Pathophysiological remodeling of the skeletal muscle microenvironment in patients with lung cancer
Battey, E.; Irazoki, A.; Wang, J.; Sorensen, J.; Modvig, J.; Themsen, S.; Voldstedlund, C.; Knos, C.; Andersen, N.; Frank, E.; Kudahl, S.; Hahn, N.; Madsen, A.; Raun, S. H.; Gavaldi, C. P.; Dmitryev, O.; Ortenblad, N.; Farup, J.; Sylow, L.
Show abstract
Background Muscle wasting, systemic inflammation, and functional decline are highly prevalent and detrimental in patients with advanced-stage non small cell lung cancer (NSCLC). Methods: In this cross sectional study, we investigated NSCLC associated muscle remodeling by analyzing skeletal muscle biopsies from patients with NSCLC (n = 18) and matched controls (n = 18) using quantitative proteomics, histology, fluorescence-activated cell sorting, gene expression profiling, and high resolution respirometry. Findings: NSCLC muscle was characterized by type II muscle fiber atrophy, greater collagen deposition, and redistribution of lipids to the extracellular matrix (ECM), together with remodeling of the inflammatory, immune, ECM and mitochondrial proteome. Additionally, mitochondrial respiratory capacity and morphology were altered in patients with NSCLC, which was associated with increased oxidative stress and dysregulated calcium handling. Concomitantly, we detected STAT3 activation and immune cell alterations, which may negatively impact skeletal muscle health in patients with NSCLC. Finally, we identified a shift in fibro-adipogenic progenitors (FAPs), favoring the CD90 subtype. Mechanistically, conditioned media from patient-derived FAPs reduced myotube width in vitro, uncovering a novel mechanism by which altered paracrine signaling from the muscle resident stromal compartment drives atrophy in cancer cachexia. Interpretation: These findings provide human evidence that altered FAP composition, mitochondrial homeostasis, calcium handling, and immune cell landscape accompany muscle wasting in NSCLC, which may inform therapeutic strategies to preserve skeletal muscle health in patients with cancer.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Tumor-induced alterations in single-nucleus transcriptome of atrophying muscles indicate enhanced protein degradation and reduced oxidative metabolism 93%
- Cell autonomous requirement of Neurofibromin (Nf1) for postnatal muscle hypertrophic growth and metabolic homeostasis 91%
- Systemic drivers and molecular mechanisms of sarcopenia in aetiology-specific end-stage liver disease 91%
Similar papers in this journal
- Fiber-type vulnerability and proteostasis reprogramming in skeletal muscle during pancreatic cancer cachexia 91%
- TGF-β Coordinates Alanine Synthesis and Import for Myofibroblast Differentiation in Pulmonary Fibrosis 91%
- Pyruvate metabolism dictates fibroblast sensitivity to GLS1 inhibition during fibrogenesis 91%
Similar papers in this journal
- Characterization of the skeletal muscle arginine methylome in health and disease reveals remodeling in Amyotrophic Lateral Sclerosis 92%
- Cancer causes dysfunctional insulin signaling and glucose transport in a muscle-type specific manner 92%
- Loss of STARD7 Impairs Mitochondrial Phospholipid Homeostasis and Contributes to Mitochondrial Myopathy 91%
Similar papers in this journal
- Succinate Dehydrogenase (SDH)-subunit C regulates muscle oxygen consumption and fatigability in an animal model of pulmonary emphysema. 91%
- Sexually distinct multi omic responses to progressive endurance exercise training in the rat lung. Findings from MoTrPAC 91%
- Targeting Pulmonary Fibrosis by SLC1A5 dependent Glutamine Transport Blockade 91%
Similar papers in this journal
- NAD+ repletion with niacin counteracts cancer cachexia 93%
- The mitochondrial mRNA stabilizing protein, SLIRP, regulates skeletal muscle mitochondrial structure and respiration by exercise-recoverable mechanisms 91%
- Transcriptomic Profiling of Skeletal Muscle Adaptations to Exercise and Inactivity 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.