Comprehensive Analysis of PD-1 Gene Expression, Immune Characteristics and Prognostic Significance in 1396 Glioma Patients

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

BACKGROUND

Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM.

METHODS

A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients' survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023.

RESULTS

From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts.

CONCLUSION

We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.

Treatment advances in high-grade gliomas

High-grade gliomas (HGG) pose significant challenges in modern tumour therapy due to the distinct biological properties and limitations of the blood-brain barrier. This review discusses recent advancements in HGG treatment, particularly in the context of immunotherapy and cellular therapy. Initially, treatment strategies focus on targeting tumour cells guided by the molecular characteristics of various gliomas, encompassing chemotherapy, radiotherapy and targeted therapy for enhanced precision. Additionally, technological enhancements are augmenting traditional treatment modalities. Furthermore, immunotherapy, emphasising comprehensive tumour management, has gained widespread attention. Immune checkpoint inhibitors, vaccines and CAR-T cells exhibit promising efficacy against recurrent HGG. Moreover, emerging therapies such as tumour treating fields (TTFields) offer additional treatment avenues for patients with HGG. The combination of diverse treatments holds promise for improving the prognosis of HGG, particularly in cases of recurrence.

IDH mutation, glioma immunogenicity, and therapeutic challenge of primary mismatch repair deficient IDH-mutant astrocytoma PMMRDIA: a systematic review

In 2021, Suwala et al. described Primary Mismatch Repair Deficient IDH-mutant Astrocytoma (PMMRDIA) as a distinct group of gliomas. In unsupervised clustering, PMMRDIA forms distinct cluster, separate from other IDH-mutant gliomas, including IDH-mutant gliomas with secondary mismatch repair (MMR) deficiency. In the published cohort, three patients received treatment with an immune checkpoint blocker (ICB), yet none exhibited a response, which aligns with existing knowledge about the decreased immunogenicity of IDH-mutant gliomas in comparison to IDH-wildtype. In the case of PMMRDIA, the inherent resistance to the standard-of-care temozolomide caused by MMR deficiency is an additional challenge. It is known that a gain-of-function mutation of IDH1/2 genes produces the oncometabolite R-2-hydroxyglutarate (R-2-HG), which increases DNA and histone methylation contributing to the characteristic glioma-associated CpG island methylator phenotype (G-CIMP). While other factors could be involved in remodeling the tumor microenvironment (TME) of IDH-mutant gliomas, this systematic review emphasizes the role of R-2-HG and the subsequent G-CIMP in immune suppression. This highlights a potential actionable pathway to enhance the response of ICB, which might be relevant for addressing the unmet therapeutic challenge of PMMRDIA.

Insights into the regulatory role of RNA methylation modifications in glioma

Epitranscriptomic abnormalities, which are highly prevalent in primary central nervous system malignancies, have been identified as crucial contributors to the development and progression of gliomas. RNA epitranscriptomic modifications, particularly the reversible modification methylation, have been observed throughout the RNA cycle. Epitranscriptomic modifications, which regulate RNA transcription and translation, have profound biological implications. These modifications are associated with the development of several cancer types. Notably, three main protein types-writers, erasers, and readers, in conjunction with other related proteins, mediate these epitranscriptomic changes. This review primarily focuses on the role of recently identified RNA methylation modifications in gliomas, such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), N7-methylguanosine (m7G), and N1-methyladenosine (m1A). We delved into their corresponding writers, erasers, readers, and related binding proteins to propose new approaches and prognostic indicators for patients with glioma.

CCR8 as a Therapeutic Novel Target: Omics-Integrated Comprehensive Analysis for Systematically Prioritizing Indications

Target identification is a crucial process in drug development, aiming to identify key proteins, genes, and signal pathways involved in disease progression and their relevance in potential therapeutic interventions. While C-C chemokine receptor 8 (CCR8) has been investigated as a candidate anti-cancer target, comprehensive multi-omics analyzes across various indications are limited. In this study, we conducted an extensive bioinformatics analysis integrating genomics, proteomics, and transcriptomics data to establish CCR8 as a promising anti-cancer drug target. Our approach encompassed data collection from diverse knowledge resources, gene function analysis, differential gene expression profiling, immune cell infiltration assessment, and strategic prioritization of target indications. Our findings revealed strong correlations between CCR8 and specific cancers, notably Breast Invasive Carcinoma (BRCA), Colon Adenocarcinoma (COAD), Head and Neck Squamous Cell Carcinoma (HNSC), Rectum adenocarcinoma (READ), Stomach adenocarcinoma (STAD), and Thyroid carcinoma (THCA). This research advances our understanding of CCR8 as a potential target for anti-cancer drug development, bridging the gap between molecular insights and creating opportunities for personalized treatment of solid tumors.

