Metabolic signatures and potential biomarkers for the diagnosis and treatment of colon cancer cachexia

Abstract

Cancer cachexia (CAC) is a debilitating condition that often arises from noncachexia cancer (NCAC), with distinct metabolic characteristics and medical treatments. However, the metabolic changes and underlying molecular mechanisms during cachexia progression remain poorly understood. Understanding the progression of CAC is crucial for developing diagnostic approaches to distinguish between CAC and NCAC stages, facilitating appropriate treatment for cancer patients. In this study, we establish a mouse model of colon CAC and categorize the mice into three groups: CAC, NCAC and normal control (NOR). By performing nuclear magnetic resonance (NMR)-based metabolomic profiling on mouse sera, we elucidate the metabolic properties of these groups. Our findings unveil significant differences in the metabolic profiles among the CAC, NCAC and NOR groups, highlighting significant impairments in energy metabolism and amino acid metabolism during cachexia progression. Additionally, we observe the elevated serum levels of lysine and acetate during the transition from the NCAC to CAC stages. Using multivariate ROC analysis, we identify lysine and acetate as potential biomarkers for distinguishing between CAC and NCAC stages. These biomarkers hold promise for the diagnosis of CAC from noncachexia cancer. Our study provides novel insights into the metabolic mechanisms underlying cachexia progression and offers valuable avenues for the diagnosis and treatment of CAC in clinical settings.

Keywords: NMR-based metabolomics; biomarker; cancer cachexia; metabolic profile; serum.

Figure 1

Characterization of a mouse model of colon CAC (A) Average daily food intake curves of NOR, NCAC, and CAC mice. (B) Tumor growth curves of NCAC and CAC mice, the tumor size is expressed as tumor volume (mm3 )=0.52×length×width2. (C) Tumor weights of CAC and NCAC mice at the time of sacrifice. (D) Percentage changes in tumor-free body weight of the mice relative to the body weight at the time of initial animal modeling. (E) Weights of epididymal adipose tissue of mice. (F) Weights of the gastrocnemius muscle of hind limbs of the mice. **P<0.01, ***P<0.001, ****P<0.0001.

Figure 2

Levels of inflammatory factors in the three groups of mouse sera (A) IL-1. (B) IL-6. (C) TLR-4. (D) TNF-α. (E) TGF-β. (F) IFN-γ. (G) Total antioxidant capacity (T-AOC). *P<0.05, **P<0.01, ***P <0.001.

Figure 3

Typical 1D ¹H-NMR spectra of sera from the three groups of mice The spectra were recorded on a Bruker Avance III 850 MHz NMR spectrometer at 298 K (pH 7.4). Spectral regions of 0.5‒4.7 ppm and 5.0‒9.0 ppm are displayed, and the water region of 4.7–5.0 ppm was removed. The region of 5.0‒9.0 ppm has been magnified 10 folds compared to another region of 0.5‒4.7 ppm for clarity. Assigned metabolites are labeled in this figure and shown in Supplementary Table S1.

Figure 4

Scores plots and cross-validation plots of PCA and OPLS-DA models based on 1D ¹H-NMR spectral data of the three groups of mouse sera (A‒C) NCAC vs NOR. (D‒F) CAC vs NCAC. (G‒I) CAC vs NOR. The ellipses indicate the 95% confidence limit. Random permutation test was performed to evaluate the reliability of the OPLS-DA model with 200 cycles.

Figure 5

VIP score-ranking significant metabolites identified from the three OPLS-DA models of mouse sera These VIP scores were obtained from pair-wise comparisons of (A) NCAC vs NOR, (B) CAC vs NCAC, and (C) CAC vs NOR. Significant metabolites were identified with VIP>1.

Figure 6

Metabolic pathway analysis for the three groups of mouse sera (A) NCAC vs NOR. (B) CAC vs NCAC. Significantly altered metabolic pathways were identified with pathway impact value (PIV)>0.1 and P<0.05, using the pathway analysis module provided by MetaboAnalyst 5.0 webserver.

Figure 7

Multivariate ROC analysis based on serum levels of differential metabolites for distinguishing between CAC and NCAC stages (A) Lysine. (B) Acetate. (C) Combination of lysine and acetate.