NOL12 is upregulated in HCC and associated with a poor prognosis
First, we downloaded the transcriptome data of LIHC and the corresponding clinical data from TCGA database, including 50 normal and 370 tumor samples. To evaluate the significance of NOL12 in HCC, these datasets were used to study the expression of NOL12 in HCC. The results showed that the mRNA expression of NOL12 in HCC tissues increased significantly (Figure 1A). This result was also fully verified in the paired analysis of HCC tissue and adjacent normal tissue in the same patient (Figure 1B). In addition, we found that the overall survival rate of the NOL12 high expression group was lower than that of the low expression group, which was statistically significant (Figure 1C). We further examined the correlation of NOL12 with the clinicopathologic features of HCC. In a subgroup analysis based on race, sex, age, grade, metastasis status and stage in the UALCAN database, the transcription level of NOL12 in patients with LIHC was significantly higher than that in normal controls, and the higher the expression of NOL12 was, the later the tumor grading and staging. (Supplementary Figure 1). The above results show that NOL12 may be a potential diagnostic marker for HCC.
Correlation of NOL12 with the Proportion of TICs
We next systematically described the pattern of tumor-infiltrating immune cells (TICs) by processing the signature gene expression profile in HCC with the CIBERSORTx algorithm. The results of Supplementary Figure 2 show the distribution landscape and correlation of infiltrating immune cells in LIHC specimens. According to the results from the difference and correlation analyses, we concluded that NOL12 might regulate the immune activity of the TME in HCC mainly through twelve kinds of TICs (Figure 2). Among them, seven kinds of TICs were negatively correlated with NOL12 expression, including naïve B cells, resting CD4+ T cell memory, activated NK cells, monocytes, M2 macrophages, resting mast cells and activated mast cells. Five kinds of TICs were positively correlated with NOL12 expression, including memory B cells, M0 macrophages, activated CD4+ T cell memory, follicular helper T cells and regulatory T cells. In addition, the results from GO enrichment analysis indicated that the functions of NOL12 correspond to immune-related activities (Figure 3A). These results suggested that the level of NOL12 might be a key indicator to reflect the state of the TME. Figure 3B shows that a variety of tumor-related signaling pathways were enriched, including the MAPK, PI3K-Akt cAMP and Ras signaling pathways.
Construction of a NOL12-Related Gene Risk Signature
We constructed a PPI network of NOL12 using the Search Tool for the Retrieval of Interacting Genes (STRING) database, with the highest confidence (0.9) (Figure 3C).
We obtained the 20 genes most closely related to NOL12 and then performed univariate Cox regression analysis to further evaluate the relationship between these coexpressed genes and the survival of patients with HCC. The results showed that 16 NOL12 coexpressed genes were closely related to the prognosis of HCC (Figure 3D). All patient samples were randomly assigned to a training set (n = 186) or a testing set (n = 184). Least absolute shrinkage and selection operator (LASSO) proportional Cox regression analysis was performed to construct an eight-gene risk signature model (Figure 3E, F). The risk score of each patient in the training and testing sets was calculated as follows: risk score = (expWDR43 * 0.00322) + (expNOP58 * 0.08182) + (expRCL1 * -0.13789) + (expUTP3 * 0.01855) + (expEBNA1BP2 * 0.16428) + (expNOP56 * 0.19824) + (expBMS1 * 0.08048) + (expRBM28 * 0.55925).
