Hepatocellular Carcinoma Prognostic Model Using DDR and ICD Gene Expression
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Solution Overview
Problem
Current hepatocellular carcinoma prognostic models based on DNA damage repair (DDR) and immunogenic cell death (ICD) gene expression are inadequate in constructing effective models, leading to poor treatment and prognosis outcomes due to high recurrence rates and variability in patient responses to chemotherapy.
Innovation Solution
A prognostic model is developed using transcription profile expression data from hepatocellular carcinoma patients to identify candidate genes through single-factor and LASSO Cox regression analysis, constructing a risk score model with genes such as FFAR3, DDX1, POLR3G, FANCL, ADA, PIK3R1, DHX58, TPT1, MGMT, SLAMF6, and EIF2AK4, and assessing prediction performance using time-dependent subject working characteristic curves and Kaplan-Meier curves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prognostic models use general transcription profile expression data, then the model construction is simple, but the prediction accuracy and prognostic precision are insufficient
Solution Approach 1:
The prognostic model is segmented into two distinct functional modules: DDR (DNA Damage Repair) gene expression module and ICD (Immunogenic Cell Death) gene expression module. Each module independently evaluates specific biological pathways, and their integration provides comprehensive prognostic prediction. This segmentation allows the model to capture different aspects of tumor biology separately while maintaining overall simplicity in construction.
2Reliability
If the prognostic model includes more gene types (DDR and ICD), then the prognostic accuracy improves, but the model complexity increases
Solution Approach 1:
The model uses universally applicable gene selection criteria that can be applied to both DDR and ICD gene sets. The same methodological framework (literature review, expression analysis, correlation with clinical outcomes) is used for both gene types, making the complex process of evaluating multiple gene types systematic and reproducible. This universal approach enhances reliability while managing complexity through standardized procedures.
3Measurement precision
If clinicians rely on traditional clinical manifestations and tumor markers for prognosis prediction, then the prediction process is simple, but the prediction accuracy deviates significantly from actual patient outcomes
Solution Approach 1:
The patent introduces gene expression profiles as an intermediary layer between traditional clinical parameters and actual patient outcomes. The DDR and ICD gene expression levels serve as molecular mediators that translate complex biological processes into quantifiable prognostic indicators. This intermediary molecular information layer bridges the gap between simple clinical observations and complex tumor behavior, significantly improving prediction accuracy without losing molecular-level insights.
Data Source
AI summary
The present disclosure relates to a construction method for a prognostic model of hepatocellular carcinoma based on DNA damage repair (DDR) and immunogenic cell death (ICD) gene expression, including the following steps of: Step 1, acquiring transcription profile expression data of multiple hepatocellular carcinoma patients; step 2, screening candidate genes based on the transcription profile expression data of multiple hepatocellular carcinoma patients; step 3, determining prognostic genes related to lifetime through single-factor Cox regression analysis based on the candidate genes; step 4, screening the genes related to the lifetime through LASSO Cox regression analysis; and step 5, assessing the prediction performance of the risk score model based on the above training dataset.