Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association Analysis
Objective: Glioblastoma multiforme (GBM) is the most malignant primary brain tumor in adults. This study aimed to identify significant prognostic biomarkers related to GBM. Methods: We collected 3 GBM and 3 healthy human brain samples for transcriptome and proteomic sequencing analysis. Differential...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-05-01
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| Series: | Technology in Cancer Research & Treatment |
| Online Access: | https://doi.org/10.1177/15330338211035270 |
| _version_ | 1828811894572449792 |
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| author | Jiabin Wang MD Shi Yan MM Xiaoli Chen MM Aowen Wang MM Zhibin Han MD Binchao Liu MM Hong Shen MD |
| author_facet | Jiabin Wang MD Shi Yan MM Xiaoli Chen MM Aowen Wang MM Zhibin Han MD Binchao Liu MM Hong Shen MD |
| author_sort | Jiabin Wang MD |
| collection | DOAJ |
| description | Objective: Glioblastoma multiforme (GBM) is the most malignant primary brain tumor in adults. This study aimed to identify significant prognostic biomarkers related to GBM. Methods: We collected 3 GBM and 3 healthy human brain samples for transcriptome and proteomic sequencing analysis. Differentially expressed genes (DEGs) between GBM and control samples were identified using the edge R package in R. Functional enrichment analyses, prediction of long noncoding RNA target genes, and protein-protein interaction network analyses were performed. Subsequently, transcriptomic and proteomic association analyses, validation using The Cancer Genome Atlas (TCGA) database, and survival and prognostic analyses were conducted. Then the hub genes directly related to GBM were screened. Finally, the expression of key genes was verified by quantitative polymerase chain reaction (qPCR). Results: Totally, 1140 transcripts and 503 proteins were significantly up- or down-regulated. A total of 25 genes were upregulated and 62 were downregulated at both the transcriptome and proteome levels. Results from TCGA database showed that 84 of these 87 genes matched with transcriptome sequencing results. A Cox regression analysis suggested that Fibronectin 1( FN1 ) was a prognostic risk factor. The qPCR results showed that FN1 was significantly upregulated in GBM samples. Conclusions: FN1 may play a role in GBM progression through ECM-receptor interaction and PI3K-Akt signaling pathways. FN1 may be considered as a prognostic biomarkers related to GBM. |
| first_indexed | 2024-12-12T09:35:15Z |
| format | Article |
| id | doaj.art-bac6213638794eae883f947fee2e464f |
| institution | Directory Open Access Journal |
| issn | 1533-0338 |
| language | English |
| last_indexed | 2024-12-12T09:35:15Z |
| publishDate | 2022-05-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Technology in Cancer Research & Treatment |
| spelling | doaj.art-bac6213638794eae883f947fee2e464f2022-12-22T00:28:45ZengSAGE PublishingTechnology in Cancer Research & Treatment1533-03382022-05-012110.1177/15330338211035270Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association AnalysisJiabin Wang MD0Shi Yan MM1Xiaoli Chen MM2Aowen Wang MM3Zhibin Han MD4Binchao Liu MM5Hong Shen MD6 Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang, Harbin, Heilongjiang Province, China Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang, Harbin, Heilongjiang Province, China Department of Pain Management, The First Affiliated Hospital of Harbin Medical University, Nangang, Harbin, Heilongjiang Province, China Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang, Harbin, Heilongjiang Province, China Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang, Harbin, Heilongjiang Province, China Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang, Harbin, Heilongjiang Province, China Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Nangang, Harbin, Heilongjiang Province, ChinaObjective: Glioblastoma multiforme (GBM) is the most malignant primary brain tumor in adults. This study aimed to identify significant prognostic biomarkers related to GBM. Methods: We collected 3 GBM and 3 healthy human brain samples for transcriptome and proteomic sequencing analysis. Differentially expressed genes (DEGs) between GBM and control samples were identified using the edge R package in R. Functional enrichment analyses, prediction of long noncoding RNA target genes, and protein-protein interaction network analyses were performed. Subsequently, transcriptomic and proteomic association analyses, validation using The Cancer Genome Atlas (TCGA) database, and survival and prognostic analyses were conducted. Then the hub genes directly related to GBM were screened. Finally, the expression of key genes was verified by quantitative polymerase chain reaction (qPCR). Results: Totally, 1140 transcripts and 503 proteins were significantly up- or down-regulated. A total of 25 genes were upregulated and 62 were downregulated at both the transcriptome and proteome levels. Results from TCGA database showed that 84 of these 87 genes matched with transcriptome sequencing results. A Cox regression analysis suggested that Fibronectin 1( FN1 ) was a prognostic risk factor. The qPCR results showed that FN1 was significantly upregulated in GBM samples. Conclusions: FN1 may play a role in GBM progression through ECM-receptor interaction and PI3K-Akt signaling pathways. FN1 may be considered as a prognostic biomarkers related to GBM.https://doi.org/10.1177/15330338211035270 |
| spellingShingle | Jiabin Wang MD Shi Yan MM Xiaoli Chen MM Aowen Wang MM Zhibin Han MD Binchao Liu MM Hong Shen MD Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association Analysis Technology in Cancer Research & Treatment |
| title | Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association Analysis |
| title_full | Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association Analysis |
| title_fullStr | Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association Analysis |
| title_full_unstemmed | Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association Analysis |
| title_short | Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association Analysis |
| title_sort | identification of prognostic biomarkers for glioblastoma based on transcriptome and proteome association analysis |
| url | https://doi.org/10.1177/15330338211035270 |
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