SLC17A9 expression levels in a pan-cancer panel and validation of the role of SLC17A9 as a novel prognostic biomarker for osteosarcoma
JUNQING LI1, FEIRAN WU1, LI SU1, HUIMIN ZHU1, JIE YAO1 and MENG ZHANG2
Minimally Invasive Spinal Surgery Center, Luoyang Orthopedic-Traumatological Hospital of Henan Province (Henan Provincial Orthopedic Hospital), Zhengzhou, Henan 450018;
2Department of Orthopedics, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, Henan 450003, P.R. China
Received November 25, 2022; Accepted April 19, 2023
DOI: 10.3892/ol.2023.13969
Abstract. Previous studies have demonstrated the involvement of the solute carrier family 17 member 9 (SLC17A9) in certain types of cancer; however, the precise role of SLC17A9 is not well defined. In the present study, a comprehensive analysis was performed to determine the involvement of SLC17A9 in a pan-cancer panel. First, data on SLC17A9 expression levels from publicly available databases were obtained to determine SLC17A9 expression profiles in various types of cancer. Next, the involvement of SLC17A9 in the prognosis of patients, stemness indices and the immune microenvironment was examined in 34 types of cancer. Furthermore, CCK-8 and colony-formation assays were performed to determine the effect of SLC17A9 on osteosarcoma (OSS) cells. In a pan-cancer panel, a difference in SLC17A9 expression levels was observed in the tumor tissues as compared with healthy tissues. Furthermore, survival analysis revealed a significant association between SLC17A9 expression levels and the prognosis of patients with various cancer types, including adrenocortical carcinoma, kidney renal clear cell carcinoma, glioblastoma, kidney renal papillary cell carcinoma, low grade glioma, liver hepatocellular carcinoma, mesothelioma, lung adenocarcinoma, skin cutaneous melanoma, uveal melanoma,
Correspondence to: Professor Jie Yao, Minimally Invasive Spinal Surgery Center, Luoyang Orthopedic-Traumatological Hospital of Henan Province (Henan Provincial Orthopedic Hospital), 100 Yongping Road, Zhengzhou, Henan 450018, P.R. China E-mail: yaojie110120@163.com
Professor Meng Zhang, Department of Orthopedics, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, 7 Weiwu Road, Zhengzhou, Henan 450003, P.R. China E-mail: zhangmeng.lh@163.com
Key words: solute carrier family 17 member 9, pan-cancer, prognosis, stemness, tumor immunity, osteosarcoma
stomach adenocarcinoma and OSS. The results of the present study revealed correlations between stemness indices, tumor immunity and SLC17A9 expression levels. Furthermore, univariate and multivariate Cox regression analyses indicated that SLC17A9 may be utilized as an independent risk factor for overall survival of patients with OSS. In vitro experiments demonstrated that SLC17A9 promotes the proliferation and viability of OSS cells. Taken together, the results of the present study suggest an association between SLC17A9 and the prog- nosis of patients as well as tumor immunity in various cancer types. SLC17A9 may serve as a novel prognostic biomarker and target for improving the prognosis of patients with OSS.
Introduction
Solute carrier family 17 member 9 (SLC17A9) is localized in lysosomes and its gene encodes for a vesicular nucleotide transporter protein, a member of the transmembrane protein family (1). It is involved in small molecule transportation in cells, specifically the active transport of ATP to lysosomes. Therefore, SLC17A9 dysfunction reduces ATP accumulation in the lysosomes, leading to cell death (1). Furthermore, studies have demonstrated that SLC17A9 is critically involved in cell viability and the physiology of lysosomes (1,2).
In colorectal cancer, a correlation was observed between enhanced SLC17A9 expression levels and a number of clinical as well as pathological characteristics of patients. In addition, the overall survival (OS) and disease-free survival of patients with colorectal cancer expressing high SLC17A9 levels in tumor tissues were poor (3). Studies have demon- strated that the survival of patients with gastric cancer expressing high SLC17A9 levels was poor (4,5). SLC17A9 affects liver hepatocellular carcinoma (LIHC) progres- sion. It is involved in the infiltration of immune cells into tumors and ferroptosis. Furthermore, a decrease in SLC17A9 expression levels inhibited the proliferation, migration and colony formation of HepG2 cells (6). By contrast, low SLC17A9 expression promotes prostate cancer cell prolifera- tion, migration and invasion, and inhibits cell apoptosis (7). Taken together, this indicates the involvement of SLC17A9
in cancer; however, its influence on the prognosis of patients has remained elusive.
Therefore, in the present study, the SLC17A9 expression levels were examined in 34 types of cancer. Next, the associa- tion between SLC17A9 expression levels and the prognosis of patients, stemness indices, immunity and drug sensitivity in these types of cancer were determined. Finally, Cell Counting Kit-8 (CCK-8) and colony-formation assays were performed to determine the effect of SLC17A9 expression levels on osteosarcoma (OSS) cells. The results of the present study indicated that SLC17A9 may serve as a prognostic marker and a therapeutic target for OSS.
Materials and methods
SLC17A9 expression levels in a pan-cancer panel. Standardized pan-cancer data were retrieved from databases including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression using the University of California Santa Cruz (UCSC) genome browser database (https://xenabrowser.net/datapages/) (8,9). The abbre- viations used for 34 types of cancer are presented in Table SI. The data on the SLC17A9 expression pattern in all samples were extracted and plotted using the R software. However, the data on SLC17A9 expression levels in corresponding healthy tissues for certain types of cancer from TCGA were missing. Thus, data on the SLC17A9 expression levels in these types of cancer and corresponding healthy tissues, including adreno- cortical carcinoma (ACC), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), acute myeloid leukemia (LAML), low grade glioma (LGG), ovarian serous cystadenocarcinoma, skin cutaneous melanoma (SKCM), testicular germ cell tumors (TGCT), thymoma (THYM), and uterine carcinosarcoma (UCS), were obtained from the Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/) database (10).
