Imprinted Gene Expression Grading for Benign and Malignant Thyroid Tumors
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Solution Overview
Problem
Current thyroid cancer diagnosis methods struggle to accurately differentiate between benign and malignant thyroid tumors, particularly follicular thyroid adenomas and carcinomas, and Hürthle cell tumors, due to limitations in early detection and reliance on morphological criteria, with existing molecular techniques lacking sensitivity and specificity.
Innovation Solution
An imprinted gene grading model that calculates the expressed quantities of genes Z1, Z11, and Z16, and optionally other genes, to grade the expression state of these genes based on loss of imprinting and copy number variation, using in-situ hybridization and microscopic imaging to diagnose thyroid tumor benignity/malignancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pathology methods are used to diagnose thyroid tumors, then the diagnosis can be made based on cell size, morphology, and invasiveness, but the accuracy in differentiating between benign and malignant tumors is insufficient and early changes cannot be detected
Solution Approach 1:
The patent replaces conventional morphological examination methods with molecular biology-based detection methods. Specifically, it uses in-situ hybridization to detect imprinted gene expression patterns at the molecular level, enabling early detection of cancerous changes before they manifest as morphological alterations. This substitution of detection mechanisms significantly improves both diagnostic accuracy and early detection capability.
Solution Approach 2:
The patent changes the detection parameter from macroscopic morphological features to molecular expression levels. By measuring the expression quantities of imprinted genes (such as Z1, Z11, and Z16) through in-situ hybridization, the method detects early molecular changes that precede morphological alterations, thereby improving early detection capability while maintaining high diagnostic accuracy.
2Reliability
If BRAF gene mutation tests are used to detect thyroid cancer, then papillary thyroid cancer can be identified, but the method cannot detect follicular thyroid carcinoma and Hürthle cell tumors
Solution Approach 1:
The patent develops a universal detection method based on imprinted gene expression analysis that can identify multiple types of thyroid tumors including papillary thyroid cancer, follicular thyroid carcinoma, and Hürthle cell tumors. By detecting changes in imprinted gene expression patterns rather than relying on tumor-type-specific mutations, the method achieves broad applicability across different thyroid cancer types while maintaining high detection specificity.
3Measurement precision
If molecular biology-based detection methods are used to detect thyroid cancer, then sensitivity is improved, but the complexity of the detection system increases
Solution Approach 1:
The patent segments the detection process into distinct functional components: sample preparation, in-situ hybridization with specific probes, microscopic imaging, and quantitative analysis of imprinted gene expression. This segmentation allows each component to be optimized independently while working together to achieve high sensitivity detection, managing the overall system complexity through modular design.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The model achieves diagnostic sensitivities of up to 97.5% in distinguishing thyroid tumor grades, providing accurate and early-stage detection by analyzing molecular markers through imprinted gene expression changes.
Implementation Method 1
performing in-situ hybridization between the probe designed by the probe designing unit and the test sample
Implementation Method 2
a is the number of cells that, after being stained with hematoxylin, show no mark in the nucleus
Data Source
Figure 1~2(e)
Figure 3(a)~3(f)
Figure 4(a)~4(f)
AI summary
A grading model for detecting the degree of benignity/malignancy of a thyroid tumor and its applications are disclosed. The model grades the changes in an imprinted gene in a thyroid tumor by calculating an expressed quantity of the imprinted gene with a loss of imprinting, an expressed quantity of the imprinted gene with a copy number variation, and a total expressed quantity of the imprinted gene. The detection model and device disclosed herein enable intuitive observation of the expression of an imprinted gene with a loss of imprinting in a tissue or cell sample taken from a patient with a thyroid tumor. By labeling the imprinted gene in situ, changes in the imprinted gene can be objectively, intuitively, and precisely detected at an early stage. Moreover, a quantitative model is provided. Thus, the disclosure contributes greatly to the diagnosis of thyroid tumors.