Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single‐Nucleus and Bulk RNA‐Seq of Hepatocellular Carcinoma
Abstract Hepatocellular carcinoma (HCC) is a major cause of cancer‐related mortality, while the hepatocyte mechanisms driving oncogenesis remains poorly understood. In this study, single‐nucleus RNA sequencing of samples from 22 HCC patients revealed 10 distinct hepatocyte subtypes, including benefi...
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Wiley
2025-04-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202412944 |
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| author | Huanhou Su Xuewen Zhou Guanchuan Lin Chaochao Luo Wei Meng Cui Lv Yuting Chen Zebin Wen Xu Li Yongzhang Wu Changtai Xiao Jian Yang Jiameng Lu Xingguang Luo Yan Chen Paul KH Tam Chuanjiang Li Haitao Sun Xinghua Pan |
| author_facet | Huanhou Su Xuewen Zhou Guanchuan Lin Chaochao Luo Wei Meng Cui Lv Yuting Chen Zebin Wen Xu Li Yongzhang Wu Changtai Xiao Jian Yang Jiameng Lu Xingguang Luo Yan Chen Paul KH Tam Chuanjiang Li Haitao Sun Xinghua Pan |
| author_sort | Huanhou Su |
| collection | DOAJ |
| description | Abstract Hepatocellular carcinoma (HCC) is a major cause of cancer‐related mortality, while the hepatocyte mechanisms driving oncogenesis remains poorly understood. In this study, single‐nucleus RNA sequencing of samples from 22 HCC patients revealed 10 distinct hepatocyte subtypes, including beneficial Hep0, predominantly malignant Hep2, and immunosuppressive Hep9. These subtypes were strongly associated with patient prognosis, confirmed in TCGA‐LIHC and Fudan HCC cohorts through hepatocyte composition deconvolution. A quantile‐based scoring method is developed to integrate data from 29 public HCC datasets, creating a Quantile Distribution Model (QDM) with excellent diagnostic accuracy (Area Under the Curve, AUC = 0.968‐0.982). QDM was employed to screen potential biomarkers, revealing that PDE7B functions as a key gene whose suppression promotes HCC progression. Guided by the genes specific to Hep0/2/9 subtypes, HCC is categorized into metabolic, inflammatory, and matrix classes, which are distinguishable in gene mutation frequencies, survival times, enriched pathways, and immune infiltration. Meanwhile, the sensitive drugs of the three HCC classes are identified, namely ouabain, teniposide, and TG‐101348. This study presents the largest single‐cell hepatocyte dataset to date, offering transformative insights into hepatocarcinogenesis and a comprehensive framework for advancing HCC diagnostics, prognostics, and personalized treatment strategies. |
| format | Article |
| id | doaj-art-ab3dd8421be04dfdb9ce77f8722e7dc6 |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
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| series | Advanced Science |
| spelling | doaj-art-ab3dd8421be04dfdb9ce77f8722e7dc62025-08-20T02:16:22ZengWileyAdvanced Science2198-38442025-04-011214n/an/a10.1002/advs.202412944Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single‐Nucleus and Bulk RNA‐Seq of Hepatocellular CarcinomaHuanhou Su0Xuewen Zhou1Guanchuan Lin2Chaochao Luo3Wei Meng4Cui Lv5Yuting Chen6Zebin Wen7Xu Li8Yongzhang Wu9Changtai Xiao10Jian Yang11Jiameng Lu12Xingguang Luo13Yan Chen14Paul KH Tam15Chuanjiang Li16Haitao Sun17Xinghua Pan18Department of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaClinical Biobank Center Microbiome Medicine Center Department of Laboratory Medicine Guangdong Provincial Clinical Research Center for Laboratory Medicine Zhujiang Hospital Southern Medical University Guangzhou 510280 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaDepartment of Hepatobiliary Surgery I General Surgery Center and Guangdong Provincial Clinical and Engineering Center of Digital Medicine Zhujiang Hospital Southern Medical University Guangzhou 510280 ChinaPrecision Regenerative Medicine Research Centre Medical Science Division and State Key Laboratory of Quality Research in Chinese Medicine Macau University of Science and Technology Macao 999078 ChinaDepartment of Psychiatry Yale University School of Medicine New Haven CT 06510 USAPrecision Regenerative Medicine Research Centre Medical Science Division and State Key Laboratory of Quality Research in Chinese Medicine Macau University of Science and Technology Macao 999078 ChinaPrecision Regenerative Medicine Research Centre Medical Science Division and State Key Laboratory of Quality Research in Chinese Medicine Macau University of Science and Technology Macao 999078 ChinaDivision of Hepatobiliopancreatic Surgery Department of General Surgery Nanfang Hospital Southern Medical University Guangzhou Guangdong 510515 ChinaClinical Biobank Center Microbiome Medicine Center Department of Laboratory Medicine Guangdong Provincial Clinical Research Center for Laboratory Medicine Zhujiang Hospital Southern Medical University Guangzhou 510280 ChinaDepartment of Biochemistry and Molecular Biology School of Basic Medical Sciences Southern Medical University and Guangdong Provincial Key Laboratory of Single Cell Technology and Application Guangzhou 510515 ChinaAbstract Hepatocellular carcinoma (HCC) is a major cause of cancer‐related mortality, while the hepatocyte mechanisms driving oncogenesis remains poorly understood. In this study, single‐nucleus RNA sequencing of samples from 22 HCC patients revealed 10 distinct hepatocyte subtypes, including beneficial Hep0, predominantly malignant Hep2, and immunosuppressive Hep9. These subtypes were strongly associated with patient prognosis, confirmed in TCGA‐LIHC and Fudan HCC cohorts through hepatocyte composition deconvolution. A quantile‐based scoring method is developed to integrate data from 29 public HCC datasets, creating a Quantile Distribution Model (QDM) with excellent diagnostic accuracy (Area Under the Curve, AUC = 0.968‐0.982). QDM was employed to screen potential biomarkers, revealing that PDE7B functions as a key gene whose suppression promotes HCC progression. Guided by the genes specific to Hep0/2/9 subtypes, HCC is categorized into metabolic, inflammatory, and matrix classes, which are distinguishable in gene mutation frequencies, survival times, enriched pathways, and immune infiltration. Meanwhile, the sensitive drugs of the three HCC classes are identified, namely ouabain, teniposide, and TG‐101348. This study presents the largest single‐cell hepatocyte dataset to date, offering transformative insights into hepatocarcinogenesis and a comprehensive framework for advancing HCC diagnostics, prognostics, and personalized treatment strategies.https://doi.org/10.1002/advs.202412944hepatocellular carcinoma classificationhepatocyte subtypemetabolic pathwayquantile distribution modelsingle‐cell transcriptomics |
| spellingShingle | Huanhou Su Xuewen Zhou Guanchuan Lin Chaochao Luo Wei Meng Cui Lv Yuting Chen Zebin Wen Xu Li Yongzhang Wu Changtai Xiao Jian Yang Jiameng Lu Xingguang Luo Yan Chen Paul KH Tam Chuanjiang Li Haitao Sun Xinghua Pan Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single‐Nucleus and Bulk RNA‐Seq of Hepatocellular Carcinoma Advanced Science hepatocellular carcinoma classification hepatocyte subtype metabolic pathway quantile distribution model single‐cell transcriptomics |
| title | Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single‐Nucleus and Bulk RNA‐Seq of Hepatocellular Carcinoma |
| title_full | Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single‐Nucleus and Bulk RNA‐Seq of Hepatocellular Carcinoma |
| title_fullStr | Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single‐Nucleus and Bulk RNA‐Seq of Hepatocellular Carcinoma |
| title_full_unstemmed | Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single‐Nucleus and Bulk RNA‐Seq of Hepatocellular Carcinoma |
| title_short | Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single‐Nucleus and Bulk RNA‐Seq of Hepatocellular Carcinoma |
| title_sort | deciphering the oncogenic landscape of hepatocytes through integrated single nucleus and bulk rna seq of hepatocellular carcinoma |
| topic | hepatocellular carcinoma classification hepatocyte subtype metabolic pathway quantile distribution model single‐cell transcriptomics |
| url | https://doi.org/10.1002/advs.202412944 |
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