SLC7A5 serves as a potential therapeutic target for osteosarcoma: a comprehensive analysis based on bioinformatics and experimental validation

Abstract

This study aimed to investigate the role of solute carrier family 7 member 5 (SLC7A5) in osteosarcoma (OS) and its potential as a therapeutic target. Pan-cancer analysis revealed that SLC7A5 is significantly overexpressed in various tumor types, with particularly prominent upregulation in osteosarcoma. Using datasets from The Cancer Genome Atlas (TCGA), we found that high SLC7A5 expression was closely associated with poor patient prognosis, tumor multifocality, and metastatic progression. Based on SLC7A5-related genes, we constructed a prognostic risk score model using LASSO regression. This model effectively stratified patients by risk, revealing significant differences in survival outcomes between the high-risk and low-risk groups. In in vitro experiments, SLC7A5 overexpression significantly promoted the proliferation, migration, and invasion of osteosarcoma cells; conversely, silencing SLC7A5 not only inhibited these cellular behaviors but also induced apoptosis. Combining RNA sequencing with pathway enrichment analysis, we found that SLC7A5 regulates the phosphorylation levels of the mTOR pathway and its downstream target S6. In vivo experiments showed that SLC7A5 overexpression accelerated the growth of mouse xenograft tumors. Consistent with the in vitro functional assays, Ki-67 and phosphorylated mTOR levels were also elevated in tumor tissues, further validating the association between SLC7A5 and mTOR-mediated tumor progression.

Introduction

Osteosarcoma is a relatively common malignant bone tumor in young populations, and its incidence has been gradually increasing [1]. It generally originates from rapidly proliferating bone cells and exhibits high malignancy with a tendency for early metastasis, leading to poor patient prognosis [2,3]. With the widespread use of immunotherapy and novel adjuvant chemotherapy regimens, the five-year survival rate of osteosarcoma patients has improved to some extent. However, the survival rate for patients with recurrent or metastatic osteosarcoma remains below 30% [4,5]. Thus, exploring the underlying molecular mechanisms of osteosarcoma is of great significance for the development of new therapeutic strategies.

The bone microenvironment is a complex structural system composed of multiple cellular components and the extracellular matrix. The complex interactions between OS cells and the bone microenvironment significantly influence various tumor behaviors, including tumor development, metastasis, angiogenesis, therapeutic response, and pre-metastatic niche formation [6-8]. Within the microenvironment, immune cells play a crucial role in predicting the prognosis of OS. For instance, CD8+ T cells and natural killer (NK) cells possess anti-tumor capabilities, whereas myeloid-derived suppressor cells, M2-type macrophages, and regulatory T (Treg) cells contribute to tumor progression [9-11]. Various cytokines and exosomes secreted by immune cells also play an indispensable regulatory role in the formation and progression of osteosarcoma [12,13].

In osteosarcoma treatment, conventional chemotherapy and surgery remain the core approaches, but strategies combining immunotherapy with targeted therapy are gradually emerging as promising alternatives. Studies have not only highlighted the critical role of the tumor microenvironment and tumor suppressor gene inactivation in various cancers but also provided new directions for osteosarcoma therapy [14-18]. Mutations in tumor suppressor genes are relatively common molecular events across multiple cancer types, with inactivation of TP53 and RB1 genes being the most representative. Such mutations often accelerate tumor initiation and progression. In osteosarcoma, these genetic alterations may also exert similar pro-tumorigenic effects and significantly influence treatment outcomes [14]. Clinical studies have also focused on the combined use of immunotherapy and chemotherapy. Although such combinations did not significantly reduce recurrence rates in uterine leiomyosarcoma [15], these findings still offer valuable insights for osteosarcoma treatment, particularly regarding combination strategies. The roles of nutritional status and inflammatory responses in cancer therapy are increasingly recognized. Lower nutritional indices have been closely associated with poorer survival outcomes, a pattern observed across multiple tumor types, suggesting potential relevance in osteosarcoma treatment [16]. Sahin et al. found that an elevated neutrophil-to-eosinophil ratio closely correlates with significantly lower overall survival and progression-free survival [18]. This finding suggests that similar immune markers could help predict the immune microenvironment in osteosarcoma (OS) [17].

The proliferation and metastasis of tumor cells are highly dependent on the uptake of exogenous amino acids [19,20]. Solute carrier family 7 member 5 (SLC7A5), also known as large neutral amino acid transporter 1, serves as a transmembrane transporter for amino acids [21]. Existing evidence indicates that SLC7A5 is overexpressed in various malignant tumors and exerts pro-tumor effects through its transport activity. Specifically, SLC7A5-mediated glutamine influx, along with the subsequent activation of the mTORC1 signaling pathway, synergistically upregulates the expression of the transcription factor c-Myc, thereby driving malignant tumor progression [22-24]. The increased expression of SLC7A5 has also been associated with unfavorable prognoses for patients [25-27]. Increased expression of SLC7A5 enhances the activation of CD4⁺ T cells, thereby stimulating immune responses [28]. Broer et al. found that 143B OS cells express a group of glutamine transporter proteins, including SLC7A5 [29]. Investigate the biological function of SLC7A5 in OS may unveil its potential use as a new tumor biomarker and therapeutic target.

In this study, we investigated the relationship between SLC7A5 expression and clinical characteristics, prognosis, and immune status in OS was investigated through an in-depth analysis of The Cancer Genome Atlas (TCGA) database. Additionally, it was unveiled that SLC7A5 promotes the proliferation of OS cells both in vitro and in vivo, suggesting that SLC7A5 has the potential to serve as a valuable clinical biomarker and target for advanced therapy in OS.

Materials and methods

Public dataset acquisition and preprocessing

In the pursuit of evaluating SLC7A5 expression among patients with cancer, the mRNA expression profiles and the corresponding clinical information were obtained from the TCGA pan-cancer cohort (https://portal.gdc.cancer.gov/). The amalgamation of multiple Gene Expression Omnibus cohorts, specifically GSE19276 [30], GSE87624 [31], and GSE99671 [32], was performed, and the mitigation of batch effects was undertaken through the application of the combat algorithm housed within the ‘sva’ package (Version 3.52.0). To ensure data consistency and comparability across samples, we performed rigorous standardization of the raw data using the transcripts per million (TPM) method for normalization. This step was crucial for achieving accurate and consistent comparisons between samples.

Analysis of prognosis and the clinicopathological correlation

The cutoff value for discerning SLC7A5 expression in sarcoma tissues as either high or low was determined based on the median expression level. The analysis of the correlation between SLC7A5 expression and various clinicopathological features, such as sex, age, tumor multifocality, residual tumor, tumor necrosis, tumor depth, and metastasis, was performed using the corresponding methodologies. Overall survival curves were generated using data from the TCGA-Sarcoma (SARC) dataset and Kaplan-Meier Plotter (https://kmplot.com/analysis/).

Screening co-expressed genes and differentially expressed genes (DEGs)

The co-expressed genes of SLC7A5 were determined using Spearman’s correlation coefficient using the TCGA-SARC dataset. Subsequently, the R package ‘Deseq2’ was employed to identify DEGs between the groups characterized by high and low SLC7A5 expression. The filtering criteria for this analysis were set at |log2 fold change (FC)| > 1 and false discovery rate (FDR) < 0.05.

Functional enrichment analysis

Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were employed to investigate potential biological processes (BPs), cellular components (CCs), and molecular functions (MFs) associated with DEGs between SLC7A5 high- and low-expression groups. A statistically significant threshold was defined as an FDR q value < 0.05. The significantly enriched pathways were visualized using bubble plots.

Establishment of an SLC7A5-derived prognostic gene model

The R package ‘glmnet’ was used to identify the top 30 DEGs that were upregulated in the SLC7A5 high-expression group. These DEGs were then subjected to a least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis to construct a prognostic model by selecting the most relevant genes from the initial pool of 30 candidate genes. The normalized expression levels of the candidate DEGs and the corresponding survival data, including time and status, were used as the independent and dependent variables, respectively, for the LASSO regression. A 10-fold cross-validation was performed to determine the penalty parameter (λ) based on the minimum criteria. Using the expression levels of the DEGs and their corresponding coefficients, a risk score was calculated for each patient. The following formula was used to calculate the risk score: risk score = (SLC7A5 × 0.1146814) + [ceramide synthase 1 (CERS1) × 0.04358834] - [tumor necrosis factor receptor-associated factor 3-interacting protein 3 (TRAF3IP3) × 0.12706772] + [regulatory factor X6 (RFX6) × 0.02177245] + [RANBP2 like and GRIP domain containing 1 (RGPD1) × 1.18127866] + [CUB and Sushi multiple domains 3 (CSMD3) × 0.03530255]. The median risk score was used to divide the patients into low- and high-risk cohorts. Kaplan-Meier analysis was conducted to compare the overall survival between these risk groups. To assess the diagnostic efficacy of the risk model, a receiver operating characteristic (ROC) curve was generated using the R packages ‘survival’, ‘survminer’, and ‘time-ROC’.

Immune infiltration and correlation analyses

An immune infiltration analysis was conducted using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. This approach allowed the exploration of the potential connection between SLC7A5 and the tumor immune microenvironment. The ssGSEA algorithm is capable of quantifying 24 known tumor-infiltrating lymphocyte cell types based on the provided expression matrix.

