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Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods

BACKGROUND: To identify prognostic genes which were associated with adrenocortical carcinoma (ACC) tumor microenvironment (TME). METHODS AND MATERIALS: Transcriptome profiles and clinical data of ACC samples were collected from The Cancer Genome Atlas (TCGA) database. We use ESTIMATE (estimation of...

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Autores principales: Li, Xiao, Gao, Yang, Xu, Zicheng, Zhang, Zheng, Zheng, Yuxiao, Qi, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6997077/
https://www.ncbi.nlm.nih.gov/pubmed/31856409
http://dx.doi.org/10.1002/cam4.2774
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author Li, Xiao
Gao, Yang
Xu, Zicheng
Zhang, Zheng
Zheng, Yuxiao
Qi, Feng
author_facet Li, Xiao
Gao, Yang
Xu, Zicheng
Zhang, Zheng
Zheng, Yuxiao
Qi, Feng
author_sort Li, Xiao
collection PubMed
description BACKGROUND: To identify prognostic genes which were associated with adrenocortical carcinoma (ACC) tumor microenvironment (TME). METHODS AND MATERIALS: Transcriptome profiles and clinical data of ACC samples were collected from The Cancer Genome Atlas (TCGA) database. We use ESTIMATE (estimation of stromal and Immune cells in malignant tumor tissues using expression data) algorithm to calculate immune scores, stromal scores and estimate scores. Heatmap and volcano plots were applied for differential analysis. Venn plots were used for intersect genes selection. We used protein‐protein interaction (PPI) networks and functional analysis to explore underlying pathways. After performing stepwise regression method and multivariate Cox analysis, we finally screened hub genes associated with ACC TME. We calculated risk scores (RS) for ACC cases based on multivariate Cox results and evaluated the prognostic value of RS shown by receiver operating characteristic curve (ROC). We investigated the association between hub genes with immune infiltrates supported by algorithm from online TIMER database. RESULTS: Gene expression profiles and clinical data were downloaded from TCGA. Lower immune scores were observed in disease with distant metastasis (DM) and locoregional recurrence (LR) than other cases (P = .0204). Kaplan‐Meier analysis revealed that lower immune scores were significantly associated with poor overall survival (OS) (P = .0495). We screened 1649 differentially expressed genes (DEGs) and 1521 DEGs based on immune scores and stromal scores, respectively. Venn plots helped us find 1122 intersect genes. After analysing by cytoHubba from Cytoscape software, 18 hub genes were found. We calculated RS and ROC showed significantly predictive accuracy (area under curve (AUC) = 0.887). ACC patients with higher RS had worse survival outcomes (P < .0001). Results from TIMER (tumor immune estimation resource) database revealed that HLA‐DOA was significantly related with immune cells infiltration. CONCLUSION: We screened a list of TME‐related genes which predict poor survival outcomes in ACC patients from TCGA database.
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spelling pubmed-69970772020-02-05 Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods Li, Xiao Gao, Yang Xu, Zicheng Zhang, Zheng Zheng, Yuxiao Qi, Feng Cancer Med Cancer Biology BACKGROUND: To identify prognostic genes which were associated with adrenocortical carcinoma (ACC) tumor microenvironment (TME). METHODS AND MATERIALS: Transcriptome profiles and clinical data of ACC samples were collected from The Cancer Genome Atlas (TCGA) database. We use ESTIMATE (estimation of stromal and Immune cells in malignant tumor tissues using expression data) algorithm to calculate immune scores, stromal scores and estimate scores. Heatmap and volcano plots were applied for differential analysis. Venn plots were used for intersect genes selection. We used protein‐protein interaction (PPI) networks and functional analysis to explore underlying pathways. After performing stepwise regression method and multivariate Cox analysis, we finally screened hub genes associated with ACC TME. We calculated risk scores (RS) for ACC cases based on multivariate Cox results and evaluated the prognostic value of RS shown by receiver operating characteristic curve (ROC). We investigated the association between hub genes with immune infiltrates supported by algorithm from online TIMER database. RESULTS: Gene expression profiles and clinical data were downloaded from TCGA. Lower immune scores were observed in disease with distant metastasis (DM) and locoregional recurrence (LR) than other cases (P = .0204). Kaplan‐Meier analysis revealed that lower immune scores were significantly associated with poor overall survival (OS) (P = .0495). We screened 1649 differentially expressed genes (DEGs) and 1521 DEGs based on immune scores and stromal scores, respectively. Venn plots helped us find 1122 intersect genes. After analysing by cytoHubba from Cytoscape software, 18 hub genes were found. We calculated RS and ROC showed significantly predictive accuracy (area under curve (AUC) = 0.887). ACC patients with higher RS had worse survival outcomes (P < .0001). Results from TIMER (tumor immune estimation resource) database revealed that HLA‐DOA was significantly related with immune cells infiltration. CONCLUSION: We screened a list of TME‐related genes which predict poor survival outcomes in ACC patients from TCGA database. John Wiley and Sons Inc. 2019-12-19 /pmc/articles/PMC6997077/ /pubmed/31856409 http://dx.doi.org/10.1002/cam4.2774 Text en © 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Biology
Li, Xiao
Gao, Yang
Xu, Zicheng
Zhang, Zheng
Zheng, Yuxiao
Qi, Feng
Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods
title Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods
title_full Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods
title_fullStr Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods
title_full_unstemmed Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods
title_short Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods
title_sort identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods
topic Cancer Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6997077/
https://www.ncbi.nlm.nih.gov/pubmed/31856409
http://dx.doi.org/10.1002/cam4.2774
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