A network analysis to identify lung cancer comorbid diseases

Abstract

Cancer patients with comorbidities face various life problems, health costs, and quality of life. Therefore, determining comorbid diseases would significantly affect the treatment of cancer patients. Because cancer disease is very complex, we can represent the relationship between cancer and its comorbidities as a network. Furthermore, the network analysis can be employed to determine comorbidities as a community detection problem because the relationship between cancer and its comorbidities forms a community. This study investigates which community detection algorithms are more appropriate to determine the comorbid of cancer. Given different community findings, this study attempted to analyze the modularity generated by the algorithm to decide the significant comorbid diseases. We retrieved lung cancer comorbid data on the basis of text mining manuscripts in PubMed, searched through disease ontologies, and calculated disease similarity. We investigate 20 algorithms using five modularity metrics and 16 fitness function evaluations to determine the significant comorbid diseases. The results show the five best modularity algorithms, namely label propagation, spinglass, Chinese whispers, Louvain, RB Pots. These five algorithms found significant comorbidities: blood vessels, immune system, bone, pancreas, and metabolic disorders, atrial cardiac septal defect, atrial fibrillation respiratory system, interstitial lung, and diabetes mellitus. The fitness function justifies the results of the community algorithm, and the ones that have a significant effect are average internal degree, size, and edges inside. This study contributes to more comprehensive knowledge and management of diseases in the healthcare context.