Comprehensive transcriptomic characterization reveals core genes and module associated with immunological changes via 1619 samples of brain glioma

Indoleamine 2,3-dioxygenase-1 involves in CD8+T cell exhaustion in glioblastoma via regulating tryptophan levels

Indoleamine 2,3-dioxygenase-1 (IDO-1) is an enzyme that catalyzes the metabolism of tryptophan (Trp). It is expressed in limited amounts in normal tissues but significantly upregulated during inflammation and infection. Various inflammatory factors, especially IFN-γ, can induce the expression of IDO-1. While extensive research has been conducted on the role of IDO-1 in tumors, its specific role in complex central nervous system tumors such as glioblastoma (GBM) remains unclear. This study aims to explore the role of IDO-1 in the development of GBM and analyze its association with tryptophan levels and CD8+T cell exhaustion in the tumor region. To achieve this, we constructed an orthotopic mouse glioblastoma tumor model to investigate the specific mechanisms between IDO-1, GBM, and CD8+T cell exhaustion. Our results showed that IDO-1 can promote CD8+T cell exhaustion by reducing tryptophan levels. When IDO-1 was knocked down in glioblastoma cells, other cells within the tumor microenvironment upregulated IDO-1 expression to compensate for the loss and enhance immunosuppressive effects. Therefore, the data suggest that the GBM microenvironment controls tryptophan levels by regulating IDO-1 expression, which plays a critical role in immune suppression. These findings support the use of immune therapy in combination with IDO-1 inhibitors or tryptophan supplementation as a potential treatment strategy.

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

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

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors

Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm-1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm-1 and 1800 cm-1 and between two types of meningiomas in the range between 2700 cm-1 and 3000 cm-1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm-1 and 2836 cm-1 while for angiomatous and atypical meningiomas - 1514 cm-1 and 2875 cm-1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

GPX8+ cancer-associated fibroblast, as a cancer-promoting factor in lung adenocarcinoma, is related to the immunosuppressive microenvironment

BACKGROUND

Cancer-associated fibroblasts (CAFs) play a crucial role in the tumor microenvironment of lung adenocarcinoma (LUAD) and are often associated with poorer clinical outcomes. This study aimed to screen for CAF-specific genes that could serve as promising therapeutic targets for LUAD.

METHODS

We established a single-cell transcriptional profile of LUAD, focusing on genetic changes in fibroblasts. Next, we identified key genes associated with fibroblasts through weighted gene co-expression network analysis (WGCNA) and univariate Cox analysis. Then, we evaluated the relationship between glutathione peroxidase 8 (GPX8) and clinical features in multiple independent LUAD cohorts. Furthermore, we analyzed immune infiltration to shed light on the relationship between GPX8 immune microenvironment remodeling. For clinical treatment, we used the tumor immune dysfunction and exclusion (TIDE) algorithm to assess the immunotherapy prediction efficiency of GPX8. After that, we screened potential therapeutic drugs for LUAD by the connectivity map (cMAP). Finally, we conducted a cell trajectory analysis of GPX8+ CAFs to show their unique function.

RESULTS

Fibroblasts were found to be enriched in tumor tissues. Then we identified GPX8 as a key gene associated with CAFs through comprehensive bioinformatics analysis. Further analysis across multiple LUAD cohorts demonstrated the relationship between GPX8 and poor prognosis. Additionally, we found that GPX8 played a role in inducing the formation of an immunosuppressive microenvironment. The TIDE method indicated that patients with low GPX8 expression were more likely to be responsive to immunotherapy. Using the cMAP, we identified beta-CCP as a potential drug-related to GPX8. Finally, cell trajectory analysis provided insights into the dynamic process of GPX8+ CAFs formation.

CONCLUSIONS

This study elucidates the association between GPX8+ CAFs and poor prognosis, as well as the induction of immunosuppressive formation in LUAD. These findings suggest that targeting GPX8+ CAFs could potentially serve as a therapeutic strategy for the treatment of LUAD.

Disco interacting protein 2 homolog A (DIP2A): A key component in the regulation of brain disorders

Disco Interacting Protein 2 Homolog A (DIP2A) is expressed throughout the body and abundantly expressed in the brain tissue. It is activated by Follistatin-like 1 (FSTL1). Activated DIP2A interacts with several pathways, such as AMPK/mTOR and AKT pathways, to contribute to many biological processes, such as oxidative stress, transcriptional regulation, and apoptosis. Dysregulated DIP2A activation has been implicated in numerous processes in the brain. If the upstream pathways of DIP2A remain globally unexplored, many proteins, including cortactin, AMPK, and AKT, have been identified as its downstream targets in the literature. Recent studies have linked DIP2A to a variety of mechanisms in many types of brain disorders, suggesting that regulation of DIP2A could provide novel diagnostic and therapeutic approaches for brain disorders. In this review, we comprehensively summarized and discussed the current research on DIP2A in various brain disorders, such as stroke, autism spectrum disorders (ASD), Alzheimer's disease (AD), dyslexia, and glioma.

Optimized risk stratification strategy for glioma patients based on the feature genes of poor immune cell infiltration patterns

BACKGROUND

Gliomas, originating from glial cells within the brain or spinal cord, are common central nervous system tumors with varying degrees of malignancy that influence the complexity and difficulty of treatment. The current strategies, including traditional surgery, radiotherapy, chemotherapy, and emerging immunotherapies, have yielded limited results. As such, our study aims to optimize risk stratification for a more precise treatment approach. We primarily identify feature genes associated with poor immune cell infiltration patterns through various omics algorithms and categorize glioma patients based on these genes to enhance the accuracy of patient prognosis assessment. This approach can underpin individualized treatment strategies and facilitate the discovery of new therapeutic targets.

METHODS

We procured datasets of gliomas and normal brain tissues from TCGA, CGGA, and GTEx databases. Clustering was conducted using the input of 287 immune cell feature genes. Hub genes linked with the poor prognosis subtype (C1) were filtered through WGCNA. The TCGA dataset served as the discovery cohort and the CGGA dataset as the external validation cohort. We constructed a prognostic model related to feature genes from poor immune cell infiltration patterns utilizing LASSO-Cox regression. Comprehensive analyses of genomic heterogeneity, tumor stemness, pathway relevance, immune infiltration patterns, treatment response, and potential drugs were conducted for different risk groups. Gene expression validation was performed using immunohistochemistry (IHC) on 98 glioma samples and 11 normal brain tissue samples.

RESULTS

Using the filtered immune cell-related genes, glioma patients were stratified into C1 and C2 subtypes through clustering. The C1 subtype exhibited a worse prognosis, with upregulated genes primarily enriched in immune response, extracellular matrix, etc., and downregulated genes predominantly enriched in neural signal transduction and neural pathway-related aspects. Seven advanced algorithms were used to elucidate immune cell infiltration patterns of different subtypes. In addition, WGCNA identified hub genes from poor immune infiltration patterns, and a prognostic model was constructed accordingly. High-risk patients demonstrated shorter survival times and higher risk scores as compared to low-risk patients. Multivariate Cox regression analysis revealed that, after adjusting for confounding clinical factors, risk score was a vital independent predictor of overall survival (OS) (P < 0.001). The established nomogram, which combined risk scores with WHO grade and age, accurately predicted glioma patient survival rates at 1, 3, and 5 years, with AUCs of 0.908, 0.890, and 0.812, respectively. This risk score enhanced the nomogram's reliability and informed clinical decision-making. We also comprehensively analyzed genomic heterogeneity, tumor stemness, pathway relevance, immune infiltration patterns, treatment response, and potential drugs for different risk groups. In addition, we conducted preliminary validation of the potential PLSCR1 gene using IHC with a large sample of gliomas and normal brain tissues.

CONCLUSION

Our optimized risk stratification strategy for glioma patients has the potential to improve the accuracy of prognosis assessment. The findings from our omics research not only enhance the understanding of the functions of feature genes related to poor immune cell infiltration patterns but also offer valuable insights for the study of glioma prognostic biomarkers and the development of individualized treatment strategies.

Comprehensive analyses for the coagulation and macrophage-related genes to reveal their joint roles in the prognosis and immunotherapy of lung adenocarcinoma patients

Purpose

This study aims to explore novel biomarkers related to the coagulation process and tumor-associated macrophage (TAM) infiltration in lung adenocarcinoma (LUAD).

Methods

The macrophage M2-related genes were obtained by Weighted Gene Co-expression Network Analysis (WGCNA) in bulk RNA-seq data, while the TAM marker genes were identified by analyzing the scRNA-seq data, and the coagulation-associated genes were obtained from MSigDB and KEGG databases. Survival analysis was performed for the intersectional genes. A risk score model was subsequently constructed based on the survival-related genes for prognosis prediction and validated in external datasets.

Results

In total, 33 coagulation and macrophage-related (COMAR) genes were obtained, 19 of which were selected for the risk score model construction. Finally, 10 survival-associated genes (APOE, ARRB2, C1QB, F13A1, FCGR2A, FYN, ITGB2, MMP9, OLR1, and VSIG4) were involved in the COMAR risk score model. According to the risk score, patients were equally divided into low- and high-risk groups, and the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. The ROC curve indicated that the risk score model had high sensitivity and specificity, which was validated in multiple external datasets. Moreover, the model also had high efficacy in predicting the clinical outcomes of LUAD patients who received anti-PD-1/PD-L1 immunotherapy.

Conclusion

The COMAR risk score model constructed in this study has excellent predictive value for the prognosis and immunotherapeutic clinical outcomes of patients with LUAD, which provides potential biomarkers for the treatment and prognostic prediction.

The Development of Immunotherapy for the Treatment of Recurrent Glioblastoma

Recurrent glioblastoma (rGBM) is a highly aggressive form of brain cancer that poses a significant challenge for treatment in neuro-oncology, and the survival status of patients after relapse usually means rapid deterioration, thus becoming the leading cause of death among patients. In recent years, immunotherapy has emerged as a promising strategy for the treatment of recurrent glioblastoma by stimulating the body's immune system to recognize and attack cancer cells, which could be used in combination with other treatments such as surgery, radiation, and chemotherapy to improve outcomes for patients with recurrent glioblastoma. This therapy combines several key methods such as the use of monoclonal antibodies, chimeric antigen receptor T cell (CAR-T) therapy, checkpoint inhibitors, oncolytic viral therapy cancer vaccines, and combination strategies. In this review, we mainly document the latest immunotherapies for the treatment of glioblastoma and especially focus on rGBM.

Myeloid cell heterogeneity in the tumor microenvironment and therapeutic implications for childhood central nervous system (CNS) tumors

Central nervous system (CNS) tumors are the most common type of solid tumors in children and the leading cause of cancer deaths in ages 0-14. Recent advances in the field of tumor biology and immunology have underscored the disparate nature of these distinct CNS tumor types. In this review, we briefly introduce pediatric CNS tumors and discuss various components of the TME, with a particular focus on myeloid cells. Although most studies regarding myeloid cells have been done on adult CNS tumors and animal models, we discuss the role of myeloid cell heterogeneity in pediatric CNS tumors and describe how these cells may contribute to tumorigenesis and treatment response. In addition, we present studies within the last 5 years that highlight human CNS tumors, the utility of various murine CNS tumor models, and the latest multi-dimensional tools that can be leveraged to investigate myeloid cell infiltration in young adults and children diagnosed with select CNS tumors.

Co-expression of fibrotic genes in inflammatory bowel disease; A localized event?

Introduction

Extracellular matrix turnover, a ubiquitous dynamic biological process, can be diverted to fibrosis. The latter can affect the intestine as a serious complication of Inflammatory Bowel Diseases (IBD) and is resistant to current pharmacological interventions. It embosses the need for out-of-the-box approaches to identify and target molecular mechanisms of fibrosis.

Methods and results

In this study, a novel mRNA sequencing dataset of 22 pairs of intestinal biopsies from the terminal ileum (TI) and the sigmoid of 7 patients with Crohn's disease, 6 with ulcerative colitis and 9 control individuals (CI) served as a validation cohort of a core fibrotic transcriptomic signature (FIBSig), This signature, which was identified in publicly available data (839 samples from patients and healthy individuals) of 5 fibrotic disorders affecting different organs (GI tract, lung, skin, liver, kidney), encompasses 241 genes and the functional pathways which derive from their interactome. These genes were used in further bioinformatics co-expression analyses to elucidate the site-specific molecular background of intestinal fibrosis highlighting their involvement, particularly in the terminal ileum. We also confirmed different transcriptomic profiles of the sigmoid and terminal ileum in our validation cohort. Combining the results of these analyses we highlight 21 core hub genes within a larger single co-expression module, highly enriched in the terminal ileum of CD patients. Further pathway analysis revealed known and novel inflammation-regulated, fibrogenic pathways operating in the TI, such as IL-13 signaling and pyroptosis, respectively.

Discussion

These findings provide a rationale for the increased incidence of fibrosis at the terminal ileum of CD patients and highlight operating pathways in intestinal fibrosis for future evaluation with mechanistic and translational studies.

Epidemiological characteristics and genetic alterations in adult diffuse glioma in East Asian populations

Understanding the racial specificities of diseases-such as adult diffuse glioma, the most common primary malignant tumor of the central nervous system-is a critical step toward precision medicine. Here, we comprehensively review studies of gliomas in East Asian populations and other ancestry groups to clarify the racial differences in terms of epidemiology and genomic characteristics. Overall, we observed a lower glioma incidence in East Asians than in Whites; notably, patients with glioblastoma had significantly younger ages of onset and longer overall survival than the Whites. Multiple genome-wide association studies of various cohorts have revealed single nucleotide polymorphisms associated with overall and subtype-specific glioma susceptibility. Notably, only 3 risk loci-5p15.33, 11q23.3, and 20q13.33-were shared between patients with East Asian and White ancestry, whereas other loci predominated only in particular populations. For instance, risk loci 12p11.23, 15q15-21.1, and 19p13.12 were reported in East Asians, whereas risk loci 8q24.21, 1p31.3, and 1q32.1 were reported in studies in White patients. Although the somatic mutational profiles of gliomas between East Asians and non-East Asians were broadly consistent, a lower incidence of EGFR amplification in glioblastoma and a higher incidence of 1p19q-IDH-TERT triple-negative low-grade glioma were observed in East Asian cohorts. By summarizing large-scale disease surveillance, germline, and somatic genomic studies, this review reveals the unique characteristics of adult diffuse glioma among East Asians, to guide clinical management and policy design focused on patients with East Asian ancestry.

Transcriptome profile and clinical characterization of ICOS expression in gliomas

Inducible co-stimulator (ICOS), an immune costimulatory molecule, has been found to play an essential role across various malignancies. This study investigated the transcriptome profile and clinical characterization of ICOS in gliomas. Clinical information and transcriptome data of 301 glioma samples were downloaded from the Chinese Glioma Genome Atlas (CGGA) dataset for analysis (CGGA301 cohort). Furthermore, the results were validated in 697 samples with RNAseq data from the TCGA glioma dataset and 325 gliomas with RNAseq data from the CGGA325 dataset. Immunohistochemistry was performed to evaluate ICOS protein expression across different WHO grades in a tissue microarray (TMA). In addition, single-cell sequencing data from CGGA and GSE 163108 datasets were used to analyze the ICOS expression across different cell types. Statistical analyses and figure production were performed with R-language. We found that ICOS was significantly upregulated in higher-grade, IDH wild type, and mesenchymal subtype of gliomas. Functional enrichment analyses revealed that ICOS was mainly involved in glioma-related immune response. Moreover, ICOS showed a robust correlation with other immune checkpoints, including the PD1/PD-L1/PD-L2 pathway, CTLA4, ICOSL (ICOS ligand), and IDO1. Subsequent Tumor Immune Dysfunction and Exclusion (TIDE) analysis revealed that GBM patients with higher ICOS expression seemed to be more sensitive to ICB therapy. Furthermore, based on seven clusters of metagenes, GSVA identified that ICOS was tightly associated with HCK, LCK, MHC-I, MHC-II, STAT1, and interferon, especially with LCK, suggesting a strong correlation between ICOS and T-cell activity in gliomas. In cell lineage analysis, Higher-ICOS gliomas tended to recruit dendritic cells, monocytes, and macrophages into the tumor microenvironment. Single-cell sequencing analysis indicated that ICOS was highly expressed by regulatory T cells (Tregs), especially in mature Tregs. Finally, patients with higher ICOS had shortened survival. ICOS was an independent prognosticator for glioma patients. In conclusion, higher ICOS is correlated with more malignancy of gliomas and is significantly associated with Treg activity among glioma-related immune responses. Moreover, ICOS could contribute as an independent prognostic factor for gliomas. Our study highlights the role of ICOS in glioma and may facilitate therapeutic strategies targeting ICOS for glioma.

Molecular markers related to patient outcome in patients with IDH-mutant astrocytomas grade 2 to 4: A systematic review

BACKGROUND

Grading and classification of IDH-mutant astrocytomas has shifted from solely histology towards histology combined with molecular diagnostics. In this systematic review, we give an overview of all currently known clinically relevant molecular markers within IDH-mutant astrocytomas grade 2 to 4.

METHODS

A literature search was performed in five electronic databases for English original papers on patient outcome with respect to a molecular marker as determined by DNA/RNA sequencing, micro-arrays, or DNA methylation profiling in IDH-mutant astrocytomas grade 2 to 4. Papers were included if molecular diagnostics were performed on tumour tissue of at least 15 IDH-mutant astrocytoma patients, and if the investigated molecular markers were not limited to the diagnostic markers MGMT, ATRX, TERT, and/or TP53.

RESULTS

The literature search identified 4508 unique articles, published between August 2012 and December 2021, of which ultimately 44 articles were included. Numerous molecular markers from these papers were significantly correlated to patient outcome. The associations between patient outcome and non-canonical IDH mutations, PI3K mutations, high expression of MSH2, high expression of RAD18, homozygous deletion of CDKN2A/B, amplification of PDGFRA, copy number neutral loss of chromosomal arm 17p, loss of chromosomal arm 19q, the G-CIMP-low DNA methylation cluster, high total CNV, and high tumour mutation burden were confirmed in multiple studies.

CONCLUSIONS

Multiple genetic and epigenetic markers are associated with survival in IDH-mutant astrocytoma patients. Commonly affected are the RB signalling pathway, the RTK-PI3K-mTOR signalling pathway, genomic stability markers, and (epigenetic) gene regulation.

Dominant Myocardial Fibrosis and Complex Immune Microenvironment Jointly Shape the Pathogenesis of Arrhythmogenic Right Ventricular Cardiomyopathy

Background

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable life-threatening myocardial disease characterized by ventricular arrhythmias and sudden cardiac death. Few studies used RNA-sequencing (RNA-seq) technology to analyze gene expression profiles, hub genes, dominant pathogenic processes, immune microenvironment in ARVC. This study aimed to explore these questions via integrated bioinformatics analysis.

Methods

RNA-sequencing datasets of GSE107475, GSE107311, GSE107156, and GSE107125 were obtained from the Gene Expression Omnibus database, including right and left ventricular myocardium from ARVC patients and normal controls. Weighted gene co-expression network analysis identified the ARVC hub modules and genes. Functional enrichment and protein-protein interaction analysis were performed by Metascape and STRING. Single-sample gene-set enrichment analysis (ssGSEA) was applied to assess immune cell infiltration. Transcription regulator (TF) analysis was performed by TRRUST.

