Promoting Prognostic Model Application: A Review Based on Gliomas

Exploring the role of Disulfidptosis in glioma progression: insights into tumor heterogeneity and therapeutic potential through single-cell RNA sequencing

BACKGROUND

Gliomas, particularly glioblastoma (GBM), are the most common and aggressive primary brain tumors in adults, characterized by high malignancy and frequent recurrence. Despite standard treatments, including surgery, radiotherapy, and chemotherapy, the prognosis for GBM remains poor, with a median survival of less than 15 months and a five-year survival rate below 10%. Tumor heterogeneity and resistance to treatment create significant challenges in controlling glioma progression. Therefore, there is an urgent need for new therapeutic targets and strategies.

OBJECTIVE

This study investigates the role of Disulfidptosis, a recently discovered form of programmed cell death, in gliomas. Unlike apoptosis and necrosis, Disulfidptosis is driven by the abnormal accumulation of intracellular disulfide bonds, leading to protein misfolding and cytoskeletal collapse, particularly in cancer cells with metabolic dysregulation. We aim to explore how glioma cells respond to Disulfidptosis and identify potential therapeutic targets by analyzing the heterogeneity of gliomas at the single-cell level using single-cell RNA sequencing (scRNA-seq).

METHODS

scRNA-seq data from glioma patients were analyzed to uncover differences in ferroptosis-related pathways, including iron metabolism and lipid peroxidation. Cellular subpopulations within gliomas were profiled to assess their sensitivity to Disulfidptosis and the underlying mechanisms. Survival analysis was conducted to evaluate the clinical relevance of Disulfidptosis-related gene expression.

RESULTS

Multiple cell subpopulations within gliomas exhibit varying sensitivities to Disulfidptosis, influenced by their metabolic properties. Dysregulated iron metabolism and antioxidant mechanisms were identified as key factors impacting Disulfidptosis sensitivity. Glioma microenvironment signaling pathways also play a role in regulating Disulfidptosis. These findings suggest that activating Disulfidptosis pathways may provide novel therapeutic strategies to overcome treatment resistance in gliomas.

CONCLUSION

This study offers new insights into the role of Disulfidptosis in glioma progression and highlights its potential as a therapeutic target. By leveraging single-cell sequencing data, the research uncovers tumor heterogeneity and identifies specific cell populations resistant to Disulfidptosis. These findings may pave the way for personalized treatment strategies to improve survival outcomes in glioma patients.

Identification of Key Immune and Cell Cycle Modules and Prognostic Genes for Glioma Patients through Transcriptome Analysis

BACKGROUND

Gliomas, the most prevalent type of primary brain tumor, stand out as one of the most aggressive and lethal types of human cancer.

METHODS & RESULTS

To uncover potential prognostic markers, we employed the weighted correlation network analysis (WGCNA) on the Chinese Glioma Genome Atlas (CGGA) 693 dataset to reveal four modules significantly associated with glioma clinical traits, primarily involved in immune function, cell cycle regulation, and ribosome biogenesis. Using the least absolute shrinkage and selection operator (LASSO) regression algorithm, we identified 11 key genes and developed a prognostic risk score model, which exhibits precise prognostic prediction in the CGGA 325 dataset. More importantly, we also validated the model in 12 glioma patients with overall survival (OS) ranging from 4 to 132 months using mRNA sequencing and immunohistochemical analysis. The analysis of immune infiltration revealed that patients with high-risk scores exhibit a heightened immune infiltration, particularly immune suppression cells, along with increased expression of immune checkpoints. Furthermore, we explored potentially effective drugs targeting 11 key genes for gliomas using the library of integrated network-based cellular signatures (LINCS) L1000 database, identifying that in vitro, both torin-1 and clofarabine exhibit promising anti-glioma activity and inhibitory effect on the cell cycle, a significant pathway enriched in the identified glioma modules.

CONCLUSIONS

In conclusion, our study provides valuable insights into molecular mechanisms and identifying potential therapeutic targets for gliomas.

Identification of prognostic risk score of disulfidptosis-related genes and molecular subtypes in glioma

Background

Programmed cell death is closely related to glioma. As a novel kind of cell death, the mechanism of disulfidptosis in glioma remains unclear. Therefore, it is of great importance to study the role of disulfidptosis-related genes (DRGs) in glioma.

