Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence
BackgroundDiabetic nephropathy (DN) is a complication of systemic microvascular disease in diabetes mellitus. Abnormal glycolysis has emerged as a potential factor for chronic renal dysfunction in DN. The current lack of reliable predictive biomarkers hinders early diagnosis and personalized therapy...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1427626/full |
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| author | Xiaoyin Wu Xiaoyin Wu Buyu Guo Buyu Guo Xingyu Chang Xingyu Chang Yuxuan Yang Yuxuan Yang Qianqian Liu Qianqian Liu Jiahui Liu Jiahui Liu Yichen Yang Yichen Yang Kang Zhang Yumei Ma Songbo Fu Songbo Fu Songbo Fu |
| author_facet | Xiaoyin Wu Xiaoyin Wu Buyu Guo Buyu Guo Xingyu Chang Xingyu Chang Yuxuan Yang Yuxuan Yang Qianqian Liu Qianqian Liu Jiahui Liu Jiahui Liu Yichen Yang Yichen Yang Kang Zhang Yumei Ma Songbo Fu Songbo Fu Songbo Fu |
| author_sort | Xiaoyin Wu |
| collection | DOAJ |
| description | BackgroundDiabetic nephropathy (DN) is a complication of systemic microvascular disease in diabetes mellitus. Abnormal glycolysis has emerged as a potential factor for chronic renal dysfunction in DN. The current lack of reliable predictive biomarkers hinders early diagnosis and personalized therapy.MethodsTranscriptomic profiles of DN samples and controls were extracted from GEO databases. Differentially expressed genes (DEGs) and their functional enrichments were identified. Glycolysis-related genes (GRGs) were selected by combining DEGs, weighted gene co-expression network, and glycolysis candidate genes. We established a diagnostic signature termed GScore via integrative machine learning framework. The diagnostic efficacy was evaluated by decision curve and calibration curve. Single-cell RNA sequence data was used to identify cell subtypes and interactive signals. The cMAP database was used to find potential therapeutic agents targeting GScore for DN. The expression levels of diagnostic signatures were verified in vitro.ResultsThrough the 108 combinations of machine learning algorithms, we selected 12 diagnostic signatures, including CD163, CYBB, ELF3, FCN1, PROM1, GPR65, LCN2, LTF, S100A4, SOX4, TGFB1 and TNFAIP8. Based on them, an integrative model named GScore was established for predicting DN onset and stratifying clinical risk. We observed distinct biological characteristics and immunological microenvironment states between the high-risk and low-risk groups. GScore was significantly associated with neutrophils and non-classical monocytes. Potential agents including esmolol, estradiol, ganciclovir, and felbamate, targeting the 12 diagnostic signatures were identified. In vitro, ELF3, LCN2 and CD163 were induced in high glucose-induced HK-2 cell lines.ConclusionAn integrative machine learning frame established a novel diagnostic signature using glycolysis-related genes. This study provides a new direction for the early diagnosis and treatment of DN. |
| format | Article |
| id | doaj-art-d8c9e55e4a7c44318911e0175bd0d01f |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-d8c9e55e4a7c44318911e0175bd0d01f2025-01-23T06:56:10ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.14276261427626Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequenceXiaoyin Wu0Xiaoyin Wu1Buyu Guo2Buyu Guo3Xingyu Chang4Xingyu Chang5Yuxuan Yang6Yuxuan Yang7Qianqian Liu8Qianqian Liu9Jiahui Liu10Jiahui Liu11Yichen Yang12Yichen Yang13Kang Zhang14Yumei Ma15Songbo Fu16Songbo Fu17Songbo Fu18School of Basic Medical Sciences, Lanzhou University, Lanzhou, ChinaThe First Clinical Medical College, Lanzhou University, Lanzhou, ChinaThe First Clinical Medical College, Lanzhou University, Lanzhou, ChinaDepartment of Endocrinology, First Hospital of Lanzhou University, Lanzhou, ChinaObstetrics and Gynecology Hospital, Fudan University, Shanghai, ChinaShanghai Key Laboratory Female Reproductive Endocrine-Related Diseases, Shanghai, ChinaThe First Clinical Medical College, Lanzhou University, Lanzhou, ChinaDepartment of Endocrinology, First Hospital of Lanzhou University, Lanzhou, ChinaThe First Clinical Medical College, Lanzhou University, Lanzhou, ChinaDepartment of Endocrinology, First Hospital of Lanzhou University, Lanzhou, ChinaThe First Clinical Medical College, Lanzhou University, Lanzhou, ChinaDepartment of Endocrinology, First Hospital of Lanzhou University, Lanzhou, ChinaThe First Clinical Medical College, Lanzhou University, Lanzhou, ChinaDepartment of Endocrinology, First Hospital of Lanzhou University, Lanzhou, ChinaXifeng District People’s Hospital, Qingyang, ChinaQilihe District People’s Hospital, Lanzhou, ChinaSchool of Basic Medical Sciences, Lanzhou University, Lanzhou, ChinaDepartment of Endocrinology, First Hospital of Lanzhou University, Lanzhou, ChinaGansu Provincial Endocrine Disease Clinical Medicine Research Center, Lanzhou, ChinaBackgroundDiabetic nephropathy (DN) is a complication of systemic microvascular disease in diabetes mellitus. Abnormal glycolysis has emerged as a potential factor for chronic renal dysfunction in DN. The current lack of reliable predictive biomarkers hinders early diagnosis and personalized therapy.MethodsTranscriptomic profiles of DN samples and controls were extracted from GEO databases. Differentially expressed genes (DEGs) and their functional enrichments were identified. Glycolysis-related genes (GRGs) were selected by combining DEGs, weighted gene co-expression network, and glycolysis candidate genes. We established a diagnostic signature termed GScore via integrative machine learning framework. The diagnostic efficacy was evaluated by decision curve and calibration curve. Single-cell RNA sequence data was used to identify cell subtypes and interactive signals. The cMAP database was used to find potential therapeutic agents targeting GScore for DN. The expression levels of diagnostic signatures were verified in vitro.ResultsThrough the 108 combinations of machine learning algorithms, we selected 12 diagnostic signatures, including CD163, CYBB, ELF3, FCN1, PROM1, GPR65, LCN2, LTF, S100A4, SOX4, TGFB1 and TNFAIP8. Based on them, an integrative model named GScore was established for predicting DN onset and stratifying clinical risk. We observed distinct biological characteristics and immunological microenvironment states between the high-risk and low-risk groups. GScore was significantly associated with neutrophils and non-classical monocytes. Potential agents including esmolol, estradiol, ganciclovir, and felbamate, targeting the 12 diagnostic signatures were identified. In vitro, ELF3, LCN2 and CD163 were induced in high glucose-induced HK-2 cell lines.ConclusionAn integrative machine learning frame established a novel diagnostic signature using glycolysis-related genes. This study provides a new direction for the early diagnosis and treatment of DN.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1427626/fulldiabetic nephropathyglycolysismachine learningdiagnostic signaturessingle cell |
| spellingShingle | Xiaoyin Wu Xiaoyin Wu Buyu Guo Buyu Guo Xingyu Chang Xingyu Chang Yuxuan Yang Yuxuan Yang Qianqian Liu Qianqian Liu Jiahui Liu Jiahui Liu Yichen Yang Yichen Yang Kang Zhang Yumei Ma Songbo Fu Songbo Fu Songbo Fu Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence Frontiers in Immunology diabetic nephropathy glycolysis machine learning diagnostic signatures single cell |
| title | Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence |
| title_full | Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence |
| title_fullStr | Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence |
| title_full_unstemmed | Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence |
| title_short | Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence |
| title_sort | identification and validation of glycolysis related diagnostic signatures in diabetic nephropathy a study based on integrative machine learning and single cell sequence |
| topic | diabetic nephropathy glycolysis machine learning diagnostic signatures single cell |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1427626/full |
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