{'fraction_non_alphanumeric': 0.039846682104926937, 'fraction_numerical': 0.03912800447177194, 'mean_word_length': 5.041244573082489, 'pattern_counts': {'":': 0, '<': 15, '<?xml version=': 0, '>': 0, 'https://': 2, 'lorem ipsum': 0, 'www.': 4, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 8, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Comprehensive Analysis of SLC17A9 and Its Prognostic Value in Hepatocellular Carcinoma

Background Solute carrier family 17 member 9 (SLC17A9) encodes a member of a family of transmembrane proteins that are involved in the transport of small molecules. SLC17A9 is involved in the occurrence and development of various cancers, but its biological role in liver hepatocellular carcinoma (LIHC) is unclear. Methods The expression level of SLC17A9 was assessed using The Cancer Genome Atlas (TCGA) database and immunohistochemistry of tumor tissues and adjacent normal liver tissues. The receiver operating characteristic (ROC) and R software package performed diagnosis and prognosis. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes functional enrichment and co-expression of SLC17A9, gene–gene interaction (GGI), and protein–protein interaction (PPI) networks were performed using R, GeneMANIA, and STRING. Western blot, real-time quantitative PCR (RT-qPCR), immunofluorescence, colony formation, wound scratch assay, ATP production assays, and high connotation were applied to determine the effect of SLC17A9 knockdown on HEPG2 (hepatocellular liver carcinoma) cells. TIMER, GEPIA, and TCGA analyzed the relationship between SLC17A9 expression and immune cells, m6A modification, and ferroptosis. Results SLC17A9 expression in LIHC tissues was higher than in normal liver tissues (p < 0.001), and SLC17A9 was related to sex, DSS (disease-specific survival), and PFI (progression-free interval) (p = 0.015, 0.006, and 0.023). SLC17A9 expression has diagnostic (AUC: 0.812; CI: 0.770–0.854) and prognostic potential (p = 0.015) in LIHC. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) functional enrichment analysis showed that SLC17A9 was closely related to neuronal cell body, presynapse, axonogenesis, PI3K/Akt signaling pathway. GGI showed that SLC17A9 was closely related to MYO5A. PPI showed that SLC17A9 was closely related to SLC18A3. SLC17A9 silencing inhibited HepG2 cells proliferation, migration, colony formation, and reduced their ATP level. SLC17A9 expression level was related to immune cells: B cells (r = 0.094, P = 8.06E-02), CD4+ T cells (r = 0.184, P = 5.95E-04), and macrophages (r = 0.137, P = 1.15E-02); m6A modification: HNRNPC (r = 0.220, p < 0.001), METTL3 (r = 0.180, p < 0.001), and WTAP (r = 0.130, p = 0.009); and ferroptosis: HSPA5 (r = 0.240, p < 0.001), SLC7A11 (r = 0.180, p < 0.001), and FANCD2 (r = 0.280, p < 0.001). Conclusion Our data show that SLC17A9 may influence LIHC progression. SLC17A9 expression correlates with tumor immune infiltration, m6A modification, and ferroptosis in LIHC and may have diagnostic and prognostic value in LIHC.