Methods

We first investigated the genetic and transcriptional alterations of 15 DRGs. Two consensus cluster analyses were used to evaluate the association between DRGs and glioma subtypes. In addition, we constructed prognostic DRG risk scores to predict overall survival (OS) in glioma patients. Furthermore, we developed a nomogram to enhance the clinical utility of the DRG risk score. Finally, the expression levels of DRGs were verified by immunohistochemistry (IHC) staining.

Results

Most DRGs (14/15) were dysregulated in gliomas. The 15 DRGs were rarely mutated in gliomas, and only 50 of 987 samples (5.07 %) showed gene mutations. However, most of them had copy number variation (CNV) deletions or amplifications. Two distinct molecular subtypes were identified by cluster analysis, and DRG alterations were found to be related to the clinical characteristics, prognosis, and tumor immune microenvironment (TIME). The DRG risk score model based on 12 genes was developed and showed good performance in predicting OS. The nomogram confirmed that the risk score had a particularly strong influence on the prognosis of glioma. Furthermore, we discovered that low DRG scores, low tumor mutation burden, and immunosuppression were features of patients with better prognoses.

Conclusion

The DRG risk model can be used for the evaluation of clinical characteristics, prognosis prediction, and TIME estimation of glioma patients. These DRGs may be potential therapeutic targets in glioma.

Identification of the Roles of Coagulation-related Signature and its Key Factor RABIF in Hepatoma Cell Malignancy

BACKGROUND

Hepatoma is a high morbidity and mortality cancer, and coagulation is a potential oncogenic mechanism for hepatoma development.

OBJECTIVE

In this study, we aimed to reveal the role of coagulation in hepatoma.

METHODS

We applied the LASSO to construct a coagulation-related risk score (CRS) and a clinical nomogram with independent validation. The heterogeneity of various aspects, including functional enrichment, SNV, CN, immunocyte infiltration, immune pathways, immune checkpoint, and genomic instability indexes, was evaluated. Besides, the prognostic value of the CRS genes was tested. We selected the critical risky gene related to coagulation from the LASSO coefficients, for which we applied transwell and clone formation assays to confirm its roles in hepatoma cell migration and clone formation ability, respectively.

RESULTS

The CRS and the nomogram predicted patients' survival with good accuracy in both two datasets. The high-CRS group was associated with higher cell cycle, DNA repair, TP53 mutation rates, amplification, and lower deletion rates at chromosome 1. For immunocyte infiltration, we noticed increased Treg infiltration and globally upregulated immune checkpoints and genomic instability indexes. Additionally, every single CRS gene affected the patient's survival. Finally, we observed that RABIF was the riskiest gene in the CRS. Its knockdown suppressed hepatoma cell migration and clone formation capability, which could be rescued by RABIF overexpression.

CONCLUSION

We built a robust CRS with great potential as a prognosis and immunotherapeutic indicator. Importantly, we identified RABIF as an oncogene, promoting hepatoma cell migration and clone formation, revealing underlying pathological mechanisms, and providing novel therapeutic targets for hepatoma treatment.

Nuclear mitochondria-related genes-based molecular classification and prognostic signature reveal immune landscape, somatic mutation, and prognosis for glioma

Background

Glioma is the most frequent malignant primary brain tumor, and mitochondria may influence the progression of glioma. The aim of this study was to analyze the role of nuclear mitochondria related genes (MTRGs) in glioma, identify subtypes and construct a prognostic model based on nuclear MTRGs and machine learning algorithms.

Methods

Samples containing both gene expression profiles and clinical information were retrieved from the TCGA database, CGGA database, and GEO database. We selected 16 nuclear MTRGs and identified two clusters of glioma. Prognostic features, microenvironment, mutation landscape, and drug sensitivity were compared between the clusters. A prognostic model based on multiple machine learning algorithms was then constructed and validated by multiple datasets.

Results

We observed significant discrepancies between the two clusters. Cluster One had higher nuclear MTRG expression, a lower survival rate, and higher immune infiltration than Cluster Two. For the two clusters, we found distinct predictive drug sensitivities and responses to immune therapy, and the infiltration of immune cells was significantly different. Among the 22 combinations of machine learning algorithms we tested, LASSO was the most effective in constructing the prognostic model. The model's accuracy was further verified in three independent glioma datasets. We identified MGME1 as a vital gene associated with infiltrating immune cells in multiple types of tumors.

Conclusion

In short, our research identified two clusters of glioma and developed a dependable prognostic model based on machine learning methods. MGME1 was identified as a potential biomarker for multiple tumors. Our results will contribute to precise medicine and glioma management.

Pan-cancer and single-cell analysis reveal the prognostic value and immune response of NQO1

Background: Overexpression of the NAD(P)H: Quinone Oxidoreductase 1 (NQOI) gene has been linked with tumor progression, aggressiveness, drug resistance, and poor patient prognosis. Most research has described the biological function of the NQO1 in certain types and limited samples, but a comprehensive understanding of the NQO1's function and clinical importance at the pan-cancer level is scarce. More research is needed to understand the role of NQO1 in tumor infiltration, and immune checkpoint inhibitors in various cancers are needed. Methods: The NQO1 expression data for 33 types of pan-cancer and their association with the prognosis, pathologic stage, gender, immune cell infiltration, the tumor mutation burden, microsatellite instability, immune checkpoints, enrichment pathways, and the half-maximal inhibitory concentration (IC50) were downloaded from public databases. Results: Our findings indicate that the NQO1 gene was significantly upregulated in most cancer types. The Cox regression analysis showed that overexpression of the NQO1 gene was related to poor OS in Glioma, uveal melanoma, head and neck squamous cell carcinoma, kidney renal papillary cell carcinoma, and adrenocortical carcinoma. NQO1 mRNA expression positively correlated with infiltrating immune cells and checkpoint molecule levels. The single-cell analysis revealed a potential relationship between the NQO1 mRNA expression levels and the infiltration of immune cells and stromal cells in bladder urothelial carcinoma, invasive breast carcinoma, and colorectal cancer. Conversely, a negative association was noted between various drugs (17-AAG, Lapatinib, Trametinib, PD-0325901) and the NQO1 mRNA expression levels. Conclusion: NQO1 expression was significantly associated with prognosis, immune infiltrates, and drug resistance in multiple cancer types. The inhibition of the NQO1-dependent signaling pathways may provide a promising strategy for developing new cancer-targeted therapies.

Prognostic and predictive impact of abnormal signal volume evolution early after chemoradiotherapy in glioblastoma

INTRODUCTION

This study was designed to explore the feasibility of semiautomatic measurement of abnormal signal volume (ASV) in glioblastoma (GBM) patients, and the predictive value of ASV evolution for the survival prognosis after chemoradiotherapy (CRT).

METHODS

This retrospective trial included 110 consecutive patients with GBM. MRI metrics, including the orthogonal diameter (OD) of the abnormal signal lesions, the pre-radiation enhancement volume (PRRCE), the volume change rate of enhancement (rCE), and fluid attenuated inversion recovery (rFLAIR) before and after CRT were analyzed. Semi-automatic measurements of ASV were done through the Slicer software.

RESULTS

In logistic regression analysis, age (HR = 2.185, p = 0.012), PRRCE (HR = 0.373, p < 0.001), post CE volume (HR = 4.261, p = 0.001), rCE_1m (HR = 0.519, p = 0.046) were the significant independent predictors of short overall survival (OS) (< 15.43 months). The areas under the receiver operating characteristic curve (AUCs) for predicting short OS with rFLAIR_3m and rCE_1m were 0.646 and 0.771, respectively. The AUCs of Model 1 (clinical), Model 2 (clinical + conventional MRI), Model 3 (volume parameters), Model 4 (volume parameters + conventional MRI), and Model 5 (clinical + conventional MRI + volume parameters) for predicting short OS were 0.690, 0.723, 0.877, 0.879, 0.898, respectively.

CONCLUSION

Semi-automatic measurement of ASV in GBM patients is feasible. The early evolution of ASV after CRT was beneficial in improving the survival evaluation after CRT. The efficacy of rCE_1m was better than that of rFLAIR_3m in this evaluation.

