile idiopathic arthritis and autoimmune uveitis

Objective

To explore the shared gene signatures and potential molecular mechanisms of polyarticular juvenile idiopathic arthritis (pJIA) and autoimmune uveitis (AU).

Method

The microarray data of pJIA and AU from the Gene Expression Omnibus (GEO) database were downloaded and analyzed. The GEO2R tool was used to identify the shared differentially expressed genes (DEGs) and genes of extracellular proteins were identified among them. Then, weighted gene co-expression network analysis (WGCNA) was used to identify the shared immune-related genes (IRGs) related to pJIA and AU. Moreover, the shared transcription factors (TFs) and microRNAs (miRNAs) in pJIA and AU were acquired by comparing data from HumanTFDB, hTFtarget, GTRD, HMDD, and miRTarBase. Finally, Metascape and g: Profiler were used to carry out function enrichment analyses of previously identified gene sets.

Results

We found 40 up-regulated and 15 down-regulated shared DEGs via GEO2R. Then 24 shared IRGs in positivity-related modules, and 18 shared IRGs in negatively-related modules were found after WGCNA. After that, 3 shared TFs (ARID1A, SMARCC2, SON) were screened. And the constructed TFs-shared DEGs network indicates a central role of ARID1A. Furthermore, hsa-miR-146 was found important in both diseases. The gene sets enrichment analyses suggested up-regulated shared DEGs, TFs targeted shared DEGs, and IRGs positivity-correlated with both diseases mainly enriched in neutrophil degranulation process, IL-4, IL-13, and cytokine signaling pathways. The IRGs negatively correlated with pJIA and AU mainly influence functions of the natural killer cell, cytotoxicity, and glomerular mesangial cell proliferation. The down-regulated shared DEGs and TFs targeted shared DEGs did not show particular functional enrichment.

Conclusion

Our study fully demonstrated the flexibility and complexity of the immune system disorders involved in pJIA and AU. Neutrophil degranulation may be considered the shared pathogenic mechanism, and the roles of ARID1A and MiR-146a are worthy of further in-depth study. Other than that, the importance of periodic inspection of kidney function is also noteworthy.

Integrated bioinformatics analysis identifies shared immune changes between ischemic stroke and COVID 19

Although COVID-19 is primarily a respiratory disease, its neurological complications, such as ischemic stroke (IS), have aroused growing concerns and reports. However, the molecular mechanisms that underlie IS and COVID-19 are not well understood. Therefore, we implemented transcriptomic analysis from eight GEO datasets consist of 1191 samples to detect common pathways and molecular biomarkers in IS and COVID-19 that help understand the linkage between them. Differentially expressed genes (DEGs) were detected for IS and COVID-19 separately for finding shared mechanisms and we found that immune-related pathways were outlined with statistical significance. JAK2, which was identified as a hub gene, was supposed to be a potential therapeutic gene targets during the immunological process of COVID-19 and IS. Besides, we found a decrease in the proportion of CD8+ T and T helper 2 cells in the peripheral circulation of both COVID and IS patients, and NCR3 expression was significantly correlated with this change. In conclusion, we demonstrated that transcriptomic analyses reported in this study could make a deeper understanding of the common mechanism and might be promising for effective therapeutic for IS and COVID-19.

Development of an immune-related gene prognostic risk model and identification of an immune infiltration signature in the tumor microenvironment of colon cancer

BACKGROUND

Colon cancer is a common and highly malignant tumor. Its incidence is increasing rapidly with poor prognosis. At present, immunotherapy is a rapidly developing treatment for colon cancer. The aim of this study was to construct a prognostic risk model based on immune genes for early diagnosis and accurate prognostic prediction of colon cancer.

METHODS

Transcriptome data and clinical data were downloaded from the cancer Genome Atlas database. Immunity genes were obtained from ImmPort database. The differentially expressed transcription factors (TFs) were obtained from Cistrome database. Differentially expressed (DE) immune genes were identified in 473 cases of colon cancer and 41 cases of normal adjacent tissues. An immune-related prognostic model of colon cancer was established and its clinical applicability was verified. Among 318 tumor-related transcription factors, differentially expressed transcription factors were finally obtained, and a regulatory network was constructed according to the up-down regulatory relationship.

RESULTS

A total of 477 DE immune genes (180 up-regulated and 297 down-regulated) were detected. We developed and validated twelve immune gene models for colon cancer, including SLC10A2, FABP4, FGF2, CCL28, IGKV1-6, IGLV6-57, ESM1, UCN, UTS2, VIP, IL1RL2, NGFR. The model was proved to be an independent prognostic variable with good prognostic ability. A total of 68 DE TFs (40 up-regulated and 23 down-regulated) were obtained. The regulation network between TF and immune genes was plotted by using TF as source node and immune genes as target node. In addition, Macrophage, Myeloid Dendritic cell and CD4⁺ T cell increased with the increase of risk score.

