Large-Scale Analysis Reveals Gene Signature for Survival Prediction in Primary Glioblastoma

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

BACKGROUND

Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM.

METHODS

A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients' survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023.

RESULTS

From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts.

CONCLUSION

We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.

An atlas of causal and mechanistic drivers of interpatient heterogeneity in glioma

Grade IV glioma, formerly known as glioblastoma multiforme (GBM) is the most aggressive and lethal type of brain tumor, and its treatment remains challenging in part due to extensive interpatient heterogeneity in disease driving mechanisms and lack of prognostic and predictive biomarkers. Using mechanistic inference of node-edge relationship (MINER), we have analyzed multiomics profiles from 516 patients and constructed an atlas of causal and mechanistic drivers of interpatient heterogeneity in GBM (gbmMINER). The atlas has delineated how 30 driver mutations act in a combinatorial scheme to causally influence a network of regulators (306 transcription factors and 73 miRNAs) of 179 transcriptional "programs", influencing disease progression in patients across 23 disease states. Through extensive testing on independent patient cohorts, we share evidence that a machine learning model trained on activity profiles of programs within gbmMINER significantly augments risk stratification, identifying patients who are super-responders to standard of care and those that would benefit from 2 nd line treatments. In addition to providing mechanistic hypotheses regarding disease prognosis, the activity of programs containing targets of 2 nd line treatments accurately predicted efficacy of 28 drugs in killing glioma stem-like cells from 43 patients. Our findings demonstrate that interpatient heterogeneity manifests from differential activities of transcriptional programs, providing actionable strategies for mechanistically characterizing GBM from a systems perspective and developing better prognostic and predictive biomarkers for personalized medicine.

Brain tumour genetic network signatures of survival

Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterized by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH-wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH-mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.

Long non-coding RNA LINC01018 inhibits human glioma cell proliferation and metastasis by directly targeting miRNA-182-5p

AIM

Accumulating evidence suggests that lncRNAs are potential biomarkers and key regulators of tumor development and progression. However, the precise function of most lncRNAs in glioma remains unknown. In this study, we explored the role of long intergenic non-protein coding RNA 1018 (LINC01018) in human glioma.

METHODS

Expression levels of LINC01018 and miR-182-5p in clinical glioma tissues and cell lines were detected by quantitative real-time PCR (qRT-PCR). Cell proliferation, migration, and invasion were determined by Cell Counting Kit-8 (CCK-8) assay and Transwell assay. Epithelial-mesenchymal transition (EMT) related proteins were measured by Western blotting. Direct relationship between LINC01018 and miR-182-5p was tested by dual-luciferase reporter assay, RNA immunoprecipitation assay (RIP), and rescue assays. Lastly, bioinformatics analyses were conducted to predict the downstream factors of LINC01018/miR-182-5p axis in glioma.

RESULTS

LINC01018 was significantly down-regulated in glioma tissues and cell lines. Overexpression of LINC01018 dramatically inhibited cell proliferation, migration, and invasion and reverse EMT process in glioma. LINC01018 directly target to miR-182-5p. Forced up-regulation of miR-182-5p reversed the inhibitory effects on proliferative and metastatic abilities of glioma cells with LINC01018 overexpression. Lastly, the bioinformatics analyses revealed that LINC01018/miR-182-5p axis mediated a cluster of downstream genes (ADRA2C, RAB6B, RAB27B, RAPGEF5, STEAP2, TAGLN3, and UNC13C), which were potential key factors in the development of glioma.

CONCLUSION

LINC01018 inhibits cell proliferation and metastasis in human glioma by targeting miR-182-5p, and should be considered as a potential therapeutic target in this cancer.

Fused inverse-normal method for integrated differential expression analysis of RNA-seq data

Background

Use of next-generation sequencing technologies to transcriptomics (RNA-seq) for gene expression profiling has found widespread application in studying different biological conditions including cancers. However, RNA-seq experiments are still small sample size experiments due to the cost. Recently, an increased focus has been on meta-analysis methods for integrated differential expression analysis for exploration of potential biomarkers. In this study, we propose a p-value combination method for meta-analysis of multiple independent but related RNA-seq studies that accounts for sample size of a study and direction of expression of genes in individual studies.

Results

The proposed method generalizes the inverse-normal method without an increase in statistical or computational complexity and does not pre- or post-hoc filter genes that have conflicting direction of expression in different studies. Thus, the proposed method, as compared to the inverse-normal, has better potential for the discovery of differentially expressed genes (DEGs) with potentially conflicting differential signals from multiple studies related to disease. We demonstrated the use of the proposed method in detection of biologically relevant DEGs in glioblastoma (GBM), the most aggressive brain cancer. Our approach notably enabled the identification of over-expressed tumour suppressor gene RAD51 in GBM compared to healthy controls, which has recently been shown to be a target for inhibition to enhance radiosensitivity of GBM cells during treatment. Pathway analysis identified multiple aberrant GBM related pathways as well as novel regulators such as TCF7L2 and MAPT as important upstream regulators in GBM.