Glioblastoma glycolytic signature predicts unfavorable prognosis, immunological heterogeneity, and ENO1 promotes microglia M2 polarization and cancer cell malignancy

Glioblastomas are the most malignant brain tumors, whose progress was promoted by aberrate aerobic glycolysis. The immune environment was highly engaged in glioblastoma formation, while its interaction with aerobic glycolysis remained unclear. Herein, we build a 7-gene Glycolytic Score (GS) by Elastic Net in the training set and two independent validating sets. The GS predicted malignant features and poor survival with good performances. Immune functional analyses and Cibersort calculation identified depressed T cells, B cells, natural killer cells immunity, and high immunosuppressive cell infiltration in the high-GS group. Also, high expressions of the immune-escape genes were discovered. Subsequently, the single-cell analyses validated the glycolysis-related immunosuppression. The functional results manifested the high-GS neoplastic cells' association with T cells, NK cells, and macrophage function regulation. The intercellular cross-talk showed strong associations between high-GS neoplastic cells and M2 macrophages/microglia in several immunological pathways. We finally confirmed that ENO1, the key gene of the GS, promoted M2 microglia polarization and glioblastoma cell malignant behaviors via immunofluorescence, clone formation, CCK8, and transwell rescue experiments. These results indicated the interactions between cancerous glycolysis and immunosuppression and glycolysis' role in promoting glioblastoma progression. Conclusively, we built a robust model and discovered strong interaction between GS and immune, shedding light on prognosis management improvement and therapeutic strategies development for glioblastoma patients.

Development of prognostic indicator based on NAD+ metabolism related genes in glioma

Background

Studies have shown that Nicotinamide adenine dinucleotide (NAD+) metabolism can promote the occurrence and development of glioma. However, the specific effects and mechanisms of NAD+ metabolism in glioma are unclear and there were no systematic researches about NAD+ metabolism related genes to predict the survival of patients with glioma.

Methods

The research was performed based on expression data of glioma cases in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Firstly, TCGA-glioma cases were classified into different subtypes based on 49 NAD+ metabolism-related genes (NMRGs) by consensus clustering. NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were gotten by intersecting the 49 NMRGs and differentially expressed genes (DEGs) between normal and glioma samples. Then a risk model was built by Cox analysis and the least shrinkage and selection operator (LASSO) regression analysis. The validity of the model was verified by survival curves and receiver operating characteristic (ROC) curves. In addition, independent prognostic analysis of the risk model was performed by Cox analysis. Then, we also identified different immune cells, HLA family genes and immune checkpoints between high and low risk groups. Finally, the functions of model genes at single-cell level were also explored.

Results

Consensus clustering classified glioma patients into two subtypes, and the overall survival (OS) of the two subtypes differed. A total of 11 NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were screened by overlapping 5,995 differentially expressed genes (DEGs) and 49 NAD+ metabolism-related genes (NMRGs). Next, four model genes, PARP9, BST1, NMNAT2, and CD38, were obtained by Cox regression and least absolute shrinkage and selection operator (Lasso) regression analyses and to construct a risk model. The OS of high-risk group was lower. And the area under curves (AUCs) of Receiver operating characteristic (ROC) curves were >0.7 at 1, 3, and 5 years. Cox analysis showed that age, grade G3, grade G4, IDH status, ATRX status, BCR status, and risk Scores were reliable independent prognostic factors. In addition, three different immune cells, Mast cells activated, NK cells activated and B cells naive, 24 different HLA family genes, such as HLA-DPA1 and HLA-H, and 8 different immune checkpoints, such as ICOS, LAG3, and CD274, were found between the high and low risk groups. The model genes were significantly relevant with proliferation, cell differentiation, and apoptosis.

Conclusion

The four genes, PARP9, BST1, NMNAT2, and CD38, might be important molecular biomarkers and therapeutic targets for glioma patients.

