Fecal Microbiome Signature for Non-Invasive RCC Risk Detection
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Solution Overview
Problem
Current methods for renal cell carcinoma (RCC) diagnosis rely heavily on imaging and invasive procedures, lacking a non-invasive, reliable biomarker for early detection.
Innovation Solution
A machine learning classification model using bacterial species abundance data, particularly Enterocloster asparagiformis enrichment, is trained to predict an increased risk of RCC based on fecal samples, with a 31-species signature for accurate discrimination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If imaging studies and needle biopsy procedures are used for RCC diagnosis, then diagnostic accuracy is improved, but patient invasiveness and procedural complexity increase
Solution Approach 1:
The patent introduces the gut microbiome as an intermediary biomarker that indirectly reflects RCC presence. Instead of directly examining renal tissue through invasive procedures, the method analyzes fecal samples containing microbial signatures that serve as mediators indicating RCC risk, thereby reducing patient invasiveness while maintaining diagnostic value
Solution Approach 2:
The patent replaces mechanical/invasive diagnostic procedures (needle biopsy, imaging) with a biochemical analysis method. Instead of physically accessing and examining renal tissue, the system substitutes this with molecular analysis of fecal microbiome compositions, eliminating the need for invasive mechanical intervention while preserving diagnostic capability
2Reliability
If imaging tests and biopsy procedures are used for RCC detection, then diagnostic reliability is improved, but procedural complexity and time consumption increase
Solution Approach 1:
The patent extracts the diagnostic function from complex imaging and biopsy procedures and relocates it to a simpler fecal sample analysis system. By isolating and analyzing specific microbial species abundances from easily obtainable fecal samples, the method extracts essential diagnostic information without requiring complex imaging equipment or multi-step biopsy procedures
Solution Approach 2:
The patent changes the diagnostic parameter from anatomical/imaging features (requiring complex imaging equipment and interpretation) to microbiome compositional parameters (species abundances, diversity indices). This parameter transformation enables diagnosis through simpler biochemical analysis of fecal samples rather than complex imaging procedures
3Measurement precision
If traditional imaging and biopsy methods are used for RCC diagnosis, then diagnostic precision is improved, but ease of screening and accessibility worsen
Solution Approach 1:
The patent enables self-service sampling by using fecal samples that patients can easily collect themselves without requiring medical professionals to perform invasive procedures. The gut microbiome naturally reflects systemic conditions including RCC, allowing individuals to contribute to their own screening process through simple, non-invasive sample collection that maintains diagnostic precision
Data Source
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AI summary
There is provided a method of detecting an increased risk of having or developing renal cancer carcinoma (RCC), comprising the steps of: a) determining the level of Enterocloster asparagiformis in a fecal sample from a human subject; b) comparing the determined level to a reference level; and c) if the determined level is higher than the reference level, concluding that the human subject has the increased risk of having or developing RCC.