Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis
Abstract Background Precision oncology’s implementation in clinical practice faces significant constraints due to the inadequacies in tools for detailed patient stratification and personalized treatment methodologies. Dysregulated tryptophan metabolism has emerged as a crucial factor in tumor progre...
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BMC
2024-12-01
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| Series: | Biology Direct |
| Online Access: | https://doi.org/10.1186/s13062-024-00576-w |
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| author | Fan Li Haiyi Hu Liyang Li Lifeng Ding Zeyi Lu Xudong Mao Ruyue Wang Wenqin Luo Yudong Lin Yang Li Xianjiong Chen Ziwei Zhu Yi Lu Chenghao Zhou Mingchao Wang Liqun Xia Gonghui Li Lei Gao |
| author_facet | Fan Li Haiyi Hu Liyang Li Lifeng Ding Zeyi Lu Xudong Mao Ruyue Wang Wenqin Luo Yudong Lin Yang Li Xianjiong Chen Ziwei Zhu Yi Lu Chenghao Zhou Mingchao Wang Liqun Xia Gonghui Li Lei Gao |
| author_sort | Fan Li |
| collection | DOAJ |
| description | Abstract Background Precision oncology’s implementation in clinical practice faces significant constraints due to the inadequacies in tools for detailed patient stratification and personalized treatment methodologies. Dysregulated tryptophan metabolism has emerged as a crucial factor in tumor progression, encompassing immune suppression, proliferation, metastasis, and metabolic reprogramming. However, its precise role in clear cell renal cell carcinoma (ccRCC) remains unclear, and predictive models or signatures based on tryptophan metabolism are conspicuously lacking. Methods The influence of tryptophan metabolism on tumor cells was explored using single-cell RNA sequencing data. Genes involved in tryptophan metabolism were identified across both single-cell and bulk-cell dimensions through weighted gene co-expression network analysis (WGCNA) and its single-cell data variant (hdWGCNA). Subsequently, a tryptophan metabolism-related signature was developed using an integrated machine-learning approach. This signature was then examined in multi-omics data to assess its associations with patient clinical features, prognosis, cancer malignancy-related pathways, immune microenvironment, genomic characteristics, and responses to immunotherapy and targeted therapy. Finally, the genes within the signature were validated through experiments including qRT-PCR, Western blot, CCK8 assay, and transwell assay. Results Dysregulated tryptophan metabolism was identified as a potential driver of the malignant transformation of normal epithelial cells. The tryptophan metabolism-related signature (TMRS) demonstrated robust predictive capability for overall survival (OS) and progression-free survival (PFS) across multiple datasets. Moreover, a high TMRS risk score correlated with increased tumor malignancy, significant metabolic reprogramming, an inflamed yet dysfunctional immune microenvironment, heightened genomic instability, resistance to immunotherapy, and increased sensitivity to certain targeted therapeutics. Experimental validation revealed differential expression of genes within the signature between RCC and adjacent normal tissues, with reduced expression of DDAH1 linked to enhanced proliferation and metastasis of tumor cells. Conclusion This study investigated the potential impact of dysregulated tryptophan metabolism on clear cell renal cell carcinoma, leading to the development of a tryptophan metabolism-related signature that may provide insights into patient prognosis, tumor biological status, and personalized treatment strategies. This signature serves as a valuable reference for further exploring the role of tryptophan metabolism in renal cell carcinoma and for the development of clinical applications based on this metabolic pathway. |
| format | Article |
| id | doaj-art-2317b3c40aa94abcba896d518222b4d1 |
| institution | DOAJ |
| issn | 1745-6150 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Biology Direct |
| spelling | doaj-art-2317b3c40aa94abcba896d518222b4d12025-08-20T02:39:51ZengBMCBiology Direct1745-61502024-12-0119112210.1186/s13062-024-00576-wIntegrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosisFan Li0Haiyi Hu1Liyang Li2Lifeng Ding3Zeyi Lu4Xudong Mao5Ruyue Wang6Wenqin Luo7Yudong Lin8Yang Li9Xianjiong Chen10Ziwei Zhu11Yi Lu12Chenghao Zhou13Mingchao Wang14Liqun Xia15Gonghui Li16Lei Gao17Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineSchool of Medicine, University of New South WalesDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineAbstract Background Precision oncology’s implementation in clinical practice faces significant constraints due to the inadequacies in tools for detailed patient stratification and personalized treatment methodologies. Dysregulated tryptophan metabolism has emerged as a crucial factor in tumor progression, encompassing immune suppression, proliferation, metastasis, and metabolic reprogramming. However, its precise role in clear cell renal cell carcinoma (ccRCC) remains unclear, and predictive models or signatures based on tryptophan metabolism are conspicuously lacking. Methods The influence of tryptophan metabolism on tumor cells was explored using single-cell RNA sequencing data. Genes involved in tryptophan metabolism were identified across both single-cell and bulk-cell dimensions through weighted gene co-expression network analysis (WGCNA) and its single-cell data variant (hdWGCNA). Subsequently, a tryptophan metabolism-related signature was developed using an integrated machine-learning approach. This signature was then examined in multi-omics data to assess its associations with patient clinical features, prognosis, cancer malignancy-related pathways, immune microenvironment, genomic characteristics, and responses to immunotherapy and targeted therapy. Finally, the genes within the signature were validated through experiments including qRT-PCR, Western blot, CCK8 assay, and transwell assay. Results Dysregulated tryptophan metabolism was identified as a potential driver of the malignant transformation of normal epithelial cells. The tryptophan metabolism-related signature (TMRS) demonstrated robust predictive capability for overall survival (OS) and progression-free survival (PFS) across multiple datasets. Moreover, a high TMRS risk score correlated with increased tumor malignancy, significant metabolic reprogramming, an inflamed yet dysfunctional immune microenvironment, heightened genomic instability, resistance to immunotherapy, and increased sensitivity to certain targeted therapeutics. Experimental validation revealed differential expression of genes within the signature between RCC and adjacent normal tissues, with reduced expression of DDAH1 linked to enhanced proliferation and metastasis of tumor cells. Conclusion This study investigated the potential impact of dysregulated tryptophan metabolism on clear cell renal cell carcinoma, leading to the development of a tryptophan metabolism-related signature that may provide insights into patient prognosis, tumor biological status, and personalized treatment strategies. This signature serves as a valuable reference for further exploring the role of tryptophan metabolism in renal cell carcinoma and for the development of clinical applications based on this metabolic pathway.https://doi.org/10.1186/s13062-024-00576-w |
| spellingShingle | Fan Li Haiyi Hu Liyang Li Lifeng Ding Zeyi Lu Xudong Mao Ruyue Wang Wenqin Luo Yudong Lin Yang Li Xianjiong Chen Ziwei Zhu Yi Lu Chenghao Zhou Mingchao Wang Liqun Xia Gonghui Li Lei Gao Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis Biology Direct |
| title | Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis |
| title_full | Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis |
| title_fullStr | Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis |
| title_full_unstemmed | Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis |
| title_short | Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis |
| title_sort | integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis |
| url | https://doi.org/10.1186/s13062-024-00576-w |
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