Abstract
Tumor heterogeneity is one of the challenges to study malignant tumors. In general, tumors are driven by combinations of mutated genes that vary greatly from patient to patient. Constructing cancer gene networks using omics data is of great significance for understanding the underlying mechanisms of cancer. In this work, we present a method for network embedding based on gene networks constructed by random forest (RFNE). Gene networks are constructed using random forests (RF) by integrating omics data from heterogeneous samples of different cancers. We assume that if genes are located on leaf nodes of the same tree, then their proximity is plus one. Summing all leaf nodes and dividing by the total number of trees in the forest yields the pairwise closeness between genes. A multi-layer weighted graph is then constructed and random roaming is used to capture distant but structurally similar gene pairs in the network. Finally, we use a network embedding approach to code genes and integrate mutation data from 5290 samples of 14 cancer types to construct patient features. We then used the lightGBM classification algorithm to make predictions for patients. Experimental results show that compared with other methods, our method has better performance than other three methods. We can separate patients with specific cancer types into several subtypes by applying an unsupervised clustering method to learn patient features. Among 14 cancer types. In most cancer types, patient subgroups revealed by RFNE are significantly linked with patient survival.
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Code Availability:
The code of RFNE is available on GitHub: github.com/Feng Li12/RFNE.
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Funding
This work has been supported by the National Natural Science Foundation of China (61902216, 61972236 and 61972226), and Natural Science Foundation of Shandong Province (No. ZR2018MF013).
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Sun, Z. et al. (2022). Construction of Gene Network Based on Inter-tumor Heterogeneity for Tumor Type Identification. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_29
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