High expression of TBRG4 in relation to unfavorable outcome and cell ferroptosis in hepatocellular carcinoma

Download and organize RNAseq data for the STAR process of the TCGA-ALL (pan cancer) project from the TCGA database (https://​portal.​gdc.​cancer.​gov), including 371 cancer samples and 50 adjacent cancer samples. Convert PFKM format RNA seq data into TPM format and extract corresponding numbers to match adjacent and cancerous samples for parsing pan cancerous data expression. Then use the R software "limma" package for log2 conversion analysis [15]. In addition, clinical information HCC patients were obtained from TCGA. RNAseq data and clinical information of 240 liver cancer samples and 202 normal samples were obtained from ICGC database (https://​dcc.​icgc.​org/​releases/​current/​Projects). Clinical information and RNA data of GSE14520 were downloaded from GEO database (https://​www.​ncbi.​nlm.​nih.​gov/​gds).

Using "survival package" for proportional risk hypothesis testing and Cox regression analysis, constructing nomogram related models and Calibration analysis using the "RMS" package for indicators with significant Univariate analysis prognosis.

Using LinkedOmics (http://​www.​Thelinkedomics.​Org) database was used to obtain genes positively and negatively correlated with TBRG4 expression with the threshold for |pearson correlation coefficient|> 0.6 and p -value < 0.01. The R package "org. Hs. eg. db" was used for ID conversion, and the package "cluster profile" was used for GO and KEGG enrichment analysis. The enriched gene categories include MF (Molecular Function), CC (Cellular Component), BP (Biological Process) and KEGG (Kyoto Encyclopedia of Genes and Genomes). For Gene set enrichment analysis (GSEA), we use "clusterProfiler" package for parsing. We ranked the TBRG4 related genes according to their correlation coefficients and obtained them from the Molecular Signatures Database (http://​www.​gsea-msigdb.​org/​gsea/​downloads.​jsp) downloaded the subset of c2.cp.kegg.v7.4.symbols.gmt to evaluate relevant pathways and molecular mechanisms. Based on the predetermined gene ranking to analysis, we set the minimum gene set to 5 and the maximum gene set to 5000, with 1000 resamples, P -value < 0.05 and FDR < 0.25 were considered statistically significant.

Build a PPI network using the GeneMANIA database to predict the interaction proteins and related functions of TBRG4. Based on the ssGSEA algorithm provided in package "GSVA", the immune infiltration of TBRG4 was calculated using the markers of 24 immune cells provided in the study of Bindea, Gabriela, et al [16].

The hepatoma cell lines LO2, Hep3B, SMC7721, HepG2 and Huh7 used in this experiment were provided by the Environmental Hormones and Reproductive Development Laboratory of Fuyang Normal University. LO2, HepG2, SMC7721 and Huh7 were cultured in DMEM medium containing 10% fetal bovine serum, 1% penicillin–streptomycin, while HepG2 was cultured in DMEM medium containing 10% fetal bovine serum, 1% penicillin–streptomycin and 1% sodium pyruvate in a cell incubator at 37 ℃ and 5% CO2. The cell culture process should follow the principle of sterility, and the medium should be changed on time. The above cell lines should be passaged at a ratio of 1:2.

Inoculate the target cells and their control cells into upper chamber without Matrigel (for migration) or with Matrigel (for invasion) and add serum-free culture medium. Add DMEM culture medium containing 20% fetal bovine serum to the bottom of the chamber, incubate at 37 ℃ in a 5% CO2 incubator for 36 to 48 h, and fix the cells that migrated to the membrane. Use crystal violet with a concentration of 0.5% for cell staining, wash and float dry, and observe under a microscope.

After 24 h of transfection, the cells were inoculated onto a 6-well plate. On the second day of cultivation, verify that the cell density meets the experimental requirements. Use a pipette gun to draw a line in a 6-well plate that is perpendicular to the bottom horizontal line. Gently clean the cells with PBS three times, remove the scratched cells, and add serum-free medium for further cultivation. Observe the healing of scratches at 0 and 24 h after marking.

