The characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis B‐related liver diseases
Abstract Hepatocellular carcinoma (HCC) is a deadly cancer that emerges from a continuous progression of liver cells from normal to abnormal, often following infections by hepatitis B/C viruses (HBV/HCV), liver fibrosis, and liver cirrhosis (LC), ultimately culminating in cancer. However, there is c...
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2024-12-01
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| Online Access: | https://doi.org/10.1002/VIW.20240031 |
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| author | Jin Xiao Hang Liu Jun Yao Shuang Yang Fenglin Shen KunPeng Bu Zhenxin Wang Fan Liu Ningshao Xia Quan Yuan Hong Shu Yueting Xiong Xiaohui Liu |
| author_facet | Jin Xiao Hang Liu Jun Yao Shuang Yang Fenglin Shen KunPeng Bu Zhenxin Wang Fan Liu Ningshao Xia Quan Yuan Hong Shu Yueting Xiong Xiaohui Liu |
| author_sort | Jin Xiao |
| collection | DOAJ |
| description | Abstract Hepatocellular carcinoma (HCC) is a deadly cancer that emerges from a continuous progression of liver cells from normal to abnormal, often following infections by hepatitis B/C viruses (HBV/HCV), liver fibrosis, and liver cirrhosis (LC), ultimately culminating in cancer. However, there is currently limited systematic molecular analysis of biomarkers at different stages of HCC progression using multi‐omics approaches. We carried out an innovative pipeline by utilizing targeted proteomics and metabolomics to identify potential biomarkers for early detection of HCC in 316 participants, including healthy adults and patients diagnosed with HBV, HCV, LC, and HCC from three independent cohorts. We first established a detailed database of candidate biomarkers for HCC containing 3059 proteins and 103 metabolites, and identified pivotal candidates implicated in the progressive trajectory of liver cancers. Through our developed DeepPRM, scheduled multiple reaction monitoring (MRM)‐targeted approach, and machine learning‐based computational pipeline, we identified an eight‐biomolecular‐based combination with an accuracy rate of 91.43% for early diagnosis of HCC, and a 12‐biomolecular‐based combination with an accuracy rate of 80.00% for detecting changes in HBV–LC progression. These two biomarker combinations significantly improved accuracy compared to traditional tumor biomarkers. Our extensive analysis provides valuable proteomic and metabolomic data resources that will contribute to a deeper understanding of liver disease progression and enhance the identification of potential therapeutic targets. |
| format | Article |
| id | doaj-art-e04071b3dc4b46eda72c85fa45d368e6 |
| institution | DOAJ |
| issn | 2688-3988 2688-268X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
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| spelling | doaj-art-e04071b3dc4b46eda72c85fa45d368e62025-08-20T02:48:58ZengWileyView2688-39882688-268X2024-12-0156n/an/a10.1002/VIW.20240031The characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis B‐related liver diseasesJin Xiao0Hang Liu1Jun Yao2Shuang Yang3Fenglin Shen4KunPeng Bu5Zhenxin Wang6Fan Liu7Ningshao Xia8Quan Yuan9Hong Shu10Yueting Xiong11Xiaohui Liu12State Key Laboratory of Vaccines for Infectious Diseases Xiang An Biomedicine Laboratory School of Public Health Xiamen University Xiamen ChinaInstitute of Biomedical Sciences and Department of Laboratory Medicine Zhongshan Hospital Fudan University Shanghai ChinaInstitute of Biomedical Sciences and Department of Laboratory Medicine Zhongshan Hospital Fudan University Shanghai ChinaInstitute of Biomedical Sciences and Department of Laboratory Medicine Zhongshan Hospital Fudan University Shanghai ChinaInstitute of Biomedical Sciences and Department of Laboratory Medicine Zhongshan Hospital Fudan University Shanghai ChinaDepartment of Clinical Laboratory and Department of Comprehensive Internal Medicine Guangxi Medical University Cancer Hospital Guangxi ChinaInstitute of Biomedical Sciences and Department of Laboratory Medicine Zhongshan Hospital Fudan University Shanghai ChinaState Key Laboratory of Vaccines for Infectious Diseases Xiang An Biomedicine Laboratory School of Public Health Xiamen University Xiamen ChinaState Key Laboratory of Vaccines for Infectious Diseases Xiang An Biomedicine Laboratory School of Public Health Xiamen University Xiamen ChinaState Key Laboratory of Vaccines for Infectious Diseases Xiang An Biomedicine Laboratory School of Public Health Xiamen University Xiamen ChinaDepartment of Clinical Laboratory and Department of Comprehensive Internal Medicine Guangxi Medical University Cancer Hospital Guangxi ChinaState Key Laboratory of Vaccines for Infectious Diseases Xiang An Biomedicine Laboratory School of Public Health Xiamen University Xiamen ChinaInstitute of Biomedical Sciences and Department of Laboratory Medicine Zhongshan Hospital Fudan University Shanghai ChinaAbstract Hepatocellular carcinoma (HCC) is a deadly cancer that emerges from a continuous progression of liver cells from normal to abnormal, often following infections by hepatitis B/C viruses (HBV/HCV), liver fibrosis, and liver cirrhosis (LC), ultimately culminating in cancer. However, there is currently limited systematic molecular analysis of biomarkers at different stages of HCC progression using multi‐omics approaches. We carried out an innovative pipeline by utilizing targeted proteomics and metabolomics to identify potential biomarkers for early detection of HCC in 316 participants, including healthy adults and patients diagnosed with HBV, HCV, LC, and HCC from three independent cohorts. We first established a detailed database of candidate biomarkers for HCC containing 3059 proteins and 103 metabolites, and identified pivotal candidates implicated in the progressive trajectory of liver cancers. Through our developed DeepPRM, scheduled multiple reaction monitoring (MRM)‐targeted approach, and machine learning‐based computational pipeline, we identified an eight‐biomolecular‐based combination with an accuracy rate of 91.43% for early diagnosis of HCC, and a 12‐biomolecular‐based combination with an accuracy rate of 80.00% for detecting changes in HBV–LC progression. These two biomarker combinations significantly improved accuracy compared to traditional tumor biomarkers. Our extensive analysis provides valuable proteomic and metabolomic data resources that will contribute to a deeper understanding of liver disease progression and enhance the identification of potential therapeutic targets.https://doi.org/10.1002/VIW.20240031biomarker discoveryhepatocellular carcinomaintegrated modelmetabolomicsproteomicsserum |
| spellingShingle | Jin Xiao Hang Liu Jun Yao Shuang Yang Fenglin Shen KunPeng Bu Zhenxin Wang Fan Liu Ningshao Xia Quan Yuan Hong Shu Yueting Xiong Xiaohui Liu The characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis B‐related liver diseases View biomarker discovery hepatocellular carcinoma integrated model metabolomics proteomics serum |
| title | The characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis B‐related liver diseases |
| title_full | The characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis B‐related liver diseases |
| title_fullStr | The characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis B‐related liver diseases |
| title_full_unstemmed | The characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis B‐related liver diseases |
| title_short | The characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis B‐related liver diseases |
| title_sort | characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis b related liver diseases |
| topic | biomarker discovery hepatocellular carcinoma integrated model metabolomics proteomics serum |
| url | https://doi.org/10.1002/VIW.20240031 |
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