Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment.

ABSTRACT:

Background

Computed tomographies (CT) are useful for identifying muscle loss in non-small lung cancer (NSCLC) cachectic patients. However, we lack consensus on the best cutoff point for pectoralis muscle loss. We aimed to characterize NSCLC patients based on muscularity, clinical data, and the transcriptional profile from the tumor microenvironment to build a cachexia classification model.

Methods

We used machine learning to generate a muscle loss prediction model, and the tumor's cellular and transcriptional profile was characterized in patients with low muscularity. First, we measured the pectoralis muscle area (PMA) of 211 treatment-naive NSCLC patients using CT available in The Cancer Imaging Archive. The cutoffs were established using machine learning algorithms (CART a

SUBMITTER: Cury SS 

PROVIDER: S-EPMC9921698 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature