18-Gene ECM Model for Hepatocellular Carcinoma Prognosis
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Solution Overview
Problem
Current clinical practices lack effective markers for predicting the prognosis of hepatocellular carcinoma patients, making it difficult to stratify risk and guide early intervention and treatment.
Innovation Solution
A gene combination model is constructed using extracellular matrix-related genes, integrating transcriptome data from hepatocellular carcinoma and normal liver tissue samples, and a tissue chip is developed to evaluate prognosis through risk scores, utilizing 18 specific genes (MMP1, EPO, MMRN1, S100A9, ADAM9, GPC1, SPP1, GLDN, FGF9, CXCL5, CST7, THBS3, ANXA10, PIK3IP1, MMP25, CLEC3B, PZP, and CLEC17A) to predict prognosis and guide treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current clinical practices are used without effective markers, then general treatment approaches are applied, but prognosis prediction capability is lacking and risk stratification cannot be performed
Solution Approach 1:
The patent segments the complex prognosis prediction problem into 18 specific extracellular matrix-related genes (MMP1, EPO, MMRN1, S100A9, ADAM9, GPC1, SPP1, GLDN, FGF9, CXCL5, CST7, THBS3, ANXA10, PIK3IP1, MMP25, CLEC3B, PZP, and CLEC17A). Each gene serves as an independent marker that can be individually measured and combined to form a comprehensive risk score, enabling reliable prognosis prediction through cumulative information from multiple segmented genetic markers.
2Measurement precision
If a gene combination model with 18 ECM genes is constructed, then prognosis prediction accuracy is improved, but model complexity increases
Solution Approach 1:
The patent merges 18 individual extracellular matrix-related genes into a unified risk score model. By combining the expression levels of these genes through a standardized formula, the model achieves high prognosis prediction accuracy while maintaining operational simplicity. The merged model allows clinicians to evaluate patient prognosis through a single integrated score rather than analyzing 18 separate gene markers individually.
3Adaptability or versatility
If risk stratification is implemented using the gene model, then precise treatment guidance is achieved, but additional testing requirements increase
Solution Approach 1:
The patent creates a universal risk score model that serves multiple functions: prognosis prediction, risk stratification, and treatment guidance. The same 18-gene panel and risk score calculation can be applied across different patient populations and clinical scenarios, providing adaptable treatment guidance without requiring separate testing protocols for each application. The model universally evaluates patients into high-risk or low-risk categories to guide appropriate intervention intensity.
Data Source
AI summary
Disclosed are a gene model for judging prognosis of hepatocellular carcinoma and a construction method and use thereof. According to the present invention, genes with differential expression are obtained by comparing data of hepatocellular carcinoma patient samples with transcriptome data of normal patient samples, and after integration with an extracellular matrix gene set, a LASSO-COX regression model is reduced to obtain a model of 18 genes. The model of the present invention can evaluate the prognosis of hepatocellular carcinoma patients, distinguish and select patients with poor prognosis, so as to guide clinicians to provide more active treatment schemes, and meanwhile avoid over treatment of low-risk hepatocellular carcinoma patients. The gene model helps to construct a tissue chip based on extracellular matrix genes, which can quickly evaluate the prognosis of the hepatocellular carcinoma patients after surgery and realize clinical transformation.