In vivo tracking of adenoviral-transduced iron oxide-labeled bone marrow-derived dendritic cells using magnetic particle imaging

BACKGROUND

Despite widespread study of dendritic cell (DC)-based cancer immunotherapies, the in vivo postinjection fate of DC remains largely unknown. Due in part to a lack of quantifiable imaging modalities, this is troubling as the amount of DC migration to secondary lymphoid organs correlates with therapeutic efficacy. Magnetic particle imaging (MPI) has emerged as a suitable modality to quantify in vivo migration of superparamagnetic iron oxide (SPIO)-labeled DC. Herein, we describe a popliteal lymph node (pLN)-focused MPI scan to quantify DC in vivo migration accurately and consistently.

METHODS

Adenovirus (Ad)-transduced SPIO+ (Ad SPIO+) and SPIO+ C57BL/6 bone marrow-derived DC were generated and assessed for viability and phenotype, then fluorescently labeled and injected into mouse hind footpads (n = 6). Two days later, in vivo DC migration was quantified using whole animal, pLN-focused, and ex vivo pLN MPI scans.

RESULTS

No significant differences in viability, phenotype and in vivo pLN migration were noted for Ad SPIO+ and SPIO+ DC. Day 2 pLN-focused MPI quantified DC migration in all instances while whole animal MPI only quantified pLN migration in 75% of cases. Ex vivo MPI and fluorescence microscopy confirmed that pLN MPI signal was due to originally injected Ad SPIO+ and SPIO+ DC.

CONCLUSION

We overcame a reported limitation of MPI by using a pLN-focused MPI scan to quantify pLN-migrated Ad SPIO+ and SPIO+ DC in 100% of cases and detected as few as 1000 DC (4.4 ng Fe) in vivo. MPI is a suitable preclinical imaging modality to assess DC-based cancer immunotherapeutic efficacy.

RELEVANCE STATEMENT

Tracking the in vivo fate of DC using noninvasive quantifiable magnetic particle imaging can potentially serve as a surrogate marker of therapeutic effectiveness.

KEY POINTS

• Adenoviral-transduced and iron oxide-labeled dendritic cells are in vivo migration competent. • Magnetic particle imaging is a suitable modality to quantify in vivo dendritic cell migration. • Magnetic particle imaging focused field of view overcomes dynamic range limitation.

Dual-sgRNA CRISPR/Cas9 knockout of PD-L1 in human U87 glioblastoma tumor cells inhibits proliferation, invasion, and tumor-associated macrophage polarization

Programmed death ligand 1 (PD-L1) plays a key role in glioblastoma multiforme (GBM) immunosuppression, vitality, proliferation, and migration, and is therefore a promising target for treating GBM. CRISPR/Cas9-mediated genomic editing can delete both cell surface and intracellular PD-L1. This systemic deliverable genomic PD-L1 deletion system can be used as an effective anti-GBM therapy by inhibiting tumor growth and migration, and overcoming immunosuppression. To target PD-L1 for CRISPR/Cas9 gene editing, we first identified two single guide RNA (sgRNA) sequences located on PD-L1 exon 3. The first sgRNA recognizes the forward strand of human PD-L1 near the beginning of exon 3 that allows editing by Cas9 at approximately base pair 82 (g82). The second sgRNA recognizes the forward strand of exon 3 that directs cutting at base pair 165 (g165). A homology-directed repair template (HDR) combined with the dual-sgRNAs was used to improve PD-L1 knockout specificity and efficiency. sgRNAs g82 and g165 were cloned into the multiplex CRISPR/Cas9 assembly system and co-transfected with the HDR template in human U87 GBM cells (g82/165 + HDR). T7E1 analysis suggests that the dual-sgRNA CRISPR/Cas9 strategy with a repair template was capable of editing the genomic level of PD-L1. This was further confirmed by examining PD-L1 protein levels by western blot and immunofluorescence assays. Western blot analysis showed that the dual-sgRNAs with the repair template caused a 64% reduction of PD-L1 protein levels in U87 cells, while immunostaining showed a significant reduction of intracellular PD-L1. PD-L1 deletion inhibited proliferation, growth, invasion and migration of U87 cells, indicating intracellular PD-L1 is necessary for tumor progression. Importantly, U87 cells treated with g82/165 + HDR polarized tumor-associated macrophages (TAM) toward an M1 phenotype, as indicated by an increase in TNF-α and a decrease in IL-4 secretions. This was further confirmed with flow cytometry that showed an increase in the M1 markers Ly6C + and CD80 +, and a decrease in the M2 marker CD206 + both in vitro and in vivo. Utilizing dual-sgRNAs and an HDR template with the CRISPR/Cas9 gene-editing system is a promising avenue for the treatment of GBM.

