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Evaluation and prediction of hepatocellular carcinoma prognosis based on molecular classification.

ABSTRACT: Purpose:Prediction of hepatocellular carcinoma (HCC) prognosis faced great difficulty due to tumor heterogeneity. We aimed to identify the prognosis-associated molecular subtypes existing in HCC patients and construct an evaluation model based on identified molecular classification. Materials and methods:Non-negative matrix factorization consensus clustering was performed using 371 HCC patients from The Cancer Genome Atlas (TCGA) to identify molecular subtypes, based on the expression profile of the survival-associated genes. Signature genes for different subtypes were identified by Significance Analysis of Microarray and Prediction Analysis for Microarrays. Model for subtype discrimination and prognosis evaluation was established using binary logistic regression. The model and its clinical implications were further validated in GSE5436 cohort and Fujian cohort. Results:Based on TCGA data, we observed two molecular subtypes with distinct clinical outcomes including significantly different overall survival, tumor differentiation, TNM stage, and vascular invasion (all P<0.05). The existence of these two molecular subtypes was further validated in five other Gene Expression Omnibus datasets. Furthermore, we constructed an evaluation model based on six subtype signature genes, which can discriminate different subtypes with the cutoff of 0.385. Meanwhile, both Cox regression analysis and stratification analysis showed that the calculated continuous prognostic value could also effectively indicate HCC prognosis, regardless of patients' clinical conditions. The prognostic evaluation model was successfully validated in GSE54236 cohort and Fujian cohort. Conclusion:Two prognostic molecular subtypes existed among HCC patients, which provided promising strategies for overcoming HCC heterogeneity and could be utilized in future clinical application for predicting HCC prognosis.

SUBMITTER: Ke K

PROVIDER: S-EPMC6225913 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Evaluation and prediction of hepatocellular carcinoma prognosis based on molecular classification.

Ke Kun K Chen Geng G Cai Zhixiong Z Huang Yanbing Y Zhao Bixing B Wang Yingchao Y Liao Naishun N Liu Xiaolong X Li Zhenli Z Liu Jingfeng J

Cancer management and research 20181105

<h4>Purpose</h4>Prediction of hepatocellular carcinoma (HCC) prognosis faced great difficulty due to tumor heterogeneity. We aimed to identify the prognosis-associated molecular subtypes existing in HCC patients and construct an evaluation model based on identified molecular classification.<h4>Materials and methods</h4>Non-negative matrix factorization consensus clustering was performed using 371 HCC patients from The Cancer Genome Atlas (TCGA) to identify molecular subtypes, based on the expres ...[more]

PMID: 30464626

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Project description:BackgroundAs an iron-dependent type of programmed cell death, ferroptosis plays an important role in the pathogenesis and progression of hepatocellular carcinoma (HCC). Long noncoding RNAs (lncRNAs) have been linked to the prognosis of patients with HCC in a number of studies. Nevertheless, the predictive value of lncRNAs (FRLs) associated with ferroptosis in HCC has not been fully elucidated.MethodsDownload RNA sequencing data and clinical profiles of HCC patients from The Cancer Genome Atlas (TCGA) database. The FRLs associated with prognosis were determined by Pearson's correlation analysis. After that, prognostic signature for FRLs was established using Cox and LASSO regression analyses. Meanwhile, survival analysis, correlation analysis of clinicopathological features, Cox regression, receiver operating characteristic (ROC) curve, and nomogram were used to analyze the FRL signature's predictive capacity. The relationship between signature risk score, immune cell infiltration, and chemotherapy drug sensitivity is further studied.ResultsIn total, 93 FRLs were found to be of prognostic value in patients with HCC. A five-FRL signature comprising AC015908.3, LINC01138, AC009283.1, Z83851.1, and LUCAT1 was created in order to enhance the prognosis prediction with HCC patients. The signature demonstrated a good predictive potency, according to the Kaplan-Meier and ROC curves. The five-FRL signature was found to be a risk factor independent of various clinical factors using Cox regression and stratified survival analysis. The high-risk group was shown to be enriched in tumorigenesis and immune-related pathways according to GSEA analysis. Additionally, immune cell infiltration, immune checkpoint molecules, and half-inhibitory concentrations differed considerably between risk groups, implying that this signature could be used to evaluate the clinical efficacy of chemotherapy and immunotherapy.ConclusionThe five-FRL risk signature is helpful for assessing the prognosis of HCC patients and improving therapy options, so it can be further applied clinically.

Project description:Background & aimsHepatocellular 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.MethodsHepatocellular 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.ResultsHepatocellular 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.ConclusionsHepatocellular carcinoma molecular subclasses can be enriched by clinicopathological indices tightly associated with deregulation of therapeutically targetable molecular pathways.

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Evaluation and prediction of hepatocellular carcinoma prognosis based on molecular classification.

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Evaluation and prediction of hepatocellular carcinoma prognosis based on molecular classification.

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