Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis

STAT3 drives the malignant progression of low-grade gliomas through modulating the expression of STAT1, FOXO1, and MYC

Low-grade glioma (LGG) is a prevalent and lethal primary brain malignancy, with most patients succumbing to recurrence and progression. The signal transducer and activator of transcription (STAT) family has long been implicated in tumor initiation and progression. However, a comprehensive evaluation of the expression status and overall function of STAT genes in LGG remains largely unreported. In this study, we investigated the association between the expression of STAT family genes and the progression of LGG. Through a comprehensive analysis that combined bioinformatics screening and validation assays, we determined that STAT1, STAT3, and STAT5A were upregulated and contributed to the malignant progression of LGG. Notably, our findings suggest that STAT3 is a critical prognostic marker that regulates the progression of LGG. STAT3 emerged as the most significant prognostic indicator governing the advancement of LGG. Additionally, our inquiry into the STAT3-binding proteins and differentially expressed-correlated genes (DEGs) revealed that STAT3 played a pivotal role in the progression of LGG by stimulating the expression of STAT1, FOXO1, and MYC. In summary, our recent study conducted a thorough analysis of the STAT family genes and revealed that directing therapeutic interventions towards STAT3 holds potential as a viable strategy for treating patients with LGG.

Cancer immune profiling unveils biomarkers, immunological pathways, and cell type score associated with glioblastoma patients' survival

Introduction

Glioblastoma (GBM), isocitrate dehydrogenase (IDH) wild-type (IDH ^wt), and grade 4 astrocytomas, IDH mutant (IDH ^mut), are the most common and aggressive primary malignant brain tumors in adults. A better understanding of the tumor immune microenvironment may provide new biomarkers and therapeutic opportunities.

Objectives

We aimed to evaluate the expression profile of 730 immuno-oncology-related genes in patients with IDH ^wt GBM and IDH ^mut tumors and identify prognostic biomarkers and a gene signature associated with patient survival.

Methods

RNA was isolated from formalin-fixed, paraffin-embedded sections of 99 tumor specimens from patients treated with standard therapy. Gene expression profile was assessed using the Pan-Cancer Immune Profiling Panel (Nanostring Technologies, Inc., Seattle, WA, USA). Data analysis was performed using nSolverSoftware and validated in The Cancer Genome Atlas. In addition, we developed a prognostic signature using the cox regression algorithm (Least Absolute Shrinkage and Selection Operator).

Results

We found 88 upregulated genes, high immunological functions, and a high macrophage score in IDH ^wt GBM compared to IDH ^mut tumors. Regarding IDH ^wt GBM, we found 24 upregulated genes in short-term survivors (STS) and overexpression of CD274 (programmed death-ligand 1, PD-L1). Immune pathways, CD45, cytotoxic, and macrophage scores were upregulated in STS. Two different prognostic groups were found based on the 12-gene signature (CXCL14, PSEN2, TNFRSF13C, IL13RA1, MAP2K1, TNFSF14, THY1, CTSL, ITGAE, CHUK, CD207, and IFITM1).

Conclusion

The elevated expression of immune-oncology-related genes was associated with worse outcome in IDH ^wt GBM patients. Increased immune functions, CD45, cytotoxic cells, and macrophage scores were associated with a more aggressive phenotype and may provide promising possibilities for therapy. Moreover, a 12 gene-based signature could predict patients' prognosis.

Transcriptome Sequencing Analysis of the Effect of β-Elemene on Colorectal Cancer from the lncRNA-miRNA-mRNA Perspective