CONCLUSION

We developed and validated twelve immune gene models for colon cancer, including SLC10A2, FABP4, FGF2, CCL28, IGKV1-6, IGLV6-57, ESM1, UCN, UTS2, VIP, IL1RL2, NGFR. This model can be used as a tool variable to predict the prognosis of colon cancer.

Epigenetically regulated lncRNAs dissect the intratumoural heterogeneity and facilitate immune evasion of glioblastomas

Background: Glioblastomas are the most common and malignant central nervous system (CNS) tumors that occupied a highly heterogeneous tumor microenvironment (TIME). Long noncoding RNAs (lncRNAs), whose expression can be modified by DNA methylation, are emerging as critical regulators in the immune system. However, knowledge about the epigenetic changes in lncRNAs and their contribution to the immune heterogeneity of glioma is still lacking. Methods: In this study, we integrated paired methylome and transcriptome datasets of glioblastomas and identified 2 robust immune subtypes based on lncRNA methylation features. The immune characteristics of glioma subtypes were compared. Furthermore, immune-related lncRNAs were identified and their relationships with immune evasion were evaluated. Results: Glioma immunophenotypes exhibited distinct immune-related characteristics as well as clinical and epigenetic features. 149 epigenetically regulated (ER) lncRNAs were recognized that possessed inverse variation in epigenetic and transcriptional levels between glioma subtypes. Immune-related lncRNAs were further identified through the investigation of their correlation with immune cell infiltrations and immune-related pathways. In particular, the 'Hot' glioma subtype with higher immunoactivity while a worse survival outcome was found to character immune evasion features. We finally prioritized candidate ER lncRNAs associated with immune evasion markers and response to glioma immunotherapy. Among them, CD109-AS1 and LINC02447 were validated as novel immunoevasive biomarkers for glioma through in vitro experiments. Conclusion: In summary, our study systematically reveals the crosstalk among DNA methylation, lncRNA, and immune regulation in glioblastomas, and will facilitate the development of epigenetic immunotherapy approaches.

Regulatory mechanism of immune-related genes in patients with hypertension

Hypertension (HT) is among the most common cardiovascular diseases in the world and is an important risk factor for stroke, myocardial infarction, heart failure, and kidney failure. Recent studies have demonstrated that activation of the immune system plays an important role in the occurrence and maintenance of HT. Thus, this research aimed to determine the immune-related biomarkers in HT. In this study, RNA sequencing data of the gene expression profiling datasets (GSE74144) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes between HT and normal samples were identified using the software limma. The immune-related genes associated with HT were screened. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the program "clusterProfiler" of the R package. The protein-protein interaction network of these differentially expressed immune-related genes (DEIRGs) was constructed based on the information from the STRING database. Finally, the TF-hub and miRNA-hub gene regulatory networks were predicted and constructed using the miRNet software. Fifty-nine DEIRGs were observed in HT. The Gene Ontology analysis indicated that DEIRGs were mainly enriched in the positive regulation of cytosolic calcium ions, peptide hormones, protein kinase B signaling, and lymphocyte differentiation. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these DEIRGs were significantly involved in the intestinal immune network for IgA production, autoimmune thyroid disease, JAK-STAT signaling pathway, hepatocellular carcinoma, and Kaposi sarcoma-associated herpesvirus infection, among others. From the protein-protein interaction network, 5 hub genes (insulin-like growth factor 2, cytokine-inducible Src homology 2-containing protein, suppressor of cytokine signaling 1, cyclin-dependent kinase inhibitor 2A, and epidermal growth factor receptor) were identified. The receiver operating characteristic curve analysis was performed in GSE74144, and all genes with an area under the curve of > 0.7 were identified as the diagnostic genes. Moreover, miRNA-mRNA and TF-mRNA regulatory networks were constructed. Our study identified 5 immune-related hub genes in patients with HT and demonstrated that they were potential diagnostic biomarkers for HT.

Common and rare variants of EGF increase the genetic risk of Alzheimer's disease as revealed by targeted sequencing of growth factors in Han Chinese

Alzheimer's disease (AD) is the most common neurodegenerative disease with high heritability. Growth factors (GFs) might contribute to the development of AD due to their broad effects on neuronal system. We herein aimed to investigate the role of rare and common variants of GFs in genetic susceptibility of AD. We screened 23 GFs in 6324 individuals using targeted sequencing. A rare-variant-based burden test and common-variant-based single-site association analyses were performed to identify AD-associated GF genes and variants. The burden test showed an enrichment of rare missense variants (p = 6.08 × 10^-4) in GF gene-set in AD patients. Among the GFs, EGF showed the strongest signal of enrichment, especially for loss-of-function variants (p = 0.0019). A common variant rs4698800 of EGF showed significant associations with AD risk (p = 3.24 × 10^-5, OR = 1.26). The risk allele of rs4698800 was associated with an increased EGF expression, whereas EGF was indeed upregulated in AD brain. These findings suggested EGF as a novel risk gene for AD.

