A novel gene signature based on five glioblastoma stem-like cell relevant genes predicts the survival of primary glioblastoma

Kunitz-type protease inhibitor TFPI2 remodels stemness and immunosuppressive tumor microenvironment in glioblastoma

Glioblastoma (GBM) tumors consist of multiple cell populations, including self-renewing glioblastoma stem cells (GSCs) and immunosuppressive microglia. Here we identified Kunitz-type protease inhibitor TFPI2 as a critical factor connecting these cell populations and their associated GBM hallmarks of stemness and immunosuppression. TFPI2 promotes GSC self-renewal and tumor growth via activation of the c-Jun N-terminal kinase-signal transducer and activator of transcription (STAT)3 pathway. Secreted TFPI2 interacts with its functional receptor CD51 on microglia to trigger the infiltration and immunosuppressive polarization of microglia through activation of STAT6 signaling. Inhibition of the TFPI2-CD51-STAT6 signaling axis activates T cells and synergizes with anti-PD1 therapy in GBM mouse models. In human GBM, TFPI2 correlates positively with stemness, microglia abundance, immunosuppression and poor prognosis. Our study identifies a function for TFPI2 and supports therapeutic targeting of TFPI2 as an effective strategy for GBM.

The Water Transport System in Astrocytes–Aquaporins

Highlights

(AQPs) are transmembrane proteins responsible for fast water movement across cell membranes, including those of astrocytes.

The expression and subcellular localization of AQPs in astrocytes are highly dynamic under physiological and pathological conditions.

Besides their primary function in water homeostasis, AQPs participate in many ancillary functions including glutamate clearance in tripartite synapses and cell migration.

Abstract

Astrocytes have distinctive morphological and functional characteristics, and are found throughout the central nervous system. Astrocytes are now known to be far more than just housekeeping cells in the brain. Their functions include contributing to the formation of the blood–brain barrier, physically and metabolically supporting and communicating with neurons, regulating the formation and functions of synapses, and maintaining water homeostasis and the microenvironment in the brain. Aquaporins (AQPs) are transmembrane proteins responsible for fast water movement across cell membranes. Various subtypes of AQPs (AQP1, AQP3, AQP4, AQP5, AQP8 and AQP9) have been reported to be expressed in astrocytes, and the expressions and subcellular localizations of AQPs in astrocytes are highly correlated with both their physiological and pathophysiological functions. This review describes and summarizes the recent advances in our understanding of astrocytes and AQPs in regard to controlling water homeostasis in the brain. Findings regarding the features of different AQP subtypes, such as their expression, subcellular localization, physiological functions, and the pathophysiological roles of astrocytes are presented, with brain edema and glioma serving as two representative AQP-associated pathological conditions. The aim is to provide a better insight into the elaborate “water distribution” system in cells, exemplified by astrocytes, under normal and pathological conditions.

A Novel Defined Pyroptosis-Related Gene Signature for Predicting Prognosis and Treatment of Glioma

Pyroptosis, a form of programmed cell death, that plays a significant role in the occurrence and progression of tumors, has been frequently investigated recently. However, the prognostic significance and therapeutic value of pyroptosis in glioma remain undetermined. In this research, we revealed the relationship of pyroptosis-related genes to glioma by analyzing whole transcriptome data from The Cancer Genome Atlas (TCGA) dataset serving as the training set and the Chinese Glioma Genome Atlas (CGGA) dataset serving as the validation set. We identified two subgroups of glioma patients with disparate prognostic and clinical features by performing consensus clustering analysis on nineteen pyroptosis-related genes that were differentially expressed between glioma and normal brain tissues. We further derived a risk signature, using eleven pyroptosis-related genes, that was demonstrated to be an independent prognostic factor for glioma. Furthermore, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to implement functional analysis of our gene set, and the results were closely related to immune and inflammatory responses in accordance with the characteristics of pyroptosis. Moreover, Gene Set Enrichment Analysis (GSEA) results showed that that the high-risk group exhibited enriched characteristics of malignant tumors in accordance with its poor prognosis. Next, we analyzed different immune cell infiltration between the two risk groups using ssGSEA. Finally, CASP1 was identified as a core gene, so we subsequently selected an inhibitor targeting CASP1 and simulated molecular docking. In addition, the inhibitory effect of belnacasan on glioma was verified at the cellular level. In conclusion, pyroptosis-related genes are of great significance for performing prognostic stratification and developing treatment strategies for glioma.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

The role m6A RNA methylation is CNS development and glioma pathogenesis

Epigenetic abnormalities play a crucial role in many tumors, including glioma. RNA methylation occurs as an epigenetic modification similar to DNA methylation and histone modification. m⁶A methylation is the most common and most intensively studied RNA methylation, which can be found throughout the RNA life cycle and exert biological functions by affecting RNA metabolism. The m⁶A modification is primarily associated with three types of protease, which are encoded by the writer, eraser and reader genes, respectively. It has been shown that the m⁶A methylation has close connections with the occurrence and development of many tumors, including glioma. In this study, the concept and the research progress of m⁶A methylation are reviewed, especially the role of m⁶A methylation in glioma. Moreover, we will discuss how glioma is paving the way to the development of new therapeutic options based on the inhibition of m⁶A deposition.

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

Malignant glioma with a mesenchymal (MES) signature is characterized by shorter survival time due to aggressive dissemination and resistance to chemoradiotherapy. Here, this study used the TCGA database as the training set and the CGGA database as the testing set. Consensus clustering was performed on the two data sets, and it was found that two groups had distinguished prognostic and molecular features. Cox analysis and Lasso regression analysis were used to construct MES signature-based risk score model of glioma. Our results show that MES signature-based risk score model can be used to assess the prognosis of glioma. Three methods (ROC curve analyses, univariate Cox regression analysis, multivariate Cox regression analysis) were used to investigate the prognostic role of texture parameters. The result showed that the MES-related gene signature was proved to be an independent prognostic factor for glioma. Furthermore, functional analysis of the gene related to the risk signature showed that the genes sets were closely related to the malignant process of tumors. Finally, FCGR2A and EHD2 were selected for functional verification. Silencing these two genes inhibited the proliferation, migration and invasion of gliomas and reduced the expression of mesenchymal marker genes. Collectively, MES-related risk signature seems to provide a novel target for predicting the prognosis and treatment of glioma.

ADAM and ADAMTS Proteins, New Players in the Regulation of Hepatocellular Carcinoma Microenvironment

Simple Summary

Members of the adamalysin family are multi-domain proteins involved in many cancer-related functions. In this review, we will examine the literature on the involvement of adamalysins in hepatocellular carcinoma progression and their importance in the tumor microenvironment where they regulate the inflammatory response and the epithelial–mesenchymal transition. We complete this review with an analysis of adamalysin expression in a large cohort of patients with hepatocellular carcinoma from The Cancer Genome Atlas (TCGA) database. These original results give a new insight into the involvement of all adamalysins in the primary liver cancer.

Abstract

The tumor microenvironment plays a major role in tumor growth, invasion and resistance to chemotherapy, however understanding how all actors from microenvironment interact together remains a complex issue. The tumor microenvironment is classically represented as three closely connected components including the stromal cells such as immune cells, fibroblasts, adipocytes and endothelial cells, the extracellular matrix (ECM) and the cytokine/growth factors. Within this space, proteins of the adamalysin family (ADAM for a disintegrin and metalloproteinase; ADAMTS for ADAM with thrombospondin motifs; ADAMTSL for ADAMTS-like) play critical roles by modulating cell–cell and cell–ECM communication. During last decade, the implication of adamalysins in the development of hepatocellular carcinoma (HCC) has been supported by numerous studies however the functional characterization of most of them remain unsettled. In the present review we propose both an overview of the literature and a meta-analysis of adamalysins expression in HCC using data generated by The Cancer Genome Atlas (TCGA) Research Network.

Molecular Subtypes Based on the Stemness Index Predict Prognosis in Glioma Patients

Glioma is the common histological subtype of malignancy in the central nervous system, with high morbidity and mortality. Glioma cancer stem cells (CSCs) play essential roles in tumor recurrence and treatment resistance. Thus, exploring the stem cell-related genes and subtypes in glioma is important. In this study, we collected the RNA-sequencing (RNA-seq) data and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. With the differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA), we identified 86 mRNA expression-based stemness index (mRNAsi)-related genes in 583 samples from TCGA RNA-seq dataset. Furthermore, these samples from TCGA database could be divided into two significantly different subtypes with different prognoses based on the mRNAsi corresponding gene, which could also be validated in the CGGA database. The clinical characteristics and immune cell infiltrate distribution of the two stemness subtypes are different. Then, functional enrichment analyses were performed to identify the different gene ontology (GO) terms and pathways in the two different subtypes. Moreover, we constructed a stemness subtype-related risk score model and nomogram to predict the prognosis of glioma patients. Finally, we selected one gene (ETV2) from the risk score model for experimental validation. The results showed that ETV2 can contribute to the invasion, migration, and epithelial-mesenchymal transition (EMT) process of glioma. In conclusion, we identified two distinct molecular subtypes and potential therapeutic targets of glioma, which could provide new insights for the development of precision diagnosis and prognostic prediction for glioma patients.

A novel gene signature based on five immune checkpoint genes predicts the survival of glioma

BACKGROUND

Glioma is the most common and fatal type of nerve neoplasm in the central nervous system. Several biomarkers have been considered for prognosis prediction, which is not accurate enough. We aimed to carry out a gene signature related to the expression of immune checkpoints which was enough for its performance in prediction.

