Early NSCLC prognosis prediction system

Abstract

The invention discloses an early NSCLC prognosis prediction system, which comprises a main effect identification module, an interaction identification module, a survival time prediction module and a high-dimensional population discrimination module, and can improve the model prediction precision from the perspective of cross-omics by establishing a sample database and molecular biomarkers-methylation and gene expression. Different from a traditional biomarker, the system is stable and minimally invasive, the sensitivity and specificity of prognosis prediction are greatly improved, the main effect, GxE and GxG interaction effects are integrated, an early-stage NSCLC survival prediction model which is high in prediction precision and has strict multi-stage independent crowd verification is constructed, the defect that an existing model is poor in prediction effect is overcome, and a high-risk crowd discrimination module is combined. People with different risks are discriminated, diseaseprognosis is evaluated scientifically and accurately, and clinicians are helped to make clinical decisions or guide adjuvant therapy, early intervention and early benefit.

Description

technical field [0001] The invention relates to the technical fields of genetic engineering and tumor medicine, in particular to an early NSCLC prognosis prediction system. Background technique [0002] Lung cancer is the leading cause of cancer death in the world, and an accurate prognostic prediction model can help clinicians make clinical decisions or guide adjuvant therapy; although patient macroscopic clinical information and tumor characteristics have been generally used as effective predictors, more and more evidence showed that molecular biomarkers can provide early warning signals; the reason is that even when the tumor size cannot be detected (<0.01cm3), tumor cells can metastasize and some biomarkers are abnormal; therefore, a Prognosis prediction models including genetic and extrinsic non-genetic factors are very valuable in clinical application. [0003] At present, although some studies have established a prognosis prediction model for early NSCLC, the pred...

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Problems solved by technology

[0003] At present, although some studies have established a prognosis prediction model for early NSCLC, the prediction effect is not good (AUC<0.8), and the sample size is small; the reasons may be: (1) The type of omics data is limited: based on omics data , existing studies have proposed biomarkers associated with lung cancer prognosis, including DNA methylation, gene expression, microRNA, and long non-coding RNA, etc.; however, most studies are limited to a single omics, which leads to poor prognosis prediction model accuracy Not ideal; (2) Insufficient information is considered: there are almost no large-scale studies that incorporate gene-environment (GxE) and gene-gene (GxG) interaction information to construct cancer prognosis prediction models

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