Predicting Diagnostic Gene Biomarkers Associated With Immune Checkpoints, N6-Methyladenosine, and Ferroptosis in Patients With Acute Myocardial Infarction

This study aimed to determine early diagnosis genes of acute myocardial infarction (AMI) and then validate their association with ferroptosis, immune checkpoints, and N6-methyladenosine (m6A), which may provide a potential method for the early diagnosis of AMI. Firstly, we downloaded microarray data from NCBI (GSE61144, GSE60993, and GSE42148) and identified differentially expressed genes (DEGs) in samples from healthy subjects and patients with AMI. Also, we performed systematic gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used STRING to predict protein interactions. Moreover, MCC and MCODE algorithms in the cytoHubba plug-in were used to screen nine key genes in the network. We then determined the diagnostic significance of the nine obtained DEGs by plotting receiver operating characteristic curves using a multiscale curvature classification algorithm. Meanwhile, we investigated the relationship between AMI and immune checkpoints, ferroptosis, and m6A. In addition, we further validated the key genes through the GSE66360 dataset and consequently obtained nine specific genes that can be used as early diagnosis biomarkers for AMI. Through screening, we identified 210 DEGs, including 53 downregulated and 157 upregulated genes. According to GO, KEGG, and key gene screening results, FPR1, CXCR1, ELANE, TLR2, S100A12, TLR4, CXCL8, FPR2 and CAMP could be used for early prediction of AMI. Finally, we found that AMI was associated with ferroptosis, immune checkpoints, and m6A and FPR1, CXCR1, ELANE, TLR2, S100A12, TLR4, CXCL8, FPR2 and CAMP are effective markers for the diagnosis of AMI, which can provide new prospects for future studies on the pathogenesis of AMI.

Multiparametric MRI Radiomics for the Early Prediction of Response to Chemoradiotherapy in Patients With Postoperative Residual Gliomas: An Initial Study

Purpose

To evaluate whether multiparametric magnetic resonance imaging (MRI)-based logistic regression models can facilitate the early prediction of chemoradiotherapy response in patients with residual brain gliomas after surgery.

Patients and Methods

A total of 84 patients with residual gliomas after surgery from January 2015 to September 2020 who were treated with chemoradiotherapy were retrospectively enrolled and classified as treatment-sensitive or treatment-insensitive. These patients were divided into a training group (from institution 1, 57 patients) and a validation group (from institutions 2 and 3, 27 patients). All preoperative and postoperative MR images were obtained, including T1-weighted (T1-w), T2-weighted (T2-w), and contrast-enhanced T1-weighted (CET1-w) images. A total of 851 radiomics features were extracted from every imaging series. Feature selection was performed with univariate analysis or in combination with multivariate analysis. Then, four multivariable logistic regression models derived from T1-w, T2-w, CET1-w and Joint series (T1+T2+CET1-w) were constructed to predict the response of postoperative residual gliomas to chemoradiotherapy (sensitive or insensitive). These models were validated in the validation group. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were applied to compare the predictive performances of these models.

Results

Four models were created and showed the following areas under the ROC curves (AUCs) in the training and validation groups: Model-Joint series (AUC, 0.923 and 0.852), Model-T1 (AUC, 0.835 and 0.809), Model-T2 (AUC, 0.784 and 0.605), and Model-CET1 (AUC, 0.805 and 0.537). These results indicated that the Model-Joint series had the best performance in the validation group, followed by Model-T1, Model-T2 and finally Model-CET1. The calibration curves indicated good agreement between the Model-Joint series predictions and actual probabilities. Additionally, the DCA curves demonstrated that the Model-Joint series was clinically useful.

Conclusion

Multiparametric MRI-based radiomics models can potentially predict tumor response after chemoradiotherapy in patients with postoperative residual gliomas, which may aid clinical decision making, especially to help patients initially predicted to be treatment-insensitive avoid the toxicity of chemoradiotherapy.