Analysis of the association between SLC17A9 expression levels and survival of patients with cancer. To determine the association between SLC17A9 expression and survival outcomes of various types of cancer, the OS, progression-free survival (PFS) and disease-specific survival (DSS) data of patients were obtained from the TCGA and TARGET cohorts. TCGA data including expression data and clinical data were obtained from the University of California, Santa Cruz (UCSC) database, which is an open, pubic database (https://xenabrowser.net/datapages/). In the UCSC database, not all tumor types contained the three survival times (OS, PFS, and DSS), some cancer types only contained OS or PFS. Survival analysis was performed using the Kaplan-Meier (KM) method with the median of SLC17A9 expression as the cut-off value and the survival curves were plotted using the survival package in R. In addition, only when P was less than 0.05 can the survival curves be graphed. Next, a univariate Cox regression analysis was performed using the forestplot R package to determine the association between SLC17A9 expression levels and survival. In addition, the association between SLC17A9 expression levels and the survival of patients in the immunotherapy cohort was determined using KM
plotter (https://kmplot.com/analysis/) (11). The detailed steps were as follows: i) Enter the KM plotter website and select the ‘start KM plotter for immunotherapy’ button; ii) enter the gene symbol ‘SLC17A9’ and select the cut-off value ‘median’; and iii) select survival ‘OS or PFS’, select follow-up threshold ‘all’, select Anti-PD-L1 treatment ‘all’, select tumor type ‘all, bladder … ‘.
Analysis of the stemness index. Stemness refers to the self-renewal and dedifferentiation properties of cells, which aid in the progression and invasion of cancer cells, thus resulting in poor prognosis of the patient (12). The two inde- pendent stemness indices are the mRNA expression-based stemness index (RNAss), which demonstrates gene expression, and the DNA methylation-based stemness index (DNAss), which reflects epigenetic features. The stemness scores of the patients were obtained from the TCGA cohort based on the UCSC database. The correlation between SLC17A9 expression levels and the stemness indices was determined by Spearman correlation analysis.
Analysis of tumor immunity. The tumor microenvironment (TME) serves a crucial role in cancer progression. The propor- tion of stromal and immune cells in the TME of patients was determined from the TCGA cohort by calculating the estima- tion of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) score (13) determined with the ESTIMATE algorithm (https://bioinformatics. mdanderson.org/public-software/estimate/). In addition, the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) tool (https://cibersortx. stanford.edu/) was used to determine the infiltration status of tumor-infiltrating immune cells (TIICs) in 34 types of cancer (14). Subsequently, the effect of SLC17A9 expression on the TME and TIICs was determined.
Drug activity analysis. To predict potential drugs targeting SLC17A9, the correlation between SLC17A9 expression levels and drug sensitivity was determined by Pearson’s correlation coefficient. Furthermore, the CellMiner database (https://discover.nci.nih.gov/cellminer/) was examined to obtain data on drug activity (15).
Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and gene set varia- tion analyses (GSVA). The clusterProfiler R package (16) was employed to perform the GO and KEGG pathway enrichment analyses to determine the functions and pathways enriched by SLC17A9. Next, the GSVA R package (17) was used to estimate the variations in key gene sets in patients with OSS.
Cell culture and transfection. OSS cells were obtained from The Cell Bank of Type Culture Collection of The Chinese Academy of Sciences. U2OS, Saos2, MG63 and HOS cells were cultured in Dulbecco’s Modified Eagle’s Medium supple- mented with 10% fetal bovine serum (both from Biological Industries), and 143 B cells were cultured in Modified Eagle’s Medium (Biological Industries) supplemented with 10% fetal bovine serum. All cells were incubated at 37℃ in a humidified atmosphere containing 5% CO2. GV492 was
used as the vector for overexpression and empty plasmid, and SLC17A9 overexpression (OE) and empty (NC) vectors were constructed and provided by Shanghai GeneChem Co., Ltd. Small interfering RNAs (siRNAs) targeting SLC17A9 (si-1, stB0012921A and si-2, stB0012921B) and negative control siRNA (si-nc, siN0000001-1-5) were designed and synthesized by Guangzhou RiboBio Co., Ltd. The si-1 and si-2 target sequences are presented in Table SII. These vectors or siRNAs were separately transfected into cells with the aid of Lipofectamine 3000 (Invitrogen; Thermo Fisher Scientific, Inc.) or riboFECT CP Transfection Kit (cat. no. C10511-05; Guangzhou RiboBio Co., Ltd). HOS and MG63 (1x106/well) cells were seeded into a 6-well plate. After 24 h, vectors and Lipofectamine 3000 (or siRNAs and riboFECT CP reagent) were mixed and left at room temperature for 15 min, then the mixture was added to each well of the 6-well plate. MG63 cells were transfected with 5 µg vector in each well. HOS cells were transfected with 100 nM siRNA per well. The transfected cells were incubated for 48 h at 37℃ in a humidified atmosphere containing 5% CO2, and then used in subsequent experiments.
Reverse transcription-quantitative PCR (RT-qPCR) and western blotting (WB). RT-qPCR and WB were performed as described previously (18). RT-qPCR reagents such as AG RNAex Pro Reagent, SYBR Green Premix Pro Taq HS qPCR kit and the Evo M-MLV RT Premix kit were purchased from Accurate Biology. The sequences of the primers used are presented in Table SII. The reagents used to perform WB, including RIPA buffer and BeyoECL Plus kit, were purchased from Beyotime Institute of Biotechnology. Furthermore, anti-GAPDH (1:1,000; cat. no. 10494-1-AP) and anti-SLC17A9 (1:500; cat. no. 26731-1-AP) antibodies were purchased from Wuhan Sanying Biotechnology, and the HRP-conjugated Affinipure Goat Anti-Rabbit IgG antibody (1:1,000; cat. no. SA00001-2) was purchased from Proteintech Group, Inc (Wuhan Sanying Biotechnology).
