Identification of a mesenchymal-related signature associated with clinical prognosis in glioma

Comprehensive benchmarking of CNN-based tumor segmentation methods using multimodal MRI data

Magnetic resonance imaging (MRI) has become an essential and a frontline technique in the detection of brain tumor. However, segmenting tumors manually from scans is laborious and time-consuming. This has led to an increasing trend towards fully automated methods for precise tumor segmentation in MRI scans. Accurate tumor segmentation is crucial for improved diagnosis, treatment, and prognosis. This study benchmarks and evaluates four widely used CNN-based methods for brain tumor segmentation CaPTk, 2DVNet, EnsembleUNets, and ResNet50. Using 1251 multimodal MRI scans from the BraTS2021 dataset, we compared the performance of these methods against a reference dataset of segmented images assisted by radiologists. This comparison was conducted using segmented images directly and further by radiomic features extracted from the segmented images using pyRadiomics. Performance was assessed using the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). EnsembleUNets excelled, achieving a DSC of 0.93 and an HD of 18, outperforming the other methods. Further comparative analysis of radiomic features confirmed EnsembleUNets as the most precise segmentation method, surpassing other methods. EnsembleUNets recorded a Concordance Correlation Coefficient (CCC) of 0.79, a Total Deviation Index (TDI) of 1.14, and a Root Mean Square Error (RMSE) of 0.53, underscoring its superior performance. We also performed validation on an independent dataset of 611 samples (UPENN-GBM), which further supported the accuracy of EnsembleUNets, with a DSC of 0.85 and an HD of 17.5. These findings provide valuable insight into the efficacy of EnsembleUNets, supporting informed decisions for accurate brain tumor segmentation.

EH domain-containing protein 2 (EHD2): Overview, biological function, and therapeutic potential

EH domain-containing protein 2 (EHD2) is a member of the EHD protein family and is mainly located in the plasma membrane, but can also be found in the cytoplasm and endosomes. EHD2 is also a nuclear-cytoplasmic shuttle protein. After entering the cell nuclear, EHD2 acts as a corepressor of transcription to inhibit gene transcription. EHD2 regulates a series of biological processes. As a key regulator of endocytic transport, EHD2 is involved in the formation and maintenance of endosomal tubules and vesicles, which are critical for the intracellular transport of proteins and other substances. The N-terminal of EHD2 is attached to the cell membrane, while its C-terminal binds to the actin-binding protein. After binding, EHD2 connects with the actin cytoskeleton, forming the curvature of the membrane and promoting cell endocytosis. EHD2 is also associated with membrane protein trafficking and receptor signaling, as well as in glucose metabolism and lipid metabolism. In this review, we highlight the recent advances in the function of EHD2 in various cellular processes and its potential implications in human diseases such as cancer and metabolic disease. We also discussed the prospects for the future of EHD2. EHD2 has a broad prospect as a therapeutic target for a variety of diseases. Further research is needed to explore its mechanism, which could pave the way for the development of targeted treatments.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the molecular feature of M2 macrophages of head and neck squamous cell carcinoma

The connection between head and neck squamous cell carcinoma (HNSC) and M2 tumour-associated macrophages is not yet fully understood. We gathered gene expression profiles and clinical data from HNSC patients in the TCGA database. Using Consensus Clustering, we categorized these patients into M2 macrophage-related clusters. We developed a M2 macrophage-related signature (MRS) through statistical analyses. Additionally, we assessed gene expression in HNSC cells using single-cell sequencing data (GSE139324). We identified three distinct M2 macrophage-related clusters in HNSC, each with different prognostic outcomes and immune characteristics. Patients with different MRS profiles exhibited variations in immune infiltration, genetic mutations and prognosis. FCGR2A may play a role in creating an immunosuppressive tumour microenvironment and could potentially serve as a therapeutic target for HNSC. Our study demonstrated that M2 macrophage-related genes significantly impact the development and progression of HNSC. The M2 macrophage-related model offered a more comprehensive assessment of HNSC patient prognosis, genetic mutations and immune features. FCGR2A was implicated in immunosuppressive microenvironments and may hold promise for the development of novel immunotherapeutic strategies for HNSC.

EHD2 overexpression promotes tumorigenesis and metastasis in triple-negative breast cancer by regulating store-operated calcium entry

With nearly all cancer deaths a result of metastasis, elucidating novel pro-metastatic cellular adaptations could provide new therapeutic targets. Here, we show that overexpression of the EPS15-Homology Domain-containing 2 (EHD2) protein in a large subset of breast cancers (BCs), especially the triple-negative (TNBC) and HER2+ subtypes, correlates with shorter patient survival. The mRNAs for EHD2 and Caveolin-1/2, structural components of caveolae, show co-overexpression across breast tumors, predicting shorter survival in basal-like BC. EHD2 shRNA knockdown and CRISPR-Cas9 knockout with mouse Ehd2 rescue, in TNBC cell line models demonstrate a major positive role of EHD2 in promoting tumorigenesis and metastasis. Mechanistically, we link these roles of EHD2 to store-operated calcium entry (SOCE), with EHD2-dependent stabilization of plasma membrane caveolae ensuring high cell surface expression of the SOCE-linked calcium channel Orai1. The novel EHD2-SOCE oncogenic axis represents a potential therapeutic target in EHD2- and CAV1/2-overexpressing BC.

Identification of an endoplasmic reticulum stress-related signature associated with clinical prognosis and immune therapy in glioma

Background

Glioma is the most common brain tumor in adults and is characterized by a short survival time and high resistance to chemotherapy. It is imperative to determine the prognosis and therapy-related targets for glioma. Endoplasmic reticulum stress (ERS), as an adaptive protective mechanism, indicates the unfolded protein response (UPR) to determine cell survival and affects chemotherapy sensitivity, which is related to the prognosis of glioma.

