A prediction model based on high serum SH2B1 in patients with non-small cell lung cancer

Background Identifying a specific biomarker will facilitate the diagnosis and prediction of non-small cell lung cancer (NSCLC). The aim of this study was to investigate the serum SH2B1 in patients with NSCLC and healthy volunteers and establish a novel prediction model. Methods A total 103 NSCLC patients and 108 healthy volunteers were selected from December 2019 to December 2020. Their serum and important clinical data were collected. Serum SH2B1 concentration was determined by ELISA. A novel prediction model for NSCLC was established according to these significant factors. Results Multivariate logistic regression analysis indicated that the chronic pulmonary diseases; NLR ≥ 2.07; hemoglobin level ≥ 136.56 g/L; albumin level ≥ 42.59 g/L and serum SH2B1 concentration ≥615.28 pg/mL were considered as statistically significant difference (p < 0.05). A comprehensive nomogram was established based on serum SH2B1 concentration combined with significant clinical indicators to predict an individual's probability of NSCLC. Conclusion The serum SH2B1 concentration ≥ 615.28 pg/mL is a significant predictive factor for NSCLC. Significantly, the prediction model based on serum SH2B1 has good stability and accuracy, which provides new insights of prediction assessment for NSCLC.