A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

A molecular signature for the G6PC3/SLC37A2/SLC37A4 interactors in glioblastoma disease progression and in the acquisition of a brain cancer stem cell phenotype

Background

Glycogen plays an important role in glucose homeostasis and contributes to key functions related to brain cancer cell survival in glioblastoma multiforme (GBM) disease progression. Such adaptive molecular mechanism is dependent on the glycogenolytic pathway and intracellular glucose-6-phosphate (G6P) sensing by brain cancer cells residing within those highly hypoxic tumors. The involvement of components of the glucose-6-phosphatase (G6Pase) system remains however elusive.

Objective

We questioned the gene expression levels of components of the G6Pase system in GBM tissues and their functional impact in the control of the invasive and brain cancer stem cells (CSC) phenotypes.

Methods

In silico analysis of transcript levels in GBM tumor tissues was done by GEPIA. Total RNA was extracted and gene expression of G6PC1-3 as well as of SLC37A1-4 members analyzed by qPCR in four human brain cancer cell lines and from clinically annotated brain tumor cDNA arrays. Transient siRNA-mediated gene silencing was used to assess the impact of TGF-β-induced epithelial-to-mesenchymal transition (EMT) and cell chemotaxis. Three-dimensional (3D) neurosphere cultures were generated to recapitulate the brain CSC phenotype.

Results

Higher expression in G6PC3, SLC37A2, and SLC37A4 was found in GBM tumor tissues in comparison to low-grade glioma and healthy tissue. The expression of these genes was also found elevated in established human U87, U251, U118, and U138 GBM cell models compared to human HepG2 hepatoma cells. SLC37A4/G6PC3, but not SLC37A2, levels were induced in 3D CD133/SOX2-positive U87 neurospheres when compared to 2D monolayers. Silencing of SLC37A4/G6PC3 altered TGF-β-induced EMT biomarker SNAIL and cell chemotaxis.

Conclusion

Two members of the G6Pase system, G6PC3 and SLC37A4, associate with GBM disease progression and regulate the metabolic reprogramming of an invasive and CSC phenotype. Such molecular signature may support their role in cancer cell survival and chemoresistance and become future therapeutic targets.

Mechanistic insights into super-enhancer-driven genes as prognostic signatures in patients with glioblastoma

BACKGROUND

Glioblastoma (GBM) is one of the most common malignant brain tumors in adults and is characterized by high aggressiveness and rapid progression, poor treatment, high recurrence rate, and poor prognosis. Although super-enhancer (SE)-driven genes haven been recognized as prognostic markers for several cancers, whether it can be served as effective prognostic markers for patients with GBM has not been evaluated.

METHODS

We first combined histone modification data with transcriptome data to identify SE-driven genes associated with prognosis in patients with GBM. Second, we developed a SE-driven differentially expressed genes (SEDEGs) risk score prognostic model by univariate Cox analysis, KM survival analysis, multivariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression. Its reliability in predicting was verified by two external data sets. Third, through mutation analysis, immune infiltration, we explored the molecular mechanisms of prognostic genes. Next, Genomics of Drug Sensitivity in Cancer (GDSC) and the Connectivity Map (cMap) database were employed to assess different sensitivities to chemotherapeutic agents and small-molecule drug candidates between high- and low-risk patients. Finally, SEanalysis database was chosen to identify SE-driven transcription factors (TFs) regulating prognostic markers which will reveal a potential SE-driven transcriptional regulatory network.

