Investigating prognostic factors for T1 stage high-grade bladder cancer and developing a multi-marker risk prediction model using immunohistochemistry

Objectives To identify prognostic factors for T1 high-grade bladder cancer (T1HG BCa), select pertinent biomarkers, and construct an immunohistochemistry-based multi-biomarker risk prediction model. Methods A retrospective case series study was performed involving 181 T1HG BCa patients who underwent transurethral resection of bladder tumor at the First Affiliated Hospital of Xi′an Jiaotong University between January 2014 and December 2018. The cohort comprised 136 males (75.1%) and 45 females (24.9%), with an age of (M(IQR)) 69(13) years (range: 36 to 88 years). Using simple random sampling, subjects were allocated to a training set (127 cases) and a validation set (54 cases) in a 7∶3 ratio. Differences between independent groups were assessed using chi-square or corrected chi-square tests. Immunohistochemical staining quantified the expression of nine biomarkers; least absolute shrinkage and selection operator (LASSO) regression identified key biomarkers to derive a risk score. The prognostic value of this score was evaluated via Kaplan-Meier survival analysis and receiver operating characteristic curve testing. Significant prognostic factors for T1HG BCa were identified through univariate and multivariate Cox regression analyses, leading to the construction of a Nomogram model. Results During follow-up of 51(41) months (range:3 to 95 months), tumor recurrence occurred in 45 patients (24.9%), progression in 28 patients (15.5%), and death in 9 patients (5.0%). LASSO regression on the training set identified six significant biomarkers: CD44, Ki67, Her2, CK20, GATA3, and CK5/6. Patients were stratified into low-risk and high-risk groups based on the training set′s median risk score (-1.416). Kaplan-Meier analysis within the training set revealed significantly higher rates of tumor recurrence and progression in the high-risk group compared to the low-risk group (all P<0.01). Validation set results confirmed these findings. Univariate and multivariate Cox regression models for tumor recurrence and progression, developed using the training set, demonstrated that bladder tumor count (HR=2.154, 95%CI: 1.021 to 4.503, P=0.043) and the risk score (HR=6.172, 95%CI: 3.250 to 11.719, P<0.01) were independent predictors of recurrence. The risk score alone (HR=3.975, 95%CI: 1.858 to 8.505, P<0.01) emerged as an independent predictor of progression. A nomogram model integrating the risk score with clinical parameters achieved a C-index of 0.914 (95%CI: 0.845 to 0.984) for recurrence prediction and 0.811 (95%CI: 0.696 to 0.926) for progression prediction. Conclusion By integrating the risk score with clinical characteristics of T1HG BCa patients, a robust risk prediction model capable of assessing the probability of tumor recurrence and progression is established, demonstrating significant prognostic predictive value.