Distant metastasis is the most consequential stage of breast cancer progression because tumor cells that travel to organs such as the bone, liver, lungs, or brain are responsible for most breast cancer-related deaths. Clinicians already use tumor stage, hormone-receptor status, HER2 expression, genomic tests, and other clinical factors to estimate risk, but these measures do not fully explain why patients with seemingly similar disease can follow very different trajectories. The new analysis adds to growing evidence that cancer prognosis may depend on molecular relationships operating within tumor cells, not simply on the level of one isolated gene.

The researchers focused on SUCLA2 and USP10 because both have biological links to cancer-related processes, although they operate in different molecular contexts. SUCLA2 encodes the beta subunit of succinyl-CoA ligase, an enzyme involved in mitochondrial energy metabolism and the tricarboxylic acid cycle. Mitochondria do more than generate energy: they also influence redox balance, biosynthesis, cell death, and the ability of cancer cells to adapt to stressful environments. USP10 encodes ubiquitin-specific peptidase 10, a deubiquitinating enzyme that removes ubiquitin tags from proteins. By regulating protein stability and signaling, deubiquitinating enzymes can affect pathways involved in DNA damage responses, cell survival, growth, and immune regulation.

Rather than examining the genes independently, the study assessed their combined expression patterns in four independent breast cancer cohorts containing information on distant metastasis-free survival. Patients were grouped according to the relationship between SUCLA2 and USP10 expression, allowing the researchers to test whether a molecular combination could distinguish risk more effectively than either biomarker alone. Survival differences were evaluated using Kaplan–Meier analyses, with patients divided according to an optimal cutoff determined through receiver operating characteristic, or ROC, analysis. Statistical significance was assessed with the log-rank test.

The strongest signal emerged in patients who had low SUCLA2 expression together with high USP10 expression. Among patients who had not received treatment, this molecular pattern was associated with significantly poorer DMFS, indicating a greater likelihood of developing distant metastases during follow-up. The result was notable because the risk pattern was not reproduced consistently when SUCLA2 or USP10 was considered separately. In other words, the prognostic information appeared to reside in the relationship between the genes rather than in the absolute expression of either one.

Treatment status substantially changed the association. In patients who received treatment, the elevated metastatic risk linked to low SUCLA2 and high USP10 was no longer observed. This finding does not establish that therapy directly neutralizes the biological effects of the gene interaction, because the analysis was observational and treatment decisions may have been influenced by clinical features that were not fully captured. However, it raises the possibility that treatment modifies the relationship between tumor metabolism, protein regulation, and metastatic behavior. It also suggests that a biomarker can perform differently in treated and untreated populations, an issue that is critical when developing clinically useful prediction tools.

The researchers further examined the product of SUCLA2 and USP10 expression, a mathematical representation of their combined activity. Kaplan–Meier curves for this interaction measure supported the idea that the joint signal could separate patients with different metastatic outcomes. Such interaction-based models are designed to capture situations in which the effect of one molecular factor depends on the level of another. This is biologically plausible in cancer, where metabolic pathways, protein turnover, stress responses, and treatment resistance are tightly interconnected. A gene that appears weakly informative on its own may become clinically meaningful when interpreted in the context of another pathway.

“Our results indicate that molecular interactions may provide more informative biomarkers than single-gene measurements,” the researchers noted. The conclusion reflects a broader shift in precision oncology. Many current approaches focus on identifying mutations or expression changes in individual genes, but tumors function as dynamic networks. A metabolic enzyme and a deubiquitinating enzyme may influence overlapping cellular systems without being part of a simple linear pathway. Their combined expression could therefore act as a proxy for a tumor state characterized by altered energy use, protein stability, stress tolerance, or metastatic capacity.

The findings could eventually help identify breast cancer patients who require closer surveillance or more intensive treatment, but substantial validation is still needed. The study relied on retrospective gene-expression and clinical datasets, and the cohorts may differ in tumor subtypes, treatment regimens, follow-up duration, and methods of molecular measurement. Laboratory experiments will be necessary to determine whether SUCLA2 and USP10 directly regulate one another or instead reflect a third biological process. Prospective clinical studies must also test whether the interaction remains predictive when adjusted for tumor stage, age, receptor status, chemotherapy, endocrine therapy, HER2-targeted treatment, and other established factors. If these results are confirmed, the SUCLA2–USP10 relationship could become both a prognostic tool and a starting point for investigating new strategies to limit metastatic progression.

Subject of Research: Not applicable

Article Title: SUCLA2-USP10 interaction rather than SUCLA2 alone correlates with metastasis in breast cancer patients

Web References: https://doi.org/10.70401/cbm.2026.0022

References: He X, Shao Y, Sun X. “SUCLA2-USP10 interaction rather than SUCLA2 alone correlates with metastasis in breast cancer patients.” Computational Biomedicine. DOI: 10.70401/cbm.2026.0022.

Image Credits: © He X, Shao Y, Sun X, 2026. Creative Commons Attribution 4.0 International License.

Keywords: breast cancer, distant metastasis-free survival, SUCLA2, USP10, gene interaction, biomarkers, precision oncology, tumor metabolism, deubiquitinating enzymes, Kaplan–Meier analysis