METHODS

Gene expression of immune checkpoints in TGGA database was filtrated. The 5 selected genes underwent verification by COX and Lasso-COX regression. Next, the selected genes were included to build a novel signature for further analysis.

RESULTS

Patients were sub-grouped into high and low risk according to the novel signature. Immune response, clinicopathologic characters, and survival showed significant differences between those 2 groups. Terms including "naive," "effector," and "IL-4" were screened out by GSEA. The results showed strong relevance between the signature and immune response.

CONCLUSIONS

We constructed a gene signature with 5 immune checkpoints. The signature predicted survival effectively. The novel signature performed more functional than previous biomarkers.

Development of a prognostic index based on immunogenomic landscape analysis in glioma

Background

Glioma is the most common intracranial tumor. The inflammatory response actively participates in the malignancy of gliomas. There is still limited knowledge about the biological function of immune‐related genes (IRGs) and their potential involvement in the malignancy of gliomas.

Methods

We screened differentially expressed and survival‐associated IRGs, and explored their potential molecular characteristics. Then we developed a prognostic index derived from seven hub IRGs. A prognostic nomogram was built to indicate the prognostic value of the prognostic index and seven IRGs. We characterized the immune infiltration landscape to analyze tumor‐immune interactions. The real‐time quantitative polymerase chain reaction assay was performed to validate bioinformatics results.

Results

The differentially expressed IRGs are involved in cell chemotaxis, cytokine activity, and the chemokine‐mediated signaling pathway. The prognostic index derived from seven IRGs had clinical prognostic value in glioma, and positively correlated with the malignant clinicopathological characteristics. A nomogram further indicated that the prognostic index and seven hub IRGs had clinical prognostic value for gliomas. We revealed that the prognostic index could reflect the state of the glioma immune microenvironment.

Conclusion

This study demonstrates the importance of an IRG‐based prognostic index as a potential biomarker for predicting malignancy in gliomas.

Predictive value of MGMT promoter methylation on the survival of TMZ treated IDH-mutant glioblastoma

Objective

O6methylguanine-DNA methyltransferase (MGMT) promoter methylation is a biomarker widely used to predict the sensitivity of IDH-wildtype glioblastoma to temozolomide therapy. Given that the IDH status has critical effects on the survival and epigenetic features of glioblastoma, we aimed to assess the role of MGMT promoter methylation in IDH-mutant glioblastoma.

Methods

This study included 187 IDH-mutant glioblastomas and used 173 IDH-wildtype glioblastomas for comparison. Kaplan-Meier curves and multivariate Cox regression were used to study the predictive effects.

Results

Compared with IDH-wildtype glioblastomas, IDH-mutant glioblastomas showed significantly higher (P < 0.0001) MGMT promoter methylation. We demonstrated that MGMT promoter methylation status, as determined by a high cutoff value (≥30%) in pyrosequencing, could be used to significantly stratify the survival of 50 IDH-mutant glioblastomas receiving temozolomide therapy (cohort A); this result was validated in another cohort of 25 IDH-mutant glioblastomas (cohort B). The median progression-free survival and median overall survival in cohort A were 9.33 and 13.76 months for unmethylated cases, and 18.37 and 41.61 months for methylated cases, and in cohort B were 6.97 and 9.10 months for unmethylated cases, and 23.40 and 26.40 months for methylated cases. In addition, we confirmed that the MGMT promoter methylation was significantly (P = 0.0001) correlated with longer OS in IDH-mutant patients with GBM, independently of age, gender distribution, tumor type (primary or recurrent/secondary), and the extent of resection.

Conclusions

MGMT promoter methylation has predictive value in IDH-mutant glioblastoma, but its cutoff value should be higher than that for IDH-wildtype glioblastoma.

Secretory Pathway Kinase FAM20C, a Marker for Glioma Invasion and Malignancy, Predicts Poor Prognosis of Glioma

Purpose

Glioblastoma (GBM) is the most lethal primary cancer in adult central nervous system, and new strategies are desperately needed. The secretory pathway kinase or kinase-like proteins (SPKKPs) have been shown to mediate multiple physiological functions by phosphorylating extracellular proteins and proteoglycans. However, their roles in cancers, especially GBM, remain poorly defined.

Methods

The least absolute shrinkage and selection operator (LASSO) regression was employed for establishing the SPKKPs signature for IDH wild type (wt) GBM prognosis. Integrative analyses with multiple datasets were employed to identify the core member of this gene family in glioma. The receiver operator characteristic (ROC) curves and immunohistochemistry were further used for evaluating its association with progressive malignancy in glioma and GBM patients’ survival, respectively. Gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to interpret its functions in GBM, which were further verified in vitro.