INTRODUCTION
Liver hepatocellular carcinoma (LIHC) is one of the most common malignant tumors in the world the most commonly diagnosed cancer in 13 countries and the leading cause of cancer death in 20 countries (1). Patients with advanced liver cancer often suffer liver failure. Liver cancer is mainly treated surgically, but it has high recurrence and metastasis. Its prognosis is very poor and its mortality rate is 8.2% (2). China has the heaviest burden of hepatitis and accounts for one in three global cases of chronic HBV infection and about 7% of HCV infections (3). Thus, effective liver cancer treatments are urgently needed.
SLC17A9 (solute carrier family 17 member 9), a vesicular nucleotide transporter (VUNT), is most abundantly expressed in the stomach, intestines, skeletal muscles, and liver (4). SLC17A9 is involved in ATP vesicular storage and exocytosis. SLC17A9 and its function were first described in bile duct cells (5). SLC17A9 is also highly enriched on lysosomes in various several cell types, including C2C12, COS-1, and HEK-293T cells, and actively transports ATP across lysosomal membranes (6). It is reported that ATP accumulates in lysosomes of astrocytes and microglia (7,8). Once SLC17A9 is damaged, it will decrease in the accumulation of ATP in lysosomes, which results in cell death. SLC17A9 is expressed in biliary epithelial cells (9), osteoblasts (10), AR42J cells, and pancreatic acinar cells (11), but its specific role in LIHC has not been explored. Immunotherapy is a targeted method for the treatment of cancer. It is a common anti-tumor method to use the body's immune system to fight tumor cells to promote the response to cancer cells (12,13). Currently, m6A modification is widely used for the treatment of various cancers (14,15). However, there are no studies on the role of SLC17A9 in LIHC, especially in the context of LIHC immunotherapy and m6A modification. Irondependent cell death has recently emerged as a novel form of cell death that occurs mainly due to an abnormal increase in intracellular iron-dependent lipid oxygen free radicals and an imbalance in redox homeostasis (16)(17)(18). However, little is known about the relationship between SLC17A9 and genes associated with iron-dependent cell death in LIHC. Here, we sought to characterize SLC17A9 expression in LIHC, its clinical significance, and its diagnostic and prognostic potential. We also examined the effect of SLC17A9 on the proliferation, migration, and colony formation capacity of LIHC cells and evaluated its biological function with the help of the database.

Database Analysis
Datasets on SLC17A9 expression in pan-cancer, LIHC, and normal liver tissues were downloaded from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) (19). Correlation between SLC17A9 expression in LIHC and clinicopathological features was analyzed using TCGA LIHC data. Receiver operating characteristic (ROC) curve analysis was done on R to detect the sensitivity and specificity of SLC17A9 expression in LIHC. Analysis of correlation between SLC17A9 expression and LIHC prognosis was done on R. LinkedOmics database (www.linkedomics.org/login.php) (20) was used to identify genes that are co-expressed with SLC17A9 in the TCGA LIHC dataset. Pearson's correlation coefficient was used for statistical analysis. Volcano plots and heatmaps were used to visualize analysis results. An R package was used for GO and KEGG pathway enrichment analysis of SLC17A9 in LIHC. GeneMANIA (www.genemania.org) and STRING (www.stringdb.org) were used to construct gene-gene interaction (GGI) and protein-protein interaction (PPI) networks (21,22), respectively. TIMER (https://cistrome.shinyapps.io/timer) (23) was used to assess correlation between SLC17A9 gene expression and immune cell infiltration. TIMER's SCNA module was used to associate SLC17A9's genetic copy number variation (CNV) with the relative abundance of tumor-infiltrating cells. The proportion of immune cells in LIHC samples expressing high and low SLC17A9 levels was identified on R using the CiberSort plugin. GEPIA (gepia.cancer-pku.cn) (24) was used to assess the relationship between SLC17A9 and immune cell markers in LIHC. Immune cell markers were obtained from the website of R&D Systems (www.rndsystems.com/cn/resources/cell-markers/ immune-cells). Correlation between SLC17A9 and m6A and ferroptosis was analyzed using TCGA LIHC datasets.

Liver Cancer Tissues Specimens
We recruited 28 patients with liver cancer who underwent surgery in Taihe Hospital, Shiyan City, Hubei province. Ethical approval for this study was granted by Taihe Hospital's medical ethics committee. The study adhered to the Helsinki Declaration and its subsequent amendments.

Western Blot Analysis
At 48 h after transfection, cells were homogenized in RIPA lysis buffer (Promega, Madison, USA) on ice for 30 min, and then, protein was measured by the Bicinchoninic Acid (BCA) assay (Beyotime, Beijing, China). Protein was separated on 10% sodium dodecyl sulfate-polyacrylamide gels and transferred onto a polyvinylidene difluoride membrane using a semiwet method; next, we blocked the membrane with 5% dried skimmed milk for 1 h and incubated overnight with a SLC17A9 rabbit anti-human monoclonal antibody (1:200, Abcam, USA) in a 4°C refrigerator. Next day, horseradish peroxidase (HRP)-conjugated secondary antibodies (1:1,000; ProteinTech Group) for 1 h at room temperature. Images were obtained using a gel imaging system.