Digest and passage the target cells and their control cells, prepare a cell suspension, count the cells, and inoculate 200 cells in a 6-well plate for 10–14 days in complete culture medium. After observing cell clones visible to the naked eye, discard the culture medium and wash the cells with PBS three times. Fix with methanol for 15 min, stain with crystal violet for 10 min, slowly wash off the dye solution with running water, and observe the formation of clones.

Take logarithmic growth phase cells, wait for the cell density to reach about 80%, and proceed with transfection according to the instructions. Using serum free medium, add Lipo8000 transfection reagent (Biyuntian, Shanghai, China) and plasmid, let stand for 10 min, slowly add to the medium and mix well. After 16 h, replace the medium. After 48 h, collect cells to test transfection efficiency.

A total of 8 HCC tumor tissue with their adjacent non-cancerous lung tissues used in this study were obtained from the Second Affiliated Hospital of Fuyang Normal University and were approved by the hospital's ethics committee. The collected tissue samples were prepared and stored by paraffin embedding, then dewaxed, and sealed in a methanol solution. After tissue sectioning, antigen repair was performed using sodium citrate EDTA. Afterwards, the slices were incubated overnight with TBRG4 antibody (Proteintech, Wuhan, China), followed by incubation of the secondary antibody and staining. Images were obtained using a scanning microscope and analyzed.

Inoculate logarithmic growth phase cells into a confocal microscope culture dish, fix with paraformaldehyde for 10 min, and then block with goat serum for 1 h. Incubate the cells with TBRG4 and TOM20 antibodies overnight. After washing with PBS three times, incubate the fluorescent secondary antibody, and then add DAPI staining solution dropwise. Use laser confocal microscope to take photos and analyze.

The RT-PCR and Western Blot experimental methods can be found in previous studies [17]. β-Actin is used as an internal control for RNA quantification standardization. Using primer sequences β-Actin (F: CTCGCCTTTGCCGATCC, R: GAATCCTTCTGACCCATGCC) and TBRG4 (F: CAGCTCACCTGGTAAAGCGAT, R: GGGAGTAGATGCTCGTTCCTTC). β-Actin, TBRG4, AKT, p-AKT, BCL2, GSK3β, ACSL4 and GPX4 antibodies are ordered from Proteintech (Wuhan, China), Beclin1 and TF antibodies are ordered from Biyuntian (Shanghai, China), and DDX56 is ordered from SANTA (St Louis, USA). All blots were cut prior to hybridization with antibodies. According to the required target proteins, the membrane was cut open and incubated in different boxes to eliminate the interference of some impurity bands. The original image of the blots used is included in Supplementary Fig. 2.

Collect logarithmic growth phase cells, add 1 mL RIPA lysate, centrifuge, add agarose beads and TBRG4 antibody, and shake overnight. Collect agarose beads, add SDS sampling buffer, and boil for 100 min. Staining gel with Fast Silver Stain Kit (Biyuntian, Shanghai, China) and use Western Blot to detect TBRG4 interacting molecules.

We first analyzed the paired sample expression of TBRG4 in the TCGA database. The results showed that TBRG4 showed abnormal elevation in 14 types of tumors, including HCC (Fig. 1A). In addition, we also used the HCC database for analysis and found that TBRG4 showed a significant increase in 10 datasets (Fig. 1B). Similarly, in the tumor cohorts of GSE14520 and ICGC, the expression level of TBRG4 also showed a significant increase (Fig. 1C and Supplemental Fig. 1A). ROC curve analysis shows that TBRG4 is a good diagnostic marker for HCC patients (Fig. 1D and 1E). In addition, we divided TCGA HCC patients into high expression and low expression groups based on the median expression of TBRG4 and analyzed the clinical characteristics of the two groups of patients (Table 1). The results showed that the abnormal high expression of TBRG4 is related to Histologic grade, Vascular invasion, Weight, BMI and AFP in HCC patients. In the ICGC patient’s cohort, the expression level of TBRG4 is positively correlated with the histologic grade of HCC (Supplemental Fig. 1B).

