QIGTD: identifying critical genes in the evolution of lung adenocarcinoma with tensor decomposition

Abstract

Background: Identifying critical genes is important for understanding the pathogenesis of complex diseases. Traditional studies typically comparing the change of biomecules between normal and disease samples or detecting important vertices from a single static biomolecular network, which often overlook the dynamic changes that occur between different disease stages. However, investigating temporal changes in biomolecular networks and identifying critical genes is critical for understanding the occurrence and development of diseases.

Methods: A novel method called Quantifying Importance of Genes with Tensor Decomposition (QIGTD) was proposed in this study. It first constructs a time series network by integrating both the intra and inter temporal network information, which preserving connections between networks at adjacent stages according to the local similarities. A tensor is employed to describe the connections of this time series network, and a 3-order tensor decomposition method was proposed to capture both the topological information of each network snapshot and the time series characteristics of the whole network. QIGTD is also a learning-free and efficient method that can be applied to datasets with a small number of samples.

Results: The effectiveness of QIGTD was evaluated using lung adenocarcinoma (LUAD) datasets and three state-of-the-art methods: T-degree, T-closeness, and T-betweenness were employed as benchmark methods. Numerical experimental results demonstrate that QIGTD outperforms these methods in terms of the indices of both precision and mAP. Notably, out of the top 50 genes, 29 have been verified to be highly related to LUAD according to the DisGeNET Database, and 36 are significantly enriched in LUAD related Gene Ontology (GO) terms, including nuclear division, mitotic nuclear division, chromosome segregation, organelle fission, and mitotic sister chromatid segregation.

Conclusion: In conclusion, QIGTD effectively captures the temporal changes in gene networks and identifies critical genes. It provides a valuable tool for studying temporal dynamics in biological networks and can aid in understanding the underlying mechanisms of diseases such as LUAD.

Keywords: Critical genes; Evolution; LUAD; Temporal network; Tensor decomposition.

Fig. 1

The module of forming the tensor and decomposing tensor. a The way of constructing temporal network into tensor. The edges, consist of inter-stage edges and intra-stage edges are both taken into consideration. The yellow one is the network of Stage I, the green one is Stage II, the blue one is Stage III and the red one is Stage IV. The black represents inter-stage network. b The decomposition of tensor

Fig. 2

The curve of fold enrichment in the top 500 genes. The x axis is the rank of genes in every method. The y axis is the score of fold enrichment. The fold enrichment could be calculated with precision and the correlation rate between all genes and LUAD

Fig. 3

The boxplot of the expression of top 10 genes. The different color in the plots is different stages. The blue box represents the expression in control. It shows that the genes identified with QIGTD are differentially expressed genes

Fig. 4

The sub networks of Top 10 genes and Top 50 genes

Fig. 5

The GO enrichment of top 50 genes. 5 Go terms chosen from the result of the enrichment are exhibited. The ribbons in different colors represents different GO terms. The number of the ribbons in gene is the number of GO terms it enriches. The boxed genes are LUAD-related genes. There are 32 genes enriched on nuclear division, 29 enriched on mitotic nuclear division, 30 on chromosome segregation, 32 on organelle fission and 25 enriched on mitotic sister chromatid segregation