Thyroid Cancer Marker Set for Benign Malignant Nodule Differentiation
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Solution Overview
Problem
Current methods for diagnosing thyroid nodules, particularly distinguishing between benign and malignant follicular thyroid carcinoma (FTC) and papillary thyroid carcinoma (PTC), suffer from lack of specificity, leading to unnecessary treatments due to poor classification results from gene expression profiling studies.
Innovation Solution
A set of specific tumor markers (PI-1 to PI-33, PII-1 to PII-64, PIII-1 to PIII-70, FI-1 to FI-147, PIV-1 to PIV-9, and PV-1 to PV-11) identified through gene expression analysis, which can be used to differentiate between benign and malignant thyroid cancer types, with preferred markers like SERPINA1 for PTC diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods (scintigraphy and fine needle aspiration followed by histology) are used for diagnosis, then the diagnostic process is established, but the specificity is insufficient leading to misclassification between benign and malignant thyroid nodules
Solution Approach 1:
The patent segments the diagnostic process by introducing a multi-gene expression profiling approach that analyzes specific gene sets (e.g., 50-200 genes) to distinguish between benign and malignant thyroid nodules. This segmentation of the diagnostic workflow into specific molecular markers improves reliability while maintaining adequate measurement precision through focused gene panel analysis.
Solution Approach 2:
The patent applies parameter changes by measuring gene expression levels of specific genes to differentiate thyroid nodule types. By changing from conventional histological parameters to molecular expression parameters, the diagnostic accuracy and specificity are both improved, resolving the contradiction between reliability and measurement precision.
2Quantity of substance
If large-scale transcript-level expression profiling technologies (cDNA microarrays, oligonucleotide arrays, SAGE) are used, then many genes are identified, but most are false positives with only a small fraction useful as diagnostic markers
Solution Approach 1:
The patent extracts a focused subset of genes (50-200 genes) from the large-scale transcript profiling data. By taking out only the most relevant genes for thyroid nodule classification, the patent reduces false positives while maintaining diagnostic utility, thus improving measurement precision without sacrificing the quantity of useful markers.
Solution Approach 2:
Instead of analyzing all identified genes, the patent applies partial action by selecting and analyzing only a specific subset of 50-200 genes that are most relevant for diagnosis. This partial approach filters out false positives while retaining sufficient diagnostic power, resolving the contradiction between quantity of genes and their usefulness.
3Adaptability or versatility
If different classification systems from various studies are applied, then each discriminates between 2 of 5 entities, but there is no or very few genes in common and classification results are poor when applied across studies
Solution Approach 1:
The patent creates a universal gene panel (50-200 genes) that can be applied across different thyroid carcinoma entities and studies. This multi-functional gene set maintains adaptability for classifying different tumor types while ensuring reliability through consistent performance across various study cohorts, resolving the contradiction between versatility and consistency.
Solution Approach 2:
The patent merges findings from multiple studies by identifying common genes across different expression profiling studies. By combining the most consistently identified genes into a unified panel, the patent achieves both adaptability for different tumor entities and reliability through cross-study validation, eliminating the inconsistency problem.
Data Source
AI summary
The present invention provides a set of moieties specific for tumor markers, in particular of follicular thyroid carcinoma (FTC) and papillary thyroid carcinoma (PTC) as well as a method for identifying markers of any genetic disease.