Phosphoproteomics Delineates Hepatocellular Carcinoma Subtypes and Pinpoints Therapeutic Targets
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ABSTRACT:
ORGANISM(S): Homo Sapiens
SUBMITTER: 
Ping Xu
PROVIDER: PXD055026 | iProX | Wed Aug 21 00:00:00 BST 2024
REPOSITORIES: iProX
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Project description:Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver cancers, the second leading cause of cancer mortality worldwide. Although HCC surgical treatment may be curative in the early stages, its five-year overall survival is only 50-70%. Advances in proteomic technologies have expanded the breadth and depth of cancer proteome characterization. Here, we present the largest characterization effort on proteomic profiling of 222 tumor and paired non-tumor tissues in clinically early HCC (Barcelona Clinic Liver Cancer (BCLC) stage 0 and A). Quantitative proteomic data identified three more-refined subtypes in the early- stage cohort of HCC (termed S-I, S-II and S-III) with different clinical outcomes. S-I retained hepatic detoxification and metabolic functions with the best prognosis, S-II increased molecular expression related to proliferation, and S-III showed distinct enrichment of tumor metastasis and immune response pathways and the poorest prognosis. The subtype specific signatures targeted by known FDA approved drugs or inhibitors under clinical investigations for HCC provide a novel resource for HCC therapeutic targets. A new mechanism of disrupted cholesterol homeostasis with aberrant accumulation of cholesteryl esters was also highlighted in S-III. Thus, this study represents the first proteomic stratification of early-stage HCC, providing insights into tumor biology and personalized targeted therapy.
2019-05-29 | PXD006512 | Pride
Project description:Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver cancers, the second leading cause of cancer mortality worldwide. Although HCC surgical treatment may be curative in the early stages, its five-year overall survival is only 50-70%. Advances in proteomic technologies have expanded the breadth and depth of cancer proteome characterization. Here, we present the largest characterization effort on proteomic profiling of 222 tumor and paired non-tumor tissues in clinically early HCC (Barcelona Clinic Liver Cancer (BCLC) stage 0 and A). Quantitative proteomic data identified three more-refined subtypes in the early- stage cohort of HCC (termed S-I, S-II and S-III) with different clinical outcomes. S-I retained hepatic detoxification and metabolic functions with the best prognosis, S-II increased molecular expression related to proliferation, and S-III showed distinct enrichment of tumor metastasis and immune response pathways and the poorest prognosis. The subtype specific signatures targeted by known FDA approved drugs or inhibitors under clinical investigations for HCC provide a novel resource for HCC therapeutic targets. A new mechanism of disrupted cholesterol homeostasis with aberrant accumulation of cholesteryl esters was also highlighted in S-III. Thus, this study represents the first proteomic stratification of early-stage HCC, providing insights into tumor biology and personalized targeted therapy.
2019-03-01 | PXD008373 | Pride
Project description:IntroductionHepatocellular carcinoma (HCC) is an aggressive malignancy with steadily increasing incidence rates worldwide and poor therapeutic outcomes. Studies show that metabolic reprogramming plays a key role in tumor genesis and progression. In this study, we analyzed the metabolic heterogeneity of epithelial cells in the HCC and screened for potential biomarkers.MethodsThe hepatic single-cell RNA sequencing (scRNA-seq) datasets of HCC patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Based on data intergration and measurement of differences among groups, the metabolic epithelial cell subpopulations were identified. The single-cell metabolic pathway was analyzed and the myeloid subpopulations were identified. Cell-cell interaction analysis and single-cell proliferation analysis were performed. The gene expression profiles of HCC patients were obtained from the GSE14520 dataset of GEO and TCGA-LIHC cohort of the UCSC Xena website. Immune analysis was performed. The differentially expressed genes (DEGs) were identified and functionally annotated. Tumor tissues from HCC patients were probed with anti-ALDOA, anti-CD68, anti-CD163, anti-CD4 and anti-FOXP3 antibodies. Results We analyzed the scRNA-seq data from 48 HCC patients and 14 healthy controls. The epithelial cells were significantly enriched in HCC patients compared to the controls (p = 0.011). The epithelial cells from HCC patients were classified into two metabolism-related subpopulations (MRSs) - pertaining to amino acid metabolism (MRS1) and glycolysis (MRS2). Depending on the abundance of these metabolic subpopulations, the HCC patients were also classified into the MRS1 and MRS2 subtype distinct prognoses and immune infiltration. The MRS2 group had significantly worse clinical outcomes and more inflamed tumor microenvironment (TME), as well as a stronger crosstalk between MRS2 cells and immune subpopulations that resulted in an immunosuppressive TME. We also detected high expression levels of ALDOA in the MRS2 cells and HCC tissues. In the clinical cohort, HCC patients with higher ALDOA expression showed greater enrichment of immunosuppressive cells including M2 macrophages and T regulatory cells.DiscussionThe glycolytic subtype of HCC cells with high ALDOA expression is associated with an immunosuppressive TME and predicts worse clinical outcomes, providing new insights into the metabolism and prognosis of HCC.