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

Pan-cancer transcriptomic analysis identified six classes of immunosenescence genes revealed molecular links between aging, immune system and cancer

Aging is a complex process that significantly impacts the immune system. The aging-related decline of the immune system, termed immunosenescence, can lead to disease development, including cancer. The perturbation of immunosenescence genes may characterize the associations between cancer and aging. However, the systematical characterization of immunosenescence genes in pan-cancer remains largely unexplored. In this study, we comprehensively investigated the expression of immunosenescence genes and their roles in 26 types of cancer. We developed an integrated computational pipeline to identify and characterize immunosenescence genes in cancer based on the expression profiles of immune genes and clinical information of patients. We identified 2218 immunosenescence genes that were significantly dysregulated in a wide variety of cancers. These immunosenescence genes were divided into six categories based on their relationships with aging. Besides, we assessed the importance of immunosenescence genes in clinical prognosis and identified 1327 genes serving as prognostic markers in cancers. BTN3A1, BTN3A2, CTSD, CYTIP, HIF1AN, and RASGRP1 were associated with ICB immunotherapy response and served as prognostic factors after ICB immunotherapy in melanoma. Collectively, our results furthered the understanding of the relationship between immunosenescence and cancer and provided insights into immunotherapy for patients.

The Dynamic Linkage between Provirus Integration Sites and the Host Functional Genome Property Alongside HIV-1 Infections Associated with Antiretroviral Therapy

(1) Background: The HIV-1 latent reservoir harboring replication-competent proviruses is the major barrier in the quest for an HIV-1 infection cure. HIV-1 infection at all stages of disease progression is associated with immune activation and dysfunctional production of proinflammatory soluble factors (cytokines and chemokines), and it is expected that during HIV-1 infection, different immune components and immune cells, in turn, participate in immune responses, subsequently activating downstream biological pathways. However, the functional interaction between HIV-1 integration and the activation of host biological pathways is presently not fully understood. (2) Methods: In this work, I used genes targeted by proviruses from published datasets to seek enriched immunologic signatures and host biological pathways alongside HIV-1 infections based on MSigDb and KEGG over-representation analysis. (3) Results: I observed that different combinations of immunologic signatures of immune cell types and proinflammatory soluble factors appeared alongside HIV-1 infections associated with antiretroviral therapy. Moreover, enriched KEGG pathways were often related to "cancer specific types", "immune system", "infectious disease viral", and "signal transduction". (4) Conclusions: The observations in this work suggest that the gene sets harboring provirus integration sites may define specific immune cells and proinflammatory soluble factors during HIV-1 infections associated with antiretroviral therapy.

Identification of immune related gene signature for predicting prognosis of cholangiocarcinoma patients

Objective

To identify the gene subtypes related to immune cells of cholangiocarcinoma and construct an immune score model to predict the immunotherapy efficacy and prognosis for cholangiocarcinoma.

Methods

Based on principal component analysis (PCA) algorithm, The Cancer Genome Atlas (TCGA)-cholangiocarcinoma, GSE107943 and E-MTAB-6389 datasets were combined as Joint data. Immune genes were downloaded from ImmPort. Univariate Cox survival analysis filtered prognostically associated immune genes, which would identify immune-related subtypes of cholangiocarcinoma. Least absolute shrinkage and selection operator (LASSO) further screened immune genes with prognosis values, and tumor immune score was calculated for patients with cholangiocarcinoma after the combination of the three datasets. Kaplan-Meier curve analysis determined the optimal cut-off value, which was applied for dividing cholangiocarcinoma patients into low and high immune score group. To explore the differences in tumor microenvironment and immunotherapy between immune cell-related subtypes and immune score groups of cholangiocarcinoma.

Results

34 prognostic immune genes and three immunocell-related subtypes with statistically significant prognosis (IC1, IC2 and IC3) were identified. Among them, IC1 and IC3 showed higher immune cell infiltration, and IC3 may be more suitable for immunotherapy and chemotherapy. 10 immune genes with prognostic significance were screened by LASSO regression analysis, and a tumor immune score model was constructed. Kaplan-Meier (KM) and receiver operating characteristic (ROC) analysis showed that RiskScore had excellent prognostic prediction ability. Immunohistochemical analysis showed that 6 gene (NLRX1, AKT1, CSRP1, LEP, MUC4 and SEMA4B) of 10 genes were abnormal expressions between cancer and paracancer tissue. Immune cells infiltration in high immune score group was generally increased, and it was more suitable for chemotherapy. In GSE112366-Crohn's disease dataset, 6 of 10 immune genes had expression differences between Crohn's disease and healthy control. The area under ROC obtained 0.671 based on 10-immune gene signature. Moreover, the model had a sound performance in Crohn's disease.