Prognostic analysis shows that HCC patients with high TBRG4 expression have poorer overall survival(OS), disease specific survival(DSS) and progress free interval(PFI), as depicted in Fig. 2A, B, and C. In the ICGC patient’s cohort, the high expression group of TBRG4 also showed poor prognosis (Supplemental Fig. 1C). AUC curve analysis indicates that TBRG4 can serve as a prognostic marker for ICGC patients (Supplemental Fig. 1D). Next, we conducted univariate and multivariate COX regression analysis using the clinical characteristics and TBRG4 expression levels of HCC patients (Fig. 2D). Univariate analysis showed that the prognosis of patients was related to the T/M staging, pathological grade, TBRG4 expression level, and tumor status. Multivariate analysis showed that only TBRG4 and tumor status were prognostic factors for HCC patients. Next, we use the “RMS” package to build nomogram related models and Calibration analysis (Fig. 2E and F) and found that TBRG4 can accurately predict the survival prognosis of hepatocellular carcinoma patients. These results suggest that TBRG4 may be one of the important prognostic factors for HCC patients.

To investigate the potential biological function of TBRG4, we utilized the LinkedOmics database and performed a Spearman test to identify genes associated with TBRG4 expression in HCC patients. Our analysis revealed that TBRG4 expression was positively correlated with 4167 genes, while it was negatively correlated with 7145 genes (Fig. 3A). We further examined the top 50 genes that showed positive correlation (Fig. 3B) and negative correlation (Fig. 3C) with TBRG4. Additionally, we identified 168 genes that exhibited a correlation coefficient greater than 0.5, indicating co-expression with TBRG4. Conduct GO, KEGG and GSEA enrichment analysis on the TBRG4 related gene set, and screen the enrichment results according to FDR < 0.01(Fig. 3D and E). The enrichment analysis results of GO and KEGG indicate that TBRG4 may be related to Non-alcoholic fatty liver disease, Oxidative phosphorylation, structural constituent of ribosome, NADH dehydrogenase (ubiquinone) activity. The GSEA analysis results show that TBRG4 is associated with multiple HCC related signaling pathways, such as Beta catenin independent wnt signaling, nonalcoholic fatty liver disease, B cell receptor and TP53 regulates metabolic genes et at. In addition, we used GSVA packages to analyze the relationship between TBRG4 expression levels and immune infiltration in HCC. The results showed that TBRG4 was positively correlated with NK CD56bright cells, Th2 cells, TFH cells and negatively correlated with DC cells, cytotoxic cells, neutrophil cells. This means that TBRG4 may be an important marker of immune infiltration in HCC.

During our molecular analysis concerning TBRG4, we observed a remarkably high correlation between TBRG4 and DEAD-Box 56 (DDX56), with a correlation coefficient of 0.812 (Fig. 4A). In the GSE14520 and ICGC cohort, DDX56 also showed a strong correlation with TBRG4 (Supplemental Fig. 1E and F). In paired sample analysis, DDX56 was significantly elevated in HCC tissue (Fig. 4B). The ROC curve and survival analysis showed that DDX56 is also an important factor in the diagnosis and prognosis of HCC patients (Fig. 4C and D). This strong correlation suggests a potential functional relationship or co-regulation between these two factors, which may be of significance in understanding their roles in biological processes. Further investigation into the interplay between TBRG4 and DDX56 is warranted to uncover the underlying mechanisms and implications for relevant pathways or functions. Therefore, we used the GeneMANIA database to construct a PPI interaction network for TBRG4 and DX56, with functions focused on ribosome biogenesis, mitochondrial gene expression and ribosome assembly et al. (Fig. 4E). Next, we used the GSCA database (http://​bioinfo.​life.​hust.​edu.​cn/​GSCA/​#/​drug) to analyze the drug sensitivity of TBRG4 and DDX56 (Fig. 4F and G). Drug names, Correlation, and FDR can be found in Supplementary Table 1. It was found that TBRG4 may be related to resistance to methotrexate and lenalidomide. For commonly used drugs in HCC, we found that TBRG4 may be associated with resistance to Vinblastine.