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Project description:Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, and prior attempts to develop genomic-based classification for HCC have yielded highly divergent results, indicating difficulty in identifying unified molecular anatomy. We performed a meta-analysis of gene expression profiles in data sets from eight independent patient cohorts across the world. In addition, aiming to establish the real world applicability of a classification system, we profiled 118 formalin-fixed, paraffin-embedded tissues from an additional patient cohort. A total of 603 patients were analyzed, representing the major etiologies of HCC (hepatitis B and C) collected from Western and Eastern countries. We observed three robust HCC subclasses (termed S1, S2, and S3), each correlated with clinical parameters such as tumor size, extent of cellular differentiation, and serum alpha-fetoprotein levels. An analysis of the components of the signatures indicated that S1 reflected aberrant activation of the WNT signaling pathway, S2 was characterized by proliferation as well as MYC and AKT activation, and S3 was associated with hepatocyte differentiation. Functional studies indicated that the WNT pathway activation signature characteristic of S1 tumors was not simply the result of beta-catenin mutation but rather was the result of transforming growth factor-beta activation, thus representing a new mechanism of WNT pathway activation in HCC. These experiments establish the first consensus classification framework for HCC based on gene expression profiles and highlight the power of integrating multiple data sets to define a robust molecular taxonomy of the disease. Surgically resected 118 tumor tissues from patients with hepatocellular carcinoma (HCC)
2009-09-08 | E-GEOD-10186 | biostudies-arrayexpress
Project description:Clinical heterogeneity of hepatocellular carcinoma (HCC) reflected in unequal outcome of treatment is poorly defined in molecular level, and molecular subtypes and their associated biomarkers have not been established to improve prognostification and treatment of HCC. Using microarray technologies, we analyzed gene expression profiling data from HCC patients, uncovered mesenchymal subtype, and identified gene expression signature associated with mesenchymal phenotype of HCC.
2020-07-01 | GSE134568 | GEO
Project description:Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, and prior attempts to develop genomic-based classification for HCC have yielded highly divergent results, indicating difficulty in identifying unified molecular anatomy. We performed a meta-analysis of gene expression profiles in data sets from eight independent patient cohorts across the world. In addition, aiming to establish the real world applicability of a classification system, we profiled 118 formalin-fixed, paraffin-embedded tissues from an additional patient cohort. A total of 603 patients were analyzed, representing the major etiologies of HCC (hepatitis B and C) collected from Western and Eastern countries. We observed three robust HCC subclasses (termed S1, S2, and S3), each correlated with clinical parameters such as tumor size, extent of cellular differentiation, and serum alpha-fetoprotein levels. An analysis of the components of the signatures indicated that S1 reflected aberrant activation of the WNT signaling pathway, S2 was characterized by proliferation as well as MYC and AKT activation, and S3 was associated with hepatocyte differentiation. Functional studies indicated that the WNT pathway activation signature characteristic of S1 tumors was not simply the result of beta-catenin mutation but rather was the result of transforming growth factor-beta activation, thus representing a new mechanism of WNT pathway activation in HCC. These experiments establish the first consensus classification framework for HCC based on gene expression profiles and highlight the power of integrating multiple data sets to define a robust molecular taxonomy of the disease.
2009-09-08 | GSE10186 | GEO
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Project description:The purpose of this study is to identify the characteristics of different HCC subtypes through next generation sequencing and metabolome analysis.The samples for transcriptome sequencing were collected from 60 cases of HCC, 30 of which were also used for whole exome sequencing and LC-MS/MS.
2023-01-31 | GSE195952 | GEO
Project description:The purpose of this experiment of high-coverage DNA sequencing of 58 frequently mutated genes in hepatocellular carcinoma (HCC) is to confirm clonal distribution of the known HCC drivers in samples corresponding to multiple regions of a tumor.
2020-01-16 | E-MTAB-5899 | biostudies-arrayexpress
Project description:Clinical heterogeneity of hepatocellular carcinoma (HCC) reflected in unequal outcome of treatment is poorly defined in molecular level, and molecular subtypes and their associated biomarkers have not been established to improve prognostification and treatment of HCC. Using reverse phase protein arrays (RPPA) technologies, we analyzed protein expression profiling data from HCC patients, uncovered mesenchymal subtype, and identified gene expression signature associated with mesenchymal phenotype of HCC.
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