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pmid:33626208

Construction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real-world data.

Xi Tian · Wen-Hao Xu · Aihetaimujiang Anwaier · Hong-Kai Wang · Fang-Ning Wan · Da-Long Cao · Wen-Jie Luo · Guo-Hai Shi · Yuan-Yuan Qu · Hai-Liang Zhang · Ding-Wei Ye

Journal of cellular and molecular medicine · 2021 doi:10.1111/jcmm.16323

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complete

Experiment

complete

Data

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:33626208
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science-record
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2026-11-01T15:41:26Z
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2026-11-01T21:24:44Z
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title
Construction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real-world data.
doi
10.1111/jcmm.16323
pmid
33626208
authors array · 11 items
[0]
Xi Tian
[1]
Wen-Hao Xu
[2]
Aihetaimujiang Anwaier
[3]
Hong-Kai Wang
[4]
Fang-Ning Wan
[5]
Da-Long Cao
[6]
Wen-Jie Luo
[7]
Guo-Hai Shi
[8]
Yuan-Yuan Qu
[9]
Hai-Liang Zhang
[10]
Ding-Wei Ye
journal
Journal of cellular and molecular medicine
year
2021
abstract
This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large-scale multiomics analysis and real-world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis-related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI's GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P < .001), TFRC (P < .001), TSC1 (P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki-67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients' prognosis using proteomic data, gene expression data and real-world data and this prognostic model showed stronger predictive value than other biomarkers (Ki-67, Beta-catenin, etc) in multi-cohorts.
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Homo sapiens
title
Human adrenocortical carcinomas (33), adenomas (22), and normal adrenal cortex (10), on Affymetrix HG_U133_plus_2 arrays
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Clinicopathological characteristics of 22 adult ACC patients (GSE10927 cohort)
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Kaplan‐Meier analysis and univariate Cox regression analysis of proteins (both P‐value < .05)
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6.531 (1.64‐26.011)
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.001
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7.007 (2.517‐19.511)
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14.431 (1.033‐201.583)
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TSC1
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.001
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.001
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16.639 (2.965‐93.39)
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.042
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.043
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18.739 (1.098‐319.658)
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.003
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20.533 (2.432‐173.352)
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.042
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.006
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51.697 (3.028‐882.701)
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.007
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.008
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68.124 (2.962‐1566.613)
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.038
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.010
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77.659 (2.787‐2164.284)
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RAD50
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.005
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.019
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158.062 (2.325‐10744.279)
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TAZ
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.003
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768.971 (10.143‐58295.167)
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Evaluating biomarkers of ACC in multiple cohorts
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External validation of the IPRPs model and comparison with other biomarkers using gene expression data
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Table 5
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Evaluating biomarkers of ACC in multiple cohorts
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TCGA (RPPA)
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TCGA (RNAseq)
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0.489
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0.571
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0.676
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0.659
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0.551
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0.853
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0.694
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0.705
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0.649
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0.939
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0.789
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0.657
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0.72
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Identification of DEGs associated with the IPRPs
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Table 6
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GO and KEGG pathways enrichment analysis of hub genes
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Description
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GO:0 140 014
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Mitotic nuclear division
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GO:0 000 280
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Organelle fission
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1.61E‐27
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GO:0 000 775
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Chromosome, centromeric region
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24
[3]
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GO:0 000 776
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21
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1.48E‐37
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GO:0 098 687
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Chromosomal region
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25
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1.04E‐36
