A prediction model for targeted drug resistance in renal cell carcinoma based on plasma proteomics analysis

Over the past decade, the prognosis of patients with metastatic renal cell carcinoma (mRCC) has significantly improved owing to the development of anti-angiogenic targeted drugs such as sunitinib, pazopanib, and sorafenib. However, biomarkers that can identify patients with mRCC who may rapidly develop drug resistance are still lacking. Approximately 25% of patients experience rapid disease progression [progression-free survival (PFS) ≤3 months]. Currently, there is a lack of effective noninvasive biomarkers to identify resistant patients prior to treatment. This study aimed to identify plasma biomarkers associated with therapeutic resistance and develop a predictive model for clinical decision-making.

Plasma proteomic analysis was conducted in 159 patients with mRCC treated with targeted therapy. Patients were divided into training and validation cohorts. Candidate protein biomarkers were initially screened using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and further validated using enzyme-linked immunosorbent assay (ELISA). A predictive nomogram was subsequently developed using logistic regression analysis and assessed for discrimination and calibration performance. Immunohistochemistry (IHC) was performed to compare protein expression levels in corresponding tumor tissue samples.

Four plasma proteins-chitotriosidase-1 (CHIT1), interleukin-6 receptor (IL-6R), neuronal cell adhesion molecule (NRCAM), and ecto-5'-nucleotidase (NT5E)-were identified as significant predictors of intrinsic resistance. The nomogram incorporating these biomarkers exhibited a high predictive accuracy, with a concordance index (C-index) of 0.956 and 0.869 for the training and validation cohorts, respectively. Notably, while the plasma concentrations of these proteins were significantly elevated in resistant patients, their expression levels in tumor tissues showed no significant differences, underscoring their utility as circulating, noninvasive biomarkers.

We developed and validated a plasma protein-based nomogram to predict mRCC resistance to targeted therapy. The four identified biomarkers allow noninvasive identification of high-risk patients and offer a practical tool for early clinical stratification. This model may assist clinicians in avoiding ineffective treatment and optimizing therapeutic strategies for patients with mRCC.