To further validate the expression of TBRG4 in HCC, we used Western blot and RT-PCR to detect the expression level of TBRG4 in liver cancer cell lines. The results showed that TBRG4 was significantly elevated in Hep3B, Huh7, SMC7721, and HepG2 tumor cells, while its expression was lower in human normal liver epithelial cells LO2 (Fig. 5A and B). In addition, we collected some HCC tumor tissues and adjacent tissues for immunohistochemical staining, and the results showed a significant increase in the proportion of TBRG4 positive cells in tumor tissues (Fig. 5C). Western blot and RT-PCR confirmed a significant increase in protein and mRNA levels of TBRG4 in tumor tissue (Fig. 5D and E). In addition, GO and KEGG enrichment analysis showed that TBRG4 is related to oxidative physiology and mitochondrial protein complex. Therefore, we used fluorescent antibodies to label mitochondria and TBRG4. We found that TBRG4 was localized on the mitochondria of HCC cells (Fig. 5F).

To verify biological function of TBRG4 in HCC cells, we used interference plasmids to reduce the expression level of TBRG4. After 24 h of plasmid transfection, the protein and mRNA expression levels of TBRG4 showed significant downregulation (Fig. 6A and B). The growth curve and clone formation experiments indicate that knocking down the expression level of TBRG4 can significantly inhibit the proliferation and clone formation ability of HCC cells (Fig. 6C, D and G). In the traswell experiment, the TBRG4 knockdown group significantly reduced the number of invading cells (Fig. 6E, H and Supplemental Fig. 1G). Similarly, in the scratch experiment, the knocking out group of cells increased the width of wound healing (Fig. 6F and I). These results indicate that downregulating the expression level of TBRG4 can significantly inhibit the proliferation, migration, and invasion ability of HCC cells.

To further explore the mechanism of TBRG4 involved in HCC progression, we used Western blot to detect signal molecules related to TBRG4. In the previous bioinformatics prediction, TBRG4 showed a strong correlation with DDX56. Studies have shown that DDX56 can inhibit the proliferation and migration of HCC cells through the PTEN/p-AKT signaling pathway [18]. We speculate that TBRG4 may participate in the AKT signaling pathway through DDX56. Western blot confirmed our hypothesis. We found that knocking down TBRG4 significantly decreased the expression of DDX56 and p-AKT, while the expression of Glycogen Synthase Kinase 3 Beta(GSK3β) significantly increased(Fig. 7A). This suggests that TBRG4 may participate in the proliferation and migration of tumor cells through the DDX56/p-AKT/GSK3β signaling pathway.

Next, we examined the changes in HCC-related apoptotic proteins after silencing TBRG4.We found that B-cell lymphoma 2 (BCL2) significantly decreased, while Cleaved-Caspase 3 expression significantly increased. This indicates that silencing TBRG4 can induce programmed cell death in HCC cells (Fig. 7A). Therefore, we examined the mitochondrial membrane potential and apoptosis ratio of Huh7 cells. It was found that after silencing TBRG4, the mitochondrial membrane potential of Huh7 cells significantly decreased, and the proportion of apoptotic cells increased (Fig. 7B and D). ROS level detection showed that silencing TBRG4 significantly increased the ROS level in Huh7 cells (Fig. 7C and D). These results suggest that silencing TBRG4 may induce iron death in HCC cells. Therefore, we examined the changes in iron death-related markers in Huh7 cells. It was found that Glutathione peroxidase 4(GPX4) and Transferrine(TF) significantly decreased, while Acyl-CoA synthetase long-chain family member 4(ACSL4) significantly increased (Fig. 7E). To better understand the mechanism of TBRG4 regulating HCC ferroptosis, we verified the binding between ferroptosis-related proteins and TBRG4 through Co-IP. The reciprocal Co-IP experiment showed that TBRG4 can specifically bind to Beclin1 (Fig. 7F). Coomassie Brilliant Blue staining also confirmed our results (Fig. 7G). In addition, interfering with and overexpressing TBRG4 can regulate the expression level of Beclin1 (Fig. 7H). These results indicate that TBRG4 may regulate ferroptosis in HCC cells through its interaction with Beclin1.