Prognostic Value of the NOL12-Related Genes Risk Signature
The patients in the training and testing sets were further divided into high- and low-risk groups, with the median risk score as the cutoff value. The results showed that in both the training and testing sets, the number of deaths in the high-risk group was significantly higher than that in the low-risk group (Figure 4A, B). The results of K-M survival analysis also confirmed that the prognosis of the low-risk group was better than that of the high-risk group (Figure 4C, D). Time-dependent receiver operating characteristic (ROC) curves were generated to estimate the efficacy of the risk signature for predicting the survival of patients with HCC. The areas under the curve (AUCs) of the 1-, 3- and 5-year risk scores were 0.762, 0.705 and 0.728 in the training sets, respectively. In the testing sets, the AUC values were 0.713, 0.662, and 0.609 at the 1-, 3- and 5-year time points, respectively (Figure 4E, F). In addition, to further evaluate the value of the risk signature in clinical application, we conducted a stratified survival analysis of patients with different clinicopathological features, including age, sex, grade, T stage, N stage, M stage, and clinical stage. For different stratifications, patients in the low-risk group had a higher overall survival rate than those in the high-risk group (Supplementary Figure 3). These results indicated that the NOL12-related risk signature can accurately predict the prognosis of patients with HCC without considering clinical factors. Univariate Cox regression analyses also showed that the risk score was an independent prognostic factor for patients with HCC (Figure 4G, H).
Construction of a Nomogram and Evaluation of Its Prediction Ability
To provide a quantitative method with clinical value to predict the probability of 1-, 3- and 5-year OS in HCC, we constructed a nomogram based on the independent prognostic factors (Figure 5A). The total scores were calculated according to the points of all variables in the nomogram, and then the 1-, 3- and 5-year survival rates of each patient with HCC could be predicted by drawing a vertical line from the total points to the survival prediction axis. The prediction values of the 1-, 3- and 5-year nomograms in the calibration plot were close to the 45-degree line in the complete dataset, which indicates that the nomogram demonstrates good prediction ability (Figure 5B). The ROC curves showed that, compared with other clinical factors related to prognosis, the nomogram had the highest accuracy in predicting HCC survival, with an AUC of 0.804 (Figure 5C). In addition, decision curve analysis (DCA) was performed to evaluate the net clinical benefits of multiple prognostic factors (Figure 5D). The results showed that using a nomogram to predict survival probability can bring more benefits to patients.
Estimation of TMB and ICIs by the Risk Signature
We analyzed the mutation profiles of each LIHC patient and associated the patient's risk score with TMB for difference and correlation analyses. The results showed that the level of TMB in the high-risk group was higher than that in the low-risk group and was positively correlated with the risk score (Figure 6A, B). Subsequently, we performed a K-M survival analysis to evaluate the effects of high and low TMB and risk scores on the prognosis of patients with HCC. As shown in Figures 6C and D, the survival rate of patients with h-TMB was significantly lower than that of patients with l-TMB. Among all the groups, the survival rate of patients with h-TMB + h-risk was the worst, while that of patients with l-TMB + l-risk was the best. Since patients with elevated TMB tend to benefit more from ICIS treatment, we further investigated the relationship between ICIS and risk scores. The results showed that the expression levels of PD-1, PD-L1, CTLA-4 and LAG3 in the high-risk group were significantly higher than those in the low-risk group and were positively correlated with the risk score, indicating that the high-risk group could benefit more from ICI treatment (Figure 6E-L).
Knockdown of NOL12 Inhibits the Proliferation and Metastasis of HCC Cells
Considering that the previous data results supported the potential tumor-promoting effect of NOL12 in HCC, a series of functional experiments were carried out in vitro. We selected HepG2 and Huh-7 cells with relatively higher endogenous NOL12 in HCC cells for knockdown experiments. Two shRNAs targeting human NOL12 (shNOL12#1, shNOL12#2) were introduced into HepG2 and Huh-7 cells to detect changes in endogenous NOL12 protein. As shown in Figure 7A, we confirmed NOL12 silencing by comparison with the corresponding negative control (shCtrol). The results of the cell proliferation assay showed that the number of cells in the NOL12-knockdown group was significantly lower than that in the control group (Figure 7B). The results of the MTT assay also showed that cell viability was significantly inhibited after NOL12 knockdown in vitro (Figure 7C), and the colony numbers of HepG2 and Huh-7 cells decreased significantly (Figure 7D, E). To further determine whether NOL12 participates in the regulation of migration and invasion processes in HCC cells, Transwell experiments were executed to examine the effect of NOL12 on cell migration and invasion. The results of Transwell assays showed that knockdown of NOL12 in HepG2 and Huh-7 cells inhibited their migration and invasion ability (Figure 8).