Deep learning-based multi-omics integration robustly predicts survival in liver cancer
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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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Particularly in cancer patient survival subtyping with multi-omics modalities, the sequence of joint representation learning through an AE followed by feature selection with univariate Cox proportional hazard modeling and unsupervised label inference has become popular.
“Particularly in cancer patient survival subtyping with multi-omics modalities, this sequence of steps has become popular [25–31, 34].”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [25]
Chaudhary et al. employed multi-omics data integration to predict prognosis of hepatocellular carcinoma and identify subgroups with significant survival differences.
“In liver cancer research, Chaudhary et al. employed multi-omics data integration to predict the prognosis of hepatocellular carcinoma (HCC) and identify subgroups with significant survival differences [112].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [112]
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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.
- Venue
- Clinical Cancer Research
- Published
- 2018-03-14
- Volume
- 24
- Issue
- 6
- Pages
- 1248-1259
- ISSN
- 1078-0432 (ISSN-L)
- 1557-3265
- 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 · 2017: 4
- 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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