Correspondence on “Explore the potential mechanisms between ertugliflozin and kidney cancer through bioinformatics analysis and Mendelian randomization study”
Year: 2026
Type: letter
Abstract: Dear Editor, We read with great interest the recent study by Xu et al[1] exploring the potential mechanisms between ertugliflozin and kidney cancer through bioinformatics analysis and Mendelian randomization (MR). While the authors provide an innovative computational framework for drug repositioning, several unavoidable methodological and biological hurdles must be addressed to ensure these findings are not over-interpreted in a clinical surgical context. First, the fundamental validity of MR hinges on the exclusion of horizontal pleiotropy, a rigor that appears insufficient given the authors’ primary reliance on the inverse variance weighted method. SGLT2 inhibitors exert broad systemic effects on hemodynamics and metabolic pathways. Considering the remarkably high odds ratio (83.70) and the disproportionately wide 95% confidence interval reported in the UK Biobank analysis, these results are highly susceptible to being driven by individual pleiotropic variants. Without implementing rigorous sensitivity analyses, such as MR-Egger regression or MR-PRESSO, to definitively account for these effects, the asserted causal inference remains speculative[2]. Second, a significant timescale mismatch exists. MR estimates the impact of lifelong genetic exposure, whereas clinical patients typically initiate ertugliflozin during middle or late age for a finite duration. This inherent bias often leads to an overestimation of a drug’s protective or pathogenic effects compared to real-world pharmacological intervention, which is a limitation increasingly recognized in recent oncological MR studies[3]. Third, the reliance on bulk RNA-sequencing overlooks intratumoral heterogeneity. Renal cell carcinoma (RCC) is characterized by extreme spatial diversity. Recent single-cell and spatial transcriptomics data suggest that bulk analysis often fails to distinguish whether gene expression changes occur within malignant cells or the complex immune microenvironment[4]. For the surgical oncologist, understanding these spatial dynamics is crucial for predicting therapeutic response and long-term surgical outcomes. Lastly, metabolic confounders hinder the transition from in silico models to the bedside. Patients prescribed ertugliflozin predominantly suffer from Type 2 Diabetes, which is an independent risk factor for RCC progression[5]. Decoupling the direct anti-tumor effect of SGLT2 inhibition from the indirect benefits of improved systemic glycemic control remains an unresolved challenge in this study. In conclusion, while these findings are hypothesis-generating, the leap from genetic correlation to clinical causation requires caution. Prospective clinical trials remain the gold standard to validate whether these mechanisms translate into tangible survival benefits for patients with kidney cancer.
Source: International Journal of Surgery
Authors: Lin Demin, Zhang Haijun, Li Dingpeng, Bai Dengyan
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,450
OpenAlex ID: W7141666358