RESULTS

First, we developed a 11-gene risk score prognostic model (NCF2, MTHFS, DUSP6, G6PC3, HOXB2, EN2, DLEU1, LBH, ZEB1-AS1, LINC01265, and AGAP2-AS1) selected from 1,154 SEDEGs, which is not only an independent prognostic factor for patients, but also can effectively predict the survival rate of patients. The model can effectively predict 1-, 2- and 3-year survival of patients and was validated in external Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) datasets. Second, the risk score was positively correlated with the infiltration of regulatory T cell, CD4 memory activated T cell, activated NK cell, neutrophil, resting mast cell, M0 macrophage, and memory B cell. Third, we found that high-risk patients showed higher sensitivity than low-risk patients to both 27 chemotherapeutic agents and 4 small-molecule drug candidates which might benefit further precision therapy for GBM patients. Finally, 13 potential SE-driven TFs imply how SE regulates GBM patient's prognosis.

CONCLUSION

The SEDEG risk model not only helps to elucidate the impact of SEs on the course of GBM, but also provides a bright future for prognosis determination and choice of treatment for GBM patients.

Enhanced effects of antagomiR-3074-3p-conjugated PEI-AuNPs on the odontogenic differentiation by targeting FKBP9

The odontogenic differentiation of dental pulp stem cells (DPSCs), which is vital for tooth regeneration, was regulated by various functional molecules. In recent years, a growing body of research has shown that miRNAs play a crucial role in the odontogenic differentiation of human dental pulp stem cells (hDPSCs). However, the mechanisms by which miRNAs regulated odontogenic differentiation of hDPSCs remained unclear, and the application of miRNAs in reparative dentin formation in vivo was also rare. In this study, we first discovered that miR-3074-3p had an inhibitory effect on odontogenic differentiation of hDPSCs and antagomiR-3074-3p-conjugated PEI-AuNPs effectively promoted odontogenic differentiation of hDPSCs in vitro. AntagomiR-3074-3p-conjugated PEI-AuNPs was further applied to the rat pulp-capping model and showed the increased formation of restorative dentin. In addition, the results of lentivirus transfection in vitro suggested that FKBP9 acted as the key target of miR-3074-3p in regulating the odontogenic differentiation of hDPSCs. These findings might provide a new strategy and candidate target for dentin restoration and tooth regeneration.

Identification of ACSF gene family as therapeutic targets and immune-associated biomarkers in hepatocellular carcinoma

Acyl-CoA synthetases (ACSs) are responsible for acyl-CoA synthesis from nonpolar hydrophilic fatty acids and play a vital role in many metabolic processes. As a category of ACS isozymes, members of ACS family (AACS, ACSF2-3, AASDH) participate in lipid metabolism; however, their expression patterns, regulatory mechanisms and effects in hepatocellular carcinoma (HCC) are poorly understood. Here, through evaluating the expression profiles of ACSF gene family, we found that upregulated AACS might be more significant and valuable in development and progression of HCC. Consequently, the mRNA expression levels of AACS and ACSF2 was accordantly increased in HCC. Kaplan-Meier plotter revealed that HCC patients with high level of AACS were highly related to a shorter overall survival time and relapse-free survival. Genetic alterations using cBioPortal revealed that the alteration rate of AACS were 5%. We also found that the functions of ACSF gene family were linked to several cancer-associated pathways, including long-term potentiation, phospholipase D signaling pathway and purine metabolism. TIMER database indicated that the AACS and ACSF2 had a strong relationship with the infiltration of six types of immune cells (macrophages, neutrophils, CD8+ T-cells, B-cells, CD4+ T-cells and dendritic cells). Next, Diseasemeth database revealed that the global methylation levels of ACSF2 was higher in HCC patients. In conclusion, this study firstly demonstrated that Acyl-CoA synthesis gene family, in particular, AACS, could be associated with immune microenvironment, thereby influencing the development and prognosis of patients with HCC.

