Multiomics integration prioritizes potential drug targets for lung cancer
Peize Meng, Zhiping Zhang, Zheng Ruan, Congcong Xu · 2026
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Description
Meng and co-authors used genetic causal inference to nominate new drug targets for lung cancer, integrating gene-expression and protein data across three large consortia. Their 2026 multiomics study screened 2,645 candidate genes with strong genetic instruments, then demanded that the gene signal and the cancer signal share the same causal variant.
Four genes passed every filter with colocalization probability above 0.80 on the posterior scale: SERPING1, THBS3, FLT4, and CHEK1. Their directions differed by subtype: higher FLT4 expression raised adenocarcinoma risk, while higher CHEK1 looked protective in squamous cell carcinoma. SERPING1 and THBS3 appeared to promote non-small cell lung cancer progression, in a disease family that makes up roughly 85% of all lung cancers.
Catalog record
| CREATOR | Peize Meng, Zhiping Zhang, Zheng Ruan, Congcong Xu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |