No abstract

Objective: Tumour associated trypsin inhibitor (TATI) is a peptide that is a marker for several tumours. TATI may also behave as an acute phase reactant in severe inflammatory disease. Overexpression of TATI predicts an unfavourable outcome for many cancers. This study aimed to evaluate the prognostic value of pre-and postoperative concentration of TATI in serum (S-TATI) of patients with renal cell carcinoma (RCC). Materials and methods: S-TATI was determined by time resolved immunofluorometric assay in preoperative and postoperative samples that were collected from 132 RCC patients, who underwent partial or complete nephrectomy in Helsinki University Hospital from May 2005 to July 2010. Results: Preoperative S-TATI was significantly associated with tumour stage, lymph-node involvement, metastatic stage, Chronic Kidney Disease Stage (CKD grade), and preoperative C-reactive protein level (p < 0.05). Postoperative S-TATI was significantly associated only with CKD grade (p < 0.001). Multivariate Cox regression analysis of postoperative S-TATI, as a continuous variable, was an independent prognostic factor for overall survival (HR ¼ 1.01, 95% CI ¼ 1.00À1.01, p ¼ 0.03) and cancer-specific survival (CSS) (HR ¼ 1.01, 95% CI ¼ 1.00À1.02, p ¼ 0.004). Conclusions: Our data suggest that elevated postoperative S-TATI may be associated with adverse prognosis in RCC patients.

After surgery of localized renal cell carcinoma, over 20% of the patients will develop distant metastases. Our aim was to develop an easy-to-use prognostic model for predicting metastasis-free survival after radical or partial nephrectomy of localized clear cell RCC. Model training was performed on 196 patients. Right-censored metastasis-free survival was analysed using LASSO-regularized Cox regression, which identified three key prediction features. The model was validated in an external cohort of 714 patients. 55 (28%) and 134 (19%) patients developed distant metastases during the median postoperative follow-up of 6.3 years (interquartile range 3.4–8.6) and 5.4 years (4.0–7.6) in the training and validation cohort, respectively. Patients were stratified into clinically meaningful risk categories using only three features: tumor size, tumor grade and microvascular invasion, and a representative nomogram and a visual prediction surface were constructed using these features in Cox proportional hazards model. Concordance indices in the training and validation cohorts were 0.755 ± 0.029 and 0.836 ± 0.015 for our novel model, which were comparable to the C-indices of the original Leibovich prediction model (0.734 ± 0.035 and 0.848 ± 0.017, respectively). Thus, the presented model retains high accuracy while requiring only three features that are routinely collected and widely available.