SPP1 and the risk score model to improve the survival prediction of patients with hepatocellular carcinoma based on multiple algorithms and back propagation neural networks

Abstract

Hepatocellular carcinoma (HCC) is associated with poor prognosis and fluctuations in immune status. Although studies have found that secreted phosphoprotein 1 (SPP1) is involved in HCC progression, its independent prognostic value and immune-mediated role remain unclear. Using The Cancer Genome Atlas and Gene Expression Omnibus data, we found that low expression of SPP1 is significantly associated with improved survival of HCC patients and that SPP1 expression is correlated with clinical characteristics. Univariate and multivariate Cox regression confirmed that SPP1 is an independent prognostic factor of HCC. Subsequently, we found that T cell CD4 memory-activated monocytes, M0 macrophages, and resting mast cells showed significant differences in penetration in the high and low SPP1 expression groups. Next, we used the Weighted Gene Co-Expression Network and Least Absolute Shrinkage Sum Selection Operator algorithms to construct a risk score for the 9-immune-related genes signature. The risk score showed a good ability to identify high and low-risk patients and improved survival prediction. We also used multivariate Cox regression to validate that risk score was significantly correlated with SPP1 and overall survival. Lastly, the Back-Propagation Neural Network confirmed the reliability of the results of multiple algorithms. In conclusion, the findings suggest that SPP1 is an independent marker of HCC survival and immunotherapy.