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pmid:33858449

Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism-related genes.

Zhihao Wang · Kidane Siele Embaye · Qing Yang · Lingzhi Qin · Chao Zhang · Liwei Liu · Xiaoqian Zhan · Fengdi Zhang · Xi Wang · Shenghui Qin

Cancer cell international · 2021 doi:10.1186/s12935-021-01915-x

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Experiment

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Data

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pmid:33858449
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title
Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism-related genes.
doi
10.1186/s12935-021-01915-x
pmid
33858449
authors array · 10 items
[0]
Zhihao Wang
[1]
Kidane Siele Embaye
[2]
Qing Yang
[3]
Lingzhi Qin
[4]
Chao Zhang
[5]
Liwei Liu
[6]
Xiaoqian Zhan
[7]
Fengdi Zhang
[8]
Xi Wang
[9]
Shenghui Qin
journal
Cancer cell international
year
2021
abstract
BACKGROUND: Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes (MRGs). METHODS: The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate cox regression analysis was performed to identify MRGs that related to overall survival (OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD. The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD. RESULTS: A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs-aldolase A (ALDOA), catalase (CAT), ectonucleoside triphosphate diphosphohydrolase-2 (ENTPD2), glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1), lactate dehydrogenase A (LDHA), and thymidylate synthetase (TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The high-risk group patients have higher levels of immune checkpoint molecules and are therefore more sensitive to immunotherapy. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cell lines and firstly found that ENTPD2 might played an important role on LUAD cells colon formation and migration. CONCLUSIONS: We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis, individualized immuno-/chemotherapeutic strategies and prognosis in patients with LUAD.
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The differentially expressed MRGs in LUAD and normal lung tissues were detected using the R package limma and the Wilcoxon test method [18].
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The differentially expressed MRGs in LUAD and normal lung tissues were detected using the R package limma and the Wilcoxon test method [18].
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All data generated or analyzed during the present study was downloaded from TCGA database, GEO database, HPA database and GDSC database.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33858449", "kind": "science-record", "revision": 7, "content_hash": "sha256:d70c82312ac29db52ff9ded9944e4bf5775f27257787373c85bf3ed8eb0f1f8d", "created_at": "2026-11-01T15:41:32Z", "updated_at": "2026-11-01T22:03:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism-related genes.", "doi": "10.1186/s12935-021-01915-x", "pmid": "33858449", "authors": ["Zhihao Wang", "Kidane Siele Embaye", "Qing Yang", "Lingzhi Qin", "Chao Zhang", "Liwei Liu", "Xiaoqian Zhan", "Fengdi Zhang", "Xi Wang", "Shenghui Qin"], "journal": "Cancer cell international", "year": 2021, "abstract": "BACKGROUND: Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes (MRGs).\nMETHODS: The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate cox regression analysis was performed to identify MRGs that related to overall survival (OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD. The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD.\nRESULTS: A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs-aldolase A (ALDOA), catalase (CAT), ectonucleoside triphosphate diphosphohydrolase-2 (ENTPD2), glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1), lactate dehydrogenase A (LDHA), and thymidylate synthetase (TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The high-risk group patients have higher levels of immune checkpoint molecules and are therefore more sensitive to immunotherapy. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cell lines and firstly found that ENTPD2 might played an important role on LUAD cells colon formation and migration.\nCONCLUSIONS: We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis, individualized immuno-/chemotherapeutic strategies and prognosis in patients with LUAD.", "claims": [{"id": "claim-method", "statement": "The differentially expressed MRGs in LUAD and normal lung tissues were detected using the R package limma and the Wilcoxon test method [18].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33858449", "locator": "sections[4] \"Differentially expressed MRGs and enrichment analysis\" paragraph 2", "quote": "The differentially expressed MRGs in LUAD and normal lung tissues were detected using the R package limma and the Wilcoxon test method [18]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during the present study was downloaded from TCGA database, GEO database, HPA database and GDSC database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33858449", "locator": "sections[31] \"notes\" paragraph 1", "quote": "All data generated or analyzed during the present study was downloaded from TCGA database, GEO database, HPA database and GDSC database."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33858449"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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