Distinguish active tuberculosis with an immune-related signature and molecule subtypes: a multi-cohort analysis
Qingqing Shan, Yangke Li, Kun Yuan, Xiao Yang, Li Yang, Jian-Qing He · 2024
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Description
Qingqing Shan and five colleagues mine thirteen public data sets to find a blood signature that splits active tuberculosis from latent infection. That split decides who needs full treatment. Network analysis across search data sets surfaced five hub genes; machine learning boiled them down to a three-gene signature.
Across all cohorts the signature told active from latent disease apart with fair accuracy and beat rival published signatures. The three genes track closely with immune cell infiltration in the blood. Active cases also split into two molecular subtypes differing in immune profiles, pathway activity, and predicted drug sensitivity. The authors offer the panel as a step toward blood-based tuberculosis triage and subtype-aware treatment.
Catalog record
| CREATOR | Qingqing Shan, Yangke Li, Kun Yuan, Xiao Yang, Li Yang, Jian-Qing He |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |