A pseudogene-signature in glioma predicts survival

UBDP1 pseudogene and UBD network competitively bind miR‑6072 to promote glioma progression

Increasing evidence suggests that pseudogenes play crucial roles in various cancers, yet their functions and regulatory mechanisms in glioma pathogenesis remain enigmatic. In the present study, a novel pseudogene was identified, UBDP1, which is significantly upregulated in glioblastoma and positively correlated with the expression of its parent gene, UBD. Additionally, high levels of these paired genes are linked with a poor prognosis for patients. In the present study, clinical samples were collected followed by various analyses including microarray for long non‑coding RNAs, reverse transcription‑quantitative PCR, fluorescence in situ hybridization and western blotting. Cell lines were authenticated and cultured then subjected to various assays for proliferation, migration, and invasion to investigate the molecular mechanisms. Bioinformatic tools identified miRNA targets, and luciferase reporter assays validated these interactions. A tumor xenograft model in mice was used for in vivo studies. In vitro and in vivo studies have demonstrated that UBDP1, localized in the cytoplasm, functions as a tumor‑promoting factor influencing cell proliferation, migration, invasion and tumor growth. Mechanistic investigations have indicated that UBDP1 exerts its oncogenic effects by decoying miR‑6072 from UBD mRNA, thus forming a competitive endogenous RNA network, which results in the enhanced oncogenic activity of UBD. The present findings offered new insights into the role of pseudogenes in glioma progression, suggesting that targeting the UBDP1/miR‑6072/UBD network may serve as a potential therapeutic strategy for glioma patients.

Systematic investigation of the prognostic impact of clonal status of somatic mutations across multiple cancer types

Tumors are genetically heterogeneous and many mutations are actually present in subclonal populations. The clonal status of mutations is valuable for accurate prognosis, clinical management. The aim of this study was to identify the clonal status of somatic mutations and systematically evaluate their prognostic values across various cancer types. We totally identified 227 clonal and 432 subclonal mutations contributed to prognosis and demonstrated the importance of clonal status in improving mutation-related clinical guidance. We further developed a customized multi-step approach to identify gene-specific prognostic patterns of clonal status at pan-cancer level and found some cancer-specific prognostic patterns. The 'subclonal-dependent risk' subpattern was one of the most common subpatterns, it usually accompanied by high genomic in-stability and high extent of intra-tumor heterogeneity and could be used to improve the accuracy of prognostic analysis. Our results revealed the importance of clonal status, especially subclonal mutation in clinical survival.

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.

Pseudogenes as Biomarkers and Therapeutic Targets in Human Cancers

Pseudogenes are commonly labeled as "junk DNA" given their perceived nonfunctional status. However, the advent of large-scale genomics projects prompted a revisit of pseudogene biology, highlighting their key functional and regulatory roles in numerous diseases, including cancers. Integrative analyses of cancer data have shown that pseudogenes can be transcribed and even translated, and that pseudogenic DNA, RNA, and proteins can interfere with the activity and function of key protein coding genes, acting as regulators of oncogenes and tumor suppressors. Capitalizing on the available clinical research, we are able to get an insight into the spread and variety of pseudogene biomarker and therapeutic potential. In this chapter, we describe pseudogenes that fulfill their role as diagnostic or prognostic biomarkers, both as unique elements and in collaboration with other genes or pseudogenes. We also report that the majority of prognostic pseudogenes are overexpressed and exert an oncogenic role in colorectal, liver, lung, and gastric cancers. Finally, we highlight a number of pseudogenes that can establish future therapeutic avenues.

The LGMN pseudogene promotes tumor progression by acting as a miR-495-3p sponge in glioblastoma

Pseudogenes, which are long noncoding RNAs that originate from protein-coding genes, have been suggested to play important roles in disease. Although studies have revealed high expression of legumain (LGMN) in many types of tumors, the regulation of LGMN remains largely unknown. Here, we found that a novel LGMN pseudogene (LGMNP1) was upregulated in glioblastoma (GBM) tissues and high LGMNP1 expression in GBM cells enhanced proliferation and invasion. Biochemical analysis showed that cytoplasmic LGMNP1 functionally targeted miR-495-3p in a manner involving an RNA-induced silencing complex. Dual-luciferase reporter assays demonstrated that LGMN was a target of miR-495-3p, and LGMN was upregulated and positively correlated with LGMNP1 in GBM. Moreover, miR-495-3p was downregulated and negatively correlated with LGMNP1 in GBM tissues. Notably, the tumor-promoting effects of LGMNP1 upregulation could be alleviated by miR-495-3p mimics. Furthermore, GBM cells overexpressing LGMNP1 exhibited more aggressive tumor progression and elevated LGMN expression in vivo. Thus, our data illustrate that LGMNP1 exerts its oncogenic activity, at least in part, as a competitive endogenous RNA (ceRNA) that elevates LGMN expression by sponging miR-495-3p. CeRNA-mediated miRNA sequestration might be a novel therapeutic strategy in GBM.

Optimized Sample Preparation Workflow for Improved Identification of Ghost Proteins

Large scale proteomic strategies rely on database interrogation. Thus, only referenced proteins can be identified. Recently, Alternative Proteins (AltProts) translated from nonannotated Alternative Open reading frame (AltORFs) were discovered using customized databases. Because of their small size which confers them peptide-like physicochemical properties, they are more difficult to detect using standard proteomics strategies. In this study, we tested different preparation workflows for improving the identification of AltProts in NCH82 human glioma cell line. The highest number of identified AltProts was achieved with RIPA buffer or boiling water extraction followed by acetic acid precipitation.

ceRNA network analysis reveals prognostic markers for glioblastoma

Glioblastoma (GBM) is a common aggressive cancer that originates in the brain, which has a poor prognosis. It is therefore crucial to understand its underlying genetic mechanisms in order to develop novel therapies. The present study aimed to identify some prognostic markers and candidate therapeutic targets for GBM. To do so, RNA expression levels in tumor and normal tissues were compared by microarray analysis. The differential expression of RNAs in normal and cancer tissues was analyzed, and a competing endogenous RNA (ceRNA) network was constructed for pathway analysis. The results revealed that RNA expression patterns were considerably different between normal and tumor samples. A ceRNA network was therefore constructed with the differentially expressed RNAs. ETS variant 5 (ETV5), myocyte enhancer factor 2C and ETS transcription factor (ELK4) were considerably enriched in the significant pathway of ‘transcriptional misregulation in cancer’. In addition, prognostic analysis demonstrated that ETV5 and ELK4 expression levels were associated with the survival time of patients with GBM. These results suggested that ELK4 and ETV5 may be prognostic markers for GBM, and that their microRNAs may be candidate therapeutic targets.

CHI3L1, NTRK2, 1p/19q and IDH Status Predicts Prognosis in Glioma

The aim of this study was to identify relevant biomarkers for the prognosis of glioma considering current molecular changes such as IDH mutation and 1p19q deletion. Gene expression profiling was performed using the TaqMan Low Density Array and hierarchical clustering using 96 selected genes in 64 patients with newly diagnosed glioma. The expression dataset was validated on a large independent cohort from The Cancer Genome Atlas (TCGA) database. A differential expression panel of 26 genes discriminated two prognostic groups regardless of grade and molecular groups of tumors: Patients having a poor prognosis with a median overall survival (OS) of 23.0 ± 9.6 months (group A) and patients having a good prognosis with a median OS of 115.0 ± 6.6 months (group B) (p = 0.007). Hierarchical clustering of the glioma TCGA cohort supported the prognostic value of these 26 genes (p < 0.0001). Among these genes, CHI3L1 and NTRK2 were identified as factors that can be associated with IDH status and 1p/19q co-deletion to distinguish between prognostic groups of glioma from the TCGA cohort. Therefore, CHI3L1 associated with NTRK2 seemed to be able to provide new information on glioma prognosis.

