Data Sheet 1_Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.zip
Objective<p>This study aimed to identify specific metabolic markers in the blood that can diagnose early-stage lung adenocarcinoma.</p>Methods<p>An untargeted metabolomics study was performed, and the participants were divided into four groups: early-stage lung adenocarcinoma group...
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2025
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| _version_ | 1852022200070045696 |
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| author | Danxiong Sun (9611467) |
| author2 | Yanhong Du (9757284) Rufang Li (15691436) Yunhui Zhang (65111) |
| author2_role | author author author |
| author_facet | Danxiong Sun (9611467) Yanhong Du (9757284) Rufang Li (15691436) Yunhui Zhang (65111) |
| author_role | author |
| dc.creator.none.fl_str_mv | Danxiong Sun (9611467) Yanhong Du (9757284) Rufang Li (15691436) Yunhui Zhang (65111) |
| dc.date.none.fl_str_mv | 2025-03-11T05:10:36Z |
| dc.identifier.none.fl_str_mv | 10.3389/fonc.2025.1535525.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Data_Sheet_1_Metabolomics_for_early-stage_lung_adenocarcinoma_diagnostic_biomarker_screening_zip/28571915 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Oncology and Carcinogenesis not elsewhere classified early-stage lung adenocarcinoma metabolomics biomarker diagnostic model liquid chromatography-mass spectrometry |
| dc.title.none.fl_str_mv | Data Sheet 1_Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.zip |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Objective<p>This study aimed to identify specific metabolic markers in the blood that can diagnose early-stage lung adenocarcinoma.</p>Methods<p>An untargeted metabolomics study was performed, and the participants were divided into four groups: early-stage lung adenocarcinoma group (E-LUAD; n = 21), healthy control group (HC, n = 17), non-cancerous lung disease group (NCC; n = 17), and advanced lung adenocarcinoma group (A-LUAD; n = 25). Plasma metabolite levels that differed in the E-LUAD group compared to the other three groups were identified via liquid chromatography–mass spectrometry (LC–MS). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed at metaX for statistical analysis. A Venn diagram was constructed to identify overlapping differential metabolites of the class comparisons. The data were randomly divided into a training set and a validation set. Based on the overlapping differential metabolites, the diagnostic model was constructed. The discrimination of the model was evaluated using the area under the curve (AUC).</p>Results<p>A total of 527 metabolites were tentatively identified in positive ion mode and 286 metabolites in negative ion mode. Compared with the HC group, 121 differential metabolites were identified. Compared with the NCC group, 67 differential metabolites were identified. Compared with the A-LUAD group, 54 differential metabolites were identified. The Venn diagram showed that 29 metabolites can distinguish E-LUAD from HC and NCC and that four metabolites can distinguish E-LUAD from HC, NCC, and A-LUAD. The feature metabolites were selected to establish the diagnostic model for E-LUAD. The AUC value of the training set was 0.918, and it was 0.983 in the validation set.</p>Conclusion<p>Blood metabolomics has potential diagnostic value for E-LUAD. More medical studies are needed to verify whether the metabolic markers identified in the current research can be applied in clinical practice.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_ebade79cf6d5714cb248252937745f49 |
| identifier_str_mv | 10.3389/fonc.2025.1535525.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28571915 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Data Sheet 1_Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.zipDanxiong Sun (9611467)Yanhong Du (9757284)Rufang Li (15691436)Yunhui Zhang (65111)Oncology and Carcinogenesis not elsewhere classifiedearly-stage lung adenocarcinomametabolomicsbiomarkerdiagnostic modelliquid chromatography-mass spectrometryObjective<p>This study aimed to identify specific metabolic markers in the blood that can diagnose early-stage lung adenocarcinoma.</p>Methods<p>An untargeted metabolomics study was performed, and the participants were divided into four groups: early-stage lung adenocarcinoma group (E-LUAD; n = 21), healthy control group (HC, n = 17), non-cancerous lung disease group (NCC; n = 17), and advanced lung adenocarcinoma group (A-LUAD; n = 25). Plasma metabolite levels that differed in the E-LUAD group compared to the other three groups were identified via liquid chromatography–mass spectrometry (LC–MS). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed at metaX for statistical analysis. A Venn diagram was constructed to identify overlapping differential metabolites of the class comparisons. The data were randomly divided into a training set and a validation set. Based on the overlapping differential metabolites, the diagnostic model was constructed. The discrimination of the model was evaluated using the area under the curve (AUC).</p>Results<p>A total of 527 metabolites were tentatively identified in positive ion mode and 286 metabolites in negative ion mode. Compared with the HC group, 121 differential metabolites were identified. Compared with the NCC group, 67 differential metabolites were identified. Compared with the A-LUAD group, 54 differential metabolites were identified. The Venn diagram showed that 29 metabolites can distinguish E-LUAD from HC and NCC and that four metabolites can distinguish E-LUAD from HC, NCC, and A-LUAD. The feature metabolites were selected to establish the diagnostic model for E-LUAD. The AUC value of the training set was 0.918, and it was 0.983 in the validation set.</p>Conclusion<p>Blood metabolomics has potential diagnostic value for E-LUAD. More medical studies are needed to verify whether the metabolic markers identified in the current research can be applied in clinical practice.</p>2025-03-11T05:10:36ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fonc.2025.1535525.s001https://figshare.com/articles/dataset/Data_Sheet_1_Metabolomics_for_early-stage_lung_adenocarcinoma_diagnostic_biomarker_screening_zip/28571915CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/285719152025-03-11T05:10:36Z |
| spellingShingle | Data Sheet 1_Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.zip Danxiong Sun (9611467) Oncology and Carcinogenesis not elsewhere classified early-stage lung adenocarcinoma metabolomics biomarker diagnostic model liquid chromatography-mass spectrometry |
| status_str | publishedVersion |
| title | Data Sheet 1_Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.zip |
| title_full | Data Sheet 1_Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.zip |
| title_fullStr | Data Sheet 1_Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.zip |
| title_full_unstemmed | Data Sheet 1_Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.zip |
| title_short | Data Sheet 1_Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.zip |
| title_sort | Data Sheet 1_Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.zip |
| topic | Oncology and Carcinogenesis not elsewhere classified early-stage lung adenocarcinoma metabolomics biomarker diagnostic model liquid chromatography-mass spectrometry |