Object. β-Elemene is an emerging antitumor Chinese medicine, but the exact mechanism of action of β-elemene in colorectal cancer (CRC) remains unclear. This study aimed to explore the mechanism of the lncRNA-miRNA-mRNA network in the process of β-elemene inhibiting CRC. Methods. RNA sequencing was performed on CRC cells from the control group (untreated) and the case group (β-elemene-treated). According to the sequencing data, we screened the differentially expressed (DE) lncRNAs, miRNAs, and mRNAs and then analyzed them by functional enrichment analyses. Through the lncRNA-miRNA-mRNA network, the key miRNAs and mRNAs involved in the process of β-elemene inhibiting CRC were further identified. Results. Totally, 607 upregulated and 599 downregulated DElncRNAs, 12 downregulated and 24 upregulated DEmiRNAs, and 3153 downregulated and 3248 upregulated DEmRNAs were identified. Through the lncRNA-miRNA-mRNA network, 3 miRNAs (miR-7109-3p, miR-4506, and miR-3182), 7 prognostic mRNAs (ALPG, DTX1, HOXD13, RIMS3, SLC16A8, SYT1, and TNNT1), and 2 key mRNAs (RIMS3 and SLC16A8) were determined to participate in the inhibitory mechanism of β-elemene in CRC. Conclusion. This study revealed for the first time that the lncRNA-miRNA-mRNA network is involved in the regulation of β-elemene in CRC, and these identified miRNAs and mRNAs could be new clinical prognostic biomarkers and therapeutic targets for CRC patients.

Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma

Background: Glioblastoma (GBM) is the most invasive brain tumors, and it is associated with high rates of recurrence and mortality. The purpose of this study was to investigate the expression of RBM8A in GBM and the potential influence of its expression on the disease.

Methods: Levels of RBM8A mRNA in GBM patients and controls were examined in The Cancer Genome Atlas (TCGA), GSE16011 and GSE90604 databases. GBM samples in TCGA were divided into RBM8A^high and RBM8A^low groups. Differentially expressed genes (DEGs) between GBM patients and controls were identified, as were DEGs between RBM8A^high and RBM8A^low groups. DEGs common to both of these comparisons were analyzed for coexpression and regression analyses. In addition, we identified potential effects of RBM8A on competing endogenous RNAs, immune cell infiltration, methylation modifications, and somatic mutations.

Results: RBM8A is expressed at significantly higher levels in GBM than control samples, and its level correlates with tumor purity. We identified a total of 488 mRNAs that differed between GBM and controls as well as between RBM8A^high and RBM8A^low groups, which enrichment analysis revealed to be associated mainly with neuroblast proliferation, and T cell immune responses. We identified 174 mRNAs that gave areas under the receiver operating characteristic curve >0.7 among coexpression module genes, of which 13 were significantly associated with overall survival of GBM patients. We integrated 11 candidate mRNAs through LASSO algorithm, then nomogram, risk score, and decision curve analyses were analyzed. We found that RBM8A may compete with DLEU1 for binding to miR-128-1-5p, and aberrant RBM8A expression was associations with tumor infiltration by immune cells. Some mRNAs associated with GBM prognosis also appear to be methylated or mutated.

Conclusions: Our study strongly links RBM8A expression to GBM pathobiology and patient prognosis. The candidate mRNAs identified here may lead to therapeutic targets against the disease.

Effects and molecular mechanisms of Achyranthes bidentata polypeptide k on proliferation of Schwann cells

Background

Achyranthes bidentata polypeptide k (ABPPk) is an active ingredient separated from the Achyranthes bidentata polypeptides (ABPP) in traditional Chinese medicine. In the present study, we investigated the promoting effects and molecular mechanisms of ABPPk on the proliferation of Schwann cells (SCs).

Methods

Primary SCs were cultured with ABPPk or nerve growth factor (NGF) in vitro, and cell viability, cell cycle, EdU assay, and the expressions of proliferating cell nuclear antigen (PCNA) and Ki67 were analyzed. In addition, RNA-seq was used for bioinformatics analysis at different time points. PCNA was detected at different time points in a rat sciatic nerve injury model to further determining the role of ABPPk in sciatic nerve injury repair.