Identification of a Five-Pseudogene Signature for Predicting Survival and Its ceRNA Network in Glioma

Background: Glioma is the most common primary brain tumor with a dismal prognosis. It is urgent to develop novel molecular biomarkers and conform to individualized schemes. Methods: Differentially expressed pseudogenes between low grade glioma (LGG) and glioblastoma multiforme (GBM) were identified in the training cohort. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox proportional hazards regression analyses were used to select pseudogenes associated with prognosis of glioma. A risk signature was constructed based on the selected pseudogenes for predicting the survival of glioma patients. A pseudogene-miRNA-mRNA regulatory network was established and visualized using Cytoscape 3.5.1. Gene Oncology (GO) and signaling pathway analyses were performed on the targeted genes to investigate functional roles of the risk signature. Results: Five pseudogenes (ANXA2P2, EEF1A1P9, FER1L4, HILS1, and RAET1K) correlating with glioma survival were selected and used to establish a risk signature. Time-dependent receiver operating characteristic (ROC) curves revealed that the risk signature could accurately predict the 1, 3, and 5-year survival of glioma patients. GO and signaling pathway analyses showed that the risk signature was involved in regulation of proliferation, migration, angiogenesis, and apoptosis in glioma. Conclusions: In this study, a risk signature with five pseudogenes was constructed and shown to accurately predict 1-, 3-, and 5-year survival for glioma patient. The risk signature may serve as a potential target against glioma.

Classifying lower grade glioma cases according to whole genome gene expression

OBJECTIVE

To identify a gene-based signature as a novel prognostic model in lower grade gliomas.

RESULTS

A gene signature developed from HOXA7, SLC2A4RG and MN1 could segregate patients into low and high risk score groups with different overall survival (OS), and was validated in TCGA RNA-seq and GSE16011 mRNA array datasets. Receiver operating characteristic (ROC) was performed to show that the three-gene signature was more sensitive and specific than histology, grade, age, IDH1 mutation and 1p/19q co-deletion. Gene Set Enrichment Analysis (GSEA) and GO analysis showed high-risk samples were associated with tumor associated macrophages (TAMs) and highly invasive phenotypes. Moreover, HOXA7-siRNA inhibited migration and invasion in vitro, and downregulated MMP9 at the protein level in U251 glioma cells.

METHODS

A cohort of 164 glioma specimens from the Chinese Glioma Genome Atlas (CGGA) array database were assessed as the training group. TCGA RNA-seq and GSE16011 mRNA array datasets were used for validation. Regression analyses and linear risk score assessment were performed for the identification of the three-gene signature comprising HOXA7, SLC2A4RG and MN1.

CONCLUSIONS

We established a three-gene signature for lower grade gliomas, which could independently predict overall survival (OS) of lower grade glioma patients with higher sensitivity and specificity compared with other clinical characteristics. These findings indicate that the three-gene signature is a new prognostic model that could provide improved OS prediction and accurate therapies for lower grade glioma patients.

Pseudogenes of annexin A2, novel prognosis biomarkers for diffuse gliomas

Diffuse gliomas is a kind of common malignant primary brain tumor. Pseudogenes have multilayered biological function in the progression of human cancers. In this study, Differentially Expressed Pseudogenes (DEPs) between glioblastomas and non-tumor controls were found by bioinformatics analysis, of which the annexin A2 pseudogenes (ANXA2P1, ANXA2P2 and ANXA2P3) were significantly up-regulated, along with the parent gene annexin A2 (ANXA2). Among four glioblastoma subtypes, ANXA2P1 and ANXA2P2 were preferentially expressed in mesenchymal subtype and less expressed in proneural subtype. Meanwhile, Pearson’s correlation analysis revealed that the expression level of ANXA2 was positively correlated with ANXA2 pseudogenes expression. Then, the expression patterns of ANXA2 and its pseudogenes were validated in diffuse glioma specimens (n=99) and non-tumor tissues (n=12) by quantitative real-time PCR (qRT-PCR). Additionally, Kaplan–Meier analysis revealed that highly expressed ANXA2 and annexin A2 pseudogenes were associated with the poor survival outcome of glioma patients. Cox regression analyses suggested that ANXA2, ANXA2P1 and ANXA2P2 were the independent prognosis factors for gliomas. Furthermore, down-regulation of ANXA2 and ANXA2 pseudogenes might contribute to the improvement of patients’ survival who received chemotherapy and radiotherapy. These results demonstrated that ANXA2 pseudogenes and ANXA2 could be used as the novel biomarkers for diagnosis, prognosis and target therapy of gliomas.