Conclusion

The prediction of tumor immune score model in predicting immune microenvironment, immunotherapy and chemotherapy in patients with cholangiocarcinoma has shown its potential for indicating the effect of immunotherapy on patients with cholangiocarcinoma.

Neuroimmune transcriptome changes in patient brains of psychiatric and neurological disorders

Neuroinflammation has been implicated in multiple brain disorders but the extent and the magnitude of change in immune-related genes (IRGs) across distinct brain disorders has not been directly compared. In this study, 1275 IRGs were curated and their expression changes investigated in 2467 postmortem brains of controls and patients with six major brain disorders, including schizophrenia (SCZ), bipolar disorder (BD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer's disease (AD), and Parkinson's disease (PD). There were 865 IRGs present across all microarray and RNA-seq datasets. More than 60% of the IRGs had significantly altered expression in at least one of the six disorders. The differentially expressed immune-related genes (dIRGs) shared across disorders were mainly related to innate immunity. Moreover, sex, tissue, and putative cell type were systematically evaluated for immune alterations in different neuropsychiatric disorders. Co-expression networks revealed that transcripts of the neuroimmune systems interacted with neuronal-systems, both of which contribute to the pathology of brain disorders. However, only a few genes with expression changes were also identified as containing risk variants in genome-wide association studies. The transcriptome alterations at gene and network levels may clarify the immune-related pathophysiology and help to better define neuropsychiatric and neurological disorders.

A random forest-based metabolic risk model to assess the prognosis and metabolism-related drug targets in ovarian cancer

As one of the most common gynecologic malignant tumors, ovarian cancer is usually diagnosed at an advanced and incurable stage because of its early asymptomatic onset. Increasing research into tumor biology has demonstrated that abnormal cellular metabolism precedes tumorigenesis, therefore it has become an area of active research in academia. Cellular metabolism is of great significance in cancer diagnostic and prognostic studies. In this study, we integrated The Cancer Genome Atlas dataset with multiple Gene Expression Omnibus ovarian cancer datasets, identified 17 metabolic pathways with prognostic values using the random forest algorithm, constructed a metabolic risk scoring model based on metabolic pathway enrichment scores, and classified patients with ovarian cancer into two subtypes. Then, we systematically investigated the differences between different subtypes in terms of prognosis, differential gene expression, immune signature enrichment, Hallmark signature enrichment, and somatic mutations. As well, we successfully predicted differences in sensitivity to immunotherapy and chemotherapy drugs in patients with different metabolic risk subtypes. Moreover, we identified 5 drug targets associated with high metabolic risk and low metabolic risk ovarian cancer phenotypes through the weighted correlation network analysis and investigated their roles in the genesis of ovarian cancer. Finally, we developed an XGBoost classifier for predicting metabolic risk types in patients with ovarian cancer, producing a good predictive effect. In light of the above study, the research findings will provide valuable information for prognostic prediction and personalized medical treatment of patients with ovarian cancer.

Convergent transcriptomic and genomic evidence supporting a dysregulation of CXCL16 and CCL5 in Alzheimer's disease

BACKGROUND

Neuroinflammatory factors, especially chemokines, have been widely reported to be involved in the pathogenesis of Alzheimer's disease (AD). It is unclear how chemokines are altered in AD, and whether dysregulation of chemokines is the cause, or the consequence, of the disease.

METHODS

We initially screened the transcriptomic profiles of chemokines from publicly available datasets of brain tissues of AD patients and mouse models. Expression alteration of chemokines in the blood from AD patients was also measured to explore whether any chemokine might be used as a potential biomarker for AD. We further analyzed the association between the coding variants of chemokine genes and genetic susceptibility of AD by targeted sequencing of a Han Chinese case-control cohort. Mendelian randomization (MR) was performed to infer the causal association of chemokine dysregulation with AD development.

RESULTS

Three chemokine genes (CCL5, CXCL1, and CXCL16) were consistently upregulated in brain tissues from AD patients and the mouse models and were positively correlated with Aβ and tau pathology in AD mice. Peripheral blood mRNA expression of CXCL16 was upregulated in mild cognitive impairment (MCI) and AD patients, indicating the potential of CXCL16 as a biomarker for AD development. None of the coding variants within any chemokine gene conferred a genetic risk to AD. MR analysis confirmed a causal role of CCL5 dysregulation in AD mediated by trans-regulatory variants.

CONCLUSIONS

In summary, we have provided transcriptomic and genomic evidence supporting an active role of dysregulated CXCL16 and CCL5 during AD development.