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Ubiquitin-related molecular classification and risk stratification of hepatocellular carcinoma.

ABSTRACT: The roles of ubiquitin-related genes in hepatocellular carcinoma (HCC) have not been thoroughly investigated. This study aimed to systematically examine ubiquitin-related genes and identify subtypes and stratify prognosis of HCC by using ubiquitin-related signatures. Survival, biological processes, tumor microenvironment (TME), and genomic alterations of the HCC subtypes were investigated. Patients with HCC were classified into two subtypes (clusters 1 and 2) with distinct survival outcomes, pathways, and genomic alterations. Cluster 2 had better prognosis than did cluster 1. Hepatitis B, hepatitis C, Janus tyrosine kinase (JAK)-signal transducer and activator of transcription (STAT) pathway, and natural killer cell-mediated cytotoxicity were enriched in cluster 1. Moreover, cluster 2 had a higher immune score and immune cell infiltrations, whereas cluster 1 had a lower immune score and immune infiltrations. Additionally, mutations, amplifications, and deletions among the phosphatidylinositol 3-kinase (PI3K)-AKT, p53, and receptor tyrosine kinase (RTK)-RAS pathways more frequently occurred in cluster 1, while those among the Hippo, MYC, and Notch signaling pathways were found in cluster 2. Finally, a prognostic signature, consisting of eight ubiquitin-related genes, was established and validated. In brief, our study established a new classification and developed a prognostic signature for HCC.

SUBMITTER: Yang S

PROVIDER: S-EPMC8138213 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background & aimsHepatocellular carcinoma (HCC) risk varies dramatically in patients with cirrhosis according to well-described, readily available predictors. We aimed to develop simple models estimating HCC risk in patients with alcohol-related liver disease (ALD)-cirrhosis or non-alcoholic fatty liver disease (NAFLD)-cirrhosis and calculate the net benefit that would be derived by implementing HCC surveillance strategies based on HCC risk as predicted by our models.MethodsWe identified 7,068 patients with NAFLD-cirrhosis and 16,175 with ALD-cirrhosis who received care in the Veterans Affairs (VA) healthcare system in 2012. We retrospectively followed them for the development of incident HCC until January 2018. We used Cox proportional hazards regression to develop and internally validate models predicting HCC risk using baseline characteristics at entry into the cohort in 2012. We plotted decision curves of net benefit against HCC screening thresholds.ResultsWe identified 1,278 incident cases of HCC during a mean follow-up period of 3.7 years. Mean annualized HCC incidence was 1.56% in NAFLD-cirrhosis and 1.44% in ALD-cirrhosis. The final models estimating HCC were developed separately for NAFLD-cirrhosis and ALD-cirrhosis and included 7 predictors: age, gender, diabetes, body mass index, platelet count, serum albumin and aspartate aminotransferase to √alanine aminotransferase ratio. The models exhibited very good measures of discrimination and calibration and an area under the receiver operating characteristic curve of 0.75 for NAFLD-cirrhosis and 0.76 for ALD-cirrhosis. Decision curves showed higher standardized net benefit of risk-based screening using our prediction models compared to the screen-all approach.ConclusionsWe developed simple models estimating HCC risk in patients with NAFLD-cirrhosis or ALD-cirrhosis, which are available as web-based tools (www.hccrisk.com). Risk stratification can be used to inform risk-based HCC surveillance strategies in individual patients or healthcare systems or to identify high-risk patients for clinical trials.Lay summaryPatients with cirrhosis of the liver are at risk of getting hepatocellular carcinoma (HCC or liver cancer) and therefore it is recommended that they undergo surveillance for HCC. However, the risk of HCC varies dramatically in patients with cirrhosis, which has implications on if and how patients get surveillance, how providers counsel patients about the need for surveillance, and how healthcare systems approach and prioritize surveillance. We used readily available predictors to develop models estimating HCC risk in patients with cirrhosis, which are available as web-based tools at www.hccrisk.com.

Project description:Hepatocellular carcinoma (HCC) is the second most lethal cancer caused by lack of effective therapies. Although promising, HCC molecular classification, which enriches potential responders to specific therapies, has not yet been assessed in clinical trials of anti-HCC drugs. We aimed to overcome these challenges by developing clinicopathological surrogate indices of HCC molecular classification.Hepatocellular carcinoma classification defined in our previous transcriptome meta-analysis (S1, S2 and S3 subclasses) was implemented in an FDA-approved diagnostic platform (Elements assay, NanoString). Ninety-six HCC tumours (training set) were assayed to develop molecular subclass-predictive indices based on clinicopathological features, which were independently validated in 99 HCC tumours (validation set). Molecular deregulations associated with the histopathological features were determined by pathway analysis. Sample sizes for HCC clinical trials enriched with specific molecular subclasses were determined.Hepatocellular carcinoma subclass-predictive indices were steatohepatitic (SH)-HCC variant and immune cell infiltrate for S1 subclass, macrotrabecular/compact pattern, lack of pseudoglandular pattern, and high serum alpha-foetoprotein (>400 ng/ml) for S2 subclass, and microtrabecular pattern, lack of SH-HCC and clear cell variants, and lower histological grade for S3 subclass. Macrotrabecular/compact pattern, a predictor of S2 subclass, was associated with the activation of therapeutically targetable oncogene YAP and stemness markers EPCAM/KRT19. BMP4 was associated with pseudoglandular pattern. Subclass-predictive indices-based patient enrichment reduced clinical trial sample sizes from 121, 184 and 53 to 30, 43 and 22 for S1, S2 and S3 subclass-targeting therapies respectively.Hepatocellular carcinoma molecular subclasses can be enriched by clinicopathological indices tightly associated with deregulation of therapeutically targetable molecular pathways.

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Ubiquitin-related molecular classification and risk stratification of hepatocellular carcinoma.

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