Immune-related matrisomes are potential biomarkers to predict the prognosis and immune microenvironment of glioma patients

The extracellular matrix (ECM) plays a vital role in the progression and metastasis of glioma and is an important part of the tumor microenvironment. The matrisome is composed of ECM components and related proteins. There have been several studies on the effects of matrisomes on the glioma immune microenvironment, but most of these studies were performed on individual glioma immune-related matrisomes rather than integral analysis. Hence, an overall analysis of all potential immune-related matrisomes in gliomas is needed. Here, we divided 667 glioma patients in The Cancer Genome Atlas (TCGA) database into low, moderate, and high immune infiltration groups. Immune-related matrisomes differentially expressed among the three groups were analyzed, and a risk signature was established. Eight immune-related matrisomes were screened, namely, LIF, LOX, MMP9, S100A4, SRPX2, SLIT1, SMOC1, and TIMP1. Kaplan-Meier analysis, operating characteristic curve analysis, and nomogram were constructed to analyze the relationships between risk signatures and the prognosis of glioma patients. The risk signature was significantly correlated with the overall survival of glioma patients. Both high- and low-risk signatures were also associated with some immune checkpoints. In addition, analysis of somatic mutations and anti-PD1/L1 immunotherapy responses in the high- and low-risk groups showed that the high-risk group had worse prognosis and a higher response to anti-PD1/L1 immunotherapy. Our analysis of immune-related matrisomes may improve understanding of the characteristics of the glioma immune microenvironment and provide direction for glioma immunotherapy development in the future.

Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma

Long noncoding ribonucleic acids (RNAs; lncRNAs) have been associated with cancer immunity regulation. However, the roles of immune cell-specific lncRNAs in glioblastoma (GBM) remain largely unknown. In this study, a novel computational framework was constructed to screen the tumor-infiltrating immune cell-associated lncRNAs (TIIClnc) for developing TIIClnc signature by integratively analyzing the transcriptome data of purified immune cells, GBM cell lines and bulk GBM tissues using six machine learning algorithms. As a result, TIIClnc signature could distinguish survival outcomes of GBM patients across four independent datasets, including the Xiangya in-house dataset, and more importantly, showed superior performance than 95 previously established signatures in gliomas. TIIClnc signature was revealed to be an indicator of the infiltration level of immune cells and predicted the response outcomes of immunotherapy. The positive correlation between TIIClnc signature and CD8, PD-1 and PD-L1 was verified in the Xiangya in-house dataset. As a newly demonstrated predictive biomarker, the TIIClnc signature enabled a more precise selection of the GBM population who would benefit from immunotherapy and should be validated and applied in the near future.

Identification of CD73 as a Novel Biomarker Encompassing the Tumor Microenvironment, Prognosis, and Therapeutic Responses in Various Cancers

CD73 is essential in promoting tumor growth by prohibiting anti-tumor immunity in many cancer types. While the mechanism remains largely unknown, our paper comprehensively confirmed the onco-immunological characteristics of CD73 in the tumor microenvironment (TME) of pan-cancer. This paper explored the expression pattern, mutational profile, prognostic value, tumor immune infiltration, and response to immunotherapy of CD73 in a continuous cohort of cancers through various computational tools. The co-expression of CD73 on cancer cells, immune cells, and stromal cells in the TME was also detected. Especially, we examined the correlation between CD73 and CD8+ (a marker of T cell), CD68+ (a marker of macrophage), and CD163+ (a marker of M2 macrophage) cells using multiplex immunofluorescence staining of tissue microarrays. CD73 expression is significantly associated with a patient's prognosis and could be a promising predictor of these cancers. High CD73 levels are strongly linked to immune infiltrations, neoantigens, and immune checkpoint expression in the TME. In particular, enrichment signaling pathway analysis demonstrated that CD73 was obviously related to activation pathways of immune cells, including T cells, macrophages, and cancer-associated fibroblasts (CAFs). Meanwhile, single-cell sequencing algorithms found that CD73 is predominantly co-expressed on cancer cells, CAFs, M2 macrophages, and T cells in several cancers. In addition, we explored the cellular communication among 14 cell types in glioblastoma (GBM) based on CD73 expression. Based on the expression of CD73 as well as macrophage and T cell markers, we predicted the methylation and enrichment pathways of these markers in pan-cancer. Furthermore, a lot of therapeutic molecules sensitive to these markers were predicted. Finally, potential anticancer inhibitors, immunotherapies, and gene therapy responses targeting CD73 were identified from a series of immunotherapy cohorts. CD73 is closely linked to clinical prognosis and immune infiltration in many cancers. Targeting CD73-dependent signaling pathways may be a promising therapeutic strategy for future tumor immunotherapy.

