Proteogenomic characterization and integrative analysis of glioblastoma multiforme

Song, Ying Chun; Lu, Gai Xia; Zhang, Hong Wei; Zhong, Xiao Ming; Cong, Xian Ling; Xue, Shao Bo; Kong, Rui; Li, Dan; Chang, Zheng; Wang, Xiao Feng; Zhang, Yun Jie; Sun, Ryan; Chai, Li; Xie, Ru Ting; Cai, Ming; Sun, Ming; Mao, Wenting; Yang, Hui; Shao, Yun; Fan, Su Yun; Wu, Ting; Xia, Qing; Lv, Zhong Wei; Fu, David A.; 于水,

doi:10.18632/oncotarget.21937

Cited by 20 publications

(17 citation statements)

References 14 publications

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“…For example, a correlation of only 17% was found between mRNAs and proteins in lung adenocarcinoma [ 28 , 34 ]; in prostate cancer, the correlation between gene expression and protein levels is also poor to moderate [ 35 ]. Song et al [ 36 ] performed proteomic profiling of eight GBs and their paired normal brain tissues and afterwards assessed overlap with RNA gene expression profiling from GEO and TCGA datasets of GBs. They found a correlation of only 2% between the differentially expressed proteins and genes from microarray.…”

Section: Discussionmentioning

confidence: 99%

Integration of transcriptome and proteome profiles in glioblastoma: looking for the missing link

et al. 2018

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BackgroundGlioblastoma (GB) is the most common and aggressive tumor of the brain. Genotype-based approaches and independent analyses of the transcriptome or the proteome have led to progress in understanding the underlying biology of GB. Joint transcriptome and proteome profiling may reveal new biological insights, and identify pathogenic mechanisms or therapeutic targets for GB therapy. We present a comparison of transcriptome and proteome data from five GB biopsies (TZ) vs their corresponding peritumoral brain zone (PBZ). Omic analyses were performed using RNA microarray chips and the isotope-coded protein label method (ICPL).ResultsAs described in other cancers, we found a poor correlation between transcriptome and proteome data in GB. We observed only two commonly deregulated mRNAs/proteins (neurofilament light polypeptide and synapsin 1) and 12 altered biological processes; they are related to cell communication, synaptic transmission and nervous system processes. This poor correlation may be a consequence of the techniques used to produce the omic profiles, the intrinsic properties of mRNA and proteins and/or of cancer- or GB-specific phenomena. Of interest, the analysis of the transcription factor binding sites present upstream from the open reading frames of all altered proteins identified by ICPL method shows a common binding site for the topoisomerase I and p53-binding protein TOPORS. Its expression was observed in 7/11 TZ samples and not in PBZ. Some findings suggest that TOPORS may function as a tumor suppressor; its implication in gliomagenesis should be examined in future studies.ConclusionsIn this study, we showed a low correlation between transcriptome and proteome data for GB samples as described in other cancer tissues. We observed that NEFL, SYN1 and 12 biological processes were deregulated in both the transcriptome and proteome data. It will be important to analyze more specifically these processes and these two proteins to allow the identification of new theranostic markers or potential therapeutic targets for GB.Electronic supplementary materialThe online version of this article (10.1186/s12867-018-0115-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Integration of transcriptome and proteome profiles in glioblastoma: looking for the missing link

et al. 2018

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“…Proteogenomic profiling of GBM tissues showed high expression of CD44 [23] and recent GBM systematic analysis further supported CD44 as a GBM cell surface antigen [26].…”

Section: Identifying Existing Drugs Targeting Gbm Signature and Cd44 mentioning

confidence: 79%

“…3). For tissue proteomics, we found several studies that performed comparative GBM vs. normal brain tissues proteome profiling [11,[20][21][22][23]. However, some of these studies either identified only limited number of proteins or the data are not downloadable.…”

Section: Identification Of Gbm Cell-surface Signature By Integration mentioning

confidence: 99%

Integration of RNA-Seq and proteomics data identifies glioblastoma multiforme surfaceome signature

Syafruddin

Nazarie

Moidu

et al. 2020

Preprint

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BackgroundGlioblastoma multiforme (GBM) is a highly lethal, stage IV brain tumor with a prevalence of approximately 2 per 10000 people globally. The cell surface proteins or surfaceome play significant roles in modulating cancer phenotypes and acting as information gateway in many oncogenic signaling pathways. Hence, surfaceome are attractive targets for cancer therapy due to their direct accessibility to drugs. Nonetheless, a comprehensive GBM surfaceome landscape has not been fully defined. Thus, the aim of this study is to define GBM-specific surfaceome genes and identify key cell surface genes that could potentially be developed as novel GBM biomarkers, or therapeutically targeted.MethodsWe integrated the RNA-Seq data from TCGA GBM (n=166) and GTEx normal brain cortex (n=408) databases to identify the significantly dysregulated surfaceome in GBM. This was followed by integrative analysis that combines transcriptomics, proteomics and protein-protein interaction network data to prioritize the high-confidence GBM surfaceome signature.ResultsAmong the 2,381 significantly dysregulated genes in GBM, 395 genes were classified as surfaceome. Via the integrative analysis, we identified 6 high-confidence GBM molecular signature, HLA-DRA, CD44, SLC1A5, EGFR, ITGB2, PTPRJ, which were significantly upregulated in GBM. The expression of these genes were validated in an independent transcriptomics database, which confirmed their upregulated expression in GBM. Importantly, high expression of CD44, PTPRJ and HLA-DRA is significantly associated with poor disease-free survival. Lastly, using the Drugbank database, we identified several clinically-approved drugs targeting the GBM molecular signature suggesting potential drug repurposing.ConclusionsIn summary, we identified and highlighted the key GBM surface-enriched repertoires that could be biologically relevant in supporting GBM pathogenesis. These genes could be further interrogated experimentally in future studies that could lead to efficient GBM diagnostic/prognostic markers or a therapeutic regimen to treat GBM.

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“…Mass spectrometry analysis of ve GBM cell lines revealed the upregulation of EGFR, CD44, PTPRJ, SLC1A5, F2R, and TSPAN6 proteins in these samples [12], whereby the expression level of these proteins were in concordance with our transcriptomics data analysis ( Figure 3). For tissue proteomics, we found several studies that performed comparative GBM vs. normal brain tissues proteome pro ling [11,[20][21][22][23]. However, some of these studies either identi ed only a limited number of proteins or the data are not downloadable.…”

Section: Resultsmentioning

confidence: 99%

Integration of RNA-Seq and proteomics data identifies glioblastoma multiforme surfaceome signature

Syafruddin

Nazarie

Moidu

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Glioblastoma multiforme (GBM) is a highly lethal, stage IV brain tumour with a prevalence of approximately 2 per 10000 people globally. The cell surface proteins or surfaceome serve as an information gateway in many oncogenic signalling pathways and are important in modulating cancer phenotypes. Dysregulation of surfaceome expression and activity have been shown to promote tumorigenesis. The expression of GBM surfaceome is a case in point; OMICS screening in cell-based system identified that this sub-proteome is largely perturbed in GBM. Additionally, since these cell surface proteins have ‘direct’ access to drugs, they are appealing targets for cancer therapy. However, a comprehensive aberrant GBM surfaceome landscape has not been fully defined. Thus, this study aimed to define GBM-specific surfaceome genes and identify key cell-surface genes that could potentially be developed as novel GBM biomarkers for therapeutic purposes. Methods: We integrated the RNA-Seq data from TCGA GBM (n=166) and GTEx normal brain cortex (n=408) databases to identify the significantly dysregulated surfaceome in GBM. This was followed by integrative analysis that combines transcriptomics, proteomics and protein-protein interaction network data to prioritize the high-confidence GBM surfaceome signature. Results: Of the 2,381 significantly dysregulated genes in GBM, 395 genes were classified as surfaceome. Via the integrative analysis, we identified 6 high-confidence GBM molecular signature, HLA-DRA, CD44, SLC1A5, EGFR, ITGB2, PTPRJ, which were significantly upregulated in GBM. The expression of these genes were validated in an independent transcriptomics database, which confirmed their upregulated expression in GBM. Importantly, high expression of CD44, PTPRJ and HLA-DRA is significantly associated with poor disease-free survival. Last, using the Drugbank database, we identified several clinically-approved drugs targeting the GBM molecular signature suggesting potential drug repurposing. Conclusions: In summary, we identified and highlighted the key GBM surface-enriched repertoires that could be biologically relevant in supporting GBM pathogenesis. These genes could be further interrogated experimentally in future studies that could lead to efficient diagnostic/prognostic markers or potential treatment options for GBM.

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Proteogenomic characterization and integrative analysis of glioblastoma multiforme

Cited by 20 publications

References 14 publications

Integration of transcriptome and proteome profiles in glioblastoma: looking for the missing link

Integration of transcriptome and proteome profiles in glioblastoma: looking for the missing link

Integration of RNA-Seq and proteomics data identifies glioblastoma multiforme surfaceome signature

Integration of RNA-Seq and proteomics data identifies glioblastoma multiforme surfaceome signature

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