Prediction system for identifying key heterogeneous molecules for driving tumor metastasis

Abstract

The invention relates to a prediction system for identifying key heterogeneity molecules for driving tumor metastasis. The prediction system comprises an input module, an analysis module and an output module, wherein the input module is used for inputting a pre-intervention protein quantitative analysis result and a post-intervention protein quantitative analysis result; the analysis module comprises a primary analysis sub-module, a secondary analysis sub-module and a calculation sorting sub-module, all the sub-modules are connected in sequence, and a sorting list of all the heterogeneity molecules is finally obtained; and the output module is used for outputting a sorting list of the heterogeneity molecules. According to the method, the key heterogeneous molecules for driving tumor metastasis can be more reasonably and accurately identified and sequenced, sequential intervention can be subsequently carried out according to the result in combination with the actual situation, and the curative effect is determined; and the research findings and evidences provide innovative ideas, specific algorithms and practical guidance for clinical treatment of drug resistance and metastasis caused by tumor heterogeneity.

Description

technical field [0001] The invention relates to a prediction system for identifying key heterogeneous molecules driving tumor metastasis, which belongs to the field of medical biotechnology. Background technique [0002] As far as the inventors know, malignant tumors are a major public health problem that seriously endangers human health, and tumor drug resistance and metastasis are one of the root causes of no cure and low survival rate for tumor patients. The biggest challenge in cancer treatment is how to overcome tumor drug resistance and metastasis caused by tumor heterogeneity. Although new methods including biological therapy and immunotherapy have brought hope to tumor treatment, the heterogeneity of tumors will make it difficult for a single-target treatment to finally overcome the tumor. The emergence of tumor cell drug resistance or metastasis makes the treatment ineffective. Since the heterogeneous tumor cell subsets are independent of each other, even if the o...

AI Technical Summary

Problems solved by technology

Although some prediction systems already exist in the prior art, for example: the invention patent application with application number CN202010811273.X and application publication number CN111763740A discloses a system based on the lncRNA molecular model to predict the curative effect and prognosis of neoadjuvant radiotherapy and chemotherapy for patients with esophageal squamous cell carcinoma, including The system for detecting the expression of three lncRNAs, SCAT1, PRKAG2-AS1 and FLG-AS1, can predict the efficacy of neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma, and can also predict the prognosis of neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma, such as overall survival rate , prognosis and recurrence-free survival rate; however, there is no technical solution with the goal of discovering and guiding tumor treatment intervention and the technical effect

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