The construction of a hypoxia-based signature identified CA12 as a risk gene affecting uveal melanoma cell malignant phenotypes and immune checkpoint expression

Uveal melanoma (UM) is a deadly intraocular neoplasm in the adult population and harbors limited therapeutic effects from the current treatment. Here, we aimed to investigate the role of hypoxia in UM progress. We adopted the Cancer Genome Atlas data set as a training cohort and Gene Expression Omnibus data sets as validating cohorts. We first used consensus clustering to identify hypoxia-related subtypes, and the C1 subtype predicted an unfavorable prognosis and exhibited high infiltration of immunocytes and globally elevated immune checkpoint expression. Besides this, the patients with the C1 subtype were predicted to respond to the PD-1 treatment. By the least absolute shrinkage and selection operator algorithm, we constructed a hypoxia risk score based on the hypoxia genes and identified 10 genes. The risk score predicted patient survival with high performance, and the high-risk group also harbored high immunocyte infiltration and immune checkpoint expression. Furthermore, we confirmed that the risk genes were upregulated under hypoxia, and knockdown of CA12 inhibited the epithelial–mesenchymal transition process, clone formation ability, and G1/S phase transformation of the UM cells. The CD276 was also downregulated when CA12 knockdown was performed. These results validate the prognostic role of the hypoxia signature in UM and demonstrate that CA12 is a critical factor for UM cell progression as well as a target to improve immunotherapeutic effects. We believe our study contributes to the understanding of hypoxia’s roles in UM and provides a novel target that will benefit future therapeutic strategy development.

Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma

Rationale: Accumulating evidence demonstrated that long noncoding RNAs (lncRNAs) involved in the regulation of the immune system and displayed a cell-type-specific pattern in immune cell subsets. Given the vital role of tumor-infiltrating lymphocytes in effective immunotherapy, we explored the tumor-infiltrating immune cell-associated lncRNA (TIIClncRNA) in low-grade glioma (LGG), which has never been uncovered yet.

Methods: This study utilized a novel computational framework and 10 machine learning algorithms (101 combinations) to screen out TIIClncRNAs by integratively analyzing the sequencing data of purified immune cells, LGG cell lines, and bulk LGG tissues.

Results: The established TIIClnc signature based on the 16 most potent TIIClncRNAs could predict outcomes in public datasets and the Xiangya in-house dataset with decent efficiency and showed better performance when compared with 95 published signatures. The TIIClnc signature was strongly correlated to immune characteristics, including microsatellite instability, tumor mutation burden, and interferon γ, and exhibited a more active immunologic process. Furthermore, the TIIClnc signature predicted superior immunotherapy response in multiple datasets across cancer types. Notably, the positive correlation between the TIIClnc signature and CD8, PD-1, and PD-L1 was verified in the Xiangya in-house dataset.

Conclusions: The TIIClnc signature enabled a more precise selection of the LGG population who were potential beneficiaries of immunotherapy.

Identification of an Inflammatory Response-Related Gene Signature to Predict Survival and Immune Status in Glioma Patients

Background

Glioma is the most common primary brain tumor with high mortality and poor outcomes. As a hallmark of cancers, inflammatory responses are crucial for their progression. The present study is aimed at exploring the prognostic value of inflammatory response-related genes (IRRGs) and constructing a prognostic IRRG signature for gliomas.

Materials and Methods

We investigated the relationship between IRRGs and gliomas by integrating the transcriptomic data for gliomas from public databases. Differentially expressed IRRGs (DE-IRRGs) were identified in the GSE4290 cohort. Further, univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were conducted to construct an IRRG signature using The Cancer Genome Atlas (TCGA) cohort. Gliomas from the Chinese Glioma Genome Atlas (CGGA) cohort were employed for independent validation. The performance of gene signature was assessed by survival and receiver operating characteristic curve analyses. The differences in clinical correlations, immune infiltrate types, immunotherapeutic response predictions, and pathway enrichment among subgroups were investigated via bioinformatic algorithms.

