Table 1 Summary of network-based approaches to analyze different cancer types, including prostate cancer.
From: Prostate cancer screening research can benefit from network medicine: an emerging awareness
Method | Network type | Database | Cases of study | Data type | Reference |
|---|---|---|---|---|---|
Mode-of-action by network identification (MNI) algorithm | Gene regulatory network | Microarray data from: GEO, Oncomine, EBI ArrayExpress (MEXP-441), Broad Institute Cancer and the St Jude Research | Non-recurrent primary and metastatic prostate cancer | Transcriptomics data | |
Drug repurposing based on human functional linkage network (FLN) | Drug-disease perturbed genes network | (1) TCGA: prostate cancer transcriptomics data, (2) OMIM: prostate mutated genes, (3) LINCS: prostate cancer cell line expression in response to more than 4000 drugs, (4) DrugBank: drug data | Prostate cancer, breast cancer, and leukemia | Transcriptomics, Genomics, Drug-target data | |
Drug repurposing based on Prostate cancer-specific genome-scale metabolic models (GEMs) | Drug-gene association network | (1) TCGA: prostate cancer transcriptomics data, (2) the Human Protein Atlas: proteome tissue proteome, (3) the Human Pathology Atlas: prostate cancer GEMs, (4) Human Metabolic Atlas: healthy prostate tissue GEMs, (5) ConnectivityMap2: gene expression data from drug-perturbed cancer cell lines | Prostate cancer | Metabolics, Proteomics, Transcriptomics, Drug-target data | |
Bayesian network-based approach (Person correlation, mutual information, Kullback Liebler) | Features association network (DAG) | Prostate MR Image Database | Prostate cancer | MR imaging data | |
Patients stratification based on network propagation (PRINCE algorithm) and clustering | Protein–Protein interaction network | (1) TCGA: ovarian, uterine, and lung adenocarcinoma somatic mutations data, (2) STRING: protein–protein interactions, (3) HumanNet: protein–protein interactions, (4) PathwayCommons: protein–protein interactions and functional gene interactions | Ovarian, uterine, and lung cancer | Genomics data, Protein–Protein interactions | |
Patients stratification based on network propagation (random walk with restart algorithm) and clustering | Protein–Protein interaction network | (1) TCGA: prostate cancer somatic mutations data, (2) STRING: protein–protein interactions TCGA: prostate cancer somatic mutations data | Prostate cancer | Genomics data, Protein–Protein interactions |