Results

A SPKKPs classifier was constructed with 3 genes of this family. This signature could effectively distinguish IDH wt GBM survival. Family with sequence similarity 20 C (FAM20C) was further identified as the core member of this family in glioma. Elevated FAM20C expression was not only closely correlated with glioma malignancy progression and the mesenchymal subtype of GBM but also indicated unfavorable survival of GBM patients. FAM20C was also found to be associated with the disrupted immune response in GBM microenvironment and was required for the migration of glioma and immune cells.

Conclusion

These data indicate that the potential of FAM20C serving as a predictive molecule and a therapeutic target for GBM.

Transcriptional Characteristics of IDH-Wild Type Glioma Subgroups Highlight the Biological Processes Underlying Heterogeneity of IDH-Wild Type WHO Grade IV Gliomas

Isocitric dehydrogenase (IDH)-wild type diffuse gliomas, which have a poorer prognosis than their IDH-mutant counterparts, are also accompanied with high heterogeneity. Here, we aimed to identify the key biological processes associated with the three groups of IDH-wild type diffuse gliomas in 323 patients. By The Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) update 3 recommendation, the three groups are Group A, diffuse astrocytic glioma, World Health Organization (WHO) grade II/III; Group B, diffuse astrocytic glioma, with one (or more) of the three genetic alterations: TERT promoter mutation, EGFR gene amplification, gain of chromosome 7 combined with loss of chromosome 10, WHO grade IV; and Group C, glioblastoma, WHO grade IV. Consistent with their histologic and genetic molecular features, we successfully identified that biological activities associated with “cell cycle” and “cell mitosis” are significantly elevated in Group B compared with Group A; microenvironment-related hallmarks “angiogenesis” and “hypoxia,” and biological processes of “extracellular matrix,” “immune response,” and “positive regulation of transcriptional activities” were more enriched in Group C than Group B. We also constructed a nine-gene signature from differentially expressed genes among the three groups to further stratify the WHO grade IV gliomas (Groups B and C) whose survival cannot be clearly stratified by current classification systems. This signature was an independent prognosis factor for WHO grade IV gliomas and had better prognostic value than other known factors in both training and validation dataset. In addition, the signature risk score was positively correlated with the amount of infiltrated immune cells, expression of immune checkpoints, and the genes enriched in biological processes of “immune response,” “cell cycle,” and “extracellular matrix.” The bioinformatic analysis results were also validated by immunohistochemistry and patient-derived cell proliferation assay. Overall, our findings revealed the key biological processes underlying the new classifications of IDH-wild type diffuse glioma. Meanwhile, we constructed a signature, which could properly stratify the prognosis, cell proliferation activates, extracellular matrix-mediated biological activities, and immune-microenvironment of IDH-wild type WHO grade IV gliomas.

Whole-transcriptome sequencing profiling identifies functional and prognostic signatures in patients with PTPRZ1-MET fusion-negative secondary glioblastoma multiforme

Gliomas are the most common type of primary brain tumor in adults with a high mortality rate. Low-grade gliomas progress to glioblastoma multiforme (GBM) in the majority of cases, forming secondary GBM (sGBM), followed by rapid fatal clinical outcomes. Protein tyrosine phosphatase receptor type Z1 (PTPRZ1)-MET proto-oncogene receptor tyrosine kinase (MET) (ZM) fusion has been identified as a biomarker for sGBM that is involved in glioma progression, but the mechanism of gliomagenesis and pathology of ZM-negative sGBM has remained to be fully elucidated. A whole-transcriptome signature is thus required to improve the outcome prediction for patients with sGBM without ZM fusion. In the present study, whole-transcriptome sequencing on 42 sGBM samples with or without ZM fusion from the Chinese Glioma Genome Atlas database identified mRNAs with differential expression between patients with and without ZM fusion and the most significant survival-associated genes were identified. A 6-gene signature was identified as a novel prognostic model reflecting survival probability in patients with ZM-negative sGBM. Clinical characteristics in patients with a high or low risk score value were analyzed with the Kaplan-Meier method and a two-sided log-rank test. In addition, ZM-negative sGBM patients with a high risk score exhibited an increase in immune cells, NF-κB-induced pathway activation and a decrease in endothelial cells compared with those with a low risk score. The present study demonstrated the potential use of a next-generation sequencing-based cancer gene signature in patients with ZM-negative sGBM, indicating possible clinical therapeutic strategies for further treatment of such patients.

