Integration of Clinicopathological And Genomic Features To Predict The Risk Stratification of TCGA Lung Adenocarcinoma And Lung Squamous Cell Carcinoma Patients

Background Predicting lung adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC) risk cohorts is a crucial step in precision oncology. Currently, clinicians and patients are informed about the patient’s risk group via staging. Recently, several machine learning approaches are reported for the stratification of LUAD and LUSC patients, but there is no study comparatively assessing the integrated modeling of the clinicopathological and genetic data of these two lung cancer types so far. Methods In our study based on 1026 patients’ clinicopathological and somatically mutated gene features, a prognostic prediction model is implemented to rank the importance of features according to their impact on risk classification. Findings By integrating the clinicopathological features and somatically mutated genes of patients, we achieved the highest accuracy; %93 for LUAD and %89 for LUSC, respectively. Our second finding is that new prognostic genes such as KEAP1 for LUAD and CSMD3 for LUSC and new clinicopathological factors such as site of resection are significantly associated with the risk stratification and can be integrated into clinical decision making. Conclusions In current clinical practice, clinicians, and patients are informed about the patient’s risk group only with cancer staging. With the feature set we propose, clinicians and patients can assess the risk group of their patients according to the patient-specific clinical and molecular parameters. Using this machine learning model we are implementing a user-friendly web interface for clinicians and lung cancer patients to predict the risk stratification of individuals and to understand the underlying clinical and molecular mechanisms. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement TUBITAK 2209A, TUSEB4583 ### 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: TCGA I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at TCGA