RNA Isolation and Real-Time Quantitative PCR Analysis
Total RNA was extracted from cells 48 h after transfection using Trizol reagent (Invitrogen). Trizol Reagen (600 ml) was added, and the samples were placed on ice for 20 min to lyse the cells. Subsequently, the cell lysate in Tirol was phase separated by addition of 120 ml of chloroform, and the RNA was precipitated by addition of 300 ml of isopropanol, and then, cDNA was synthesized by utilizing the TaqMan Reverse Transcription Reagents kit (TaKaRa). Real-time quantitative PCR (RT-qPCR) analysis was performed with different primer sequences using the SYBR Green Master Mix (TaKaRa). The primers used for RT-PCR were in Table 1.

Immunofluorescence Experiment
Cells were fixed with 4% formaldehyde for 1 h and permeabilized with 0.5% Triton X-100 at room temperature. They were then blocked with 1% Bovine Serum Albumin (BSA) for 2 h then incubated with primary antibody at 4°C, overnight. Next, they were incubated with secondary antibody 2 h and nuclei counterstained 2-(4-Amidinophenyl)-6-indolecarbamidine dihydrochloride (DAPI). They were then examined and imaged fluorescent confocal microscopy.

Colony Formation Experiment
The cells were seeded onto 12-well plates at 100 cells per well and cultured for 2 weeks. They were then fixed with 4% Paraformaldehyde (PFA) for 10 min and rinsed with Phosphate Buffered Saline (PBS) before staining with crystal violet. They were then examined by microscopy and imaged.

Wound Scratch Assay
HepG2 cells (1 × 10 6 per well) were seeded onto 12-well plates and cultured until confluent. A white gun was used to make vertical scratch along the ruler to ensure uniform scratches in each well. The image was taken at 0, 12, 24, and 48 h.

ATP Detection Assay
Control and siSLC17A9 groups cells were cultured for 72 h, and their culture media and cells were collected. Cellular ATP levels were then measured using an ATP bioluminescent assay kit (Beyotime, China).

High Connotation Cell Imaging Analysis
Control and siSLC17A9 groups were suspended at 1 × 10 4 /ml and seeded onto 96-well plates in six replicate wells. After 24 h of culture, the 96-well plates were moved into a high-connotation instrument. High-connotation cell images were then taken using a 10× objective. Multiple visual fields were selected per well for monitoring and cell growth recording.

Statistical Analysis
Gene expression analysis was done using Pearson correlation analysis. Other experimental results were analyzed using twotailed t-test. Data were expressed as mean ± SD. P < 0.05 indicated statistically significant differences.

SLC17A9 Is Highly Expressed in LIHC Tissues And Is Related to Gender, DSS, and PFI
We analyzed the expression level of SLC17A9 in TCGA pan-cancer dataset ( Figure 1A). The results showed that SLC17A9 was highly expressed in 12 cancer types, including breast cancer, colonic adenocarcinoma, and LIHC. Low SLC17A9 expression was found in 21 cancers, including cervical cancer and cholangiocarcinoma. Analysis of SLC17A9 expression in LIHC vs. normal tissues in paired and unmatched samples revealed that SLC17A9 expression was significantly higher in LIHC tissues than in normal tissues (p < 0.001; Figures 1B, C). ROC analysis showed that the area under the ROC curve was 0.812. The sensitivity and specificity of predicting SLC17A9 expression in liver cancer were 63.6% and 96%, respectively ( Figure 1D). Correlation analysis revealed that high SLC17A9 expression correlated with poor prognosis ( Figure 1E). Relative to patients with low SLC17A9 expression, those with high SLC17A9 expression had significantly shorter survival (p = 0.015). Correlation analysis of TCGA data revealed correlation between SLC17A9 expression and some clinical features. High SLC17A9 expression correlated positively with gender, disease-specific survival (DSS), and progression-free interval (PFI) (p = 0.015, 0.006, and 0.023, respectively; Table 2). To confirm this finding, we carried out IHC analysis and found that SLC17A9 is mainly localized in the cytosol, with modest presence in the nucleus. IHC results showed that the protein level of SLC17A9 in tumor tissues was significantly higher than that in adjacent normal tissues ( Figure 1F). These results indicate that SLC17A9 has a potential carcinogenic effect on the progression of LIHC.

