Constructing cancer-specific patient similarity network with clinical significance

Clinical molecular genetic testing and molecular imaging dramatically increase the quantity of clinical data. Combined with the extensive application of electronic health records, medical data ecosystem is forming, which summons big-data-based medicine model. We tried to use big data analytics to search for similar patients in a cancer cohort and to promote personalized patient management. In order to overcome the weaknesses of most data processing algorithms that rely on expert labelling and annotation, we uniformly adopted one-hot encoding for all types of clinical data, calculating Euclidean distance to measure patient similarity, and subgrouping via unsupervised learning model. Overall survival was investigated to assess the clinical validity and clinical relevance of the model. Thereafter, we built a high-dimensional network cPSN (clinical patient similarity network). When performing overall survival analysis, we found Cluster\_2 had the longest survival rates while Cluster\_5 had the worst prognosis among all subgroups. Because patients in the same subgroup share some clinical characteristics, clinical feature analysis found that Cluster_2 harbored more lower distal GCs than upper proximal GCs, shedding light on the debates. Overall, we constructed a cancer-specific cPSN with excellent interpretability and clinical significance, which would recapitulate patient similarity in the real-world. The constructed cPSN model is scalable, generalizable, and performs well for various data types. The constructed cPSN could be used to accurately "locate" interested patients, classify the patient into a disease subtype, support medical decision making, and predict clinical outcomes. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement National Natural Science Foundation of China (#91846302). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Ethics Committee of Changhai Hospitall, Naval Military Medical University. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors