Expression Profile Analysis Identifies a Novel Seven Immune-Related Gene Signature to Improve Prognosis Prediction of Glioblastoma

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.

Prognostic Significance of mRNA Expression RBBP8 or Its Methylation in Gliomas

Retinoblastoma-binding protein 8 (RBBP8) affects the prognosis of patients with malignancies through various mechanisms. However, its function in gliomas is unknown. Our study explored the effects of RBBP8 on the prognosis of glioma patients, as well as its regulatory role in the glioma immune microenvironment. We used various bioinformatics methods to analyze the transcriptional profiles and methylation data of RBBP8 in gliomas from multiple databases. Our results showed that the mRNA and protein expression of RBBP8 in gliomas was higher than that in normal tissues and positively correlated with malignant clinical features such as age and WHO grade. A Kaplan-Meier analysis showed that patients with high RBBP8 expression had a poor prognosis. Cox regression demonstrated that RBBP8 was an independent risk indicator and had good diagnostic value for the poor prognosis of glioma. Importantly, RBBP8 was positively correlated with many well-known immune checkpoints (e.g., CTLA4 and PDL-1). Finally, a gene set enrichment analysis revealed that RBBP8 was remarkably enriched in cancer-related pathways such as cell cycle, DNA replication and so on. In conclusion, this study is the first to elaborate on the value of RBBP8 in the pathological process of glioma for anti-tumor immunotherapy. In addition, the expression of RBBP8 and its methylation site, cg05513509, may provide potential targets for glioma therapy.

Identification of SSBP1 as a ferroptosis-related biomarker of glioblastoma based on a novel mitochondria-related gene risk model and in vitro experiments

Background

Glioblastoma (GBM) is the most common primary malignant brain tumor that leads to lethality. Several studies have demonstrated that mitochondria play an important role in GBM and that mitochondria-related genes (MRGs) are potential therapeutic targets. However, the role of MRGs in GBM remains unclear.

Methods

Differential expression and univariate Cox regression analyses were combined to screen for prognostic differentially-expressed (DE)-MRGs in GBM. Based on LASSO Cox analysis, 12 DE-MRGs were selected to construct a risk score model. Survival, time dependent ROC, and stratified analyses were performed to evaluate the performance of this risk model. Mutation and functional enrichment analyses were performed to determine the potential mechanism of the risk score. Immune cell infiltration analysis was used to determine the association between the risk score and immune cell infiltration levels. CCK-8 and transwell assays were performed to evaluate cell proliferation and migration, respectively. Mitochondrial reactive oxygen species (ROS) levels and morphology were measured using a confocal laser scanning microscope. Genes and proteins expression levels were investigated by quantitative PCR and western blotting, respectively.

Results

We identified 21 prognostic DE-MRGs, of which 12 DE-MRGs were selected to construct a prognostic risk score model for GBM. This model presented excellent performance in predicting the prognosis of patients with GBM and acted as an independent predictive factor. Functional enrichment analysis revealed that the risk score was enriched in the inflammatory response, extracellular matrix, and pro-cancer-related and immune related pathways. Additionally, the risk score was significantly associated with gene mutations and immune cell infiltration in GBM. Single-stranded DNA-binding protein 1 (SSBP1) was considerably upregulated in GBM and associated with poor prognosis. Furthermore, SSBP1 knockdown inhibited GBM cell progression and migration. Mechanistically, SSBP1 knockdown resulted in mitochondrial dysfunction and increased ROS levels, which, in turn, increased temozolomide (TMZ) sensitivity in GBM cells by enhancing ferroptosis.

Conclusion

Our 12 DE-MRGs-based prognostic model can predict the GBM patients prognosis and 12 MRGs are potential targets for the treatment of GBM. SSBP1 was significantly upregulated in GBM and protected U87 cells from TMZ-induced ferroptosis, which could serve as a prognostic and therapeutic target/biomarker for GBM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03657-4.

Bioinformatics analysis and identification of hub genes and immune-related molecular mechanisms in chronic myeloid leukemia

BACKGROUND

Chronic myeloid leukemia (CML) is a malignant hyperplastic tumor of the bone marrow originating from pluripotent hematopoietic stem cells. The advent of tyrosine kinase inhibitors (TKIs) has greatly improved the survival rate of patients with CML. However, TKI-resistance leads to the disease recurrence and progression. This study aimed to identify immune-related genes (IRGs) associated with CML progression.

METHODS

We extracted the gene's expression profiles from the Gene Expression Omnibus (GEO). Bioinformatics analysis was used to determine the differentially expressed IRGs of CML and normal peripheral blood mononuclear cells (PBMCs). Functional enrichment and gene set enrichment analysis (GSEA) were used to explore its potential mechanism. Hub genes were identified using Molecular Complex Detection (MCODE) and the CytoHubba plugin. The hub genes' diagnostic value was evaluated using the receiver operating characteristic (ROC). The relative proportions of infiltrating immune cells in each CML sample were evaluated using CIBERSORT. Quantitative real-time PCR (RT-qPCR) was used to validate the hub gene expression in clinical samples.