Co-Expressed Genes of SLC17A9 in LIHC
Analysis of genes' co-expression with SLC17A9 in LIHC on LinkedOmics found that SLC17A9 expression positively correlated with 4,084 genes and negatively correlated with 3,702 genes ( Figure 2D). The bubble map GO function analysis showed that SLC17A9 coexpression was mainly associated with neuronal cell body, presynapse, and axonogenesis ( Figure 2E).

Effects of SLC17A9 Knockdown on the Function and ATP Levels of Hepatoma Cells
Expression of SLC17A9 in HepG2 cells was down regulated by treatment of the cells with siRNA. Effectiveness of SLC17A9 knockdown in liver cancer cell line was determined by Western blot and RT-qPCR analysis that showed stable siSLC17A9 ( Figures 4A, B). There was a significant difference in SLC17A9 expression level (P = 0.0083 and 0.002273) between siSLC17A9r e l a t e d H e p G 2 c e l l s a n d c o n t r o l H e p G 2 c e l l s . Immunofluorescence assays showed that SLC17A9 was localized in the cell cytoplasm, and the expression level in the siSLC17A9 group was significantly lower relative to the expression level of SLC17A9 in the control group ( Figure 4C). The colony-forming ability of cells after SLC17A9 knockdown showed a significant decrease relative the colony-forming ability of the control group (P = 0.0007; Figure 4D). Wound healing assay showed that the cell migration ability of the siSLC17A9 group was significantly reduced compared with the migration ability of the control group (P = 0.0069; Figure 4E). The results showed significantly lower HepG2 cell ATP content in the siSLC17A9 group relative to the ATP content in the control group (P < 0.0001; Figure 4F). Moreover, cells in the siSLC17A9 group showed a significant decrease in cell proliferation ability compared with the proliferate rate of cells in the control group (P < 0.05; Figure 4G).

SLC17A9 Expression Level Is Correlated With Infiltration of Tumor Immune Cells
The relationship between SLC17A9 and various immune cells in LIHC was explored using TIMER database. The findings showed that the expression level of SLC17A9 was correlated with infiltration of B cells (r = 0.094, P = 8.06E-02), CD4 + T cells (r = 0.184, P = 5.95E-04), macrophages (r = 0.137, P = 1.15E-02), neutrophils (r = 0.145, P = 7.12E-03), and CD8 + T cells (r = 0.027, p = 6.05) ( Figure 5A). In addition, analysis using date retrieved from GEPIA database showed that SLC17A9 expression level was correlated with levels of immune marker genes of Tfh, Th1, Th17, M1 macrophage, M2 macrophage, Tumor associated macrophage (TAM), and natural killer cell (NK) cells (Table 3). Furthermore, TCGA LIHC analysis showed that SLC17A9 expression level was positively correlated with the infiltration level of CD4 + T cells and macrophages ( Figure 5B). Patients were assigned to high and low expression groups to explore differences in immune infiltration levels under different SLC17A9 expression levels. The findings showed that infiltration of naive B cells (P = 0.015), naive CD4 T cells (P = 0.023), memory resting CD4 T cells (P = 0.033), T-cell regulatory (Tregs) cells (P = 0.036), and monocytes (P < 0.001) were significantly different between the high and low expression groups ( Figure 5C).