Quercetin sensitizes glioblastoma to t-AUCB by dual inhibition of Hsp27 and COX-2 in vitro and in vivo

Background

Evidences indicate that inflammatory process plays pivotal role in tumor disease. Soluble epoxide hydrolase inhibitors (sEHIs) have been shown to participate in anti-inflammation and tumorigenesis by protecting epoxyeicosatrienoic acids (EETs). Although we have previously revealed some effects of t-AUCB on glioma in vitro, further investigations are needed to demonstrate its effects on glioblastoma growth in vivo and how to strengthen its antitumor effect.

Methods

CCK-8 kit was used to test cell growth. Cell migration capacity was performed by wound healing assays. Transwell assay was used to test cell invasion potency. Cell-cycle analysis and cell apoptosis was performed by flow cytometry. The activity of caspase-3 in cells was measured using caspase-3 activity assay kits. Total RNA was extracted from cells lysated by TRIzol reagent. qRT-PCR was performed by ABI 7500 fast RT- PCR system. Lipofectamine RNAiMAX Transfection Reagent (Invitrogen) was used for siRNA transfection. Western blootting was used to test protein expression. Tumor cell xenograft mouse models were used for in vivo study. The SPSS version 17.0 software was applied for statistical analysis.

Results

Our data shown that t-AUCB inhibits cell proliferation, migration and invasion and induces cell cycle G1 phase arrest in vitro but induces no cell apoptosis; increased Hsp27 activation and following COX-2 overexpression confer resistance to t-AUCB treatment in glioblastoma both in vitro and in vivo; quercetin sensitizes glioblastoma to t-AUCB by dual inhibition of Hsp27 and COX-2 in vitro and in vivo.

Conclusions

These results indicate that combination of t-AUCB and quercetin may be a potential approach to treating glioblastoma.

Integrated analysis of genome-wide DNA methylation, gene expression and protein expression profiles in molecular subtypes of WHO II-IV gliomas

Background

Glioma is the most common malignant primary brain tumor among adults, among which glioblastoma (GBM) exhibits the highest malignancy. Despite current standard chemoradiation, glioma is still invariably fatal. A further insight into the molecular background of glioma is required to improve patient outcomes.

Method

Previous studies evaluated molecular genetic differences through comparing different grades of glioma. Here, we integrated DNA methylation, RNA sequencing and protein expression data sets of WHO grade II to IV gliomas, to screen for dysregulated genes in subtypes during malignant progression of glioma.

Results

We propose a list of universal genes (UG) as novel glioma biomarkers: 977 up-regulated genes and 114 down-regulated genes, who involved in cell cycle, Wnt receptor signaling pathway and fatty acid metabolic process. Poorer survival was associated significantly with the high expression of 977 up-regulated genes and low expression of 114 down-regulated in UG (P <0.001).

Conclusion

To our knowledge, this was the first study that focused on subtypes to detect dysregulated genes that could contribute to malignant progression. Furthermore, the differentially expressed genes profile may lead to the identification of new therapeutic targets for glioma patients.

Pseudogenes in Human Cancer

Recent advances in the analysis of RNA sequencing data have shown that pseudogenes are highly specific markers of cell identity and can be used as diagnostic and prognostic markers. Furthermore, genetically engineered mouse models have recently provided compelling support for a causal link between altered pseudogene expression and cancer. In this review, we discuss the most recent milestones reached in the pseudogene field and the use of pseudogenes as cancer classifiers.