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Molecular subtypes of human skeletal muscle in cancer cachexia

Nature (2025)Cite this article

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Abstract

Cancer-associated muscle wasting is associated with poor clinical outcomes1, but its underlying biology is largely uncharted in humans2. Unbiased analysis of the RNAome (coding and non-coding RNAs) with unsupervised clustering using integrative non-negative matrix factorization3 provides a means of identifying distinct molecular subtypes and was applied here to muscle of patients with colorectal or pancreatic cancer. Rectus abdominis biopsies from 84 patients were profiled using high-throughput next-generation sequencing. Integrative non-negative matrix factorization with stringent quality metrics for clustering identified two highly coherent molecular subtypes within muscle of patients with cancer. Patients with subtype 1 (versus subtype 2) showed clinical manifestations of cachexia: high-grade weight loss, low muscle mass, atrophy of type IIA and type IIX muscle fibres, and reduced survival. On the basis of differential expression between the subtypes, we identified biological processes that may contribute to cancer-associated loss of muscle mass and function, including altered posttranscriptional regulation and perturbation of neuronal systems; cytokine storm and cellular immune response; pathways related to extracellular matrix; and metabolic abnormalities spanning xenobiotic metabolism, haemostasis, signal transduction, embryonic and/or pluripotent stem cells, and amino acid metabolism. Differential expression between subtypes indicated the involvement of multiple intertwined higher-order gene regulatory networks, suggesting that network interactions of (hub) long non-coding RNAs, microRNAs and mRNAs could represent targets for future research.

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Fig. 1: Research design and cachexia classification.
Fig. 2: Subtype identification, genome-wide RNAome profiles, association with clinical, radiological and histological parameters.
Fig. 3: Multilayered RNA cross-talk and hub lncRNAs.
Fig. 4: Top Differentially Expressed canonical pathways in subtype 1 versus subtype 2.
Fig. 5: Comparison of canonical pathways in rat model compared with human molecular subtype.

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Data availability

The data generated during this study are available in the Supplementary Information and have been deposited at GEO (https://www.ncbi.nlm.nih.gov/geo/), being accessible through GEO series accession codes GSE254877 (RNA-seq), GSE254878 (small RNA-seq) and GSE292052 (rat RNA-seq) embedded in GEO super series GSE292053.

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Acknowledgements

We thank the Canadian Institute of Health Research (CIHR) for providing operating research grants.

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Author notes

  1. These authors contributed equally: Vickie E. Baracos, Sambasivarao Damaraju

Authors and Affiliations

  1. Department of Oncology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada

    Bhumi J. Bhatt, Sunita Ghosh & Vickie E. Baracos

  2. Department of Laboratory Medicine and Pathology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada

    Bhumi J. Bhatt & Sambasivarao Damaraju

  3. Department of Agricultural, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Science, University of Alberta, Edmonton, Alberta, Canada

    Vera Mazurak & Sambasivarao Damaraju

  4. Unité de Nutrition Humaine (UNH), INRAE, Université Clermont Auvergne, Clermont-Ferrand, France

    Aurélien Q. Brun

  5. Departments of Surgery and Oncology, Arnie-Charbonneau Cancer Institute, University of Calgary, Calgary, Alberta, Canada

    Oliver Bathe

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Contributions

B.J.B., V.E.B. and S.D. performed conceptualization and overall data analysis. Next-generation sequencing, molecular and bioinformatical analysis, and unsupervised clustering techniques and interpretations were performed by B.J.B. and S.D. The collection, assembly and statistical analysis of clinical, demographic, histological and radiological data were performed by B.J.B. and V.E.B. The human muscle biopsy histology was carried out by A.Q.B. The accrual of human skeletal muscle biopsies was carried out by O.B. The preclinical model was developed by V.M. S.G. provided statistical advice. B.J.B., V.E.B. and S.D. wrote the manuscript. All authors approved the final version.

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Correspondence to Vickie E. Baracos or Sambasivarao Damaraju.

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Extended data figures and tables

Extended Data Fig. 1 Correlation between muscle change over time in lumbar, thoracic and thigh musculature.

Total muscle cross-sectional area was determined in the 3rd lumbar vertebra, at the 4th thoracic vertebra and thigh 11.25 mm below the lesser trochanter of the femur. Muscle change over time is expressed as % for N = 39 patients. Statistic Pearson r. Inset: cross correlations between L3, T4 and leg muscle change over time. These regions include different muscles: abdomen at L3 (rectus abdominis, lateral and oblique abdominis muscles, quadratus lumborum, psoas, paraspinal (multifidus, erector spinae), thigh (rectus femoris, sartorius, vastus intermedius, vastus lateralis, adductor, gracilis, gluteus maximus, biceps femoris and semitendinosus) and chest at the 4th thoracic vertebra (pectoralis, external intercostal, serratus anterior, teres major, subscapularis, infraspinatus, rhomboid major, erector spinae, trapezius). The high degree of intercorrelation of muscle change over time these regions suggests that cancer-associated muscle atrophy is systemic.

Extended Data Fig. 2 Pathway map of cytokine-cytokine interactions.

The pathway map (generated using KEGG database) depicts the ligand-receptor interactions between chemokines, cytokines, TGF and TNF gene family. The genes highlighted in red were more highly expressed in subtype 1.

Extended Data Fig. 3 Pathway map of ECM receptor interactions.

The pathway map (generated using KEGG database) depicts the ligand-receptor interactions between extracellular matrix (ECM) gene family. The genes highlighted in red were more highly expressed in subtype 1.

Extended Data Fig. 4 GSEA results of protein/amino acid metabolism.

Amino acid catabolic processes enriched in subtype 1 are shown in in panel a and c for men and women, respectively. Heatmap representation (panel b and d) of genes comprising core enrichment are represented with the expression intensity, red representing genes with high expression and blue representing low expression.

Extended Data Fig. 5 Bubble plot of pathway identities and p-values extracted from suppl. Table 2 of Neyroud et al. 24.

Functional annotation terms were filtered at a P-value cut-off of <0.0001 from GSE271521. Mouse muscle mRNAs were subjected to DAVID bioinformatics analysis (DAVID Functional Annotation Bioinformatics Microarray Analysis) to identify enriched biological processes/pathways. Biological process categories (left side column) represent themes of high differential expression between subtype 1 (versus subtype 2) in male patients in our clinical study sample; this specific comparison was chosen because the mice were of male sex. Size of dots represents p-value significance as depicted in the bubble plot. The color of the bubble represents different pathway categories mentioned on the left side of the figure. Results are presented based on the temporal analysis from Day 8 through to the study endpoint (Day 15–18).

Extended Data Table 1 Clinical and radiological features of subtypes
Full size table
Extended Data Table 2 Histological parameters associated with radiologically determined SMI categories
Full size table

Supplementary information

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Bhatt, B.J., Ghosh, S., Mazurak, V. et al. Molecular subtypes of human skeletal muscle in cancer cachexia. Nature (2025). https://doi.org/10.1038/s41586-025-09502-0

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