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pmid:35432534

Expression Profiles of HOXC6 Predict the Survival of Glioblastoma Patients and Correlate with Cell Cycle.

Zhen-Hang Li · Yue Ma · Yan Zhou · Zhen-Hua Huang

Journal of oncology · 2022 doi:10.1155/2022/8656865

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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title
Expression Profiles of HOXC6 Predict the Survival of Glioblastoma Patients and Correlate with Cell Cycle.
doi
10.1155/2022/8656865
pmid
35432534
authors array · 4 items
[0]
Zhen-Hang Li
[1]
Yue Ma
[2]
Yan Zhou
[3]
Zhen-Hua Huang
journal
Journal of oncology
year
2022
abstract
The goal of this study was to investigate the homeobox (HOX) gene expression status and its prognostic value in glioblastoma multiforme (GBM) and to uncover the biological processes related to its expression. The prognostic value of HOX genes in GBM was systematically investigated by a genome-wide analysis of HOX gene expression profiles in GBM patient samples in The Cancer Genome Atlas (TCGA) project (microarray dataset) and validation datasets. Using the differentially expressed gene (DEG) analysis and a Cox regression model, we discovered that the HOXC6 could stratify patients into significantly different survival (p = 0.0012, log-rank test) groups in the training cohort. TCGA RNA-seq and GSE16011 datasets were used for validation. Multivariate Cox and stratification analysis indicated that HOXC6 was an independent prognostic factor after adjusting for other clinical covariates. Bioinformatic analysis suggested that the HOXC6 might be involved in the cell cycle-related biological processes and pathways that are well established in the context of glioblastoma tumorigenesis. We further explored the bioinformatic implications by gene set enrichment analysis (GSEA). Tumor cell biology experiments verified the role of HOXC6 in proliferation and cell cycle progression. In conclusion, HOXC6 might be a candidate biomarker gene for individual treatment optimization of glioblastoma. HOXC6 expression has a significant prognostic value and is related to the cell cycle process in glioblastoma.
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Using the differentially expressed gene (DEG) analysis and a Cox regression model, we discovered that the HOXC6 could stratify patients into significantly different survival (p = 0.0012, log-rank test) groups in the training cohort.
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Using the differentially expressed gene (DEG) analysis and a Cox regression model, we discovered that the HOXC6 could stratify patients into significantly different survival (p = 0.0012, log-rank test) groups in the training cohort.
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The preprocessed normalized expression data and clinical data of 525 GBM Microarray (training cohort) and 153 GBM RNA-seq samples belong to the TCGA dataset, retrieved from the website http://cancergenome.nih.gov/. The normalized expression data and clinical data of the GSE16011 dataset comprised of 155 GBM samples were retrieved from the Gene Expression Omnibus website.
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The preprocessed normalized expression data and clinical data of 525 GBM Microarray (training cohort) and 153 GBM RNA-seq samples belong to the TCGA dataset, retrieved from the website http://cancergenome.nih.gov/. The normalized expression data and clinical data of the GSE16011 dataset comprised of 155 GBM samples were retrieved from the Gene Expression Omnibus website.
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title
Intrinsic Gene Expression Profiles of Gliomas are a Better Predictor of Survival than Histology
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The preprocessed normalized expression data and clinical data of 525 GBM Microarray (training cohort) and 153 GBM RNA-seq samples belong to the TCGA dataset, retrieved from the website http://cancergenome.nih.gov/. The normalized expression data and clinical data of the GSE16011 dataset comprised of 155 GBM samples were retrieved from the Gene Expression Omnibus website.
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. Material and Methods 2.3. Cell Culture and Transfection The human glioblastoma cell lines LN229 and
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Using the differentially expressed gene (DEG) analysis and a Cox regression model, we discovered that the HOXC6 could stratify patients into significantly different survival (p = 0.0012, log-rank test) groups in the training cohort.
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The prognostic value of HOX genes in GBM was systematically investigated by a genome-wide analysis of HOX gene expression profiles in GBM patient samples in The Cancer Genome Atlas (TCGA) project (microarray dataset) and validation datasets. Using the differentially expressed gene (DEG) analysis and a Cox regression model, we discovered that the HOXC6 could stratify patients into significantly different survival (p = 0.0012, log-rank test) groups in the training cohort. TCGA RNA-seq and GSE16011 datasets were used for validation. Multivariate Cox and stratification analysis indicated that HOXC6 was an independent prognostic factor after adjusting for other clinical covariates. Bioinformatic analysis suggested that the HOXC6 might be involved in the cell cycle-related biological processes and pathways that are well established in the context of glioblastoma tumorigenesis. We further explored the bioinformatic implications by gene set enrichment analysis (GSEA). Tumor cell biology experiments verified the role of HOXC6 in proliferation and cell cycle progression. In conclusion, HOXC6 might be a candidate biomarker gene for individual treatment optimization of glioblastoma. 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