Results

Three ARVC hub modules with 25 hub genes were identified. Functional enrichment analysis of the hub genes indicated that myocardial fibrosis was the dominant pathogenic process. Higher myocardial fibrosis activity existed in ARVC than in normal controls. A complex immune microenvironment was discovered that type 2 T helper cell, type 1 T helper cell, regulatory T cell, plasmacytoid dendritic cell, neutrophil, mast cell, central memory CD4 T cell, macrophage, CD56dim natural killer cell, myeloid-derived suppressor cell, memory B cell, natural killer T cell, and activated CD8 T cell were highly infiltrated in ARVC myocardium. The immune-related hub module was enriched in immune processes and inflammatory disease pathways, with hub genes including CD74, HLA-DRA, ITGAM, CTSS, CYBB, and IRF8. A positive linear correlation existed between immune cell infiltration and fibrosis activity in ARVC. NFKB1 and RELA were the shared TFs of ARVC hub genes and immune-related hub module genes, indicating the critical role of NFκB signaling in both mechanisms. Finally, the potential lncRNA-miRNA-mRNA interaction network for ARVC hub genes was constructed.

Conclusion

Myocardial fibrosis is the dominant pathogenic process in end-stage ARVC patients. A complex immune microenvironment exists in the diseased myocardium of ARVC, in which T cell subsets are the primary category. A tight relationship exists between myocardial fibrosis activity and immune cell infiltration. NFκB signaling pathway possibly contributes to both mechanisms.

Sp1 induced gene TIMP1 is related to immune cell infiltration in glioblastoma

Tumor immune microenvironment exerts a profound effect on the population of infiltrating immune cells. Tissue inhibitor of matrix metalloproteinase 1 (TIMP1) is frequently overexpressed in a variety of cells, particularly during inflammation and tissue injury. However, its function in cancer and immunity remains enigmatic. In this study, we find that TIMP1 is substantially up-regulated during tumorigenesis through analyzing cancer bioinformatics databases, which is further confirmed by IHC tissue microarrays of clinical samples. The TIMP1 level is significantly increased in lymphocytes infiltrating the tumors and correlated with cancer progression, particularly in GBM. Notably, we find that the transcriptional factor Sp1 binds to the promoter of TIMP1 and triggers its expression in GBM. Together, our findings suggest that the Sp1-TIMP1 axis can be a potent biomarker for evaluating immune cell infiltration at the tumor sites and therefore, the malignant progression of GBM.

Glioma-Associated Microglia Characterization in the Glioblastoma Microenvironment through a 'Seed-and Soil' Approach: A Systematic Review

Background and aim: Ever since the discovery of tumor-associated immune cells, there has been growing interest in the understanding of the mechanisms underlying the crosstalk between these cells and tumor cells. A "seed and soil" approach has been recently introduced to describe the glioblastoma (GBM) landscape: tumor microenvironments act as fertile "soil" and interact with the "seed" (glial and stem cells compartment). In the following article, we provide a systematic review of the current evidence pertaining to the characterization of glioma-associated macrophages and microglia (GAMs) and microglia and macrophage cells in the glioma tumor microenvironment (TME). Methods: An online literature search was launched on PubMed Medline and Scopus using the following research string: "((Glioma associated macrophages OR GAM OR Microglia) AND (glioblastoma tumor microenvironment OR TME))". The last search for articles pertinent to the topic was conducted in February 2022. Results: The search of the literature yielded a total of 349 results. A total of 235 studies were found to be relevant to our research question and were assessed for eligibility. Upon a full-text review, 58 articles were included in the review. The reviewed papers were further divided into three categories based on their focus: (1) Microglia maintenance of immunological homeostasis and protection against autoimmunity; (2) Microglia crosstalk with dedifferentiated and stem-like glioblastoma cells; (3) Microglia migratory behavior and its activation pattern. Conclusions: Aggressive growth, inevitable recurrence, and scarce response to immunotherapies are driving the necessity to focus on the GBM TME from a different perspective to possibly disentangle its role as a fertile 'soil' for tumor progression and identify within it feasible therapeutic targets. Against this background, our systematic review confirmed microglia to play a paramount role in promoting GBM progression and relapse after treatments. The correct and extensive understanding of microglia-glioma crosstalk could help in understanding the physiopathology of this complex disease, possibly opening scenarios for improvement of treatments.

APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance

BACKGROUND

Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions.

METHODS

Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review.

FINDINGS

APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10-69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness.

INTERPRETATION

APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO).

FUNDING

National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).