Results

In total, 37 DE-IRRGs were determined, of which 31 were found to be associated with survival. Ultimately, eight genes were retained to construct an IRRG signature that further classified glioma patients into two groups; the high-risk group suffered a poorer outcome as compared to the low-risk group. Furthermore, the high-risk group was significantly correlated with several risk factors, including older age, higher tumor grade, IDH wild type, 1p19q noncodel, and MGMT unmethylation. The nomogram was constructed by integrating the risk scores and other independent clinical characteristics. Moreover, the high-risk group had a greater immune infiltration and was most likely to benefit from immunotherapy. Gene set enrichment analysis suggested that immune and oncogenic pathways were enriched in high-risk glioma patients.

Conclusion

We constructed a signature composed of eight IRRGs for gliomas, which could effectively predict survival and guide decision-making for treatment.

Transcriptional Patterns of Lower-Grade Glioma Patients with Distinct Ferroptosis Levels, Immunotherapy Response, and Temozolomide Sensitivity

Background

Many studies have defined a critical role for ferroptosis in cancer progression and therapy, but it is unclear how ferroptosis regulates tumor immunity or tumor microenvironment (TME).

Methods

In this study, 24 ferroptosis-regulators were assessed by nonnegative matrix factorization (NMF) consensus clustering to identify ferroptosis patterns in lower-grade gliomas (LGGs). Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method and single sample gene set enrichment analysis (ssGSEA) were used to quantify immune cell infiltrations. The PCA algorithm was used to develop the ferroptosis-related score (FRscore) to measure ferroptosis levels.

Results

Two LGG subgroups named ferroptosis-related clusters 1 (FRC1) and 2 (FRC2), with distinct ferroptosis levels, immune infiltrations, and clinical outcomes were determined in 1,407 LGG samples. A well-designed scoring system was developed to evaluate the ferroptosis levels in LGG patients based on the FRSig gene profile and divided patients into low- and high-FRscore subgroups. Patients with low FRscores had lower ferroptosis levels and prolonged survival time and were expected to benefit from immune checkpoint blockade (ICB) therapy and showed higher sensitivity to TMZ chemotherapy. Findings also showed that the PI3K-AKT-mTOR pathway is activated by ferroptosis induction in SW1088 cells.

Conclusions

This study highlights the critical role of ferroptosis in TME formation and shaping, and quantitatively assessing ferroptosis levels in individual tumors can help to define the intratumor microenvironment and formulate precise treatment strategies for LGG patients.

A Novel Risk Score Model Based on Eleven Extracellular Matrix-Related Genes for Predicting Overall Survival of Glioma Patients

Gliomas are the most common lethal primary brain tumors with variable survival outcomes for patients. The extracellular matrix (ECM) is linked with clinical prognosis of glioma patients, but it is not commonly used as a clinical indicator. Herein, we investigated changes in ECM-related genes (ECMRGs) via analyzing the transcriptional data of 938 gliomas from TCGA and CGGA datasets. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, a 11-ECMRG signature that is strongly linked with overall survival (OS) in glioma patients was identified. This signature was characterized by high-risk and low-risk score patterns. We found that the patients in the high-risk group are significantly linked with malignant molecular features and worse outcomes. Univariate and multivariate Cox regression analyses suggested that the signature is an independent indicator for glioma prognosis. The prediction accuracy of the signature was verified through time-dependent receiver operating characteristic (ROC) curves and calibration plots. Further bioinformatics analyses implied that the ECMRG signature is strongly associated with the activation of multiple oncogenic and metabolic pathways and immunosuppressive tumor microenvironment in gliomas. In addition, we confirmed that the high-risk score is an indicator for a therapy-resistant phenotype. In addition to bioinformatics analyses, we functionally verified the oncogenic role of bone morphogenetic protein 1 (BMP1) in gliomas in vitro.

Large-Scale Single-Cell and Bulk Sequencing Analyses Reveal the Prognostic Value and Immune Aspects of CD147 in Pan-Cancer