Cell-proliferation and colony-formation assays. The prolif- eration of cells was determined using the CCK-8 assay (Beyotime Institute of Biotechnology) following the manufac- turer’s protocols. For this assay, 2x103 cells/well were seeded and cultured in a 96-well plate at 37℃ for 0, 24, 48 and 72 h. For the colony-formation assay, 1,000 cells/well were seeded and incubated in a 6-well plate and incubated for 14 days, with the media being changed every 2 days. Next, the colonies were stained at room temperature with 1% crystal violet for 20 min. The colonies that had formed (colonies with ≥50 cells) were manually counted under a microscope.
Statistical analysis. Bioinformatics and statistical analyses, such as the normalization and transformation of RNA sequencing data, and the correlation, survival, CIBERSORT, ESTIMATE, GSVA, GO and KEGG analyses, were performed using the R software (version 4.0.0; https://www.r-project.org/). Furthermore, the coxph algorithm of the survival R package was used to perform univariate and multivariate Cox regres- sion analyses. Pearson correlation analysis was performed to determine the correlation between SLC17A9 expression levels and the activity of the drugs from the CellMiner database. All quantitative data from in vitro experiments are expressed as
the mean ± standard deviation of three independent experi- ments. One-way analysis of variance followed by Tukey’s post hoc test was performed using the GraphPad Prism 8.0 Software (Dotmatics) to determine differences across groups. A two-tailed P<0.05 was considered to indicate a statistically significant difference.
Results
SLC17A9 expression levels in pan-cancer panel. The differ- ences in SLC17A9 expression profiles between pan-cancer tumor and normal samples were analyzed after obtaining data of the SLC17A9 expression profiles for pan-cancer using publicly available data. A significant increase in SLC17A9 expression levels was observed in 15 types of cancer (Fig. 1A and B), including LUAD, COAD, BRCA, KIRP, DLBC, head and neck squamous cell carcinoma (HNSC), LAML, KIRC, LIHC, BLCA, READ, STAD, LUSC, THCA and UCEC. However, a significant decrease in SLC17A9 expression levels was observed in the tissues of patients with ACC (Fig. 1B).
Significance of SLC17A9 expression levels in predicting the prognosis of patients. Univariate Cox regression and survival analyses were performed to determine the association between SLC17A9 expression levels and the prognosis of patients in 34 types of cancer. The univariate Cox regression analysis demonstrated that SLC17A9 expression was an independent prognostic factor associated with the OS of patients with various types of cancer, such as OSS, ACC, LGG, KIRC, MESO, KIRP, SKCM and UVM (Fig. 2A). Furthermore, the KM analysis demonstrated that patients with high SLC17A9 expression and types of cancer such as KIRC, LGG, OSS or UVM had a reduced OS duration compared with patients with low SLC17A9 expression. On the contrary, the OS duration of patients with high SLC17A9 expression levels and BRCA was increased compared with that of patients with low SLC17A9 expression levels (Fig. S1).
The SLC17A9 expression levels were an independent risk factor for DSS in patients with types of cancer such as KIRC, ACC, KIRP, LGG, MESO, LIHC, SKCM, LUAD, STAD and UVM (Fig. 2B). KM analysis demonstrated that the DSS duration of patients with high SLC17A9 expression levels and KIRC, LGG or UVM was reduced compared with that of patients with low SLC17A9 expression levels. However, the DSS duration of patients with high SLC17A9 expression levels and LUAD was increased compared with that of patients with low SLC17A9 expression levels (Fig. S1). Forest plots demon- strated a significant association between SLC17A9 expression levels and the PFS of patients with glioblastoma (GBM), LGG, KIRC, LUAD, PRAD, THYM, STAD and UVM (Fig. 2C). In addition, KM analysis demonstrated that the PFS duration of patients with high SLC17A9 expression levels and KIRC, LGG, PRAD or UVM was reduced compared with that of patients with low SLC17A9 expression levels (Fig. S1). The DSS and PFS of patients with high SLC17A9 expression levels and LUAD were increased compared with those of patients with low SLC17A9 expression levels.
The association between the SLC17A9 expression levels and the prognosis of patients from the immunotherapy cohort
A
Type
Normal
Tumor
9
SLC17A9 expression
A
2
O
BLCA
BRCA
CESC
CHOL
COAD
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LIHC
LUAD
LUSC
PAAD
PCPG
PRAD
READ
STAD
THCA
UCEC
B
6
5
5
Log2 (TPM+1)
-
8
5
3
4
4
4
6
3
3
2
3
4
2
2
2
2
1
1
0
0
0
0
ACC T (n=77); N (n=128)
DLBC T (n=47); N (n=337)
LAML
T (n=173); N (n=70)
LGG T (n=518); N (n=207)
OV T (n=426); N (n=88)
7
5
5
6
6
+
+
5
Log2 (TPM+1)
10
+
4
3
3
3
3
~
2
2
~
-
1
-
-
0
0
SKCM T (n=461); N (n=558)
TGCT T (n=137); N (n=165)
THYM T (n=118); N (n=339)
UCS T (n=57); N (n=78)
was also investigated using the KM plotter web-based tool. The results revealed that in several types of cancer, including bladder cancer, esophaseal adenocarcinoma, GBM, hepato- cellular carcinoma, melanoma, HNSC, non-small cell lung cancer, non-squamous lung carcinoma and urothelial cancer, the OS of patients with high SLC17A9 expression levels in the antibody directed against programmed cell death-1 ligand 1 (anti-PD-L1) therapy cohort was increased (Fig. S2A).