Conclusions

The proposed meta-analysis method generalizes the existing inverse-normal method by providing a way to establish differential expression status for genes with conflicting direction of expression in individual RNA-seq studies. Hence, leading to further exploration of them as potential biomarkers for the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04859-9.

Identification of Prognostic Genes in Gliomas Based on Increased Microenvironment Stiffness

With a median survival time of 15 months, glioblastoma multiforme is one of the most aggressive primary brain cancers. The crucial roles played by the extracellular matrix (ECM) stiffness in glioma progression and treatment resistance have been reported in numerous studies. However, the association between ECM-stiffness-regulated genes and the prognosis of glioma patients remains to be explored. Thus, using bioinformatics analysis, we first identified 180 stiffness-dependent genes from an RNA-Seq dataset, and then evaluated their prognosis in The Cancer Genome Atlas (TCGA) glioma dataset. Our results showed that 11 stiffness-dependent genes common between low- and high-grade gliomas were prognostic. After validation using the Chinese Glioma Genome Atlas (CGGA) database, we further identified four stiffness-dependent prognostic genes: FN1, ITGA5, OSMR, and NGFR. In addition to high-grade glioma, overexpression of the four-gene signature also showed poor prognosis in low-grade glioma patients. Moreover, our analysis confirmed that the expression levels of stiffness-dependent prognostic genes in high-grade glioma were significantly higher than in low-grade glioma, suggesting that these genes were associated with glioma progression. Based on a pathophysiology-inspired approach, our findings illuminate the link between ECM stiffness and the prognosis of glioma patients and suggest a signature of four stiffness-dependent genes as potential therapeutic targets.

Comprehensive Landscape of STEAP Family Members Expression in Human Cancers: Unraveling the Potential Usefulness in Clinical Practice Using Integrated Bioinformatics Analysis

The human Six-Transmembrane Epithelial Antigen of the Prostate (STEAP) family comprises STEAP1-4. Several studies have pointed out STEAP proteins as putative biomarkers, as well as therapeutic targets in several types of human cancers, particularly in prostate cancer. However, the relationships and significance of the expression pattern of STEAP1-4 in cancer cases are barely known. Herein, the Oncomine database and cBioPortal platform were selected to predict the differential expression levels of STEAP members and clinical prognosis. The most common expression pattern observed was the combination of the over- and underexpression of distinct STEAP genes, but cervical and gastric cancer and lymphoma showed overexpression of all STEAP genes. It was also found that STEAP genes’ expression levels were already deregulated in benign lesions. Regarding the prognostic value, it was found that STEAP1 (prostate), STEAP2 (brain and central nervous system), STEAP3 (kidney, leukemia and testicular) and STEAP4 (bladder, cervical, gastric) overexpression correlate with lower patient survival rate. However, in prostate cancer, overexpression of the STEAP4 gene was correlated with a higher survival rate. Overall, this study first showed that the expression levels of STEAP genes are highly variable in human cancers, which may be related to different patients’ outcomes.

Novel Biomarker Genes for Prognosis of Survival and Treatment of Glioma

Glioblastoma multiforme (GBM) is the most aggressive malignant primary central nervous system tumor. Although surgery, radiotherapy, and chemotherapy treatments are available, the 5-year survival rate of GBM is only 5.8%. Therefore, it is imperative to find novel biomarker for the prognosis and treatment of GBM. In this study, a total of 141 differentially expressed genes (DEGs) in GBM were identified by analyzing the GSE12657, GSE90886, and GSE90598 datasets. After reducing the data dimensionality, Kaplan-Meier survival analysis indicated that expression of PTPRN and RIM-BP2 were downregulated in GBM tissues when compared with that of normal tissues and that the expression of these genes was a good prognostic biomarker for GBM (p<0.05). Then, the GSE46531 dataset and the Genomics of Drug Sensitivity in Cancer (GDSC) database were used to examine the relationship between sensitivity radiotherapy (RT) and chemotherapy for GBM and expression of PTPRN and RIM-BP2. The expression of PTPRN was significantly high in RT-resistant patients (p<0.05) but it was not related to temozolomide (TMZ) resistance. The expression level of RIM-BP2 was not associated with RT or TMZ treatment. Among the chemotherapeutic drugs, cisplatin and erlotinib had a significantly good treatment effect for glioma with expression of PTPRN or RIM-BP2 and in lower-grade glioma (LGG) with IDH mutation. (p < 0.05). The tumor mutational burden (TMB) score in the low PTPRN expression group was significantly higher than that in the high PTPRN expression group (p=0.013), with a large degree of tumor immune cell infiltration. In conclusion, these findings contributed to the discovery process of potential biomarkers and therapeutic targets for glioma patients.

neoDL: a novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival

Background

Neoantigen based personalized immune therapies achieve promising results in melanoma and lung cancer, but few neoantigen based models perform well in IDH wild-type GBM, and the association between neoantigen intrinsic features and prognosis remain unclear in IDH wild-type GBM. We presented a novel neoantigen intrinsic feature-based deep learning model (neoDL) to stratify IDH wild-type GBMs into subgroups with different survivals.

