Abstract

Simple SummaryColorectal cancer is one of the most common cancers and the second leading cause of deaths due to cancer. In this study, we developed a neural model based on only four lncRNAs to predict the overall survival rate of colorectal cancer patients. Moreover, we validated the value of analysing the lncRNA MYOSLID, one of the hub lncRNAs in our model, which promotes colorectal cancer by regulating necroptosis. Our study offered some essential insights into predicting the prognosis of colorectal cancer patients and may help to assist diagnosis and treatment in the future.AbstractNecroptosis is a newly defined form of programmed cell death that plays an important role in cancers. However, necroptosis-related lncRNAs (NRLs) involved in colorectal cancer (CRC) have not yet been thoroughly studied. Methods: In this study, a 4-NRL model was developed based on the least absolute shrinkage and selection operator (LASSO) algorithm. A series of informatic, in vitro and in vivo analyses were applied to validate the prognostic value of the model and the potential function of the hub lncRNA MYOSLID. Results: The model exhibited an excellent capacity for the prediction of overall survival and other clinicopathological features of CRC patients using Kaplan–Meier (K–M) survival curves and receiver operating characteristic (ROC) curves. Furthermore, a significant difference in the levels of immune cells, such as CD4 memory T cells and activated mast cells, between two risk groups was observed. The low-risk patients had a higher expression of immune checkpoints, such as PDCD1 (PD-1) and CD274 (PD-L1). The levels of MYOSLID, a hub lncRNA in our model, were higher in CRC tissues than in normal tissues. Knockdown of MYOSLID induced necroptosis and inhibited the proliferation of CRC cells in vitro and in vivo. Interestingly, knockdown of MYOSLID also increased the percentage of CD4+