RESULTS

A total of 31 differentially expressed IRGs were identified. GO analyses revealed that the modules were typically enriched in the receptor ligand activity, cytokine activity, and endopeptidase activity. KEGG enrichment analysis of IRGs revealed that CML involved Th17 cell differentiation, the NF-kappa B signaling pathway, and cytokine-cytokine receptor interaction. A total of 10 hub genes were selected using the PPI network. GSEA showed that these hub genes were related to the gamma-interferon immune response, inflammatory response, and allograft rejection. ROC curve analysis suggested that six hub genes may be potential biomarkers for CML diagnosis. Further analysis indicated that immune cells were associated with the pathogenesis of CML. The RT-qPCR results showed that proteinase 3 (PRTN3), cathepsin G (CTSG), matrix metalloproteinase 9 (MMP9), resistin (RETN), eosinophil derived neurotoxin (RNase2), eosinophil cationic protein (ECP, RNase3) were significantly elevated in CML patients' PBMCs compared with healthy controls.

CONCLUSION

These results improved our understanding of the functional characteristics and immune-related molecular mechanisms involved in CML progression and provided potential diagnostic biomarkers and therapeutic targets.

Prognostic Gene Expression Signature in Patients With Distinct Glioma Grades

Background

Glioma is the most common type of primary brain tumor in adults. Patients with the most malignant form have an overall survival time of <16 months. Although considerable progress has been made in defining the adapted therapeutic strategies, measures to counteract tumor escape have not kept pace, due to the developed resistance of malignant glioma. In fact, identifying the nature and role of distinct tumor-infiltrating immune cells in glioma patients would decipher potential mechanisms behind therapy failure.

Methods

We integrated into our study glioma transcriptomic datasets from the Cancer Genome Atlas (TCGA) cohort (154 GBM and 516 LGG patients). LM22 immune signature was built using CIBERSORT. Hierarchical clustering and UMAP dimensional reduction algorithms were applied to identify clusters among glioma patients either in an unsupervised or supervised way. Furthermore, differential gene expression (DGE) has been performed to unravel the top expressed genes among the identified clusters. Besides, we used the least absolute shrinkage and selection operator (LASSO) and Cox regression algorithm to set up the most valuable prognostic factor.

Results

Our study revealed, following gene enrichment analysis, the presence of two distinct groups of patients. The first group, defined as cluster 1, was characterized by the presence of immune cells known to exert efficient antitumoral immune response and was associated with better patient survival, whereas the second group, cluster 2, which exhibited a poor survival, was enriched with cells and molecules, known to set an immunosuppressive pro-tumoral microenvironment. Interestingly, we revealed that gene expression signatures were also consistent with each immune cluster function. A strong presence of activated NK cells was revealed in cluster 1. In contrast, potent immunosuppressive components such as regulatory T cells, neutrophils, and M0/M1/M2 macrophages were detected in cluster 2, where, in addition, inhibitory immune checkpoints, such as PD-1, CTLA-4, and TIM-3, were also significantly upregulated. Finally, Cox regression analysis further corroborated that tumor-infiltrating cells from cluster 2 exerted a significant impact on patient prognosis.

Conclusion

Our work brings to light the tight implication of immune components on glioma patient prognosis. This would contribute to potentially developing better immune-based therapeutic approaches.

Comprehensive Analysis of CD163 as a Prognostic Biomarker and Associated with Immune Infiltration in Glioblastoma Multiforme

Background

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignancy in adults with high aggression. The prognosis of GBM patients is poor. There is a critical need for novel biomarkers for the prognosis and therapy of GBM.

Methods

Differentially expressed genes (DEGs) in GBM were screened using TCGA cohort. Univariate and multivariate Cox regression analyses were performed on DEGs to identify the optimal prognosis-related genes. qRT-PCR was performed to verify the result.

Results

A total of 5216 DEGs, including 2785 upregulated and 2458 downregulated genes, were obtained. Enrichment analysis revealed that these DEGs were mainly involved in the p53 signaling pathway and cell cycle, immune response, and MAPK signaling pathways. Moreover, the top 50 DEGs were associated with drug resistance or drug sensitivity. Prognosis analysis revealed that GBM patients with a high expression of CD163 and CHI3L2 had a poor overall survival, prognosis-free survival, and disease-specific survival. The univariate and multivariate analyses revealed that CD163 and age were independent factors affecting the prognosis of GBM patients. A validation study revealed that CD163 was upregulated in GBM tissues and associated with poor overall survival. Moreover, further analysis revealed that CD163 showed significant correlation with immune cells, immune biomarkers, chemokines, and chemokine receptors. We also identified several CD163-associated kinase, miRNA, and transcription factor targets in GBM, including LCK, miR-483, and ELF1.

Conclusions

In conclusion, our study suggested CD163 as a prognostic biomarker and associated it with immune infiltration in GBM.