APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment
IntroductionDiabetic nephropathy is the leading cause of end-stage renal disease, which imposes a huge economic burden on individuals and society, but effective and reliable diagnostic markers are still not available.MethodsDifferentially expressed genes (DEGs) were characterized and functional enri...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1102634/full |
| _version_ | 1797902574797455360 |
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| author | Kuipeng Yu Kuipeng Yu Kuipeng Yu Shan Li Chunjie Wang Chunjie Wang Yimeng Zhang Luyao Li Xin Fan Lin Fang Haiyun Li Huimin Yang Jintang Sun Xiangdong Yang Xiangdong Yang |
| author_facet | Kuipeng Yu Kuipeng Yu Kuipeng Yu Shan Li Chunjie Wang Chunjie Wang Yimeng Zhang Luyao Li Xin Fan Lin Fang Haiyun Li Huimin Yang Jintang Sun Xiangdong Yang Xiangdong Yang |
| author_sort | Kuipeng Yu |
| collection | DOAJ |
| description | IntroductionDiabetic nephropathy is the leading cause of end-stage renal disease, which imposes a huge economic burden on individuals and society, but effective and reliable diagnostic markers are still not available.MethodsDifferentially expressed genes (DEGs) were characterized and functional enrichment analysis was performed in DN patients. Meanwhile, a weighted gene co-expression network (WGCNA) was also constructed. For further, algorithms Lasso and SVM-RFE were applied to screening the DN core secreted genes. Lastly, WB, IHC, IF, and Elias experiments were applied to demonstrate the hub gene expression in DN, and the research results were confirmed in mouse models and clinical specimens.Results17 hub secretion genes were identified in this research by analyzing the DEGs, the important module genes in WGCNA, and the secretion genes. 6 hub secretory genes (APOC1, CCL21, INHBA, RNASE6, TGFBI, VEGFC) were obtained by Lasso and SVM-RFE algorithms. APOC1 was discovered to exhibit elevated expression in renal tissue of a DN mouse model, and APOC1 is probably a core secretory gene in DN. Clinical data demonstrate that APOC1 expression is associated significantly with proteinuria and GFR in DN patients. APOC1 expression in the serum of DN patients was 1.358±0.1292μg/ml, compared to 0.3683±0.08119μg/ml in the healthy population. APOC1 was significantly elevated in the sera of DN patients and the difference was statistical significant (P > 0.001). The ROC curve of APOC1 in DN gave an AUC = 92.5%, sensitivity = 95%, and specificity = 97% (P < 0.001).ConclusionsOur research indicates that APOC1 might be a novel diagnostic biomarker for diabetic nephropathy for the first time and suggest that APOC1 may be available as a candidate intervention target for DN. |
| first_indexed | 2024-04-10T09:19:44Z |
| format | Article |
| id | doaj.art-5901ffe6f60f4e4b85f350766e7159dd |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-10T09:19:44Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-5901ffe6f60f4e4b85f350766e7159dd2023-02-20T15:01:02ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-02-011410.3389/fendo.2023.11026341102634APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experimentKuipeng Yu0Kuipeng Yu1Kuipeng Yu2Shan Li3Chunjie Wang4Chunjie Wang5Yimeng Zhang6Luyao Li7Xin Fan8Lin Fang9Haiyun Li10Huimin Yang11Jintang Sun12Xiangdong Yang13Xiangdong Yang14Department of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of Blood Purification, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaLaboratory of Basic Medical Sciences, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaLaboratory of Basic Medical Sciences, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of Geriatric Medicine, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of General Practice, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaLaboratory of Basic Medical Sciences, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of Nephrology, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaDepartment of Blood Purification, Qilu Hospital of Shandong University, Jinan, Shandong, ChinaIntroductionDiabetic nephropathy is the leading cause of end-stage renal disease, which imposes a huge economic burden on individuals and society, but effective and reliable diagnostic markers are still not available.MethodsDifferentially expressed genes (DEGs) were characterized and functional enrichment analysis was performed in DN patients. Meanwhile, a weighted gene co-expression network (WGCNA) was also constructed. For further, algorithms Lasso and SVM-RFE were applied to screening the DN core secreted genes. Lastly, WB, IHC, IF, and Elias experiments were applied to demonstrate the hub gene expression in DN, and the research results were confirmed in mouse models and clinical specimens.Results17 hub secretion genes were identified in this research by analyzing the DEGs, the important module genes in WGCNA, and the secretion genes. 6 hub secretory genes (APOC1, CCL21, INHBA, RNASE6, TGFBI, VEGFC) were obtained by Lasso and SVM-RFE algorithms. APOC1 was discovered to exhibit elevated expression in renal tissue of a DN mouse model, and APOC1 is probably a core secretory gene in DN. Clinical data demonstrate that APOC1 expression is associated significantly with proteinuria and GFR in DN patients. APOC1 expression in the serum of DN patients was 1.358±0.1292μg/ml, compared to 0.3683±0.08119μg/ml in the healthy population. APOC1 was significantly elevated in the sera of DN patients and the difference was statistical significant (P > 0.001). The ROC curve of APOC1 in DN gave an AUC = 92.5%, sensitivity = 95%, and specificity = 97% (P < 0.001).ConclusionsOur research indicates that APOC1 might be a novel diagnostic biomarker for diabetic nephropathy for the first time and suggest that APOC1 may be available as a candidate intervention target for DN.https://www.frontiersin.org/articles/10.3389/fendo.2023.1102634/fullDNbiomarkerdiagnosticmachine learning algorithmsAPOC1 |
| spellingShingle | Kuipeng Yu Kuipeng Yu Kuipeng Yu Shan Li Chunjie Wang Chunjie Wang Yimeng Zhang Luyao Li Xin Fan Lin Fang Haiyun Li Huimin Yang Jintang Sun Xiangdong Yang Xiangdong Yang APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment Frontiers in Endocrinology DN biomarker diagnostic machine learning algorithms APOC1 |
| title | APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment |
| title_full | APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment |
| title_fullStr | APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment |
| title_full_unstemmed | APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment |
| title_short | APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment |
| title_sort | apoc1 as a novel diagnostic biomarker for dn based on machine learning algorithms and experiment |
| topic | DN biomarker diagnostic machine learning algorithms APOC1 |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1102634/full |
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