Identification of a seven-gene signature predicting clinical outcome of liver cancer based on tumor mutational burden
Yunlong Cui, Ning Jiang
Abstract
The total number of somatic mutations may affect the prognosis of cancer, so we applied bioinformatics methods to investigate the association between the TMB (tumor mutational burden)-related differentially expressed genes (DEGs) and the prognosis of hepatocellular carcinoma (HCC). We calculated the TMB value of the patients with HCC in TCGA database and identified the differentially expressed genes between the high-TMB and low-TMB patients. We performed functional enrichment analysis and LASSO Cox regression analysis of the DEGs, and seven genes were screened to establish a risk score model. A nomogram based on the risk scores was drawn to assess the predictive outcomes compared to the actual outcomes. The expression level of the seven genes was verified in cancer cell lines. Moreover, we explored the difference in immune cells infiltration and immune checkpoints between the high-risk and low-risk groups. The results showed that the DEGs between the high-TMB and low-TMB patients were enriched in extracellular matrix organization. A seven-gene risk score model (PAGE1, CHGA, OGN, MMP7, TRIM55, MAGEA6, and MAGEA12) was established for predicting HCC prognosis. Patients with lower risk scores had longer survival time and lower mortality rate. The nomogram based on risk scores and TNM staging showed good performance and reliability in predicting the clinical outcomes. Significant differences in cell infiltration and checkpoints were found between the high-risk and low-risk groups. Our study demonstrated a seven-gene signature and a nomogram based on the risk score model to predict the prognosis of HCC. Some of the newly identified DEGs may be potential biomarkers or therapeutic targets.