Deciphering lung adenocarcinoma heterogeneity: a multi-omics approach reveals nuclear division fibroblasts as prognosticators and therapeutic targets - PubMed

3 hours ago

Lung adenocarcinoma (LUAD) is a major cause of cancer-related deaths globally.
Lung-associated fibroblasts (LAFs) play a significant role in tumorigenesis and the tumor microenvironment (TME).
The study used a multi-omics approach to analyze LAF heterogeneity in LUAD.
Data included bulk RNA from 2719 patients, scRNA sequencing from 368,904 cells, and spatial transcriptomics from 15,673 spots.
Nine LAF subtypes were identified, with nuclear division LAFs (nLAFs) linked to LUAD prognosis.
A five-gene nLAFs risk score (nLRS) model was developed using machine learning, outperforming 49 other models.
High nLRS groups showed distinct biological functions and immune cell infiltration in the TME.
High nLRS patients may resist immunotherapy but respond better to chemotherapy and targeted therapies.
The study proposes a five-gene signature from nLAFs as a potential prognostic biomarker for LUAD.

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