Furthermore, a significant association was observed between the SLC17A9 expression levels and the OS of patients with bladder carcinoma (Fig. S2B) in the anti-PD-L1 therapy cohort. Thus, SLC17A9 may be a potential prognostic marker for various types of cancer.
Analysis of stemness indices and tumor immunity. The correla- tion between the stemness indices and the SLC17A9 expression
| A | Overall survival | B | Disease-specific survival | C | Progression-free survival | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| P-value | Hazard ratio | P-value | Hazard ratio | P-value | Hazard ratio | |||||||
| ACC | 0.011 | 2.032(1.179-3.502) | ACC | 0.023 | 1.939(1.093-3.437) | ACC | 0.309 | 1.304(0.782-2.174) | ||||
| BLCA | 0.838 | 1.017(0.864-1.197) | BLCA | 0.611 | 0.947(0.769-1.167) | BLCA | 0.867 | 1.014(0.859-1.197) | ||||
| BRCA | 0.276 | 0.898(0.740-1.090) | BRCA | 0.527 | 1.077(0.856-1.354) | BRCA | 0.571 | 1.052(0.882-1.255) | ||||
| CESC | 0.791 | 1.034(0.806-1.326) | CESC | 0.905 | 1.017(0.766-1.352) | CESC | 0.116 | 1.195(0.957-1.493) | ||||
| CHOL | 0.418 | 0.829(0.526-1.306) | CHOL | 0.348 | 0.793(0.488-1.287) | CHOL | 0.493 | 1.156(0.763-1.753) | ||||
| COAD | 0.358 | 1.080(0.917-1.272) | COAD | 0.487 | 1.078(0.873-1.331) | COAD | 0.685 | 0.969(0.834-1.127) | ||||
| DLBC | 0.941 | 1.033(0.438-2.436) | DLBC | 0.806 | 0.866(0.276-2.717) | DLBC | 0.288 | 1.508(0.707-3.216) | ||||
| ESCA | 0.740 | 1.028(0.873-1.211) | ESCA | 0.794 | 1.026(0.846-1.244) | ESCA | 0.513 | 0.953(0.824-1.102) | ||||
| GBM | 0.002 | 1.899(1.270-2.842) | GBM | 0.002 | 1.942(1.273-2.962) | GBM | <0.001 | 2.178(1.396-3.400) | ||||
| HNSC | 0.232 | 0.883(0.720-1.083) | HNSC | 0.089 | 0.786(0.595-1.037) | HNSC | 0.190 | 0.865(0.696-1.075) | ||||
| KICH | 0.961 | 1.039(0.222-4.867) | KICH | 0.589 | 1.515(0.336-6.832) | KICH | 0.755 | 1.216(0.356-4.160) | ||||
| KIRC | <0.001 | 1.645(1.429-1.894) | KIRC | <0.001 | 1.959(1.665-2.303) | KIRC | <0.001 | 1.741(1.497-2.026) | ||||
| KIRP | 0.004 | 1.695(1.189-2.416) | KIRP | 0.020 | 1.678(1.087-2.591) | KIRP | 0.166 | 1.290(0.900-1.851) | ||||
| LAML | 0.149 | 0.854(0.689-1.058) | LGG | <0.001 | 3.333(2.194-5.062) | LGG | <0.001 | 3.079(2.108-4.498) | ||||
| LGG | <0.001 | 3.248(2.159-4.887) | LIHC | 0.008 | 1.348(1.082-1.678) | LIHC | 0.067 | 1.137(0.991-1.305) | ||||
| LIHC | 0.237 | 1.100(0.939-1.289) | LUAD | 0.018 | 0.791(0.652-0.960) | LUAD | 0.006 | 0.825(0.720-0.945) | ||||
| LUAD | 0.099 | 0.887(0.769-1.023) | LUSC | 0.519 | 0.916(0.703-1.195) | LUSC | 0.979 | 1.003(0.821-1.225) | ||||
| LUSC | 0.507 | 1.057(0.897-1.245) | MESO | 0.032 | 1.736(1.048-2.874) | MESO | 0.206 | 1.301(0.866-1.955) | ||||
| MESO | 0.025 | 1.552(1.056-2.282) | ||||||||||
| OV | 0.955 | 0.995(0.824-1.200) | OV | 0.952 | 1.005(0.854-1.182) | |||||||
| OSS | 0.004 | 1.598(1.165-2.192) | PAAD | 0.908 | 1.012(0.827-1.238) | PAAD | 0.890 | 1.012(0.857-1.195) | ||||
| OV | 0.902 | 0.989(0.830-1.178) | ||||||||||
| PAAD | 0.392 | 1.081(0.905-1.291) | PCPG | 0.313 | 0.368(0.053-2.569) | PCPG | 0.168 | 0.625(0.320-1.220) | ||||
| PCPG | 0.394 | PRAD | 0.502 | 2.556(0.165-39.510) | PRAD | <0.001 | 3.154(1.710-5.817) | |||||
| PRAD | 0.702 | 0.564(0.151-2.108) 1.529(0.173-13.515) | READ | 0.068 | 1.697(0.961-2.996) | READ | 0.207 | 1.241(0.887-1.735) | ||||
| READ | 0.521 | 1.145(0.758-1.729) | SARC | 0.791 | 1.033(0.812-1.316) | SARC | 0.965 | 1.004(0.835-1.208) | ||||
| SARC | 0.692 | 1.045(0.840-1.301) | SKCM | 0.048 | 0.880(0.775-0.999) | SKCM | 0.468 | 0.966(0.879-1.061) | ||||
| SKCM | 0.042 | 0.883(0.784-0.995) | STAD | 0.015 | 1.259(1.046-1.517) | STAD | 0.016 | 1.212(1.036-1.418) | ||||
| STAD | 0.113 | 1.125(0.973-1.302) | TGCT | 0.087 | 3.308(0.839-13.040) | TGCT | 0.548 | 0.869(0.550-1.373) | ||||
| TGCT | 0.123 | 2.765(0.761-10.049) | THCA | 0.584 | 1.367(0.446-4.191) | THCA | 0.868 | 0.963(0.618-1.500) | ||||
| THCA | 0.659 | 1.196(0.540-2.648) | THYM | 0.150 | 3.131(0.662-14.806) | THYM | 0.018 | 2.687(1.185-6.095) | ||||
| THYM | 0.442 | 1.713(0.434-6.759) | UCEC | 0.097 | 1.223(0.964-1.553) | UCEC | 0.305 | 1.097(0.919-1.309) | ||||
| UCEC | 0.262 | 1.124(0.916-1.379) | UCS | 0.341 | 1.153(0.860-1.544) | UCS | 0.509 | 1.103(0.824-1.476) | ||||
| UCS | 0.457 | 1.117(0.835-1,494) | UVM | <0.001 | 3.236(1.727-6.062) | UVM | <0.001 | 3.596(2.053-6.299) | ||||
| UVM | <0.001 | 3.191(1.733-5.874) | 1.0 4.0 2.0 0.50 | |||||||||
| 0.50 0.25 | 1.0 4.0 2.0 8.0 | 0.062 | 8.00 4.00 1.00 2.00 16.00 0.250 | Hazard ratio | ||||||||
| Hazard ratio | Hazard ratio | |||||||||||
Figure 2. Forest plots demonstrating the prognostic value of solute carrier family 17 member 9 expression levels in patients in 34 types of cancer, including (A) overall survival, (B) disease-specific survival and (C) progression-free survival.
levels exhibited differences among various cancer types. A negative correlation was revealed between SLC17A9 expression levels and the RNAss stemness index in various types of cancer, such as ACC, CESC, GBM, BLCA, HNSC, KICH, LUAD, KIRP, KIRC, LUSC, PCPG, LGG, PRAD, TGCT, SKCM and THCA. However, a positive correlation was observed between SLC17A9 expression levels and the RNAss stemness index in other types of cancer, such as PAAD and STAD (Fig. 3A). In addition, the SLC17A9 expression level was negatively corre- lated with the DNAss stemness index in BLCA, CESC, LAML and TGCT, and positively correlated in other types of cancer, such as HNSC, BRCA, KIRC, LGG, MESO, KIRP, PAAD, THCA, PRAD, SARC, PCPG, STAD and UVM (Fig. 3A).
The ESTIMATE algorithm was used to calculate the stromal and immune scores of patients. A positive correlation was observed between the SLC17A9 expression levels and the immune score of patients with BRCA, KIRC, GBM, ACC, HNSC, ESCA, UCEC, KIRP, LGG, KICH, SKCM, LUAD, BLCA, MESO, OSS, PCPG, SARC, LUSC, TGCT, PRAD, THYM, THCA and UVM. However, a negative correlation was observed between the SLC17A9 expression level and the immune score of patients with COAD, LAML, PAAD and STAD (Fig. 3B). Furthermore, the results revealed a positive correlation between the SLC17A9 expression level and the stromal scores of patients with ACC, BLCA, ESCA, ESCA, KICH, GBM, HNSC, KIRP, LGG, LUSC, MESO, PRAD, SKCM, TGCT, THYM, SARC, THCA and UCEC. However, a negative correlation was observed between the SLC17A9 expression level and the stromal scores of patients with LAML, OSS, PAAD and STAD (Fig. 3B). Taken together, these results suggest a significant correlation between the SLC17A9 expres- sion level and stemness indices such as RNAss and DNAss, as well as the immune and stromal scores of patients with BLCA, HNSC, LGG, PAAD, TGCT, PRAD, KIRP, STAD and THCA.
Next, whether there was a correlation between SLC17A9 expression and the levels of 22 types of TIIC was determined. The results demonstrated a positive correlation between SLC17A9 expression and nine TIICs, including plasma cells, naïve and memory B cells, memory-activated CD4 T cells, resting natural killer (NK) cells, regulatory T (Treg) cells, M0 macrophages, eosinophils and neutrophils. On the contrary, a negative correlation was observed between SLC17A9 expres- sion levels and the levels of other types of immune cell, such as naïve CD4 T cells, activated NK cells, T follicular helper (Tfh) cells, monocytes, M1 and M2 macrophages, resting and activated dendritic cells, and activated mast cells (Fig. 3C). Furthermore, differences in TIIC levels were observed among patients with different types of cancer. In patients with ACC or DLBC, there was a strong positive correlation between the SLC17A9 expression levels and immune cells such as neutrophils and resting NK cells, respectively. Furthermore, SLC17A9 expression levels were revealed to be positively correlated with resting memory CD4 T cells in ESCA and to be negatively correlated with monocytes in LAML. In patients with PRAD, THYM or UVM, there was a positive correlation between the SLC17A9 expression levels and immune cells such as Treg cells, plasma cells and M1 macrophages, respectively (Fig. 3C). However, no significant correlation was observed between SLC17A9 expression levels and the infiltration of 22 immune cell types in patients with BLCA or UCS (Fig. 3C). These results indicate a correlation between the SLC17A9 expression level and the degree of immune-cell infiltration in different types of cancer.
SLC17A9 enhances OSS cell proliferation and viability. In the dataset GSE16088, an increase in SLC17A9 expression levels was observed in the tissues of patients with OSS compared with those in healthy bone tissues (Fig. 4A). Furthermore, in
A
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVMMM
RNASS ☒
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DNAss ☒ ☐
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0.8
0.6
0.4
0.2
B
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OSS
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
0
UCS
UVM
-0.2
-0.4
-0.6
Immune score
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☒
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☒
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-0.8
-1
Stromal score
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C
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OSS
OV
PAAD PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
All
UCS
UVM
1
2
#
₹
:
-
:
.
