LCAT Molecular Marker for HCC Recurrence Prediction
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Solution Overview
Problem
Current methods for diagnosing and treating hepatocellular carcinoma (HCC) are inadequate, particularly in unresectable cases, with high recurrence rates and unsatisfactory median survival times, and there is a need for effective molecular markers to predict recurrence.
Innovation Solution
A method involving the use of lecithin-cholesterol acyltransferase (LCAT) for HCC diagnosis, treatment, and recurrence prediction, utilizing a risk assessment model constructed with the LASSO regression algorithm and transcriptome sequencing data to identify LCAT as a high-risk recurrence gene, activating immune cells and serving as a molecular marker.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If hepatectomy is performed on unresectable HCC patients, then long-term survival opportunity is improved, but HCC recurrence rate increases significantly
Solution Approach 1:
The patent applies preliminary action by using LASSO regression analysis on KEGG metabolism-related gene datasets to identify LCAT as a high-risk recurrence gene before surgery. This allows clinicians to predict recurrence risk in advance and develop targeted prevention strategies, addressing the high recurrence problem before it manifests after hepatectomy
Solution Approach 2:
The patent implements feedback by constructing a risk assessment model that evaluates LCAT expression levels and provides recurrence risk predictions. This feedback mechanism allows for postoperative monitoring and intervention based on molecular markers, helping to control recurrence rates while maintaining survival benefits
2Measurement precision
If LCAT is used as a molecular marker for HCC diagnosis and recurrence prediction, then diagnostic precision and prognosis prediction are improved, but the complexity of the assessment model increases
Solution Approach 1:
The patent applies the extraction principle by isolating LCAT from the complex KEGG metabolism-related gene dataset (182 genes) through LASSO regression analysis. This extracts the most critical single marker for recurrence prediction, achieving high diagnostic precision while simplifying the model compared to using the entire gene set
Solution Approach 2:
The patent uses parameter changes by applying LASSO regression with penalty parameter lambda to transform the complex multi-gene dataset into a simplified model based on LCAT expression levels. This parameter adjustment process maintains predictive accuracy while reducing model complexity to a manageable single-marker system
Data Source
AI summary
A method for applying lecithin-cholesterol acyltransferase (LCAT) on hepatocellular carcinoma (HCC) diagnosis, HCC treatment, and HCC recurrence prediction is provided, including extracting a Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolism-related gene data set from Gene Expression Omnibus (GEO) and processing the KEGG metabolism-related gene data set to obtain a KEGG metabolism-related gene set; integrating a data set in the GEO by a least absolute shrinkage and selection operator (LASSO) regression algorithm based on the KEGG metabolism-related gene set and constructing a risk assessment model; intersecting results obtained by performing difference analysis on a postoperative tumor of a patient undergoing hepatectomy and transcriptome sequencing data of normal tissues surrounding the postoperative tumor of the patient undergoing the hepatectomy to apply on the HCC diagnosis, the HCC treatment, and the HCC recurrence prediction in clinic.