Results

We found that ABPPk could effectively promote the proliferation of SCs, while ABPPk and NGF had different molecular mechanisms for their proliferation at different time points. Weighted gene co-expression network analysis (WGCNA) showed that ABPPk was mainly involved in the positive regulation of cell proliferation and epigenetic regulation of cell proliferation, while the main cell proliferation-related modules that NGF participated in were attenuation of negative regulation of cell proliferation and positive regulation of cell cycle. There were significant differences in the genes involved in different modules between the two groups, and ABPPk differed from NGF in the biological process of SC migration, differentiation, movement, and development in terms of action time and key genes. Functional enrichment analysis revealed ABPPk had more advantages and participation in the axon extension and vascular system areas. Furthermore, ABPPk significantly promoted the proliferation of SCs in vivo.

Conclusions

Through in vitro and in vivo studies, we identified the promoting effects of ABPPk on the proliferation of SCs. Using high-throughput sequencing technology, our work more comprehensively revealed the characteristics and mechanism of ABPPk on SCs. These results further enrich an understanding of the positive function and molecular mechanism of ABPPk in peripheral nerve regeneration and are conducive to the discovery of new therapeutic targets for peripheral nerve regeneration.

Scouting for common genes in the heterogenous hypoxic tumor microenvironment and their validation in glioblastoma

Investigating the therapeutic and prognostic potential of genes in the heterogeneous hypoxic niche of glioblastoma. We have analyzed RNA expression of U87MG cells cultured in hypoxia compared to normoxia. Common differentially expressed genes (DEGs) from GSE45301 and GSE18494 and their functional enrichment was performed using MetaScape and PANTHER. Hub genes and their ontology were identified using MCode cytoHubba and ClueGO and validated with GlioVis, Oncomine, HPA and PrognoScan. Using the GEO2R analysis of GSE45301 and GSE18494 datasets, we have found a total of 246 common DEGs (180 upregulated and 66 downregulated) and identified 2 significant modules involved in ribosome biogenesis and TNF signaling. Meta-analysis of key genes of each module in cytoHubba identified 17 hub genes (ATF3, BYSL, DUSP1, EGFR, JUN, ETS1, LYAR, NIP7, NOLC1, NOP2, NOP56, PNO1, RRS1, TNFAIP3, TNFRSF1B, UTP15, VEGFA). Of the 17 hub genes, ATF3, BYSL, EGFR, JUN, NIP7, NOLC1, PNO1, RRS1, TNFAIP3 and VEGFA were identified as hypoxia signatures associated with poor prognosis in Glioma. Ribosome biogenesis emerged as a vital contender of possible therapeutic potential with BYSL, NIP7, NOLC1, PNO1 and RRS1 showing prognostic value.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02987-2.

Identification of Hub Genes of Mesio Temporal Lobe Epilepsy and Prognostic Biomarkers of Brain Low-grade Gliomas Based on Bioinformatics Analysis

Mesio temporal lobe epilepsy (MTLE) syndrome is the most common form of intractable epilepsies. Meanwhile, seizures are common in patients with cancer as a consequence of brain tumors, including brain low-grade gliomas (LGG). However, the underlying molecular mechanisms of MTLE remain poorly understood. Also, the relationship between MTLE and LGG needs our attention. In this study, we aimed to investigate the hub genes and potential mechanism in MTLE, and the relationship between MTLE and LGG, the gene expression profiles (GSE88992) were downloaded from the Gene Expression Omnibus (GEO) database. Difference analysis for MTLE versus control groups under the three time points was conducted to select the differentially expressed genes (DEGs). Time series clustering analysis was used to select the trend genes. Then a series of bioinformatics analyses including functional enrichment analysis, protein–protein interaction (PPI) network and module analyses, and transcription factor (TF) and miRNA prediction were performed. Also, the overall survival analysis and expression of hub genes in LGG were performed using UALCAN from TCGA database. At 6 h, there were 351 upregulated and 80 downregulated DEGs. At 12 h, there were 499 upregulated and 231 downregulated DEGs. Additionally, 532 upregulated and 402 downregulated DEGs were obtained at 24 h. After time series clustering analysis of the DEGs, we obtained 323 uptrend and 248 downtrend genes. We identified 10 key genes with higher degrees, including C3, TIMP1, PENK, CKAP4, etc. Five PPI modules were identified by MCODE. TF analysis predicted four TFs: JUN, STAT3, NR4A2, and Myc. A total of 26,834 miRNA–mRNA pairs were predicted. Moreover, survival analysis of UALCAN suggested that C3, TIMP1, PENK, GNG2, CKAP4, TNC, JUN, STAT3, NR4A2, and Myc can be potential biomarkers for the prognosis of LGG. In summary, DEGs and hub genes were identified in the present study, which provides novel insight into the development of MTLE.

