Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients - Tu, Pinghua; Li, Xinjun; Cao, Lingli; Zhong, Minghua; Xie, Zhibin; Wu, Zhanling (Frontiers in Oncology, 2022)
FAM83A gene is related to the invasion and metastasis of a variety of tumors. However, the abnormal immune cells infiltration driven by the gene is poorly understood in the pathogenesis and prognosis of NSCLC. Based on the TCGA and GEO databases, we used COX regression and machine learning algorithms (CIBERSORT, random forest, and back propagation neural network) to study the prognostic value of FAM83A and immune infiltration characteristics in NSCLC. High FAM83A expression was significantly associated with poor prognosis of NSCLC patients (p = 0.00016), and had excellent prognostic independence. At the same time, the expression level of FAM83A is significantly related to the T, N, and Stage. Subsequently, based on machine learning strategies, we found that the infiltration level of naive B cells was negatively correlated with the expression of FAM83A. The low infiltration of naive B cells was significantly related to the poor overall survival rate of NSCLC (p = 0.0072). In addition, Cox regression confirmed that FAM83A and naive B cells are risk factors for the prognosis of NSCLC patients. The nomogram combining FAM83A and naive B cells (C-index = 0.748) has a more accurate prognostic ability than the Stage (C-index = 0.651) system. Our analysis shows that abnormal infiltration of naive B cells driven by FAM83A is a key factor in prognostic prediction of NSCLC patients.