Lung adenocarcinoma recurrence prediction method and system based on multi-omics data analysis

Abstract

The invention discloses a lung adenocarcinoma recurrence prediction method and system based on multi-omics data analysis, and relates to the technical field of precision medicines.The lung adenocarcinoma recurrence prediction method comprises the steps that folate metabolome data, transcriptome expression profiles and methylation level data of a target gene promoter region of tumor tissue of a lung adenocarcinoma patient are collected; constructing a metabolism-appearance correlation topological graph based on a spatial adjacent relationship, screening a significant causal relationship between methylation change and metabolic fluctuation through causal analysis, and generating a methylation metabolism significant causal edge set; fusing the simulated metabolic pathway characteristics with the methylation metabolism significant causal edge set, constructing a lung adenocarcinoma recurrence risk prediction model, and analyzing risk factor fluctuation through a time sequence to trigger a high-risk early warning signal; by constructing a metabolic appearance correlation topological graph and screening a significant causal edge set, causal correlation modeling of multi-omics data in a spatial dimension is realized, the robustness of a recurrence prediction model is improved, and collaborative enhancement of prediction performance is realized; and the accuracy of lung adenocarcinoma recurrence prediction is obviously improved.

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