Systematic Analysis of 4-gene Prognostic Signature in Patients with Diffuse Gliomas Based on Gene Expression Profiles

Background: Diffuse gliomas are a group of diseases that contain different degrees of malignancy and complex heterogeneity. Previous studies proposed biomarkers for certain grades of gliomas, but few of them have conducted a systematic analysis of different grades to search for molecular markers.

Methods: WGCNA was used to find significant genes associated with malignant progression of diffuse glioma in TCGA glioma sequencing expression data and the GEO expression profile-merge meta dataset. Lasso regression was used for potential model building and the best model was selected by CPE, IDI, and C_index. Risk score model was used to evaluate the gene signature prognostic power. Multi-omics data, including CNV, methylation, clinical traits, and mutation, were used for model evaluation.

Results: We found out 67 genes significantly associated with malignant progression of diffuse glioma by WGCNA. Next, we established a new 4 gene molecular marker (KDELR2, EMP3, TIMP1, and TAGLN2). Multivariate cox analysis identified the risk score of the 4 genes as an independent predictor of prognosis in patients with diffuse gliomas, and its predictive power was independent of the histopathological grades of glioma. Further, we had confirmed in five independent test datasets and the risk score remained good predictive power. The combination of the prognosis model with specific molecular characteristics possessed a better predictive power. Furthermore, we divided the low-risk group into three subtypes: LowRisk_IDH1^wt, LowRisk_IDH1^mut/ATRX^mut, and LowRisk_IDH1^mut/ATRX^wt by combining IDH1 mutation with ATRX mutation, which possessed obvious survival difference. In further analysis, we found that the 4 gene prognosis model possessed multi-omics features.

Conclusion: We established a malignant-related 4-gene molecular marker by glioma expression profile data from multiple microarrays and sequencing data. The four markers had good predictive power on the overall survival of glioma patients and were associated with gliomas' clinical and genetic backgrounds, including clinical features, gene mutation, methylation, CNV, signal pathways.

SAMD9 Is Relating With M2 Macrophage and Remarkable Malignancy Characters in Low-Grade Glioma

Immunoreactions regulated by TAMs (Tumor-associated macrophages) play a pivotal role in tumorigenesis and metastasis. In recent decades, treatments based on immune regulation have achieved revolutionary breakthroughs in cancer targeted therapies. The phenotypes of TAMs in gliomas are more heterogeneous and inherently complex than can be simply defined by classification into the M1 and M2 polarized states. The detailed mechanisms surrounding infiltrating macrophage phenotype and glioma characteristics remain undefined. SAMD9 (Sterile Alpha Motif Domain-Containing Protein 9) was found to be highly expressed in glioma and closely related to histological and genetic features in CGGA and TCGA databases. Simultaneously, we present evidence to show that there was a positive association between SAMD9 and malignancy characters in LGG. Univariable and Multivariate proportional hazard Cox analysis showed that SAMD9 was an independent prognostic factor for LGG. Surprisingly, Gene Ontology (GO) analysis showed SAMD9 expression level was remarkably well correlated with immunological responses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis supported the connection with immune responses and tumorigenesis. Immune infiltration analysis demonstrated that high SAMD9 expression resulted in an accumulation of macrophages by CIBERSORT and TIMER databases, especially positively related to macrophage total marker gene AIF1 and Macrophage M2 marker gene CD163. IHC staining further indicated a high correlation of SAMD9 with those specific macrophage markers in the immune response. Human THP-1 cells were induced into M2 macrophages, which were then co-cultured with LN229 cells. Silencing of SAMD9 by shRNA in LN229 cells attenuated the infiltration abilities of M2 macrophage. SAMD9 explored immune response via relating of M2 macrophage in vitro. Our results revealed SAMD9 acted as the malignancy characters in LGG, enrichment with M2 macrophage.

The Influence of Gene Aberrations on Survival in Resected IDH Wildtype Glioblastoma Patients: A Single-Institution Study

This prospective population-based study on a group of 132 resected IDH-wildtype (IDH-wt) glioblastoma (GBM) patients assesses the prognostic and predictive value of selected genetic biomarkers and clinical factors for GBM as well as the dependence of these values on the applied therapeutic modalities. The patients were treated in our hospital between June 2006 and June 2015. Clinical data and tumor samples were analyzed to determine the frequencies of TP53, MDM2, EGFR, RB1, BCR, and CCND1 gene aberrations and the duplication/deletion statuses of the 9p21.3, 1p36.3, 19q13.32, and 10p11.1 chromosome regions. Cut-off values distinguishing low (LCN) and high (HCN) copy number status for each marker were defined. Additionally, MGMT promoter methylation and IDH1/2 mutation status were investigated retrospectively. Young age, female gender, Karnofsky scores (KS) above 80, chemoradiotherapy, TP53 HCN, and CCND1 HCN were identified as positive prognostic factors, and smoking was identified as a negative prognostic factor. Cox proportional regression models of the chemoradiotherapy patient group revealed TP53 HCN and CCND1 HCN to be positive prognostic factors for both progression-free survival and overall survival. These results confirmed the influence of key clinical factors (age, KS, adjuvant oncotherapy, and smoking) on survival in GBM IDH-wt patients and demonstrated the prognostic and/or predictive importance of CCND1, MDM2, and 22q12.2 aberrations.

Integrative analysis of mRNA and miRNA sequencing data for gliomas of various grades

Background

The purpose of this study was to characterize subtype-specific patterns of mRNA and miRNA expression of gliomas using The Cancer Genome Atlas (TCGA) data to search for genetic determinants that predict prognosis in terms of overall survival and to create interaction networks for grade 2 and 3 (G2 and G3) astrocytomas, oligodendrogliomas and grade 4 (G4) glioblastoma multiforme. Based on open-access TCGA data, 5 groups were formed: astrocytoma G2 (n = 58), astrocytoma G3 (n = 128), oligodendroglioma G2 (n = 102), oligodendroglioma G3 (n = 72) and glioblastoma G4 (n = 564); normal samples of brain tissue were also analysed (n = 15). Data of patient age, sex, survival and expression patterns of mRNA and miRNA were extracted for each sample. After stratification of the data into groups, a differential analysis of expression was carried out, genes and miRNAs that affect overall survival were identified and gene set enrichment analysis (GSEA) and interaction analysis were performed.

Results

A total of 939 samples of glial tumours were analysed, for which subtype-specific expression profiles of genes and miRNAs were identified and networks of mRNA-miRNA interactions were constructed. Genes whose aberrant expression level was associated with survival were determined, and pairwise correlations between differential gene expression (DEG) and differential miRNA expression (DE miRNA) were calculated.

Conclusions

The developed panel of genes and miRNAs allowed us to differentiate glioma subtypes and evaluate prognosis in terms of the overall survival of patients. The regulatory miRNA-mRNA pairs unique to the five glioma subtypes identified in this study can stimulate the development of new therapeutic approaches based on subtype-specific mechanisms of oncogenesis.

Functional genomics of AP-2α and AP-2γ in cancers: in silico study

BACKGROUND

Among all causes of death, cancer is the most prevalent and is only outpaced by cardiovascular diseases. Molecular theory of carcinogenesis states that apoptosis and proliferation are regulated by groups of tumor suppressors or oncogenes. Transcription factors are example of proteins comprising representatives of both cancer-related groups. Exemplary family of transcription factors which exhibits dualism of function is Activating enhancer-binding Protein 2 (AP-2). Scientific reports concerning their function in carcinogenesis depend on particular family member and/or tumor type which proves the issue to be unsolved. Therefore, the present study examines role of the best-described AP-2 representatives, AP-2α and AP-2γ, through ontological analysis of their target genes and investigation what processes are differentially regulated in 21 cancers using samples deposited in Genomic Data Analysis Center (GDAC) Firehose.

METHODS

Expression data with clinical annotation was collected from TCGA-dedicated repository GDAC Firehose. Transcription factor targets were obtained from Gene Transcription Regulation Database (GTRD), TRANScription FACtor database (TRANSFAC) and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Monocle3 R package was used for global samples profiling while Protein ANalysis THrough Evolutionary Relationships (PANTHER) tool was used to perform gene ontology analysis.

RESULTS

With RNA-seq data and Monocle3 or PANTHER tools we outlined differences in many processes and signaling pathways, separating tumor from normal tissues or tumors from each other. Unexpectedly, a number of alterations in basal-like breast cancer were identified that distinguished it from other subtypes, which could bring future clinical benefits.

CONCLUSIONS

Our findings indicate that while the AP-2α/γ role remains ambiguous, their activity is based on processes that underlie the cancer hallmarks and their expression could have potential in diagnosis of selected tumors.

Cancer classification based on chromatin accessibility profiles with deep adversarial learning model

Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify distinct clusters from different cancer types. Numerous analyses have been conducted for this propose. Still, the methods they used always do not directly support the high-dimensional omics data across the whole genome (Such as ATAC-seq profiles). In this study, based on the deep adversarial learning, we present an end-to-end approach ClusterATAC to leverage high-dimensional features and explore the classification results. On the ATAC-seq dataset and RNA-seq dataset, ClusterATAC has achieved excellent performance. Since ATAC-seq data plays a crucial role in the study of the effects of non-coding regions on the molecular classification of cancers, we explore the clustering solution obtained by ClusterATAC on the pan-cancer ATAC dataset. In this solution, more than 70% of the clustering are single-tumor-type-dominant, and the vast majority of the remaining clusters are associated with similar tumor types. We explore the representative non-coding loci and their linked genes of each cluster and verify some results by the literature search. These results suggest that a large number of non-coding loci affect the development and progression of cancer through its linked genes, which can potentially advance cancer diagnosis and therapy.

CeRNA Network Analysis Representing Characteristics of Different Tumor Environments Based on 1p/19q Codeletion in Oligodendrogliomas

Simple Summary

Oligodendroglioma (OD) is a subtype of glioma occurring in the central nervous system. The 1p/19q codeletion is a prognostic marker of OD with an isocitrate dehydrogenase (IDH) mutation and is associated with a clinically favorable overall survival (OS). The long non-coding RNAs (lncRNAs) protects the mRNA from degradation by binding with the same miRNA by acting as a competitive endogenous RNA (ceRNA). Recently, although there is an increasing interest in lncRNAs on glioma studies, however, studies regarding their effects on OD and the 1p/19q codeletion remain limited. In our study, we performed in silico analyses using low-grade gliomas from datasets obtained from The Cancer Genome Atlas to investigate the effects of ceRNA with 1p/19q codeletion on ODs. We constructed 16 coding RNA–miRNA–lncRNA networks and the ceRNA network participated in ion channel activity, insulin secretion, and collagen network and extracellular matrix (ECM) changes. In conclusion, our results can provide insights into the possibility in the different tumor microenvironments and OS following 1p/19q codeletion through changes in the ceRNA network.

