Objective To screen long non-coding RNAs (lncRNAs) relevant to programmed cell death (PCD) and construct a nomogram model predicting prognosis of hepatocellular carcinoma (HCC). Methods The HCC patients selected from The Cancer Genome Atlas (TCGA) were randomly divided into training set and validation set according to 1∶1 sampling. The lncRNAs relevant to PCD were screened by Pearson correlation analysis, and which associated with overall survival in the training set were screened by univariate Cox proportional hazards regression (abbreviation as “Cox regression”), and then multivariate Cox regression was further used to analyze the prognostic risk factors of HCC patients, and the risk score function model was constructed. According to the median risk score of HCC patients in the training set, the HCC patients in each set were assigned into a high-risk and low-risk, and then the Kaplan-Meier method was used to draw the overall survival curve, and the log-rank test was used to compare the survival between the HCC patients with high-risk and low-risk. At the same time, the area under receiver operating characteristic curve (AUC) was used to evaluate the value of the risk score function model in predicting the 1-, 3-, and 5-year overall survival rates of HCC patients in the training set, validation set, and integral set. Then the nomogram was constructed based on the risk score function model and factors validated in clinic, and its predictive ability for the prognosis of HCC patients was evaluated. Results A total of 374 patients with HCC were downloaded from the TCGA, of which 342 had complete clinicopathologic data, including 171 in the training set and 171 in the validation set. Finally, 8 lncRNAs genes relevant to prognosis (AC099850.3, LINC00942, AC040970.1, AC022613.1, AC009403.1, AL355974.2, AC015908.3, AC009283.1) were screened out, and the prognostic risk score function model was established as follows: prognostic risk score=exp₁×β₁+exp₂×β₂...+exp_i×β_i (exp_i was the expression level of target lncRNA, β_i was the coefficient of multivariate Cox regression analysis of target lncRNA). According to this prognostic risk score function model, the median risk score was 0.89 in the training set. The patients with low-risk and high-risk were 86 and 85, 86 and 85, 172 and 170 in the training set, validation set, and integral set, respectively. The overall survival curves of HCC patients with low-risk drawn by Kaplan-Meier method were better than those of the HCC patients with high-risk in the training set, validation set, and integral set (P<0.001). The AUCs of the prognostic risk score function model for predicting the 1-, 3-, and 5-year overall survival rates in the training set were 0.814, 0.768, and 0.811, respectively, in the validation set were 0.799, 0.684, and 0.748, respectively, and in the integral set were 0.807, 0.732, and 0.784, respectively. The multivariate Cox regression analysis showed that the prognostic risk score function model was a risk factor affecting the overall survival of patients with HCC [<0.89 points as a reference, RR=1.217, 95%CI (1.151, 1.286), P<0.001]. The AUC (95%CI) of the prognostic risk score function model for predicting the overall survival rate of HCC patients was 0.822 (0.796, 0.873). The AUCs of the nomogram constructed by the prognostic risk score function model in combination with clinicopathologic factors to predict the 1-, 3-, and 5-year overall survival rates were 0.843, 0.839, and 0.834. The calibration curves of the nomogram of 1-, 3-, and 5-year overall survival rates in the training set were close to ideal curve, suggesting that the predicted overall survival rate by the nomogram was more consistent with the actual overall survival rate. Conclusion The prognostic risk score function model constructed by the lncRNAs relevant to PCD in this study may be a potential marker of prognosis of the patients with HCC, and the nomogram constructed by this model is more effective in predicting the prognosis (overall survival) of patients with HCC.

Citation： XIE Tan, ZHANG Zeyu, WANG Shiming, WANG Feitong. A nomogram prognosis prediction model for programmed cell death of hepatocellular carcinoma based on TCGA database. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2023, 30(7): 842-848. doi: 10.7507/1007-9424.202302028 Copy