A pyroptosis-related signature predicts prognosis and indicates immune microenvironment infiltration in glioma

Identification of an Immune signature assisted prognosis, and immunotherapy prediction for IDH wildtype glioblastoma

IDH-wildtype glioblastoma (GBM) is the most common and malignant primary brain tumor. The purpose of this study is to establish a prognostic gene signature for IDH-wildtype GBM. RNA sequencing data of normal brain tissue and GBM patients were obtained from TCGA, CGGA, GEO and the GTEx databases. Identification of prognostic differentially expressed genes (DEGs) with | log2 fold change | > 0.5 and adjust p < 0.05 in TCGA and CGGA databases by "limma" method. By LASSO regression analysis and multivariate Cox analysis, a 3-gene prognostic signature composed of FMOD, MXRA5 and RAB36 was established. The 3-gene prognostic risk model is validated by TCGA and GSE43378 datasets. The expression of FMOD, MXRA5 and RAB36 in GBM patients was significantly higher than that in normal brain tissues in CCGA, TCGA and GSE29796 data sets. In order to further verify this result, total RNA was extracted from tumors and paracancerous tissues of 9 GBM patients. RT-PCR results showed that the expression of FMOD, MXRA5 and RAB36 in tumor tissues of most patients was higher than that in paracancerous tissues. The results of GSEA showed that the pathway enrichment of the 3-gene signature was mainly related to tumor immunity. Immune cell infiltration analyzed by ssGSEA showed that there were significant differences in macrophages between high- and low-risk groups. Immune checkpoint genes correlation analysis showed that PD-L1 gene expression is closely related to risk score. Our study identifies a prognostic-associated risk model and provides a potential effective immunotherapy target for IDH-wildtype GBM patients.

A cancer-associated fibroblasts related risk score (CAFscore) helps to guide prognosis and personal treatment for Glioblastoma

BACKGROUND

Recent studies have identified the presence of cancer-associated fibroblasts (CAFs) within glioblastoma (GBM), yet their biological roles and underlying mechanisms remain poorly understood. This study aimed to construct a CAF-related prognostic model to guide patient prognosis and treatment strategies.

METHOD

We employed various bioinformatics methods, including enrichment analysis, Weighted Gene Co-expression Network Analysis (WGCNA), Lasso regression analysis, and machine learning techniques such as XGBoost and Random Forest, to develop a novel risk index termed CAFscore. Patients were stratified into high and low CAFscore groups for subsequent survival analysis. The area under the curve (AUC) and concordance index (C-index) for CAFscore were calculated and compared against other clinical characteristics and existing prognostic models. Drug sensitivity assessments were conducted using the Oncopredict package. Functional validation of key genes was performed through scratch and invasion assays in GBM cells.

RESULTS

Our analyses revealed four core CAF-related genes, leading to the establishment of CAFscore. Notably, patients in the high CAFscore group exhibited significantly reduced survival and exhibited enrichment in epithelial-mesenchymal transition (EMT) and inflammation response pathways. Furthermore, CAFscore showed a significant negative correlation with the sensitivity to irinotecan and its analogs, while demonstrating a positive correlation with sensitivity to 505,124 (a TGFβRI inhibitor). LRP10 emerged as a central gene within the CAFscore, displaying markedly elevated expression in GBM and a strong association with CAF infiltration. Silencing LRP10 significantly inhibited the invasive capabilities of GBM cells.

CONCLUSION

This study presented the first CAF related prognostic model (CAFscore) in GBM, and demonstrated that the model could effectively guide patient prognosis and potentially inform personalized treatment strategies. The core gene of CAFscore, LRP10, was significantly overexpressed in GBM and might play a pivotal role in regulating CAF infiltration as well as tumor invasion and metastasis, highlighting LRP10 as a promising therapeutic target for GBM management.

Identification and validation of an ECM organization-related gene signature as a prognostic biomarker and therapeutic target for glioma patients

BACKGROUND

Glioma is the most common and devastating form of malignant brain tumor, with a poor prognosis. Extracellular matrix (ECM) organization is a crucial determinant of glioma invasion and progression. However, the clinical significance of ECM organization in glioma patients remains unclear.

OBJECTIVE

To evaluate the prognostic value of ECM organization-related genes in glioma patients and identify potential therapeutic targets.

METHODS

Bulk RNA-sequencing and corresponding clinical data for patients with glioma were downloaded from TCGA and GEO databases. Differentially expressed ECM organization genes were identified, and an ECM organization-related gene prognostic model was then generated. Furthermore, the prognostic model has validated in the Chinese Glioma Genome Atlas (CGGA) dataset. The role of TIMP1 in glioma cells by using various functional assays revealed their underlying mechanism in vitro.

RESULTS

We identified and validated a nine-gene signature (TIMP1, SERPINE1, PTX3, POSTN, PLOD3, PDPN, LOXL1, ITGA2, and COL8A1) related to ECM organization as a robust prognostic biomarker for glioma. Time-dependent ROC curve analysis confirmed the specificity and sensitivity of the signature. The signature was closely related to an immunosuppressive phenotype, and its combination with immune checkpoints served as a good predictor for patients' clinical outcomes. Notably, single-cell RNA sequencing analysis revealed high expression of TIMP1 in astrocytes and oligodendrocyte progenitor cells in glioma patients. Last, we show that TIMP1 regulates glioma cell growth and invasion via the AKT/GSK3β signaling pathway.

CONCLUSION

This study provides promising insights into predicting glioma prognosis and identifying a potential therapeutic target in TIMP1.

A novel model based on necroptosis-related genes for predicting immune status and prognosis in glioma

Background

Glioma is a highly aggressive brain cancer with a poor prognosis. Necroptosis is a form of programmed cell death occurring during tumor development and in immune microenvironments. The prognostic value of necroptosis in glioma is unclear. This study aimed to develop a prognostic glioma model based on necroptosis.

Methods

A necroptosis-related risk model was constructed by Cox regression analysis based on The Cancer Genome Atlas (TCGA) training set, validated in two Chinese Glioma Genome Atlas (CGGA) validation sets. We explored the differences in immune infiltration and immune checkpoint genes between low and high risk groups and constructed a nomogram. Moreover, we compiled a third validation cohort including 43 glioma patients. The expression of necroptosis-related genes was verified in matched tissues using immunochemical staining in the third cohort, and we analyzed their relationship to clinicopathological features.

Results

Three necroptosis-related differentially expressed genes (EZH2, LEF1, and CASP1) were selected to construct the prognostic model. Glioma patients with a high risk score in the TCGA and CGGA cohorts had significantly shorter overall survival. The necroptosis-related risk model and nomogram exhibited good predictive performance in the TCGA training set and the CGGA validation sets. Furthermore, patients in the high risk group had higher immune infiltration status and higher expression of immune checkpoint genes, which was positively correlated with poorer outcomes. In the third validation cohort, the expression levels of the three proteins encoded by EZH2, LEF1, and CASP1 in glioma tissues were significantly higher than those from paracancerous tissues. They were also closely associated with disease severity and prognosis.

Conclusions

Our necroptosis-related risk model can be used to predict the prognosis of glioma patients and improve prognostic accuracy, which may provide potential therapeutic targets and a theoretical basis for treatment.