Expression of SLC17A9 Is Correlated With m6A Methylation Regulator in LIHC
m6A modification is a potential target for designing drugs for tumor treatment. Therefore, the correlation between expression level of SLC17A9 and levels of m6A-related genes was explored using TCGA LIHC datasets (Figures 6A, B). The results showed that SLC17A9 expression was significantly correlated with level of METTL3 (r = 0.181, P < 0.001), WTAP (r = 0.134, P =  Figure 6C). Figure 6D shows that m6A regulators expression change in tumor and normal tissues. On the basis of the above data, we screened two genes, METTL3 and YTHDF1, which not only increased expression in esophageal cancer, but also had the closest relationship with SLC17A9. Next, we used Western blots and RT-qPCR approaches to validate the functions of METTL3 and YTHDF1 genes at RNA and protein levels. The findings from Western blots showed significantly lower protein expression levels of METTL3 and YTHDF1 in the siSLC17A9 group compared with the expression levels in the control group (P < 0.0001, P = 0.03942; Figures 6E, F). RT-qPCR results showed significantly lower mRNA expression levels of METTL3 and YTHDF1 in the siSLC17A9 group compared with the expression levels in the control group (P = 0.038579, P = 0.002157; Figures 6G, H).

SLC17A9 Is Strongly Associated With Expression Levels of Iron Deficiency-Related Genes in LIHC
Further, the correlation between SLC17A9 and ferroptosis gene was explored using TCGA LIHC dataset ( Figures 7A, B). The results showed that SLC17A9 expression level was significantly correlated with expression levels of HSPA5 (r = 0.240, p < 0.  results using RT-qPCR analysis. The mRNA expression levels of ACSL4, CISD1, and ATP5MC3 in the siSLC17A9 group were significantly lower relative to the mRNA expression levels in the control group (P = 0.0481, P < 0.0001, P = 0.0048; Figures 7D-F).

