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pmid:33221754

An eleven metabolic gene signature-based prognostic model for clear cell renal cell carcinoma.

Yue Wu · Xian Wei · Huan Feng · Bintao Hu · Bo Liu · Yang Luan · Yajun Ruan · Xiaming Liu · Zhuo Liu · Shaogang Wang · Jihong Liu · Tao Wang

Aging · 2020 doi:10.18632/aging.104088

Paper

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Experiment

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Data

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Analysis

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Execution

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title
An eleven metabolic gene signature-based prognostic model for clear cell renal cell carcinoma.
doi
10.18632/aging.104088
pmid
33221754
authors array · 12 items
[0]
Yue Wu
[1]
Xian Wei
[2]
Huan Feng
[3]
Bintao Hu
[4]
Bo Liu
[5]
Yang Luan
[6]
Yajun Ruan
[7]
Xiaming Liu
[8]
Zhuo Liu
[9]
Shaogang Wang
[10]
Jihong Liu
[11]
Tao Wang
journal
Aging
year
2020
abstract
In this study, we performed bioinformatics and statistical analyses to investigate the prognostic significance of metabolic genes in clear cell renal cell carcinoma (ccRCC) using the transcriptome data of 539 ccRCC and 72 normal renal tissues from TCGA database. We identified 79 upregulated and 45 downregulated (n=124) metabolic genes in ccRCC tissues. Eleven prognostic metabolic genes (NOS1, ALAD, ALDH3B2, ACADM, ITPKA, IMPDH1, SCD5, FADS2, ACHE, CA4, and HK3) were identified by further analysis. We then constructed an 11-metabolic gene signature-based prognostic risk score model and classified ccRCC patients into high- and low-risk groups. Overall survival (OS) among the high-risk ccRCC patients was significantly shorter than among the low-risk ccRCC patients. Receiver operating characteristic (ROC) curve analysis of the prognostic risk score model showed that the areas under the ROC curve for the 1-, 3-, and 5-year OS were 0.810, 0.738, and 0.771, respectively. Thus, our prognostic model showed favorable predictive power in the TCGA and E-MTAB-1980 ccRCC patient cohorts. We also established a nomogram based on these eleven metabolic genes and validated internally in the TCGA cohort, showing an accurate prediction for prognosis in ccRCC.
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Then, the log-rank test was used to compare the differences in overall survival times between the two groups.
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sections[17] "Evaluation of the 11-metabolic gene signature-based prognostic model" paragraph 4
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Then, the log-rank test was used to compare the differences in overall survival times between the two groups.
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We downloaded the transcriptome data consisting of 72 normal renal and 539 ccRCC tissue samples from The Cancer Genome Atlas database (TCGA, https://portal.gdc.cancer.gov/). We also downloaded the clinical data of all the ccRCC patients.
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sections[12] "RNA-seq data analysis of ccRCC patients" paragraph 2
quote
We downloaded the transcriptome data consisting of 72 normal renal and 539 ccRCC tissue samples from The Cancer Genome Atlas database (TCGA, https://portal.gdc.cancer.gov/). We also downloaded the clinical data of all the ccRCC patients.
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KEGG pathway and GO enrichment analysis of differentially expressed metabolic genes.
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Multivariate Cox regression analysis to identify prognosis-related metabolic genes.
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Evaluation of the 11-metabolic gene signature-based prognostic risk score model
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Table 2
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Multivariate Cox regression analysis to identify prognosis-related metabolic genes.
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NOS1
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0.0154
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0.9095
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0.1425
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0.5058
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0.0490
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1.0502
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0.0583
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0.8396
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0.4011
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1.1958
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0.2212
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0.8084
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0.4189
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1.2477
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0.1045
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2.1166
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0.0343
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The prognostic value of different clinical parameters.
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Evaluation of the 11-metabolic gene signature-based prognostic risk score model
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Functional enrichment analysis of the differentially expressed metabolic genes
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KEGG pathway and GO enrichment analysis of differentially expressed metabolic genes.
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6.93E-7
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[1]
monooxygenase activity
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1.75E-8
[3]
8.24E-7
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[1]
transferase activity, transferring glycosyl groups
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4.15E-8
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0.000002
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vitamin binding
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5.28E-8
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0.000002
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[1]
oxidoreductase activity, acting on the aldehyde or oxo group of donors
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0.000003
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0.000084
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[1]
oxidoreductase activity, acting on CH-OH group of donors
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0.000003
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0.000095
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metabolic pathways
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drug metabolism
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2.43E-12
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3.71E-10
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[1]
retinol metabolism
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3.41E-12
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3.71E-10
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[1]
chemical carcinogenesis
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6.20E-11
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3.62E-9
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[1]
purine metabolism
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6.67E-11
[3]
3.62E-9
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[0]
[1]
porphyrin and chlorophyll metabolism
[2]
1.24E-9
[3]
5.76E-8
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[0]
[1]
steroid hormone biosynthesis
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3.52E-9
[3]
1.43E-7
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[1]
metabolism of xenobiotics by cytochrome P450
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3.97E-9
[3]
1.44E-7
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carbon metabolism
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6.20E-8
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0.000002
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asset:data:geo:E-MTAB-1980 not downloaded
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asset:data:geo:E-MTAB-1980 licence not read
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method-1
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Then, the log-rank test was used to compare the differences in overall survival times between the two groups.
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normalise_counts
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count_matrix_with_condition
evidence object · 3 fields
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0.8
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edgeR
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network_analysis
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Network / pathway analysis
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pathway_enrich
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true
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GO enrichment
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0.7
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ROC curve
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capability:computational_analysis
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Automated Assays Laravel application and simulated adapters
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: log_rank_test.
capability object · 4 fields
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The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
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E-MTAB-1980
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[0]
We downloaded the transcriptome data consisting of 72 normal renal and 539 ccRCC tissue samples from The Cancer Genome Atlas database (TCGA, https://portal.gdc.cancer.gov/). We also downloaded the clinical data of all the ccRCC patients.
basis
The paper names E-MTAB-1980 in ArrayExpress and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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source:pmid:33221754
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run_analysis
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cell renal cell carcinoma (ccRCC) using the transcriptome data of 539 ccRCC and 72 normal renal tissue
[1]
tabolic genes (n=274). MATERIALS AND METHODS RNA-seq data analysis of ccRCC patients We downloade
[2]
ata analysis, we first pre-processed the raw RNA-seq data with the edgeR package (http://www.bioc
[3]
olic genes in ccRCC. We downloaded the ccRCC transcriptome data for 72 normal renal tissue samples and
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paper_text_order:234
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Complete file 
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33221754", "kind": "science-record", "revision": 8, "content_hash": "sha256:670730eff13c5fe2b19b04b1a6ddbcf1a7bffb799bd994253cb03cf3db1b1112", "created_at": "2026-11-01T15:41:16Z", "updated_at": "2026-11-01T22:02:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An eleven metabolic gene signature-based prognostic model for clear cell renal cell carcinoma.", "doi": "10.18632/aging.104088", "pmid": "33221754", "authors": ["Yue Wu", "Xian Wei", "Huan Feng", "Bintao Hu", "Bo Liu", "Yang Luan", "Yajun Ruan", "Xiaming Liu", "Zhuo Liu", "Shaogang Wang", "Jihong Liu", "Tao Wang"], "journal": "Aging", "year": 2020, "abstract": "In this study, we performed bioinformatics and statistical analyses to investigate the prognostic significance of metabolic genes in clear cell renal cell carcinoma (ccRCC) using the transcriptome data of 539 ccRCC and 72 normal renal tissues from TCGA database. We identified 79 upregulated and 45 downregulated (n=124) metabolic genes in ccRCC tissues. Eleven prognostic metabolic genes (NOS1, ALAD, ALDH3B2, ACADM, ITPKA, IMPDH1, SCD5, FADS2, ACHE, CA4, and HK3) were identified by further analysis. We then constructed an 11-metabolic gene signature-based prognostic risk score model and classified ccRCC patients into high- and low-risk groups. Overall survival (OS) among the high-risk ccRCC patients was significantly shorter than among the low-risk ccRCC patients. Receiver operating characteristic (ROC) curve analysis of the prognostic risk score model showed that the areas under the ROC curve for the 1-, 3-, and 5-year OS were 0.810, 0.738, and 0.771, respectively. Thus, our prognostic model showed favorable predictive power in the TCGA and E-MTAB-1980 ccRCC patient cohorts. We also established a nomogram based on these eleven metabolic genes and validated internally in the TCGA cohort, showing an accurate prediction for prognosis in ccRCC.", "claims": [{"id": "claim-method", "statement": "Then, the log-rank test was used to compare the differences in overall survival times between the two groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33221754", "locator": "sections[17] \"Evaluation of the 11-metabolic gene signature-based prognostic model\" paragraph 4", "quote": "Then, the log-rank test was used to compare the differences in overall survival times between the two groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded the transcriptome data consisting of 72 normal renal and 539 ccRCC tissue samples from The Cancer Genome Atlas database (TCGA, https://portal.gdc.cancer.gov/). 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{"score": 0.7, "band": "medium", "quote": "ROC curve"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["logrank_test"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: log_rank_test."}, "capability": {"tier": "R1", "capability_id": 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["E-MTAB-1980"], "resolved_accessions": ["E-MTAB-1980"], "statements": ["We downloaded the transcriptome data consisting of 72 normal renal and 539 ccRCC tissue samples from The Cancer Genome Atlas database (TCGA, https://portal.gdc.cancer.gov/). We also downloaded the clinical data of all the ccRCC patients."], "basis": "The paper names E-MTAB-1980 in ArrayExpress and the accession resolves in the repository. 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MATERIALS AND METHODS RNA-seq data analysis of ccRCC patients We downloade", "ata analysis, we first pre-processed the raw RNA-seq data with the edgeR package (http://www.bioc", "olic genes in ccRCC. We downloaded the ccRCC transcriptome data for 72 normal renal tissue samples and"], "order_basis": "paper_text_order:234", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["logrank_test"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Then, the log-rank test was used to compare the differences in overall survival times between the two groups."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}