.
2
A
-
B cells naive
-
-
B cells memory
-
.
Plasma cells
-
-
T cells CD8
.
T cells CD4 naive
-
T cells CD4 memory resting
-1
-
-
T cells CD4 memory activated
.
T cells follicular helper
.
-
T cells regulatory (Tregs)
T cells gamma delta
-
NK cells resting
-
NK cells activated
-
Monocytes
-
Macrophages MO
.
Macrophages M1
-
Macrophages M2
-
Dendritic cells resting
-
Dendritic cells activated
-
Mast cells resting
Mast cells activated
Eosinophils
-
-
-
Neutrophils
the TCGA-TARGET dataset, the OS (Figs. 4B and S1) and relapse-free survival (Fig. 4B) of patients with high SLC17A9 expression levels were poor compared with those of patients with low SLC17A9 expression levels. Univariate and multivar- iate Cox regression analyses of the TCGA-TARGET dataset demonstrated that SLC17A9 may be an independent risk factor for the OS of patients with OSS (Fig. 4C).
Next, RT-qPCR was performed to determine SLC17A9 expression levels in several OSS cell lines (Fig. 4D). HOS and MG63 cells were used for the subsequent experiments since SLC17A9 levels were high in HOS and low in MG63 cells. HOS cells were transfected with siRNAs (si-nc, si-1 and si-2) and MG63 cells were transfected with SLC17A9-NC or SLC17A9-OE vectors. Finally, RT-qPCR and WB analyses were performed to confirm the knockdown and overexpression of SLC17A9 in HOS and MG63 cells, respectively (Fig. 4E). The results of the CCK-8 assay revealed a decrease in the proliferation capacity of SLC17A9-knockdown HOS cells (si-1 or si-2) compared with cells transfected with si-NC. In addition, there was an increase in the proliferation of MG63 cells transfected with the SLC17A9-OE vector compared
with the cells transfected with SLC17A9-NC (Fig. 4F). The colony-formation assays indicated a considerable reduction in the colony number of HOS cells with SLC17A9 knockdown, while there was a substantial increase in the colony numbers of MG63 cells overexpressing SLC17A9 (Fig. 4G). These results indicate that SLC17A9 has a role to enhance the proliferation and viability of OSS cells. Thus, SLC17A9 may be significantly involved in enhancing OSS progression.
GSVA and GO and KEGG pathway enrichment analyses. Next, GO and KEGG pathway enrichment analyses were performed to determine the functions and pathways enriched by SLC17A9 in OSS. Fig. 5A reveals that SLC17A9 was enriched in biological processes such as ossification, connec- tive tissue and cartilage development, the bone morphogenetic protein signaling pathway and odontogenesis. In addition, SLC17A9 was enriched in cellular component terms such as the collagen-containing extracellular matrix. The molecular function terms enriched by SLC17A9 were extracellular matrix structural constituents, receptor serine/threonine kinase and TGF-ß receptor binding, growth factors activity,
A
GSE16088
B
1.00
TCGA-TARGET
1.00
TCGA-TARGET
Relative SLC17A9 mRNA expression
10
p=0.01
Overall survival
Relapse free survival
0.75
0.75
9
0.50
0.50
8
0.25
p=0.038
0.25
7
p=0.004
0.00
0.00
6
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
0
1
2
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4
5
6
7
8
9
10
Time (years)
Time (years)
5
High
43
39
24
17
9
7
5
4
4
4
2
1
1
1
1
1
High
26
18
10
10
10
5
2
2
2
1
1
Normal (N=3)
SLC17A9
SLC17A9
Tumor
Low
43
39
31
28
25
19
11
8
7
3
1
0
0
0
0
0
0
Low
27
26
16
14
13
11
7
5
4
1
0
(N=14)
0
1
2
3
4
5
6
7
8
11
2 13 1
4
15
16
0
1
2
3
4
5
6
7
8
9
Time (years)
10
Time (years)
C
Univariate COX regression analysis for OS in TCGA-TARGET
Multivariate COX regression analysis for OS in TCGA-TARGET
0.12 0.25 0.50
1.0
2.0
4.0
8.0
0.25
0.50
1.0
2.0
4.0
8.0
D
E
Hazard ratio
Hazard ratio
Relative SLC17A9 mRNA expression
0.6
Relative SLC17A9 mRNA expression
HOS
Relative SLC17A9 mRNA expression
MG63
10
1.0
8
HOS
MG63
0.4
si-nc
si-1
si-2
NC
OE
6
SLC17A9
0.2
0.5
4
2
GAPDH
0.0
0.0
0
MG63
U20
S
Saos2
HOS
143B
si-nc
si-1
si-2
NC
OE
F
HOS
G
300
HOS
1.6
si-nc
HOS
si-1
OD (450 nm)
Number of colonies
1.2
si-2
si-nc
si-1
si-2
200
**
0.8
100
0.4
0
0.0
si-nc
si-1
si-2
0
24
48
72
h
1.5
MG63
MG63
NC
MG63
300
OE
OD (450 nm)
Number of colonies
1.0-
NC
OE
200
0.5-
100
0.0
0
24
48
72
0
h
NC
OE
| pvalue | Hazard ratio | |
|---|---|---|
| Age | 0.504 | 0.612 (0.145-2.583) |
| Gender | 0.304 | 0.681 (0.328-1.416) |
| M | <0.001 | 4.770 (2.285-9.954) |
| SLC17A9 | 0.004 | 1.598 (1.165-2.192) |
| pvalue | Hazard ratio | |
|---|---|---|
| Age | 0.974 | 1.025 (0.232-4.536) |
| Gender | 0.441 | 0.741 (0.345-1.588) |
| M | <0.001 | 4.700 (2.224-9.933) |
| SLC17A9 | 0.003 | 1.619 (1.175-2.232) |
Figure 4. Cellular functions of SLC17A9 in OSS. (A) Differential expression of SLC17A9 in tumor tissues compared with normal tissues in OSS (accession no. GSE16088). (B) Association between high SLC17A9 expression levels and poor OS and relapse-free survival of patients from the TCGA-TARGET cohort. (C) Univariate and multivariate Cox regression analysis of the OS of patients from the TCGA-TARGET cohort. (D) SLC17A9 expression levels in OSS cells. (E) SLC17A9-knockdown HOS cells and SLC17A9-OE MG63 cells were constructed. Reverse transcription-PCR and western blot analysis were used to deter- mine SLC17A9 expression levels in these cells. (F) Effect of SLC17A9 on the proliferation of HOS and MG63 cells. (G) Effect of SLC17A9 on the viability of HOS and MG63 cells. ** P<0.01 and *** P<0.001 vs. NC. SLC17A9, solute carrier family 17 member 9; M, metastasis; OSS, osteosarcoma; OS, overall survival; TCGA, The Cancer Genome Atlas; TARGET, Therapeutically Applicable Research to Generate Effective Treatments; si-nc, negative control small interfering RNA; si-1/2, small interfering RNA targeting SLC17A9; NC, empty overexpression vector; OE, overexpression; OD, optical density.
racemase and epimerase activity. The KEGG pathway enrich- ment analysis demonstrated that SLC17A9 was enriched
in the MAPK, Hippo, focal adhesion and TGF-ß signaling pathways (Fig. 5B). Finally, GSVA was performed to compare
A
Ossification
B
Connective tissue development
Cartilage development-
Count
KEGG
BMP signaling pathway
0
5
Odontogenesis
qvalue
Odontogenesis of dentin-containing tooth
10
MAPK signaling pathway
0.10
15
Hippo signaling pathway
0.15
Collagen-containing extracellular matrix
0.20
Postsynaptic membrane
20
Endoplasmic reticulum lumen
25
0.25
Focal adhesion
0.30
Myofibril-
8
Sarcolemma
qvalue
TGF-beta signaling pathway
Count
Neuromuscular junction
4
Growth factor activity-
0.04
Protein digestion and absorption
6
Extracellular matrix structural constituent-
0.08
Glycosaminoglycan biosynthesis
8
Transforming growth factor beta receptor binding-
0.12
chondroitin sulfate/dermatan sulfate
10
Racemase and epimerase activity-
류
Transmembrane receptor protein serine/threonine kinase binding
0.16
0.04
0.06
0.08
Receptor serine/threonine kinase binding
GeneRatio
0.025
0.050
GeneRatio
0.075
C
SLC17A9
SLC17A9
2
KEGG_CIRCADIAN_RHYTHM_MAMMAL
High
Low
KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS
1
KEGG_STARCH_AND_SUCROSE_METABOLISM
KEGG_ABC_TRANSPORTERS
0
KEGG_GLYCINE_SERINE_AND_THREONINE_METABOLISM
KEGG_HISTIDINE_METABOLISM
-1
KEGG_PHENYLALANINE_METABOLISM
-2
KEGG_SULFUR_METABOLISM
KEGG_GLYOXYLATE_AND_DICARBOXYLATE_METABOLISM
KEGG_VIBRIO_CHOLERAE_INFECTION
the pathways enriched in patients with OSS with high or low SLC17A9 expression levels. The GSVA results revealed significant differences in pathways such as circadian rhythm in mammals, starch and sucrose metabolism, ABC trans- porters, glycine serine/threonine, histidine, phenylalanine, sulfur, glyoxylate and dicarboxylate metabolism and Vibrio cholera infection between the high and low expression groups (Fig. 5C).
Drug sensitivity analysis. Data on 263 anticancer drugs were retrieved from the CellMiner database. Next, Pearson correlation analysis was performed to determine the correla- tion between SLC17A9 expression levels and the activity of these drugs in NCI-60 cells (15). The scatterplot in Fig. 6 revealed the correlation analysis results: A positive correla- tion was observed between SLC17A9 expression levels and the sensitivity to drugs such as vorinostat, asparaginase, chelerythrine, hypothemycin, PX-316, entinostat, acrichine, LDK-378 and imexon. A negative correlation was observed between SLC17A9 expression levels and the sensitivity to drugs including ibrutinib, afatinib and sonidegib.
Discussion
A previous study has demonstrated the involvement of SLC17A9, a transmembrane protein, in small-molecule
transportation (1). Furthermore, SLC17A9 controls ATP accu- mulation in lysosomes, thus altering cell survival (1). Recent studies have demonstrated that SLC17A9 has a role in cancer development (6,7). In LIHC, SLC17A9 acts as a tumor promoter gene that influences tumor progression (6). In PRAD, SLC17A9 acts as a tumor suppressor that attenuates prolifera- tion, apoptosis and metastasis of cancer cells (7). However, the role of SLC17A9 in other types of cancer has remained elusive. Therefore, in the present study, SLC17A9 expression levels were comprehensively analyzed in a pan-cancer panel. Next, the impact of SLC17A9 on patient prognosis, tumor immunity and stemness was determined in various types of cancer. Finally, the functions of SLC17A9 were verified in OSS cells.
The results of the present study revealed an increase in SLC17A9 expression levels in the tumor tissues of 15 types of cancer, including COAD (3), STAD (4) and LIHC (6,19). However, a decrease in SLC17A9 expression levels was observed in ACC tissues. In addition, a significant differ- ence in the status of methylation of the SLC17A9 promoter was observed in tumor tissues compared with normal tissues in 14 types of cancer. Extensive perturbations in DNA methylation alter the expression levels of genes regulating tumorigenesis, thus affecting the progression of cancer and the prognosis of the patient (20). These results indicate the involvement of SLC17A9 in the onset and progression of different types of cancer.