DISCUSSION
SLC17A9 is a vesicle nucleotide transporter involved in ATP transport and is highly localized on lysosomes (25). Reduced SLC17A9 levels cause lysosomal ATP accumulation, lysosomal function impairment, and cell death. Mounting evidence has associated the lysosome with cancer. During tumorigenesis, the lysosome location and lysosomal membrane permeability change, releasing vast amounts of cathepsin into the cytoplasm, which, together with matrix metalloproteinases and blood plasminogen activation system, promotes tumorigenesis by degrading the extracellular matrix. Thus, SLC17A9 expression directly or indirectly affects tumorigenesis. Past studies have implicated SLC17A9 upregulation in gastric (26), liver (27), and colon cancer (28). However, its expression and function in liver cancer have not been determined. Using IHC, we show that SLC17A9 upregulated in LIHC and further knocked out the SLC17A9 gene for in vitro experiments. We find that SLC17A9 silencing HepG2 markedly suppressed ATP production as well as cell proliferation, migration, and colony formation. In LIHC, SLC17A9 expression significantly correlated with sex, DSS, and PFI, indicating that it may have prognostic value in LIHC. ROC curve analysis highlighted the diagnostic value of SLC17A9 expression in LIHC. Second, we find that patients with LIHC expressing high SLC17A9 levels had worse prognosis than those with low SLC17A9 levels, indicating that SLC17A9 has potential diagnostic and prognostic value in LIHC. KEGG pathway enrichment analysis showed that SLC17A9 expression was mainly associated with PI3K/Akt signaling pathway, neuroactive ligand-receptor interaction, HPV infection, MAPK signaling, passive transmembrane transporter activity, and channel activity. It is reported that LLGL2 drives carcinogenic PI3K/AKT signaling in LIHC to promote LIHC (29). In addition, TMOD3 thought to promote liver cancer progression via MAPK/ERK signaling (30). These metabolic pathways can directly or indirectly affect tumorigenesis and highlight potential multi-target and multi-pathway synergistic treatment for LIHC. We speculate that SLC17A9 may interact with these pathways to promote liver cancer progression.
GO term enrichment analysis showed that SLC17A9 expression is associated with many aspects of BP, CC, and MF and are mainly associated with neuronal cell body, presynapse, and axonogenesis, indicating that SLC17A9 may be involved in protein binding, plasma membrane, membrane composition, nucleus, and other biological functions. GGI and PPI analysis of co-expressed genes revealed the strongest correlation to be between MYO5A, SLC18A3, and SLC17A9. MYO5A is a key component of the myosin V family, which participates in transport vesicle formation, protein transcription, and tumor progression (31). Studies have implicated MYO5A in laryngeal squamous cell carcinoma and esophageal squamous cell carcinoma (32,33). SLC18A3 belongs to the family of vesicular acetylcholine transporters. Various acetylcholine signaling pathways can regulate a range of cellular functions, including proliferation, differentiation, and cytoskeleton integrity (34). L cells from small cell lung cancer are reported to secrete acetylcholine and promote tumor proliferation (35). These results suggest that the expression of MYO5A and SLC18A3 affects tumorigenesis, which provide a theoretical basis in treatment of liver cancer.
Analysis of the relationship between SLC17A9 and immune cell infiltration in LIHC revealed that SLC17A9 expression correlates with B cells, CD4 + T cells, macrophages, neutrophils, and immune marker genes for Tfh, Th1, Th17, M1 macrophage, M2 macrophage, TAM, and NK cells. It was also positively associated with the degree of infiltration by infiltration CD4 + T cells and macrophages. CiberSort analysis showed that naive B cells, naive CD4 + T cells, memory resting CD4 + T cells, regulatory T cells (Tregs), and monocytes differed significantly with SLC17A9 expression. Together, these results confirmed that SLC17A9 was associated with tumor infiltration by immune cells in LIHC, especially B cells, CD4 + T cells, and macrophages. Macrophages have been associated with immunosuppression and angiogenesis, which provides sufficient nutrition for tumor proliferation (36). SLC17A9 expression and vesicular ATP exocytosis by macrophages can activate the P2 receptors and participate in macrophage activation expression (37). We speculate that high SLC17A9 expression in LIHC may activate macrophages, which influence LIHC proliferation.   Here, analysis on various databases found that SLC17A9 expression positively correlated with METTL3, YTHDF1, and WTAP expression. Western blot analysis and RT-qPCR analysis revealed that siSLC17A9 silencing reduced the expression of METTL3 and YTHDF1 significantly relative the control group. RNA-seq analysis revealed that HNNPC was specifically upregulated in LIHC. It is reported that METTL3 overexpression promotes LIHC growth in vivo and in vitro (38) and that this correlates with poor prognosis in LIHC. On the basis of these findings, we point out that the SLC17A9 modification by m6A enhances its mRNA stability, thereby promoting LIHC growth.
Iron cell death is reported to play an important role in tumorigenesis. Here, database analysis showed that SLC17A9 expression correlated with the expression of various genes, including lACSL4, CISD1, and ATP5MC3. These observations were verified by RT-qPCR analysis, which showed that ACSL4, CISD1, and ATP5MC3 expression was significantly lower in siSLC17A9 cells relative to the controls. ACSL4 is an enzyme that regulates lipid composition. It can also promote ferroptosis. It is reported that ACSL4 promotes LIHC via the ERK/FBW7/c-Myc axis (39). Numerous studies have reported CISD1 expression in LIHC. ATP5MC3 has been associated with colon cancer (40). We speculate whether SLC17A9 interaction with other genes like ACSL4, CISD1, and ATP5MC3 affects iron-dependent cell death in LIHC.
The core of our article is to discuss the expression of SLC17A9 in hepatocellular carcinoma and further transfect siRNA to verify it by in vivo experiments. Some information related to immune infiltration, m6A, and iron death were added, and several m6A-related factors with strong correlation were screened to verify the protein expression level and RNA expression level. Our results do show that the expression levels of METTL3 and YTHDF1 in the siSLC17A9 group are lower than those in the control group, but our results are only superficial and only validate individual factors, and no batch in-depth research has been done, which is one of our defects and deficiencies.
To our knowledge, this is the fi r s t s t u d y t o comprehensively analyze SLC17A9 expression in LIHC with regards to cellular function. SLC17A9 upregulation in LIHC correlates with poor prognosis and has a significant correlation with biology, highlighting its potential therapeutic and diagnostic potential.

DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary materials. Further inquiries can be directed to the corresponding author.

ETHICS STATEMENT
The studies involving human participants were reviewed and approved by Ethics Committee of Taihe Hospital Affiliated of Hubei University of Medicine. Written informed consent for