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GO:0 008 017
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Microtubule binding
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1.65E‐08
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GO:0 035 173
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GO:0 015 631
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1.83E‐07
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hsa04110
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7.26E‐12
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hsa04914
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Progesterone‐mediated oocyte maturation
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1.13E‐10
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7.02E‐10
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asset:data:geo:GSE10927 not downloaded
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asset:data:geo:GSE10927 licence not read
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cox_ph
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time_to_event_with_censoring
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“Kaplan‐Meier analysis was performed based on the median protein abundance value and univariate Cox regression was used to evaluate the prognostic value of candidate proteins.”
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Automated Assays Laravel application and simulated adapters
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cox_ph
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capability object · 4 fields
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capability:computational_analysis
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tier
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GSE10927
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GSE10927
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The datasets analysed during the current study available from the corresponding author on reasonable request.
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The paper names GSE10927 in NCBI’s GEO database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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cox_ph
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[0]
ated proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of
[1]
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nger predictive value than other biomarkers (Ki-67, Beta-catenin, etc) in multi-cohorts.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33626208", "kind": "science-record", "revision": 6, "content_hash": "sha256:316c444b89764a352943b5b1715c7449ae34e389c44e1cfaef7bdd13307038ca", "created_at": "2026-11-01T15:41:26Z", "updated_at": "2026-11-01T21:24:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real-world data.", "doi": "10.1111/jcmm.16323", "pmid": "33626208", "authors": ["Xi Tian", "Wen-Hao Xu", "Aihetaimujiang Anwaier", "Hong-Kai Wang", "Fang-Ning Wan", "Da-Long Cao", "Wen-Jie Luo", "Guo-Hai Shi", "Yuan-Yuan Qu", "Hai-Liang Zhang", "Ding-Wei Ye"], "journal": "Journal of cellular and molecular medicine", "year": 2021, "abstract": "This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large-scale multiomics analysis and real-world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis-related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI's GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P < .001), TFRC (P < .001), TSC1 (P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki-67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). 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"read_table", "reader_status": "available", "identifier": "section[7]/table-wrap[1]#jcmm16323-tbl-0003", "version": null, "note": "Clinicopathological characteristics of 39 adult ACC patients (Fudan University Shanghai Cancer Center cohort)", "structure": {"pmcid": "PMC8051734", "section_index": 7, "section_title": "Validation of the IPRPs model in a cohort from the Fudan University Shanghai Cancer Center (FUSCC) in China", "row_count": 23, "column_count": 2}, "table": {"label": "Table 3", "caption": "Clinicopathological characteristics of 39 adult ACC patients (Fudan University Shanghai Cancer Center cohort)", "rows": [["Characteristics", "Entire cohort (N = 39)"], ["N (%)"], ["Age"], ["<70 years", "34 (87.2)"], ["≥70 years", "5 (12.8)"], ["Gender"], ["Male", "19 (48.7)"], ["Female", "20 (51.3)"], ["AJCC stage"], ["I‐II", "14 (35.9)"], ["III‐IV", "25 (64.1)"], ["T Stage a"], ["T1 ‐ T2", "20 (51.3)"], ["T3 ‐ T4", "19 (48.7)"], ["N stage a"], ["N0", "19 (48.7)"], ["N1", "20 (51.3)"], 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["ECADHERIN", ".006", ".004", "0.133 (0.033‐0.527)"], ["INPP4B", ".002", ".019", "0.137 (0.026‐0.722)"], ["ERALPHA", ".004", ".050", "0.164 (0.027‐0.999)"], ["SMAC", ".003", ".002", "0.188 (0.065‐0.543)"], ["PKCALPHA_pS657", ".024", ".001", "0.281 (0.133‐0.594)"], ["EPPK1", ".027", ".011", "0.343 (0.151‐0.779)"], ["FASN", ".039", ".015", "1.943 (1.139‐3.316)"], ["CYCLINB1", ".000", ".001", "2.198 (1.407‐3.434)"], ["PEA15", ".028", ".033", "2.209 (1.066‐4.577)"], ["TFRC", ".001", ".000", "2.771 (1.681‐4.567)"], ["YAP_pS127", ".014", ".030", "2.984 (1.109‐8.031)"], ["PARP1", ".002", ".002", "3.23 (1.553‐6.72)"], ["ERK2", ".000", ".000", "5.629 (2.168‐14.618)"], ["RBM15", ".008", ".008", "6.531 (1.64‐26.011)"], ["FIBRONECTIN", ".001", ".000", "7.007 (2.517‐19.511)"], ["DVL3", ".002", ".010", "10.978 (1.754‐68.72)"], ["IGF1R_pY1135Y1136", ".012", ".047", "14.431 (1.033‐201.583)"], ["TSC1", ".001", ".001", "16.639 (2.965‐93.39)"], ["JAK2", ".042", ".043", "18.739 (1.098‐319.658)"], 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"section_title": "External validation of the IPRPs model and comparison with other biomarkers using gene expression data", "row_count": 8, "column_count": 13}, "table": {"label": "Table 5", "caption": "Evaluating biomarkers of ACC in multiple cohorts", "rows": [["Biomarker evaluation", "AUC", "C‐index", "NRI"], ["Cohorts", "TCGA (RPPA)", "TCGA (RNAseq)", "GSE10927 (RNA seq)", "FUSCC IHC)", "TCGA (RPPA)", "TCGA (RNAseq)", "GSE10927 (RNAseq)", "FUSCC (IHC)", "TCGA (RPPA)", "TCGA (RNAseq)", "GSE10927 (RNAseq)", "FUSCC (IHC)"], ["CTNNB1", "0.4", "0.704", "0.732", "‐", "0.564", "0.659", "0.657", "‐", "ns", "‐0.02", "0.06", "‐"], ["IGF2", "‐", "0.313", "‐", "‐", "‐", "0.575", "‐", "‐", "‐", "ns", "‐", "‐"], ["TP53", "0.489", "0.87", "0.571", "‐", "0.676", "0.659", "0.551", "‐", "‐0.02", "ns", "ns", "‐"], ["MKI67", "‐", "0.862", "0.786", "0.621", "‐", "0.853", "0.671", "0.694", "‐", "0.37", "ns", "ns"], ["SF‐1", "‐", "0.71", "‐", "‐", "‐", "0.665", "‐", "‐", "‐", "‐0.07", "ns", "‐"], ["Risk 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