Methods

Our research used the TCGA database as the training group and the CGGA database as the testing group. Lasso regression and Cox analysis were performed to construct an ERS signature-based risk score model in glioma. Three methods (time-dependent receiver operating characteristic analysis and multivariate and univariate Cox regression analysis) were applied to assess the independent prognostic effect of texture parameters. Consensus clustering was used to classify the two clusters. In addition, functional and immune analyses were performed to assess the malignant process and immune microenvironment. Immunotherapy and anticancer drug response prediction were adopted to evaluate immune checkpoint and chemotherapy sensitivity.

Results

The results revealed that the 7-gene signature strongly predicts glioma prognosis. The two clusters have markedly distinct molecular and prognostic features. The validation group result revealed that the signature has exceptional repeatability and certainty. Functional analysis showed that the ERS-related gene signature was closely associated with the malignant process and prognosis of tumors. Immune analysis indicated that the ERS-related gene signature is strongly related to immune infiltration. Immunotherapy and anticancer drug response prediction indicated that the ERS-related gene signature is positively correlated with immune checkpoint and chemotherapy sensitivity.

Conclusions

Collectively, the ERS-related risk model can provide a novel signature to predict glioma prognosis and treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02709-y.

Construction of Molecular Subtypes and Related Prognostic and Immune Response Models Based on M2 Macrophages in Glioblastoma

OBJECTIVES

To identify the molecular subtypes of glioblastoma multiforme (GBM) related to M2 macrophage-based prognostic genes, then to preliminarily explore their biological functions and construct immunotherapy response gene models.

MATERIAL AND METHODS

We used R language to analyze GBM microarray data, and other tools, including xCell and CIBERSORTx, to identify subtypes of GBM that related to M2 macrophages. The process started with the exploration of biological functions of the two subtypes by pathway analyses and GSEA, and continued with a combined procedure of constructing an M2 macrophage-related prognostic gene model and exploring the immune treatment response for GBM.

RESULTS

A high abundance of M2 macrophages in GBM was associated with poor prognosis. According to M2 macrophage-related prognostic genes, GBM was divided into two subtypes (cluster A and cluster B). The differential gene enrichment analysis of the two clusters showed that cluster A was less enriched in M2 macrophages and had immunopotential. The M2score, which was constructed based on M2 macrophage-related prognostic genes, was not only related to the survival and prognosis of patients with GBM, but also predictive of the effectiveness of immunotherapy in these patients. This result has been effectively verified in an external data set.

CONCLUSION

GBM was successfully divided into two subtypes according to M2-macrophage-related prognostic genes. In GBM, a high M2score may indicate better clinical outcome and enhancement of the immunotherapy response.

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

Glioma is the most common primary malignant brain tumor with limited treatment options and poor prognosis. To investigate the potential relationships between transcriptional characteristics and clinical phenotypes, we applied weighted gene co-expression network analysis (WGCNA) to construct a free-scale gene co-expression network yielding four modules in gliomas. Turquoise and yellow modules were positively correlated with the most malignant glioma subtype (IDH-wildtype glioblastomas). Of them, genes in turquoise module were mainly involved in immune-related terms and were regulated by NFKB1, RELA, SP1, STAT1 and STAT3. Meanwhile, genes in yellow module mainly participated in cell-cycle and division processes and were regulated by E2F1, TP53, E2F4, YBX1 and E2F3. Furthermore, 14 genes in turquoise module were screened as hub genes. Among them, five prognostic hub genes (TNFRSF1B, LAIR1, TYROBP, VAMP8, and FCGR2A) were selected to construct a prognostic risk score model via LASSO method. The risk score of this immune-related gene signature is associated with clinical features, malignant phenotype, and somatic alterations. Moreover, this signature showed an accurate prediction of prognosis across different clinical and pathological subgroups in three independent datasets including 1619 samples. Our results showed that the high-risk group was characterized by active immune-related activities while the low-risk group enriched in neurophysiological-related pathway. Importantly, the high-risk score of our immune signature predicts an enrichment of glioma-associated microglia/macrophages and less response to immune checkpoint blockade (ICB) therapy in gliomas. This study not only provides new insights into the molecular pathogenesis of glioma, but may also help optimize the immunotherapies for glioma patients.

Tac2-N Promotes Glioma Proliferation and Indicates Poor Clinical Outcomes

As the most common tumor of central nervous system in adults, glioma is characterized with poor prognosis. Tac2-N (TC2N) is a newly discovered protein that play potential roles in lung cancer and breast cancer progression. Here we aimed to investigate the expression, clinical significance, and function of TC2N in glioma. The mRNA level of TC2N in glioma patients was extracted from TCGA datasets. Immunohistochemistry staining was conducted to test protein expression of TC2N in glioma tissues. Chi-square test was used to assess correlations between TC2N expression and patients' clinicopathological characteristics. Kaplan-Meier method was used to plot survival curves. The prognostic predictive role of TC2N was evaluated by univariate and multivariate analyses. Knockdown assays were performed in U87 and U251 cell lines, respectively. Cell proliferation, colony formation, and subcutaneous mice xenografts were used to reveal the tumor-related role of TC2N in glioma. Compared with normal brain tissues, the mRNA level of TC2N was significantly higher in glioma tissues, whose dysregulated higher mRNA level was correlated with poorer overall survival. Similarly, higher protein expression of TC2N was observed in cases with larger tumor size and advanced WHO grades. Univariate and multivariate analyses identified TC2N as a novel independent prognostic factor of gliomas. In vitro and in vivo data demonstrated that TC2N interference can remarkably prevent glioma cell proliferation and tumor growth. In conclusion, high TC2N expression is significantly correlated with poor overall survival of glioma patients via enhancing tumor growth.