Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease Using Integrated Bioinformatics and Machine Learning
Jinbao Shi, Aliang Xu, Liuying Huang, Shaojie Liu, Binxuan Wu, Zuhong Zhang · 2024
Read Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease Using Integrated Bioinformatics and Machine Learning by Jinbao Shi, Aliang Xu, Liuying Huang, Shaojie Liu, Binxuan Wu, Zuhong Zhang (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11586269.txt.
Description
Jinbao Shi and co-authors mine gene and immune data for marker genes of chronic kidney disease, in a 2024 study. The disease tangles immune balance and gene activity in ways that are still being mapped. The team set out to find a small set of genes that could flag the disease early and point to how it grows.
They compare immune cell mixes and gene activity in kidney patients against healthy controls, finding over 2,300 changed genes. Machine learning then narrows the field to four marker genes: RGS1, IL4I1, NR4A3, and SOCS3. A scoring chart built on the four genes sorted patients from controls with high accuracy, and checks on a fresh data set and clinic samples backed the result. The four genes also track with immune cell levels, which hints they help drive the disease, not just mark it.
Catalog record
| CREATOR | Jinbao Shi, Aliang Xu, Liuying Huang, Shaojie Liu, Binxuan Wu, Zuhong Zhang |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |