image: Figure 1: Overview of the MULGONET framework
Credit: Wei Lan, Zhentao Tang, Haibo Liao et al.
Predicting tumor recurrence at the molecular level emerges as a critical challenge, especially for patients with complex multi-omics profiles. Traditional prognostic models relying on single biomarkers fail to sufficiently capture the interactions between genomic, epigenetic and transcriptomic drivers.
In particular, multi-omics integration face two key obstacles: the reliance on empirical feature selection limiting cross-cancer applicability and the enormous computational complexity in interpreting biological pathways. To that end, in a study published in Fundamental Research by a team from Central South University and Guangxi University, led by Prof. Jianxin Wang and Dr. Wei Lan, the researchers proposed a novel framework that enables precise and interpretable recurrence risk prediction.
"When analyzing multi-omics data from cancers such as bladder and gastric cancer, traditional machine learning models struggle to capture pathway-level interactions," explains Lan. “Our new framework, MULGONET, overcomes this limitation through two key advances.”
The MULGONET framework
1. Gene ontology guided architecture by building a hierarchical network based on more than 11,000 Gene Ontology (go) terms, MULGONET automatically links genes (such as Cdk6) to biological processes (such as "cell cycle regulation" go:0051726), eliminating the need for manual feature selection. This enabled the application to trans-cancer, with AUPR scores of 0.774 (bladder cancer), 0.873 (pancreatic cancer) and 0.702 (gastric cancer).
2. Effective multi-omics fusion The model's attention-based fusion mechanism processes 3D omics data (gene × omics layer × GO term) in less than 2 hours on standard hardware, while existing tools require more than 8 hours.
"Our framework can not only predict recurrence, but also identify driver pathways," says Lan. "For example, in pancreatic cancer, a high attributable score of Wnt5a in the" G protein-coupled receptor signaling pathway "(go:0007186) is associated with early recurrence."
The team has opened its framework to accelerate community-driven applications. "We hope that MULGONET can stimulate new research on the interpretability of multi-omics," adds Wang.
The study, published in KeAi’s Fundamental Research, directly supports precision oncology by revealing viable targets of Rock1 in metastasis.
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Contact the author: Jianxin Wang, National Engineering Laboratory of Medical Big Data Application Technology, Central South University, Hunan 410083, China, jxwang@csu.edu.cn
The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 200 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).
Journal
Fundamental Research
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable
Article Title
MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery.
COI Statement
The authors declare that they have no conflicts of interest in this work.