Suppression of SLC39A6‐CREB1 axis in liver cancer causes PCK1‐mediated mitochondrial dysfunction

Dear Editor, Hepatocellular carcinoma is one of the most common cancers worldwide and represents a major global health-care challenge. Infection with hepatitis virus, drinking excessive alcohol or suffering from non-alcoholic fatty liver disease are all known risk factors for liver hepatocellular carcinoma (LIHC). A complete understanding of the molecular mechanisms underlying LIHC progression and discovering new master regulators and their functional mechanism is of great significance which will contribute to the identification of novel diagnosis and therapeutic targets in LIHC treatment. Zinc, an essential trace element, plays various pivotal roles in numerous biochemical and physiological processes, such as growth, survival and metabolism. Notably, in liver, zinc is needed for activating many enzymes, such as glutamate dehydrogenase, and superoxide dismutase. Over time, the expanding experimental and clinical evidence had begun to determine the role of zinc in the development of individual cancers. As one of the 14 members of zinc transporter SLC39A, SLC39A6 (ZIP6) facilitates the influx of Zinc into the cytosol. Despite ZIP6 participates in cancer cell proliferation and migration, little is known about the association of ZIP6with the progression of human LIHC. Here, our study suggested a novel ZIP6-CREB1 axis promoting liver cancer growth, providing mechanistic insights explaining in part how ZIP6 functions in liver cancer, and ZIP6, act as a novel regulator of cancer progression, could be an attractive therapeutic target for LIHC. The process of bioinformatics analysis in the research is depicted in Supplementary Figure S1. First, an non-negative matrix factorization cluster analysis was performed including these 14 SLC39A-related genes, and k = 2 was determined by the comprehensive correlation coefficient. The Cancer Genome Atlas-Liver hepatocellular carcinoma (TCGA-LIHC) samples were then divided into two different clusters (Cluster 1 and Cluster 2). Consensus matrix heat maps with k = 2 showed clear boundaries and minimal interference between subgroups, showing stable clusters in the samples (Figure 1A; Supplementary Figure 2A–G). And we compared the survival rates between two groups with different SLC39A statuses, compared to Cluster 2, patients in Cluster 1 had a worse prognosis (Figure 1B). In order to better understand the SLC39A6 status between the two clusters, the volcano plot of differentially expressed genes (DEGs) were shown in Supplementary Figure 2H, and data showed that these DEGs were significantly related to cell proliferation and metabolic process, such as cell cycle and glycolysis (Supplementary Figure 2I). The TCGA dataset was used to obtain RNA-sequencing expression profiles. Ultimately, 371 samples were selected for analysis. Survival differences between the groups were compared using the log-rank test. SLC39A2, SLC39A6, SLC39A10, as well as the SLC39A14 (all localized to the cell membrane) risk score was compared using receiver operating characteristic (ROC) analysis. Based on this analysis, we performed LASSO regression to screen for SLC39As that were related to prognosis (Supplementary Figure 3A). Using the 4-mRNA signature, we calculated the scores, ranked them and classified patients with LIHC patients into highand low-risk groups by the median value. As the risk score of patients with LIHC increased, the expression levels of risk genes (SLC39A6 and SLC39A10) were obviously upregulated (Supplementary Figure 3B). The Kaplan–Meier survival curve and the log-rank test both concluded that patients with a high-risk score died more frequently than those with a low-risk score (Supplementary Figure 3C). In addition, the time dependent ROC analysis indicated the area under curve for 1-, 3and 5-year overall survival (OS) were 0.674, 0.646 and 0.642, respectively (Supplementary Figure 3D). These results confirm the validity of our risk scoring model, and ZIP6 is a prognostic risk factor for LIHC. Subsequently, we obtained the gene expression data from GES136247 and found that SLC39A6 was upregulated in liver cancer samples (Figure 1C). Furthermore, Hepatocellular carcinoma expression atlas (HCCDB) website and University of ALabama at Birmingham Cancer (UALCAN) webserver revealed SLC39A6 expression was elevated as the tumour stage and nodal metastasis status progressed (Supplementary Figure 4A,B). Kaplan–Meier analysis and Cox regression model (forest plot) displayed that reduced SLC39A6 levels were correlated with better OS (Supplementary Figure 4C–E). Combining the findings above, SLC39A6 can be used as a diagnostic and prognostic biomarker in LIHC patients. To further investigate the oncogenic role of SLC39A6 in LIHC, we observe the differentially expressed genes in the two groups, that is SLC39A6-high expression group (50%, N = 186 or 25%, N = 93) and SLC39A6-low expression group (50%, N = 185 or 25%, N = 92), respectively, to obtain the clues about the function of SLC39A6. KEGG analysis of the differentially expressed genes in the two groups indicated a notable enrichment of cell cycle, pyruvate metabolism and glycolysis pathway, and the results of GO analysis also suggested in the group with low SLC39A6 expression, the pathways involved in Received: 27 March 2023 Revised: 20 June 2023 Accepted: 26 June 2023