Inter-organ cross-talk in human cancer cachexia revealed by spatial metabolomics.

Cancer cachexia patients Diagnostics of cachexia Inter-organ cross-talk Machine learning Metabolomics classifier Spatial metabolomics

Journal

Metabolism: clinical and experimental
ISSN: 1532-8600
Titre abrégé: Metabolism
Pays: United States
ID NLM: 0375267

Informations de publication

Date de publication:
17 Sep 2024
Historique:
received: 19 04 2024
revised: 11 09 2024
accepted: 12 09 2024
medline: 20 9 2024
pubmed: 20 9 2024
entrez: 19 9 2024
Statut: aheadofprint

Résumé

Cancer cachexia (CCx) presents a multifaceted challenge characterized by negative protein and energy balance and systemic inflammatory response activation. While previous CCx studies predominantly focused on mouse models or human body fluids, there's an unmet need to elucidate the molecular inter-organ cross-talk underlying the pathophysiology of human CCx. Spatial metabolomics were conducted on liver, skeletal muscle, subcutaneous and visceral adipose tissue, and serum from cachectic and control cancer patients. Organ-wise comparisons were performed using component, pathway enrichment and correlation network analyses. Inter-organ correlations in CCx altered pathways were assessed using Circos. Machine learning on tissues and serum established classifiers as potential diagnostic biomarkers for CCx. Distinct metabolic pathway alteration was detected in CCx, with adipose tissues and liver displaying the most significant (P ≤ 0.05) metabolic disturbances. CCx patients exhibited increased metabolic activity in visceral and subcutaneous adipose tissues and liver, contrasting with decreased activity in muscle and serum compared to control patients. Carbohydrate, lipid, amino acid, and vitamin metabolism emerged as highly interacting pathways across different organ systems in CCx. Muscle tissue showed decreased (P ≤ 0.001) energy charge in CCx patients, while liver and adipose tissues displayed increased energy charge (P ≤ 0.001). We stratified CCx patients by severity and metabolic changes, finding that visceral adipose tissue is most affected, especially in cases of severe cachexia. Morphometric analysis showed smaller (P ≤ 0.05) adipocyte size in visceral adipose tissue, indicating catabolic processes. We developed tissue-based classifiers for cancer cachexia specific to individual organs, facilitating the transfer of patient serum as minimally invasive diagnostic markers of CCx in the constitution of the organs. These findings support the concept of CCx as a multi-organ syndrome with diverse metabolic alterations, providing insights into the pathophysiology and organ cross-talk of human CCx. This study pioneers spatial metabolomics for CCx, demonstrating the feasibility of distinguishing cachexia status at the organ level using serum.

Sections du résumé

BACKGROUND BACKGROUND
Cancer cachexia (CCx) presents a multifaceted challenge characterized by negative protein and energy balance and systemic inflammatory response activation. While previous CCx studies predominantly focused on mouse models or human body fluids, there's an unmet need to elucidate the molecular inter-organ cross-talk underlying the pathophysiology of human CCx.
METHODS METHODS
Spatial metabolomics were conducted on liver, skeletal muscle, subcutaneous and visceral adipose tissue, and serum from cachectic and control cancer patients. Organ-wise comparisons were performed using component, pathway enrichment and correlation network analyses. Inter-organ correlations in CCx altered pathways were assessed using Circos. Machine learning on tissues and serum established classifiers as potential diagnostic biomarkers for CCx.
RESULTS RESULTS
Distinct metabolic pathway alteration was detected in CCx, with adipose tissues and liver displaying the most significant (P ≤ 0.05) metabolic disturbances. CCx patients exhibited increased metabolic activity in visceral and subcutaneous adipose tissues and liver, contrasting with decreased activity in muscle and serum compared to control patients. Carbohydrate, lipid, amino acid, and vitamin metabolism emerged as highly interacting pathways across different organ systems in CCx. Muscle tissue showed decreased (P ≤ 0.001) energy charge in CCx patients, while liver and adipose tissues displayed increased energy charge (P ≤ 0.001). We stratified CCx patients by severity and metabolic changes, finding that visceral adipose tissue is most affected, especially in cases of severe cachexia. Morphometric analysis showed smaller (P ≤ 0.05) adipocyte size in visceral adipose tissue, indicating catabolic processes. We developed tissue-based classifiers for cancer cachexia specific to individual organs, facilitating the transfer of patient serum as minimally invasive diagnostic markers of CCx in the constitution of the organs.
CONCLUSIONS CONCLUSIONS
These findings support the concept of CCx as a multi-organ syndrome with diverse metabolic alterations, providing insights into the pathophysiology and organ cross-talk of human CCx. This study pioneers spatial metabolomics for CCx, demonstrating the feasibility of distinguishing cachexia status at the organ level using serum.

Identifiants

pubmed: 39299512
pii: S0026-0495(24)00262-2
doi: 10.1016/j.metabol.2024.156034
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

156034

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare no competing financial interests.

Auteurs

Na Sun (N)

Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany.

Tanja Krauss (T)

Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany.

Claudine Seeliger (C)

Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany; ZIEL Institute for Food and Health, Technical University of Munich, Freising-Weihenstephan, Germany.

Thomas Kunzke (T)

Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany.

Barbara Stöckl (B)

Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany; Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany.

Annette Feuchtinger (A)

Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany.

Chaoyang Zhang (C)

Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany.

Andreas Voss (A)

Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany.

Simone Heisz (S)

Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany.

Olga Prokopchuk (O)

Department of Surgery, Klinikum rechts der Isar, University Hospital of the Technical University of Munich, Munich, Germany.

Marc E Martignoni (ME)

Department of Surgery, Klinikum rechts der Isar, University Hospital of the Technical University of Munich, Munich, Germany.

Klaus-Peter Janssen (KP)

Department of Surgery, Klinikum rechts der Isar, University Hospital of the Technical University of Munich, Munich, Germany.

Melina Claussnitzer (M)

The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Institute of Nutritional Science, University of Hohenheim, 70599 Stuttgart, Germany; Department of Medicine, Harvard Medical School, Boston, MA, USA.

Hans Hauner (H)

Else Kröner Fresenius Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany; ZIEL Institute for Food and Health, Technical University of Munich, Freising-Weihenstephan, Germany; Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany. Electronic address: hans.hauner@tum.de.

Axel Walch (A)

Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany. Electronic address: axelkarl.walch@helmholtz-munich.de.

Classifications MeSH