Evaluation and prediction of hepatocellular carcinoma prognosis based on molecular classification - PubMed (original) (raw)
Evaluation and prediction of hepatocellular carcinoma prognosis based on molecular classification
Kun Ke et al. Cancer Manag Res. 2018.
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.
Keywords: HCC heterogeneity; hepatocellular carcinoma; molecular classification; prognosis evaluation; transcriptome.
Conflict of interest statement
Disclosure The authors report no conflicts of interest in this work.
Figures
Figure 1
Flow chart for the construction of the prognostic evaluation model. Abbreviations: HCC, hepatocellular carcinoma; TCGA, The Cancer Genome Atlas; NMF, non-negative matrix factorization; PAM, Prediction Analysis for Microarrays; SAM, Significance Analysis of Microarray.
Figure 2
Molecular subtype identification by NMF consensus clustering in TCGA and GSE54236 cohorts. Notes: (A) NMF clustering using all the 774 prognostic signature genes. Left: the clustering results using k=2–5 are shown for TCGA cohort and GSE54236 cohort. Right: the cophenetic correlation coefficient under corresponding k values. For both cohorts, optimal number of classifications were chosen with k=2, with highest cophenetic correlation coefficients and most harmonious models. The OS difference between the two molecular subtypes in (B) TCGA cohort and (C) GSE54236 cohort is illustrated. The _P_-values were calculated by log-rank test. Abbreviations: NMF, non-negative matrix factorization; MST, median survival time; OS, overall survival; TCGA, The Cancer Genome Atlas.
Figure 3
Transcriptome features of the two molecular subtypes. Notes: Enrichment of KEGG and Reactome pathways of signature genes for (A) molecular subtype 1 and (B) molecular subtype 2. Only the top ten significantly enriched pathways are shown (the full list of significantly enriched pathways are given in
Table S5
). Differences of mean expression (_z_-score) of signature genes for molecular subtype 1 and molecular subtype 2 between HCC tumor and adjacent normal tissue in (C) TCGA cohort and (D) GSE54236 cohort are provided. _P_-values were calculated by Wilcoxon signed-rank test. (E) Heatmap of the top 30 signature genes with highest PAM scores in molecular subtype 1 and molecular subtype 2. Abbreviations: HCC, hepatocellular carcinoma; PAM, Prediction Analysis for Microarrays; TCGA, The Cancer Genome Atlas.
Figure 4
Validation of the prognostic evaluation model. Notes: Kaplan–Meier survival analysis of HCC patients with high prognostic value and low prognostic value in GSE54236 cohort (A) without excluding and (B) excluding patients with negative silhouette width. The difference of (C) OS and (D) RFS was compared between HCC patients with high prognostic value and low prognostic value. The _P_-values were assessed by log-rank test. Abbreviation: HCC, hepatocellular carcinoma; OS, overall survival; RFS, recurrence free survival.
Figure 5
Subgroup analysis of OS for patients with different levels of prognostic values. Notes: Kaplan–Meier curves for OS between HCC patients with high prognostic values and low prognostic values in (A) Fujian cohort and (B) TCGA cohort, according to different clinical features: TNM stages, Edmondson–Steiner tumor differentiation, and vascular invasion. Abbreviations: HCC, hepatocellular carcinoma; OS, overall survival; TCGA, The Cancer Genome Atlas.
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