Systematically characterize the clinical and biological significances of 1p19q genes in 1p/19q non-codeletion glioma

1p/19q codeletion, which leads to the abnormal expression of 1p19q genes in oligodendroglioma, is associated with chemosensitivity and favorable prognosis. Here, we aimed to explore the clinical implications of 1p19q gene expression in 1p/19q non-codel gliomas. We analyzed expression of 1p19q genes in 668 1p/19q non-codel gliomas obtained from The Cancer Genome Atlas (n = 447) and the Chinese Glioma Genome Atlas (n = 221) for training and validation, respectively. The expression of 1p19q genes was significantly correlated with the clinicopathological features and overall survival of 1p/19q non-codel gliomas. Then, we derived a risk signature of 25 selected 1p19q genes that not only had prognosis value in total 1p/19q non-codel gliomas but also had prognosis value in stratified gliomas. The prognosis value of the risk signature was superior than known clinicopathological features in 1p/19q non-codel gliomas and was also highly associated with the following features: loss of CDKN2A/B copy number in mutant-IDH-astrocytoma; telomerase reverse transcriptase (TERT) promoter mutation, combined chromosome 7 gain/chromosome 10 loss and epidermal growth factor receptor amplification in wild-type-IDH-astrocytoma; classical and mesenchymal subtypes in glioblastoma. Furthermore, genes enriched in the biological processes of cell division, extracellular matrix, angiogenesis significantly correlated to the signature risk score, and this is also supported by the immunohistochemistry and cell biology experiments. In conclusion, the expression profile of 1p19q genes is highly associated with the malignancy and prognosis of 1p/19q non-codel gliomas. A 25-1p19q-gene signature has powerfully predictive value for both malignant molecular pathological features and prognosis across distinct subgroups of 1p/19q non-codel gliomas.

m6A RNA methylation regulators contribute to malignant progression and have clinical prognostic impact in gliomas

N6-methyladenosine (m⁶A) RNA methylation, associated with cancer initiation and progression, is dynamically regulated by the m⁶A RNA methylation regulators (“writers”, “erasers” and “readers”). Here, we demonstrate that most of the thirteen main m⁶A RNA methylation regulators are differentially expressed among gliomas stratified by different clinicopathological features in 904 gliomas. We identified two subgroups of gliomas (RM1/2) by applying consensus clustering to m⁶A RNA methylation regulators. Compared with the RM1 subgroup, the RM2 subgroup correlates with a poorer prognosis, higher WHO grade, and lower frequency of IDH mutation. Moreover, the hallmarks of epithelial-mesenchymal transition and TNFα signaling via NF-κB are also significantly enriched in the RM2 subgroup. This finding indicates that m⁶A RNA methylation regulators are closely associated with glioma malignancy. Based on this finding, we derived a risk signature, using seven m⁶A RNA methylation regulators, that is not only an independent prognostic marker but can also predict the clinicopathological features of gliomas. Moreover, m⁶A regulators are associated with the mesenchymal subtype and TMZ sensitivity in GBM. In conclusion, m⁶A RNA methylation regulators are crucial participants in the malignant progression of gliomas and are potentially useful for prognostic stratification and treatment strategy development.

Upregulated expression of MMP family genes is associated with poor survival in patients with esophageal squamous cell carcinoma via regulation of proliferation and epithelial‑mesenchymal transition

Matrix metalloproteinases (MMPs) are involved in the cleavage of several components of the extracellular matrix and serve important roles in tumor growth, metastasis and invasion. Previous studies have focused on the expression of one or several MMPs in esophageal squamous cell carcinoma (ESCC); however, in the present study, the transcriptomics of all 23 MMPs were systematically investigated with a focus on the prognostic value of the combination of MMPs. In this study, 8 overlapping differentially expressed genes of the MMP family were identified based on data obtained from Gene Expression Omnibus and The Cancer Genome Atlas. The prognostic value of these MMPs were investigated; the receiver operating characteristic curves, survival curves and nomograms showed that the combination of 6 selected MMPs possessed a good predictive ability, which was more accurate than the prediction model based on Tumor‑Node‑Metastasis stage. Gene set enrichment analysis and gene co‑expression analysis were performed to investigate the potential mechanism of action of MMPs in ESCC. The MMP family was associated with several signaling pathways, such as epithelial‑mesenchymal transition (EMT), Notch, TGF‑β, mTOR and P53. Cell Counting Kit‑8, colony formation, wound healing assays and western blotting were used to determine the effect of BB‑94, a pan‑MMP inhibitor, on proliferation and migration of ESCC cells. BB‑94 treatment decreased ESCC cell growth, migration and EMT. Therefore, MMPs may serve both as diagnostic and prognostic biomarkers of ESCC, and MMP inhibition may be a promising preventive and therapeutic strategy for patients with ESCC.

Gene Expression Profiling Stratifies IDH-Wildtype Glioblastoma With Distinct Prognoses

Objectives: In the present study, we aimed to determine the candidate genes that may function as biomarkers to further distinguish patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM), which are heterogeneous with respect to clinical outcomes.

Materials and Methods: We selected 41 candidate genes associated with overall survival (OS) using univariate Cox regression from IDH-wildtype GBM patients based on RNA sequencing (RNAseq) expression data from the Chinese Glioma Genome Atlas (CGGA, n = 105) and The Cancer Genome Atlas (TCGA, n = 139) cohorts. Next, a seven-gene-based risk signature was formulated according to Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm in the CGGA RNAseq database as a training set, while another 525 IDH-wildtype GBM patient TCGA datasets, consisting of RNA sequencing and microarray data, were used for validation. Patient survival in the low- and high-risk groups was calculated using Kaplan-Meier survival curve analysis and the log-rank test. Uni-and multivariate Cox regression analysis was used to assess the prognosis value. Gene oncology (GO) and gene set enrichment analysis (GSEA) were performed for the functional analysis of the seven-gene-based risk signature.

Results: We developed a seven-gene-based signature, which allocated each patient to a risk group (low or high). Patients in the high-risk group had dramatically shorter overall survival than their low-risk counterparts in three independent cohorts. Univariate and multivariate analysis showed that the seven-gene signature remained an independent prognostic factor. Moreover, the seven-gene risk signature exhibited a striking prognostic validity, with AUC of 78.4 and 73.9%, which was higher than for traditional “age” (53.7%, 62.4%) and “GBM sub-type” (57.7%, 52.9%) in the CGGA- and TCGA-RNAseq databases, respectively. Subsequent bioinformatics analysis predicted that the seven-gene signature was involved in the inflammatory response, immune response, cell adhesion, and apoptotic process.

Conclusions: Our findings indicate that the seven-gene signature could be a potential prognostic biomarker. This study refined the current classification system of IDH-wildtype GBM and may provide a novel perspective for the research and individual therapy of IDH-wildtype GBM.

The clinical significance of collagen family gene expression in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is a subtype of esophageal cancer with high incidence and mortality. Due to the poor 5-year survival rates of patients with ESCC, exploring novel diagnostic markers for early ESCC is emergent. Collagen, the abundant constituent of extracellular matrix, plays a critical role in tumor growth and epithelial-mesenchymal transition. However, the clinical significance of collagen genes in ESCC has been rarely studied. In this work, we systematically analyzed the gene expression of whole collagen family in ESCC, aiming to search for ideal biomarkers.

METHODS

Clinical data and gene expression profiles of ESCC patients were collected from The Cancer Genome Atlas and the gene expression omnibus databases. Bioinformatics methods, including differential expression analysis, survival analysis, gene sets enrichment analysis (GSEA) and co-expression network analysis, were performed to investigate the correlation between the expression patterns of 44 collagen family genes and the development of ESCC.

RESULTS

A total of 22 genes of collagen family were identified as differentially expressed genes in both the two datasets. Among them, COL1A1, COL10A1 and COL11A1 were particularly up-regulated in ESCC tissues compared to normal controls, while COL4A4, COL6A5 and COL14A1 were notably down-regulated. Besides, patients with low COL6A5 expression or high COL18A1 expression showed poor survival. In addition, a 7-gene prediction model was established based on collagen gene expression to predict patient survival, which had better predictive accuracy than the tumor-node-metastasis staging based model. Finally, GSEA results suggested that collagen genes might be tightly associated with PI3K/Akt/mTOR pathway, p53 pathway, apoptosis, cell cycle, etc.

CONCLUSION

Several collagen genes could be potential diagnostic and prognostic biomarkers for ESCC. Moreover, a novel 7-gene prediction model is probably useful for predicting survival outcomes of ESCC patients. These findings may facilitate early detection of ESCC and help improves prognosis of the patients.

Identification of a Specific Gene Module for Predicting Prognosis in Glioblastoma Patients

Introduction: Glioblastoma (GBM) is the most common and malignant variant of intrinsic glial brain tumors. The poor prognosis of GBM has not significantly improved despite the development of innovative diagnostic methods and new therapies. Therefore, further understanding the molecular mechanism that underlies the aggressive behavior of GBM and the identification of appropriate prognostic markers and therapeutic targets is necessary to allow early diagnosis, to develop appropriate therapies and to improve prognoses.

Methods: We used a weighted gene co-expression network analysis (WGCNA) to construct a gene co-expression network with 524 glioblastoma samples from The Cancer Genome Atlas (TCGA). A risk score was then constructed based on four module genes and the patients' overall survival (OS) rate. The prognostic and predictive accuracy of the risk score were verified in the GSE16011 cohort and the REMBRANDT cohort.

Results: We identified a gene module (the green module) related to prognosis. Then, multivariate Cox analysis was performed on 4 hub genes to construct a Cox proportional hazards regression model from 524 glioblastoma patients. A risk score for predicting survival time was calculated with the following formula based on the top four genes in the green module: risk score = (0.00889 × EXP_CLEC5A) + (0.0681 × EXP_FMOD) + (0.1724 × EXP_FKBP9) + (0.1557 × EXP_LGALS8). The 5-year survival rate of the high-risk group (survival rate: 2.7%, 95% CI: 1.2–6.3%) was significantly lower than that of the low-risk group (survival rate: 8.8%, 95% CI: 5.5–14.1%).

Conclusions: This study demonstrated the potential application of a WGCNA-based gene prognostic model for predicting the survival outcome of glioblastoma patients.

Microarray gene profiling analysis of glioblastoma cell line U87 reveals suppression of the FANCD2/Fanconi anemia pathway by the combination of Y15 and temozolomide

INTRODUCTION

A recent study showed that a combination of Y15 (a FAK autophosphorylation inhibitor) with temozolomide (TMZ) treatment was effective in glioblastoma (GBM) therapy. In this study, we further investigated the pathways and genes that are differentially expressed in Y15 and TMZ treated U87 cells via bioinformatics analysis.

MATERIAL AND METHODS

The microarray gene profiling analysis screened out genes with differential expression in U87 cells treated with TMZ and Y15. Gene set enrichment analysis (GSEA) identified the key GO terms and KEGG pathways in TMZ + Y15 treated U87 cells. The functional partner genes of TMZ were predicted by the STICH database. FANCD2 expression in U87 cells was detected by qRT-PCR. MTT assay and colony formation assay were conducted for cell viability detection, and flow cytometry was performed for cell apoptosis detection. Western blot was conducted to determine the expression levels of the downstream proteins of the Fanconi anemia (FA) pathway, FAN1 and BRCA2.

RESULTS

The FA pathway was suppressed in U87 cells after treatment with TMZ and Y15. Genes involved in this pathway, including FANCD2, were also down-regulated. FANCD2 knockdown could restrain viability and promote apoptosis of U87 cells, as well as enhancing the inhibitory effect of TMZ + Y15 treatment. FANCD2 could regulate the FA pathway as the protein expression levels of FAN1 and BRCA2 were modulated by FANCD2.

CONCLUSIONS

The FA pathway and FANCD2 are down-regulated in U87 cells treated with TMZ and Y15. FANCD2 down-regulation by TMZ + Y15 treatment suppressed growth of U87 cells through inhibiting the FA pathway.

Systematically profiling the expression of eIF3 subunits in glioma reveals the expression of eIF3i has prognostic value in IDH-mutant lower grade glioma

Background

Abnormal expression of the eukaryotic initiation factor 3 (eIF3) subunits plays critical roles in tumorigenesis and progression, and also has potential prognostic value in cancers. However, the expression and clinical implications of eIF3 subunits in glioma remain unknown.

Methods

Expression data of eIF3 for patients with gliomas were obtained from the Chinese Glioma Genome Atlas (CGGA) (n = 272) and The Cancer Genome Atlas (TCGA) (n = 595). Cox regression, the receiver operating characteristic (ROC) curves and Kaplan–Meier analysis were used to study the prognostic value. Gene oncology (GO) and gene set enrichment analysis (GSEA) were utilized for functional prediction.

Results

In both the CGGA and TCGA datasets, the expression levels of eIF3d, eIF3e, eIF3f, eIF3h and eIF3l highly were associated with the IDH mutant status of gliomas. The expression of eIF3b, eIF3i, eIF3k and eIF3m was increased with the tumor grade, and was associated with poorer overall survival [All Hazard ratio (HR) > 1 and P < 0.05]. By contrast, the expression of eIF3a and eIF3l was decreased in higher grade gliomas and was associated with better overall survival (Both HR < 1 and P < 0.05). Importantly, the expression of eIF3i (located on chromosome 1p) and eIF3k (Located on chromosome 19q) were the two highest risk factors in both the CGGA [eIF3i HR = 2.068 (1.425–3.000); eIF3k HR = 1.737 (1.166–2.588)] and TCGA [eIF3i HR = 1.841 (1.642–2.064); eIF3k HR = 1.521 (1.340–1.726)] databases. Among eIF3i, eIF3k alone or in combination, the expression of eIF3i was the more robust in stratifying the survival of glioma in various pathological subgroups. The expression of eIF3i was an independent prognostic factor in IDH-mutant lower grade glioma (LGG) and could also predict the 1p/19q codeletion status of IDH-mutant LGG. Finally, GO and GSEA analysis showed that the elevated expression of eIF3i was significantly correlated with the biological processes of cell proliferation, mRNA processing, translation, T cell receptor signaling, NF-κB signaling and others.

Conclusions

Our study reveals the expression alterations during glioma progression, and highlights the prognostic value of eIF3i in IDH-mutant LGG.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0867-1) contains supplementary material, which is available to authorized users.

ADAMTSL4, a Secreted Glycoprotein, Is a Novel Immune-Related Biomarker for Primary Glioblastoma Multiforme

Background

Researches on immunotherapy of glioblastoma multiforme (GBM, WHO grade IV) have increased exponentially in recent years. As a targeted therapy, a series of biomarkers have been identified in local tumor tissue, while circulating marker which could be detected in the body fluids is still lacking. ADAMTSL4, a secreted glycoprotein, was earlier found to play a critical role in a prognostic signature for primary GBM (pGBM). We aimed to investigate the role of ADAMTSL4 at transcriptome level and its relationship with clinical practice in pGBM.

