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pmid:38983159

A novel amino acid metabolism-related gene signature to predict the overall survival of esophageal squamous cell carcinoma patients.

Kebing Chen · Zhizhong Lin · Yongshi Shen · Yijin Lin · Juhui Chen · Guy D Eslick · Yuanmei Chen · Yuanji Xu · Yong Xin

Journal of thoracic disease · 2024 doi:10.21037/jtd-24-818

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:38983159
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science-record
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2026-11-01T20:29:03Z
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2026-11-01T22:12:12Z
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paper object · 10 fields
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title
A novel amino acid metabolism-related gene signature to predict the overall survival of esophageal squamous cell carcinoma patients.
doi
10.21037/jtd-24-818
pmid
38983159
authors array · 9 items
[0]
Kebing Chen
[1]
Zhizhong Lin
[2]
Yongshi Shen
[3]
Yijin Lin
[4]
Juhui Chen
[5]
Guy D Eslick
[6]
Yuanmei Chen
[7]
Yuanji Xu
[8]
Yong Xin
journal
Journal of thoracic disease
year
2024
abstract
Background: Esophageal squamous cell carcinoma (ESCC) has a poor early detection rate, prognosis, and survival rate. Effective prognostic markers are urgently needed to assist in the prediction of ESCC treatment outcomes. There is accumulating evidence of a strong relationship between cancer cell growth and amino acid metabolism. This study aims to determine the relationship between amino acid metabolism and ESCC prognosis. Methods: This study comprehensively evaluates the association between amino acid metabolism-related gene (AAMRG) expression profiles and the prognosis of ESCC patients based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the expression of prognosis-related genes. Results: A univariate Cox regression analysis of TCGA data identified 18 prognosis-related AAMRGs. The gene expression profiles of 90 ESCC tumor and normal tissues were obtained from the GSE20347 and GSE67269 datasets. Two differently expressed genes (DEGs) were considered as ESCC prognosis-related genes; and they were branched-chain amino acid transaminase 1 (BCAT1) and methylmalonic aciduria and homocystinuria type C protein (MMACHC). These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients. Both BCAT1 and MMACHC expression were verified by RT-qPCR. A prognostic nomogram that incorporated clinical factors and BCAT1 and MMACHC gene expression was constructed, and the calibration plots showed that it had good prognostic performance. Conclusions: The AAMRG signature established in our study is efficient and could be used in clinical settings to predict the early prognosis of ESCC patients.
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To evaluate the ability of the AAMRGs to predict the survival of patients with ESCC, we used TCGA-ESCC dataset as the test set, and we conducted a single-factor Cox regression analysis to screen the prognostic-related genes with the threshold set at a P value <0.05.
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sections[5] "Single-factor prognostic screening" paragraph 2
quote
To evaluate the ability of the AAMRGs to predict the survival of patients with ESCC, we used TCGA-ESCC dataset as the test set, and we conducted a single-factor Cox regression analysis to screen the prognostic-related genes with the threshold set at a P value <0.05.
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Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-818/dss
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sections[26] "fn-group" paragraph 1
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Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-818/dss
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These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients.
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sections[0] "Abstract" paragraph 3
quote
These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients.
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These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients.
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Analysis of gene expression in esophageal squamous cell carcinoma (ESCC)
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title
Integrative analysis of miRNAs and matched gene expression in ESCC
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2026-11-01T15:39:55Z
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To evaluate the ability of the AAMRGs to predict the survival of patients with ESCC, we used TCGA-ESCC dataset as the test set, and we conducted a single-factor Cox regression analysis to screen the prognostic-related genes with the threshold set at a P value <0.05.
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time quantitative polymerase chain reaction (RT-qPCR) was used to verify the expression of progno
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BCAT1 and MMACHC expression were verified by RT-qPCR. A prognostic nomogram that incorporated cli
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To evaluate the ability of the AAMRGs to predict the survival of patients with ESCC, we used TCGA-ESCC dataset as the test set, and we conducted a single-factor Cox regression analysis to screen the prognostic-related genes with the threshold set at a P value <0.05.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38983159", "kind": "science-record", "revision": 3, "content_hash": "sha256:cfe720db20b13cc916187e08c34848f3264b7818632f543df48db9f177ede8ce", "created_at": "2026-11-01T20:29:03Z", "updated_at": "2026-11-01T22:12:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel amino acid metabolism-related gene signature to predict the overall survival of esophageal squamous cell carcinoma patients.", "doi": "10.21037/jtd-24-818", "pmid": "38983159", "authors": ["Kebing Chen", "Zhizhong Lin", "Yongshi Shen", "Yijin Lin", "Juhui Chen", "Guy D Eslick", "Yuanmei Chen", "Yuanji Xu", "Yong Xin"], "journal": "Journal of thoracic disease", "year": 2024, "abstract": "Background: Esophageal squamous cell carcinoma (ESCC) has a poor early detection rate, prognosis, and survival rate. Effective prognostic markers are urgently needed to assist in the prediction of ESCC treatment outcomes. There is accumulating evidence of a strong relationship between cancer cell growth and amino acid metabolism. This study aims to determine the relationship between amino acid metabolism and ESCC prognosis.\nMethods: This study comprehensively evaluates the association between amino acid metabolism-related gene (AAMRG) expression profiles and the prognosis of ESCC patients based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the expression of prognosis-related genes.\nResults: A univariate Cox regression analysis of TCGA data identified 18 prognosis-related AAMRGs. The gene expression profiles of 90 ESCC tumor and normal tissues were obtained from the GSE20347 and GSE67269 datasets. Two differently expressed genes (DEGs) were considered as ESCC prognosis-related genes; and they were branched-chain amino acid transaminase 1 (BCAT1) and methylmalonic aciduria and homocystinuria type C protein (MMACHC). These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients. Both BCAT1 and MMACHC expression were verified by RT-qPCR. A prognostic nomogram that incorporated clinical factors and BCAT1 and MMACHC gene expression was constructed, and the calibration plots showed that it had good prognostic performance.\nConclusions: The AAMRG signature established in our study is efficient and could be used in clinical settings to predict the early prognosis of ESCC patients.", "claims": [{"id": "claim-method", "statement": "To evaluate the ability of the AAMRGs to predict the survival of patients with ESCC, we used TCGA-ESCC dataset as the test set, and we conducted a single-factor Cox regression analysis to screen the prognostic-related genes with the threshold set at a P value <0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38983159", "locator": "sections[5] \"Single-factor prognostic screening\" paragraph 2", "quote": "To evaluate the ability of the AAMRGs to predict the survival of patients with ESCC, we used TCGA-ESCC dataset as the test set, and we conducted a single-factor Cox regression analysis to screen the prognostic-related genes with the threshold set at a P value <0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-818/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38983159", "locator": "sections[26] \"fn-group\" paragraph 1", "quote": "Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-818/dss"}], "state": "complete"}, {"id": "claim-target-result", "statement": "These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38983159", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients.", "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:38983159"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE20347"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE20347"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38983159", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jtd-24-818", "sha256": 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