Results

We first derived intrinsic features for each neoantigen associated with survival, followed by applying neoDL in TCGA data cohort(AUC = 0.988, p value < 0.0001). Leave one out cross validation (LOOCV) in TCGA demonstrated that neoDL successfully classified IDH wild-type GBMs into different prognostic subgroups, which was further validated in an independent data cohort from Asian population. Long-term survival IDH wild-type GBMs identified by neoDL were found characterized by 12 protective neoantigen intrinsic features and enriched in development and cell cycle.

Conclusions

The model can be therapeutically exploited to identify IDH wild-type GBM with good prognosis who will most likely benefit from neoantigen based personalized immunetherapy. Furthermore, the prognostic intrinsic features of the neoantigens inferred from this study can be used for identifying neoantigens with high potentials of immunogenicity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04301-6.

IGFBP2 Is a Potential Master Regulator Driving the Dysregulated Gene Network Responsible for Short Survival in Glioblastoma Multiforme

Only 2% of glioblastoma multiforme (GBM) patients respond to standard therapy and survive beyond 36 months (long-term survivors, LTS), while the majority survive less than 12 months (short-term survivors, STS). To understand the mechanism leading to poor survival, we analyzed publicly available datasets of 113 STS and 58 LTS. This analysis revealed 198 differentially expressed genes (DEGs) that characterize aggressive tumor growth and may be responsible for the poor prognosis. These genes belong largely to the Gene Ontology (GO) categories “epithelial-to-mesenchymal transition” and “response to hypoxia.” In this article, we applied an upstream analysis approach that involves state-of-the-art promoter analysis and network analysis of the dysregulated genes potentially responsible for short survival in GBM. Binding sites for transcription factors (TFs) associated with GBM pathology like NANOG, NF-κB, REST, FRA-1, PPARG, and seven others were found enriched in the promoters of the dysregulated genes. We reconstructed the gene regulatory network with several positive feedback loops controlled by five master regulators [insulin-like growth factor binding protein 2 (IGFBP2), vascular endothelial growth factor A (VEGFA), VEGF165, platelet-derived growth factor A (PDGFA), adipocyte enhancer-binding protein (AEBP1), and oncostatin M (OSMR)], which can be proposed as biomarkers and as therapeutic targets for enhancing GBM prognosis. A critical analysis of this gene regulatory network gives insights into the mechanism of gene regulation by IGFBP2 via several TFs including the key molecule of GBM tumor invasiveness and progression, FRA-1. All the observations were validated in independent cohorts, and their impact on overall survival has been investigated.

Bcl2l10 induces metabolic alterations in ovarian cancer cells by regulating the TCA cycle enzymes SDHD and IDH1

Bcl2‑like‑10 (Bcl2l10) has both oncogenic and tumor suppressor functions depending on the type of cancer. It has been previously demonstrated that the suppression of Bcl2l10 in ovarian cancer SKOV3 and A2780 cells causes cell cycle arrest and enhances cell proliferation, indicating that Bcl2l10 is a tumor suppressor gene in ovarian cancer cells. The aim of the present study was to identify possible downstream target genes and investigate the underlying mechanisms of action of Bcl2l10 in ovarian cancer cells. RNA sequencing (RNA‑Seq) was performed to obtain a list of differentially expressed genes (DEGs) in Bcl2l10‑suppressed SKOV3 and A2780 cells. The RNA‑Seq data were validated by reverse transcription‑quantitative PCR (RT‑qPCR) and western blot analysis, and the levels of metabolites after Bcl2l10‑knockdown were measured using colorimetric assay kits. Pathway enrichment analysis revealed that the commonly downregulated genes in SKOV3 and A2780 cells after Bcl2l10‑knockdown were significantly enriched in metabolic pathways. The analysis of the DEGs identified from RNA‑Seq and validated by RT‑qPCR revealed that succinate dehydrogenase complex subunit D (SDHD) and isocitrate dehydrogenase 1 (IDH1), which are key enzymes of the TCA cycle that regulate oncometabolite production, may be potential downstream targets of Bcl2l10. Furthermore, Bcl2l10‑knockdown induced the accumulation of succinate and isocitrate through the downregulation of SDHD and IDH1. The present study was the first to elucidate the metabolic regulatory functions of Bcl2l10 in ovarian cancer cells, and the results indicated that Bcl2l10 may serve as a potential therapeutic target in ovarian cancer.

A universal molecular prognostic score for gastrointestinal tumors

Colorectal and gastric cancers are a leading cause of cancer deaths in developed countries. Precise estimation of prognosis is important with regard to clinical decision making for individuals with such cancers. We here comprehensively compiled a complete atlas of prognostic genes based on an integrated meta-analysis of one of the largest assembled colorectal cancer cohorts. A simple yet robust machine learning approach was then applied to establish a universal molecular prognostic score (mPS_colon) that relies on the expression status of only 16 genes and which was validated with independent data sets. This score was found to be an independent prognostic indicator in multivariate models including cancer stage, to be valid independent of tumor characteristics or patient ethnicity, and to be also applicable to gastric cancer. We conclude that mPS_colon is a universal prognostic classifier for patients with gastrointestinal cancers and that it should prove informative for optimization of personalized therapy for such patients.