Abstract
Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality
rate. The search for a new biomarker could help the prognosis of HCC patients.
We identified the glycolytic gene set associated with HCC and the glycolytic
lncRNA based on TCGA and MsigDB databases. According to these lncRNAs, K-means
clustering, and regression analysis were performed on the patients. Two groups
of HCC patients with different lncRNA expression levels were obtained based on
K-means clustering results. The results of difference analysis and enrichment
analysis showed that DEmRNA in the two HCC populations with significant survival
differences was mainly enriched in transmembrane transporter complex, RNA
polymerase II specificity, cAMP signaling pathway, and calcium signaling
pathway. In addition, a prognostic model of HCC with 4 DElncRNAs was constructed
based on regression analysis. ROC curve analysis showed that the model had good
predictive performance. Drug predictionresults showed that the efficacy of JQ1,
niraparib, and teniposide was higher in the low-risk group than in the high-risk
group. In conclusion, this study preliminarily identified glycolytic-related
prognostic features of lncRNAs in HCC and constructed a risk assessment model.
The results of this study are expected to guide the prognosis assessment of
clinical HCC patients.
Keywords
hepatocellular carcinoma - glycolysis - nonnegative matrix factorization - K-means - random forests - prognostic signature
