ArticleCancer medicine2020
Identification of prognostic genes in adrenocortical carcinoma microenvironment based on bioinformatic methods.
Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 98 citations in OpenAlex.
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- Liquid biopsy approaches and immunotherapy in colorectal cancer for precision medicine: Are we there yet?Frontiers in oncology · 2022Review
- A hypoxia risk score for prognosis prediction and tumor microenvironment in adrenocortical carcinoma.Frontiers in genetics · 2022Article
- Molecular genotyping of adrenocortical carcinoma: a systematic analysis of published literature 2019-2021.Current opinion in oncology · 2022Review
- C1R, CCL2, and TNFRSF1A Genes in Coronavirus Disease-COVID-19 Pathway Serve as Novel Molecular Biomarkers of GBM Prognosis and Immune Infiltration.Disease markers · 2022Article
- Multi-omics analysis of mScientific reports · 2021Article
- Identification of tumor microenvironment-related prognostic genes in colorectal cancer based on bioinformatic methods.Scientific reports · 2021Article
- Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer.Journal of cellular and molecular medicine · 2021Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo 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.
resultsGene 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.
conclusionWe screened a list of TME-related genes which predict poor survival outcomes in ACC patients from TCGA database.
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