Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network

Abstract

Background Cancer is a heterogeneous disease in which tumor genes cooperate as well as adapt and evolve to the changing conditions for individual patients. It is a meaningful task to discover the personalized cancer driver genes that can provide diagnosis and target drug for individual patients. However, most of existing methods mainly ranks potential personalized cancer driver genes by considering the patient-specific nodes information on the gene/protein interaction network. These methods ignore the personalized edge weight information in gene interaction network, leading to false positive results. Results In this work, we presented a novel algorithm (called PDGPCS) to predict the Personalized cancer Driver Genes based on the Prize-Collecting Steiner tree model by considering the personalized edge weight information. PDGPCS first constructs the personalized weighted gene interaction network by integrating the personalized gene expression data and prior known gene/protein interaction network knowledge. Then the gene mutation data and pathway data are integrated to quantify the impact of each mutant gene on every dysregulated pathway with the prize-collecting Steiner tree model. Finally, according to the mutant gene’s aggregated impact score on all dysregulated pathways, the mutant genes are ranked for prioritizing the personalized cancer driver genes. Experimental results on four TCGA cancer datasets show that PDGPCS has better performance than other personalized driver gene prediction methods. In addition, we verified that the personalized edge weight of gene interaction network can improve the prediction performance. Conclusions PDGPCS can more accurately identify the personalized driver genes and takes a step further toward personalized medicine and treatment. The source code of PDGPCS can be freely downloaded from https://github.com/NWPU-903PR/PDGPCS.

Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields

“…The top 50 candidate driver genes for cancer predicted using the PDGCN method were subjected to survival analysis using the online tool GEPIA2 (Gene Expression Profiling Interactive Analysis, http://gepia2.cancer-pku.cn) [38]. Genes with logrank p < 0.05 were considered significant biomarker genes [39]. Moreover, among these significant biomarker genes, those not in the CGC dataset were considered novel candidate biomarker genes.…”

Identifying cancer driver genes in individual tumours

“…Unlike all the other algorithms discussed, pDriver also considers miRNA driver genes. Finally, PDGPCS [60] is a unique algorithm that utilises paired-SSN to create de novo patient networks, but then identifies controllers of this network using a very similar approach to PRODIGY. Namely, dysregulated pathways are identified using pathway enrichment of DEGs (based on a log2 fold-change threshold between paired tumour and normal samples), and then for each mutated gene and each dysregulated pathway, a PCST model is used to rank drivers using differential expression as a node prize and making the paired-SSN edge-weights inversely proportional to edge costs.…”

“… Software De novo Network Strategy Driver-Prioritisation / Network Control Strategy Data Type Primary Language Year Ref. Personalised Network Control (PNC) Paired-SSN NCUA Paired-Tumour/ Normal MATLAB 2019 Guo et al [54] pDriver LIONESS MMS miRNA and mRNA, Tumour Only R 2021 Pham et al [59] PDGPCS Paired-SSN PCST Paired-Tumour/ Normal MATLAB 2022 Zhang et al [60] …”

Genomic landscape in Saudi patients with hepatocellular carcinoma using whole-genome sequencing: a pilot study

“…Many studies have suggested prize-collecting Steiner tree (PCST) algorithms as potential methods to identify cancer driver genes (15) and cancer-related signaling pathways (16). A PCST algorithm was demonstrated for two breast cancer signatures (17).…”