Methods

A cohort of 88 pGBM patients with RNA-seq data from the Chinese Glioma Genome Atlas (CGGA) was analyzed, and 168 pGBM patients from TCGA were included as validation. Several bioinformatic methods and predictive tools were applied to investigate the ADAMTSL4-associated immune microenvironment status.

Results

We found that ADAMTSL4 was enriched in GBM (WHO grade IV), especially for those with IDH1/2 wild-type and MGMT unmethylated groups. According to the TCGA classification scheme, ADAMTSL4 can act as a potential marker for subtypes with poorer prognosis. Bioinformatic analyses revealed that ADAMTSL4 was significantly correlated to the immune-related processes in GBM (WHO grade IV), especially representing the infiltration of immune cells and complicated tumor microenvironment. Clinically, high expression of ADAMTSL4 was an independent indicator for poor prognosis.

Conclusion

The expression of ADAMTSL4 is closely related to the clinicopathologic characteristics of pGBM. Meanwhile, it may play a critical role in immune-related processes. As a secreted glycoprotein, ADAMTSL4 is a promising circulating biomarker for pGBM, deserving further investigations.

A novel analytical model of MGMT methylation pyrosequencing offers improved predictive performance in patients with gliomas

The methylation status of the promoter of MGMT gene is a crucial factor influencing clinical decision-making in patients with gliomas. MGMT pyrosequencing results are often dichotomized by a cut-off value based on an average of several tested CpGs. However, this method frequently results in a "gray zone", representing a dilemma for physicians. We therefore propose a novel analytical model for MGMT methylation pyrosequencing. MGMT CpG heterogeneity was investigated in 213 glioma patients in two tested cohorts: cohort A in which CpGs 75-82 were tested and cohort B in which CpGs 72-78 were tested. The predictive performances of the novel and traditional averaging models were compared in 135 patients who received temozolomide using receiver operating characteristic curves and Kaplan-Meier curves, and in patients stratified according to isocitrate dehydrogenase gene mutation status. The results were validated in an independent cohort of 65 consecutive patients with high-grade gliomas from the Chinese Glioma Genome Atlas database. Heterogeneity of MGMT promoter CpG methylation level was observed in most gliomas. The optimal cut-off value for each individual CpG varied from 4-16%. The current analysis defined MGMT promoter methylation as occurring when at least three CpGs exceeded their respective cut-off values. This novel analysis could accurately predict the prognosis of patients in the methylation "gray zone" according to the standard averaging method, and improved the area under the curves from 0.67, 0.76, and 0.67 to 0.70, 0.84, and 0.72 in cohorts A, B, and the validation cohort, respectively, demonstrating superiority of this analytical method in all three cohorts. Furthermore, the advantages of the novel analysis were retained regardless of WHO grade and isocitrate dehydrogenase gene mutation status. In conclusion, this novel analytical model offers an improved clinical predictive performance for MGMT pyrosequencing results and is suitable for clinical use in patients with gliomas.

Combinations of four or more CpGs methylation present equivalent predictive value for MGMT expression and temozolomide therapeutic prognosis in gliomas

AIMS

The pyrosequencing (PSQ) has been regarded as the gold standard for MGMT promoter methylation testing in gliomas. However, various CpG combinations are currently used in clinical practice. We aimed to clarify how and how many CpGs combined is robust enough to predict MGMT mRNA expression and therapeutic prognosis of patients.

METHODS

Total 223 patients with WHO III/IV gliomas were enrolled from Chinese Glioma Genome Atlas, including two independent cohorts, the eight-site cohort (with CpGs 75-82 tested) and the seven-site cohort (with CpGs 72-78 tested). Spearman's correlation and ROC curves were employed to investigate the value of different CpG combinations on predicting MGMT mRNA expression. The ROC curves and Kaplan-Meier steps were performed to compare the TMZ therapeutic prognostic values of different CpG combinations.

RESULTS

The methylation level of all individual CpG and CpG combinations for the eleven CpGs (CpGs 72-82), significantly correlated to MGMT mRNA expression (Spearman, all P < 0.0001), could effectively predict the mRNA expression (AUC, 0.86-0.91 in the eight-site cohort, 0.83-0.90 in the seven-site cohort). Moreover, the correlation coefficients and the predictive values presented equivalent when four or more CpGs combinedly used (AUC, 0.88-0.90 in the eight-site cohort, 0.87-0.88 in the seven-site cohort). Finally, similar results were also observed when using selected CpG combinations to predict therapeutic prognosis of patients.

CONCLUSIONS

Four-CpG combinations of pyrosequencing are sufficient for evaluating the methylation status of MGMT and predicting therapeutic prognosis in gliomas.