CD147 plays an important role in promoting tumor proliferation and inhibiting cancer cell apoptosis in the tumor microenvironment. However, the mechanisms by which CD147 is involved in tumorigenesis remains unclear. This study systematically analyzed the prognostic value and immune characteristics of CD147 in 31 cancer types. The expression levels and mutant landscapes of CD147 in pan-cancer were explored. The Kaplan-Meier (KM) analysis was applied to analyze the prognostic value of CD147. The immune characteristics of CD147 in the tumor microenvironment were evaluated via TIMER 2.0 and R package (immunedeconv). We also explored the expression of CD147 on tumor cells and stromal cells through Gene Set Variation Analysis and single-cell sequencing analysis. The co-expression of CD147 and macrophage markers CD68 and CD163 in pan-cancer was detected using multiplex immunofluorescence staining on tissue microarrays. CD147 was found to be overexpressed in almost all cancer types, which was related to poor outcome. CD147 expression exhibited a strong association with immune infiltrates, immune checkpoint molecules, and neoantigen levels in the tumor microenvironment. In addition, CD147 was expressed on various cell types in the tumor microenvironment, including tumor cells, macrophages, T cells, monocytes, fibroblasts, etc. Furthermore, multiplex immunofluorescence revealed the co-expression pattern of CD147 and macrophage markers CD68 and CD163 in many tumor types. Finally, the immunotherapy response and sensitive small molecule drugs based on CD147 expression were predicted. In sum, CD147 has a significant relationship with the clinical outcome and immune infiltrates in multiple cancer types. Inhibiting the CD147-dependent signaling pathways might be a promising therapeutic strategy for tumor immunotherapy.

Characterization of aging tumor microenvironment with drawing implications in predicting the prognosis and immunotherapy response in low-grade gliomas

Aging tumor microenvironment (aging TME) is emerging as a hot spot in cancer research for its significant roles in regulation of tumor progression and tumor immune response. The immune and stromal scores of low-grade gliomas (LGGs) from TCGA and CGGA databases were determined by using ESTIMATE algorithm. Differentially expressed genes (DEGs) between high and low immune/stromal score groups were identified. Subsequently, weighted gene co-expression network analysis (WGCNA) was conducted to screen out aging TME related signature (ATMERS). Based on the expression patterns of ATMERS, LGGs were classified into two clusters with distinct prognosis via consensus clustering method. Afterwards, the aging TME score for each sample was calculated via gene set variation analysis (GSVA). Furthermore, TME components were quantified by MCP counter and CIBERSORT algorithm. The potential response to immunotherapy was evaluated by Tumor Immune Dysfunction and Exclusion analysis. We found that LGG patients with high aging TME scores showed poor prognosis, exhibited an immunosuppressive phenotype and were less likely to respond to immunotherapy compared to those with low scores. The predictive performance of aging TME score was verified in three external datasets. Finally, the expression of ATMERS in LGGs was confirmed at protein level through the Human Protein Atlas website and western blot analysis. This novel aging TME-based scoring system provided a robust biomarker for predicting the prognosis and immunotherapy response in LGGs.

An Immune-Related Prognostic Signature for Predicting Clinical Outcomes and Immune Landscape in IDH-Mutant Lower-Grade Gliomas

BACKGROUND

IDH mutation is the most common in diffuse LGGs, correlated with a favorable prognosis. However, the IDH-mutant LGGs patients with poor prognoses need to be identified, and the potential mechanism leading to a worse outcome and treatment options needs to be investigated.

METHODS

A six-gene immune-related prognostic signature in IDH-mutant LGGs was constructed based on two public datasets and univariate, multivariate, and LASSO Cox regression analysis. Patients were divided into low- and high-risk groups based on the median risk score in the training and validation sets. We analyzed enriched pathways and immune cell infiltration, applying the GSEA and the immune evaluation algorithms.

RESULTS

Stratification and multivariate Cox analysis unveiled that the six-gene signature was an independent prognostic factor. The signature (0.806/0.795/0.822) showed a remarkable prognostic performance, with 1-, 3-, and 5-year time-dependent AUC, higher than for grade (0.612/0.638/0.649) and 1p19q codeletion status (0.606/0.658/0.676). High-risk patients had higher infiltrating immune cells. However, the specific immune escape was observed in the high-risk group after immune activation, owing to increasing immunosuppressive cells, inhibitory cytokines, and immune checkpoint molecules. Moreover, a novel nomogram model was developed to evaluate the survival in IDH-mutant LGGs patients.

CONCLUSION

The six-gene signature could be a promising prognostic biomarker, which is promising to promote individual therapy and improve the clinical outcomes of IDH-mutant gliomas. The study also refined the current classification system of IDH-mutant gliomas, classifying patients into two subtypes with distinct immunophenotypes and overall survival.