Developing a Nomogram for Preoperative Prediction of Cervical Cancer Lymph Node Metastasis by Multiplex Immunofluorescence

Abstract

Objective: We aimed to explore the potential mechanism of pelvic lymph node (pLN) metastasis of cervical cancer (CC) by multiplex immunofluorescence (mIF) and construct a nomogram for preoperative prediction of pLN metastasis in patients with CC. Methods: A series of 90 patients with CC (2009 FIGO, IB1-IIA2) was retrospectively assayed with metastatic pLN or not. Tissue microarray (TMA) were prepared and tumor-infiltrating immune markers were assessed by mIF. Pearson correlation coefficients (R), linear regression and spatial proximity analysis were applied to study the potential mechanism of these markers. Multivariable logistic regression analysis and nomogram were used to develop the predicting model. Results: We concluded that T cells may interact with NK cells and macrophages through PD-1 and PD-L1 to promote pLN metastasis of CC. Multivariable logistic regression analysis and nomogram construct a predictive model and area under curve (AUC) can reach 0.843. By internally validation with the remaining 40 percent of cases, a new ROC curve was emerged and the AUC reaching 0.888. Conclusions: This study presents an immune nomogram, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CC. Keywords: cervical cancer, multiplex immunofluorescence, nomogram, internally validation