Related Experiment Video
Updated: May 28, 2026
Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
The SLC25A45-TML Axis as a Biological Foundation for a Multivariable Plasma Metabolite Signature for High-Precision
Liang Zhao1, Raghothama Chaerkady1, Naseruddin Höti1
1Complete Omics Inc., Baltimore, MD 21227, USA.
Background:
Prostate cancer remains a significant global health burden, yet current diagnostic reliance on PSA screening is heavily hampered by limited specificity and high rates of overdiagnosis.
Methods:
To address this clinical bottleneck, we utilized a highly sensitive Complete360®-MyMeta targeted-metabolomics platform to perform high-resolution profiling of 43 metabolites across the carnitine, polyamine, and methylation networks in plasma from a discovery cohort of all-stage (I-IV) PCa patients and healthy controls.
Results:
Our analysis identified 28 significantly altered metabolites (p < 0.05), revealing profound systemic metabolic reprogramming characterized by the depletion of circulating TML and putrescine, alongside the elevation of L-acetylcarnitine and sarcosine. These systemic shifts are consistent with a localized tumoral "metabolic sink", wherein upregulated mitochondrial TML import via the SLC25A45 transporter actively fuels fatty acid oxidation, while parallel androgen signaling drives massive polyamine synthesis. Translating these mechanistic insights into a clinical tool, we developed a multivariable diagnostic signature utilizing mathematically stable bipartite metabolic ratios. An optimized, cross-validated model combining L-acetylcarnitine/TML and sarcosine/putrescine effectively mitigated physiological noise to achieve robust diagnostic separation, yielding an area under the curve (AUC) of 0.99.
Conclusions:
Ultimately, this study provides a discovery-phase proof-of-concept for the SLC25A45-TML axis as a mechanistically grounded, stage-independent liquid biopsy, offering a rational, non-invasive framework to significantly improve PCa detection.
