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