Nomograms for prognostic risk assessment in glioblastoma multiforme: Applications and limitations

Glioblastoma multiforme (GBM) is the most common and aggressive form of brain cancer. Prognosis evaluation is of great significance in guiding individualized treatment and monitoring of GBM. By integrating different prognostic variables, nomograms simplify the statistical risk prediction model into numerical estimates for death or recurrence, and are hence widely applied in prognosis prediction. In the past two decades, the application of high-throughput profiling technology and the establishment of TCGA database and other public data deposits have provided opportunities to identify cancer-related molecules and prognostic biomarkers. As a result, both molecular features and clinical characteristics of cancer have been reported to be the key factors in nomogram model construction. This article comprehensively reviewed 35 studies of GBM nomograms, analyzed the present situation of GBM nomograms, and discussed the role and significance of nomograms in personalized risk assessment and clinical treatment decision-making. To facilitate the application of nomograms in the prognostic prediction of GBM patients, a website has been established for the online access of nomograms based on the studies of this review, which is called Consensus Nomogram Spectrum for Glioblastoma (CNSgbm) and is accessible through https://bioinfo.henu.edu.cn/nom/NomList.jsp.

The Prognostic Value and Immunological Role of STEAP1 in Pan-Cancer: A Result of Data-Based Analysis

Purpose. This study is aimed at systematically analyzing the expression, function, and prognostic value of six transmembrane epithelial antigen of the prostate 1 (STEAP1) in various cancers. Methods. The expressions of STEAP1 between normal and tumor tissues were analyzed using TCGA and GTEx. Clinicopathologic data was collected from GEPIA and TCGA. Prognostic analysis was conducted by Cox proportional hazard regression and Kaplan-Meier survival. DNA methylation, mutation features, and molecular subtypes of cancers were also investigated. The top-100 coexpressed genes with STEAP1 were involved in functional enrichment analysis. ESTIMATE algorithm was used to analyze the correlation between STEAP1 and immunity value. The relationships of STEAP1 and biomarkers including tumor mutational burden (TMB), microsatellite instability (MSI), and stemness score as well as chemosensitivity were also illustrated. Results. Among 33 cancers, STEAP1 was overexpressed in 19 cancers such as cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma, and lymphoid neoplasm diffuse large B cell lymphoma while was downregulated in 5 cancers such as adrenocortical carcinoma, breast invasive carcinoma (BRCA), and kidney chromophobe renal cell carcinoma. STEAP1 has significant prognostic relationships in multiple cancers. 15 cancers exhibited differences of DNA methylation including bladder urothelial carcinoma, BRCA, and CESC. STEAP1 expression was positively correlated to immune molecules especially in thyroid carcinoma and negatively especially in uveal melanoma. STEAP1 was associated with TMB and MSI in certain cancers. In addition, STEAP1 was connected with increased chemosensitivity of drugs such as trametinib and pimasertib. Conclusions. STEAP1 was an underlying target for prognostic prediction in different cancer types and a potential biomarker of TMB, MSI, tumor microenvironment, and chemosensitivity.

Screening and prognostic value of potential biomarkers for ovarian cancer

Background

Ovarian cancer is a common gynecological malignant tumor that greatly threatens women's health, so we screened potential biomarkers of ovarian cancer and analyzed their prognostic value.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to analyze the ovarian cancer-related genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of ovarian cancer-related genes. The survival-related genes were screened out through the least absolute shrinkage and selection operator (LASSO) method. Multivariate Cox regression model and stepwise regression analysis were performed to construct the risk model. The receiver operating characteristic (ROC) and the area under the ROC curve (AUC) were used to evaluate the prediction accuracy of risk score model. Finally, gene set enrichment analysis (GSEA) and immune cell infiltration analysis were performed to investigate the biological function and immune cell infiltration.

Results

A total of 111 genes were found to have common effects on survival. These genes were mainly involved in metabolism, protein phosphorylation and immune-related signaling pathways. Seven risk genes (AP3D1, DCAF10, FBXO16, LRFN4, PTPN2, SAYSD1, ZNF426) were screened out. Among these genes, AP3D1 and LRFN4 are risk genes and DCAF10, FBXO16, PTPN2, SAYSD1, and ZNF426 are protective genes. These findings suggest that risk status may be an independent prognostic factor. The risk score had a high predictive value for the prognosis of ovarian cancer. In addition, GSEA revealed that the biological function of genes expressed in patients at a high risk was mostly related to immune-related function. The contents of CD4⁺ T cells, macrophages, myeloid dendritic cells (mDC) and neutrophils were high in samples at a high risk for ovarian cancer.

Conclusions

The abnormal expression of AP3D1, DCAF10, FBXO16, LRFN4, PTPN2, SAYSD1 and ZNF426 is highly related to the progression of ovarian cancer. These seven genes can be used as independent prognostic markers of ovarian cancer. This study not only adds evidence to the pathogenesis of ovarian cancer but also provides scientific basis for judging the prognosis of ovarian cancer.

The predictive prognostic values of CBFA2T3, STX3, DENR, EGLN1, FUT4, and PCDH7 in lung cancer

BACKGROUND

Lung cancer is one of the most malignant tumors. However, neither the pathogenesis of lung cancer nor the prognosis markers are completely clear. The purpose of this study is to screen the diagnostic or prognostic markers of lung cancer.

METHODS

TCGA and GEO datasets were used to analyze the relationship between lung cancer-related genes and lung cancer samples. Common differential genes were screened, and a univariate Cox regression analysis was used to screen survival related genes. A univariable Cox proportional hazards regression analysis was used to verify the genes and construct risk model. The key factors affecting the prognosis of lung cancer were determined by univariate and multivariate regression analyses. The ROC curve, AUC and the survival of each risk gene was analyzed. Finally, the biological functions of high- and low-risk patients were explored by GSEA and an immune-infiltration analysis.

RESULTS

Based on the common differential genes, 13 genes significantly related to lung cancer survival were identified. Eight risk genes (CBFA2T3, DENR, EGLN1, FUT2, FUT4, PCDH7, PHF14, and STX3) were screened out. The results showed that risk status may be an independent prognostic factor, and the risk score predicted the prognosis of lung cancer. CBFA2T3 and STX3 are protective genes, while DENR, EGLN1, FUT4 and PCDH7 are dangerous genes. These 6 genes can be used as independent lung cancer prognosis markers. The corresponding biological functions of genes expressed in high-risk patients were mostly related to tumor proliferation and inflammatory infiltration. Neutrophil, CD8+T, Macrophage M0, Macrophage M1- and mDC-activated cells were high in high-risk status samples.

CONCLUSIONS

CBFA2T3, STX3, DENR, EGLN1, FUT4, and PCDH7 are important participants in the occurrence and development of lung cancer. High-risk patients display serious inflammatory infiltration. This study not only provides insight into the mechanism of occurrence and development of lung cancer, but also provides potential targets for targeted therapy of lung cancer.

Regulatory Roles of Six-Transmembrane Epithelial Antigen of the Prostate Family Members in the Occurrence and Development of Malignant Tumors

The human six-transmembrane epithelial antigen of the prostate (STEAP) proteins, which include STEAP1-4 and atypical STEAP1B, contain six transmembrane domains and are located in the cell membrane. STEAPs are considered archaeal metal oxidoreductases, based on their heme groups and F420H2:NADP+ oxidoreductase (FNO)-like structures, and play an important role in cell metal metabolism. Interestingly, STEAPs not only participate in biological processes, such as molecular transport, cell cycling, immune response, and intracellular and extracellular activities, but also are closely related to the occurrence and development of several diseases, especially malignant tumors. Up to now, the expression patterns of STEAPs have been found to be diverse in different types of tumors, with controversial participation in different aspects of malignancy, such as cell proliferation, migration, invasion, apoptosis, and therapeutic resistance. It is clinically important to explore the potential roles of STEAPs as new immunotherapeutic targets for the treatment of different malignant tumors. Therefore, this review focuses on the molecular mechanism and function of STEAPs in the occurrence and development of different cancers in order to understand the role of STEAPs in cancer and provide a new theoretical basis for the treatment of diverse cancers.