Abstract

Oligodendroglioma (OD) is a subtype of glioma occurring in the central nervous system. The 1p/19q codeletion is a prognostic marker of OD with an isocitrate dehydrogenase (IDH) mutation and is associated with a clinically favorable overall survival (OS); however, the exact underlying mechanism remains unclear. Long non-coding RNAs (lncRNAs) have recently been suggested to regulate carcinogenesis and prognosis in cancer patients. Here, we performed in silico analyses using low-grade gliomas from datasets obtained from The Cancer Genome Atlas to investigate the effects of ceRNA with 1p/19q codeletion on ODs. Thus, we selected modules of differentially expressed genes that were closely related to 1p/19q codeletion traits using weighted gene co-expression network analysis and constructed 16 coding RNA–miRNA–lncRNA networks. The ceRNA network participated in ion channel activity, insulin secretion, and collagen network and extracellular matrix (ECM) changes. In conclusion, ceRNAs with a 1p/19q codeletion can create different tumor microenvironments via potassium ion channels and ECM composition changes; furthermore, differences in OS may occur. Moreover, if extrapolated to gliomas, our results can provide insights into the consequences of identical gene expression, indicating the possibility of tracking different biological processes in different subtypes of glioma.

Multivariate analysis reveals differentially expressed genes among distinct subtypes of diffuse astrocytic gliomas: diagnostic implications

Diagnosis and classification of gliomas mostly relies on histopathology and a few genetic markers. Here we interrogated microarray gene expression profiles (GEP) of 268 diffuse astrocytic gliomas-33 diffuse astrocytomas (DA), 52 anaplastic astrocytomas (AA) and 183 primary glioblastoma (GBM)-based on multivariate analysis, to identify discriminatory GEP that might support precise histopathological tumor stratification, particularly among inconclusive cases with II-III grade diagnosed, which have different prognosis and treatment strategies. Microarrays based GEP was analyzed on 155 diffuse astrocytic gliomas (discovery cohort) and validated in another 113 tumors (validation set) via sequential univariate analysis (pairwise comparison) for discriminatory gene selection, followed by nonnegative matrix factorization and canonical biplot for identification of discriminatory GEP among the distinct histological tumor subtypes. GEP data analysis identified a set of 27 genes capable of differentiating among distinct subtypes of gliomas that might support current histological classification. DA + AA showed similar molecular profiles with only a few discriminatory genes overexpressed (FSTL5 and SFRP2) and underexpressed (XIST, TOP2A and SHOX2) in DA vs AA and GBM. Compared to DA + AA, GBM displayed underexpression of ETNPPL, SH3GL2, GABRG2, SPX, DPP10, GABRB2 and CNTN3 and overexpression of CHI3L1, IGFBP3, COL1A1 and VEGFA, among other differentially expressed genes.

Identification of gene expression and DNA methylation of SERPINA5 and TIMP1 as novel prognostic markers in lower-grade gliomas

Background

Lower-grade gliomas (LGGs) is characteristic with great difference in prognosis. Due to limited prognostic biomarkers, it is urgent to identify more molecular markers to provide a more objective and accurate tumor classification system for LGGs.

Methods

In the current study, we performed an integrated analysis of gene expression data and genome-wide methylation data to determine novel prognostic genes and methylation sites in LGGs.

Results

To determine genes that differentially expressed between 44 short-term survivors (<2 years) and 48 long-term survivors (≥2 years), we searched LGGs TCGA RNA-seq dataset and identified 106 differentially expressed genes. SERPINA5 and TIMP1 were selected for further study. Kaplan-Meier plots showed that SERPINA5 and TIMP1 expression were significantly correlated with overall survival (OS) and relapse-free survival (RFS) in TCGA LGGs patients. We next validated the correlation between the candidate genes expression and clinical outcome in CGGA LGGs patients. Multivariate analysis showed that TIMP1 mRNA expression had a significant prognostic value independent of other variables (HR = 4.825, 95% CI = 1.370-17.000, P = 0.014). Then, differential methylation sites were identified from differentially candidate gene expression groups, and all four methylation sites were significantly negatively correlated with gene expression (spearman r <  - 0.5, P < 0.0001). Moreover, hyper-methylation of four methylation sites indicated better OS (P < 0.05), and three of them also shown statistical significantly association with better RFS, except for SERPINA5 cg15509705 (P = 0.0762).

Conclusion

Taken together, these findings indicated that the gene expression and methylation of SERPINA5 and TIMP1 may serve as prognostic predictors in LGGs and may help to precise the current histology-based tumors classification system and to provide better stratification for future clinical trials.