» Articles » PMID: 33989895

Integrating Multi-omics Data Through Deep Learning for Accurate Cancer Prognosis Prediction

Overview
Journal Comput Biol Med
Publisher Elsevier
Date 2021 May 14
PMID 33989895
Citations 38
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Genomic information is nowadays widely used for precise cancer treatments. Since the individual type of omics data only represents a single view that suffers from data noise and bias, multiple types of omics data are required for accurate cancer prognosis prediction. However, it is challenging to effectively integrate multi-omics data due to the large number of redundant variables but relatively small sample size. With the recent progress in deep learning techniques, Autoencoder was used to integrate multi-omics data for extracting representative features. Nevertheless, the generated model is fragile from data noises. Additionally, previous studies usually focused on individual cancer types without making comprehensive tests on pan-cancer. Here, we employed the denoising Autoencoder to get a robust representation of the multi-omics data, and then used the learned representative features to estimate patients' risks.

Results: By applying to 15 cancers from The Cancer Genome Atlas (TCGA), our method was shown to improve the C-index values over previous methods by 6.5% on average. Considering the difficulty to obtain multi-omics data in practice, we further used only mRNA data to fit the estimated risks by training XGboost models, and found the models could achieve an average C-index value of 0.627. As a case study, the breast cancer prognosis prediction model was independently tested on three datasets from the Gene Expression Omnibus (GEO), and shown able to significantly separate high-risk patients from low-risk ones (C-index>0.6, p-values<0.05). Based on the risk subgroups divided by our method, we identified nine prognostic markers highly associated with breast cancer, among which seven genes have been proved by literature review.

Conclusion: Our comprehensive tests indicated that we have constructed an accurate and robust framework to integrate multi-omics data for cancer prognosis prediction. Moreover, it is an effective way to discover cancer prognosis-related genes.

Citing Articles

Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis.

Mao R, Wan L, Zhou M, Li D Brief Bioinform. 2025; 26(2).

PMID: 40067266 PMC: 11894944. DOI: 10.1093/bib/bbaf108.


A generative deep neural network for pan-digestive tract cancer survival analysis.

Xu L, Lan T, Huang Y, Wang L, Lin J, Song X BioData Min. 2025; 18(1):9.

PMID: 39871331 PMC: 11771125. DOI: 10.1186/s13040-025-00426-z.


From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.

Tanaka M Biomedicines. 2025; 13(1).

PMID: 39857751 PMC: 11761901. DOI: 10.3390/biomedicines13010167.


MulitDeepsurv: survival analysis of gastric cancer based on deep learning multimodal fusion models.

Mao S, Liu J Biomed Opt Express. 2025; 16(1):126-141.

PMID: 39816158 PMC: 11729289. DOI: 10.1364/BOE.541570.


GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection.

Lin J, Deng W, Wei J, Zheng J, Chen K, Chai H J Cell Mol Med. 2024; 28(23):e70221.

PMID: 39628446 PMC: 11615516. DOI: 10.1111/jcmm.70221.