Cited by 1,091Hepatocellular Carcinoma Treatment and PrognosisCancer-related molecular mechanisms researchGene expression and cancer classification

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Deep learning-based multi-omics integration robustly predicts survival in liver cancer

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Chaudhary, Kumardeep, Poirion, Olivier B., Lu, Liangqun, Garmire, Lana X.·2018·Clinical Cancer Research

DOI 10.1158/1078-0432.ccr-17-0853PMC PMC6050171

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Abstract

Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill this gap, we present a deep learning (DL)–based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We built the DL-based, survival-sensitive model on 360 HCC patients' data using RNA sequencing (RNA-Seq), miRNA sequencing (miRNA-Seq), and methylation data from The Cancer Genome Atlas (TCGA), which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL-based model provides two optimal subgroups of patients with significant survival differences (P = 7.13e−6) and good model fitness [concordance index (C-index) = 0.68]. More aggressive subtype is associated with frequent TP53 inactivation mutations, higher expression of stemness markers (KRT19 and EPCAM) and tumor marker BIRC5, and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n = 230, C-index = 0.75), NCI cohort (n = 221, C-index = 0.67), Chinese cohort (n = 166, C-index = 0.69), E-TABM-36 cohort (n = 40, C-index = 0.77), and Hawaiian cohort (n = 27, C-index = 0.82). This is the first study to employ DL to identify multi-omics features linked to the differential survival of patients with HCC. Given its robustness over multiple cohorts, we expect this workflow to be useful at predicting HCC prognosis prediction. Clin Cancer Res; 24(6); 1248–59. ©2017 AACR.

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Authors: Chaudhary, K., Poirion, O. B., Lu, L., & Garmire, L. X.
Affiliations: University of Hawaii Cancer Center (US)
University of Hawaiʻi at Mānoa (US)
Corresponding author: Garmire, Lana X.
ORCID: Chaudhary, Kumardeep
Poirion, Olivier B.
Lu, Liangqun
Garmire, Lana X.
Venue: Clinical Cancer Research
Publisher: American Association for Cancer Research (AACR)
Published: 2018-03-14
Volume: 24
Issue: 6
Pages: 1248-1259
ISSN: 1078-0432 (ISSN-L)
1557-3265
DOI: 10.1158/1078-0432.ccr-17-0853
PMC: PMC6050171
OpenAlex: W2951209146
Cited by: 1,091
References: 71
Citations by year: 2026: 95 · 2025: 135 · 2024: 159 · 2023: 152 · 2022: 152 · 2021: 171 · 2020: 124 · 2019: 71 · 2018: 28
FWCI: 38.36
Type: journal article
Language: en
Open access: green
doi.org
Locations: Clinical Cancer Research (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: NIEHS (K01ES025434)
NIH/NIGMS (P20 COBRE GM103457)
NICHD (R01 HD084633)
NLM (R01LM012373)
Hawaii Community Foundation (14ADVC-64566)
Topics: Health Sciences › Medicine › Hepatology › Hepatocellular Carcinoma Treatment and Prognosis
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords: hepatocellular carcinoma, survival prediction, prognosis, multi-omics integration, deep learning, RNA-seq, miRNA sequencing, DNA methylation, p53 inactivation, stem cell markers, Wnt signaling, Akt signaling, survivin, external validation, prognostic subgroups
MeSH terms: Deep Learning, Algorithms, Humans, Liver Neoplasms, Prognosis, Biomarkers, Tumor, Reproducibility of Results, Proportional Hazards Models, Computational Biology, Gene Expression Profiling, Genomics, Proteomics, Gene Regulatory Networks, Metabolomics, Transcriptome

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Deep learning-based multi-omics integration robustly predicts survival in liver cancer

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