SLC17A9, Vorinostat Cor=0.446, P<0.001
SLC17A9, Asparaginase
SLC17A9, Chelerythrine
SLC17A9, Hypothemycin Cor=0.370, P=0.004
Cor=0.390, P=0.002
Cor=0.378, P=0.003
2
2
3
1
1
2
2
0
0
0
1
-1
0
-1
-2
2
-3
1
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
SLC17A9, Ibrutinib
SLC17A9, Afatinib
SLC17A9, PX-316
SLC17A9, Entinostat
Cor =- 0.347, P=0.007
Cor =- 0.337, P=0.008
Cor=0.335, P=0.009
Cor=0.329, P=0.010
3
4
2
2
IC50
2
3
1
1
1
2
0
0
0
1
-1
-1
-1
0
2
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
SLC17A9, Acrichine
SLC17A9, LDK-378
SLC17A9, Sonidegib
SLC17A9, Imexon Cor=0.313, P=0.015
2
Cor=0.316, P=0.014
Cor=0.315, P=0.014
Cor =- 0.313, P=0.015
1
2
4
4
3
0
1
2
-1
2
0
1
-2
-1
0
0
-3
-2
-1
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
Expression of SLC17A9
Survival analysis for multiple types of cancer was performed using univariate Cox regression analysis and the KM method. The results revealed an association between SLC17A9 expression levels and the prognosis of patients with various types of cancer, including COAD (3), STAD (4,5), LIHC (6,19) and KIRC (21), consistent with previous studies. A correlation was observed between high SLC17A9 expres- sion levels and longer PFS and DSS of patients with LUAD. SLC17A9 was able to suppress tumorigenesis in LUAD and PRAD (7). In the anti-PD-L1 therapy cohort, high SLC17A9 expression levels were associated with an increase in the OS of patients. Therefore, the potential mechanisms by which SLC17A9 affects anti-PD-L1 treatment are worth investi- gating. These results suggest that SLC17A9 may be a promising prognostic marker in various tumor types.
A previous study has demonstrated a close association between stemness, the status of cancer progression and metas- tasis and poor prognosis of patients in a pan-cancer panel (12). Therefore, the association between SLC17A9 expression levels and the two stemness indices was investigated. A significant association was observed between SLC17A9 expression levels and RNAss in 18 types of cancer, including GBM, LGG and PRAD. In addition, a significant association was observed between SLC17A9 expression levels and DNAss in 17 types of cancer, including TGCT, THCA and UVM. Furthermore, these
stemness indices were associated with intratumor heteroge- neity, immune microenvironment and immune response (12). Therefore, the role of SLC17A9 in different types of cancer should be further investigated.
Previous studies have demonstrated that the TME serves a significant role in tumor onset and progression; thus, targeting the TME appears to be a promising approach in the treat- ment of cancer (22,23). First, the results of the present study indicated a significant difference in the SLC17A9 expression levels in six immune subtypes of 13 types of cancer. Next, the stromal and immune scores were calculated to determine the status of the TME of patients. The results of the present study indicated a positive correlation between the SLC17A9 expres- sion levels and the stromal scores in 18 types of cancer, as well as the immune scores in 23 types of cancer. However, a signifi- cant negative correlation was observed between the SLC17A9 expression levels and the stromal as well as the immune scores of patients with LAML, PAAD and STAD.
In addition, the results of the present study demonstrated a significant correlation between the SLC17A9 expression levels and the infiltration of immune cells in 32 types of cancer. The results of the present study indicated a negative correla- tion between the SLC17A9 expression levels and the immune cells such as Tfh and Treg cells in patients with OSS. Tfh cells express high PD1 levels and are closely associated with
antitumor immunity. In addition, Tfh-like cells are present in several types of cancer (24). A previous study has revealed the synergistic effect of combining therapies targeting Treg cells and other treatment strategies (25). These results suggest synergistic immunotherapy may be used to target SLC17A9 for treating patients with OSS.
However, the role of SLC17A9 in cancer remains contro- versial. Therefore, in vitro experiments were performed using OSS cells to determine the functions of SLC17A9. The results suggested that SLC17A9 enhances OSS cell proliferation and viability. Next, the underlying mechanisms of SLC17A9 were investigated. The KEGG pathway enrichment analysis indi- cated that SLC17A9 was enriched in the MAPK, Hippo, focal adhesion and TGF-ß signaling pathways. Furthermore, several known drugs targeting SLC17A9 were identified.
Of note, the present study has certain limitations. First, data were obtained, analyzed and integrated from several publicly available databases, which primarily included patients of Caucasian ethnicity. This may cause bias due to ethnicity. Furthermore, the SLC17A9 protein significantly influences the occurrence and progression of cancer; therefore, it is neces- sary to determine the effect of SLC17A9 protein expression in multiple types of cancer. In addition, the underlying mechanisms of the role of SCL17A9 remain to be elucidated. Thus, additional studies should be performed to design new SLC17A9-based therapeutic strategies for improving patient survival.
In conclusion, the results of the present study demonstrate that SCL17A9 was differentially expressed and significantly associated with tumor immunity as well as the prognosis of patients with various types of cancer. It was demonstrated that SLC17A9 functions as a tumor promoter in OSS. SLC17A9 may be a novel prognostic biomarker and a potential target for synergistic immunotherapy of OSS.
Acknowledgements
Not applicable.
Funding
No funding was received.
Availability of data and materials
All data generated or analyzed during this study are included in this published article.
Authors’ contributions
MZ and JY conceptualized and supervised the study. JL performed the bioinformatic analysis and statistical analysis. MZ performed the in vitro experiments. FW, LS and HZ performed the data curation and interpretation. JL wrote the original manuscript. MZ and JY confirm the authenticity of all the raw data. All authors read and approved the final version of the manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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