Cell culture

The osteoblast cells and OS cell lines MG-63, HOS, and U2-OS were obtained from BeNa Culture Collection, Beijing Beina Chunglian Institute of Biotechnology. Cell cultures were maintained in DMEM medium (Cytiva; cat. no. SH30022.02), supplemented with 10% fetal bovine serum (Corning; cat. no. 35-081-CV) and 1% penicillin-streptomycin (Solarbio; cat. no. P1400). The cells were incubated at 37°C and 5% CO₂. Subculturing was carried out when cell density reached 75-85% confluence.

Transfection

Lentiviruses containing an overexpression or an interfering sequence for SLC7A5 were procured from Shanghai Hanheng Biotech Co., Ltd. OS cells were transfected with either lenti-oeSLC7A5, lenti-shSLC7A5 or relative negative control, and polybrene (8 µg/ml) was introduced to augment the efficiency of infection. A total of 72 h after infection, cells were collected for further assays.

RNA isolation and reverse transcription-quantitative PCR (RT-qPCR)

Cells were subjected to total RNA extraction using the RNA extraction kit (Solarbio; cat. no. R1200). The isolated RNA was subsequently reverse transcribed into cDNAs with M-MLV reverse transcriptase (Promega; cat. no. M1701). The primer sequences employed were as follows: SLC7A5-forward (F), 5’-GCATCGGCTTCACCATCATC-3’; SLC7A5-reverse (R), 5’-ACCACCTGCATGAGCTTCTGAC-3’; GAPDH-F, 5’-GACCTGACCTGCCGTCTAG-3’; GAPDH-R, 5’-AGGAGTGGG TGTCGCTGT-3’. Quantitative PCR was performed using an ABI 7500 system, and the relative mRNA expression levels of SLC7A5 were quantified using the 2^-ΔΔCq method.

Western blotting

Cellular protein was isolated from each group utilizing a radioimmunoprecipitation assay lysate (Beyotime; cat. no. P0013C). The concentration of proteins was determined through the bicinchoninic acid method (Beyotime; cat. no. P0011). Protein samples (50 μg each) were subjected to polyacrylamide gel electrophoresis and then transferred to a polyvinylidene fluoride membrane. The membrane was blocked with 5% skimmed milk for 1 h at room temperature. After blocking, it was incubated overnight with the primary antibody for SLC7A5 (ProteinTech; cat. no. 28670-1-AP; 1:5000), mTOR (Abclonal; cat. no. A2445; 1:2000), Phospho-mTOR (Abclonal; cat. no. AP0094; 1:1000), S6 (Abclonal; cat. no. A6058; 1:1000), Phospho-S6 (Abclonal; cat. no. AP1328; 1:20000) and β-actin (ProteinTech; cat. no. 81115-1-RR; 1:5000). The next day, the secondary antibody (ProteinTech; cat. no. SA00001-2; 1:2000) was added and incubated for 1 h at room temperature for development.

Cell Counting Kit-8 (CCK-8) assay

Cell proliferation in MG63 and HOS cells was assessed both before transfection and on days 1, 2, 3, and 4 following transfections, utilizing a CCK-8 assay kit (Absin, cat. no. abs50003). The cells were cultured with the CCK-8 reagent for 4 h, after which the optical density was measured at 450 nm employing a microplate reader (Tecan; Infinite F50).

Cell apoptosis detection

For cell apoptosis analysis, the Annexin V-FITC apoptosis detection kit (Absin; cat. no. abs50001) was employed. The staining procedure followed the manufacturer’s guidelines. In brief, cells were double-stained with 5 µl Annexin V-FITC and 10 µl propidium iodide in each tube. After gentle vortexing, the samples were incubated in the dark at room temperature for 15 min. Subsequently, the samples were injected and examined using a flow cytometer (Beckman; CytoFLEX S).

Wound scratch assay

After trypsinization and centrifugation, cells were seeded at a predetermined density into 6-well plates containing complete culture medium. Following overnight adhesion, three parallel and vertical scratches were made perpendicular to the plate using a 1 mL pipette tip. Images of the initial scratch morphology were captured (0 hours). After 24 hours of culture, images were taken at the same positions. Cell migration ability was evaluated by measuring the changes in scratch width.

Transwell assay

Transfected cells were trypsinized, resuspended in serum-free medium, and adjusted to a density of 1 × 10⁵ cells/mL. The upper surface of the Transwell membrane was coated with Matrigel (BD Biosciences; cat. no. 356234; 1:8 dilution) and incubated at 37°C for 30 min to allow gelation. A total of 100 µL of the cell suspension, containing 1 × 10⁴ cells, was carefully seeded into the upper chamber, while the lower chamber was filled with complete medium. After overnight incubation, fixation, staining, and analysis were performed following the migration assay procedure described above.

RNA-sequencing

We collected transfected MG63 and HOS cells (2 × 10⁶ cells) and extracted total RNA using a TRIzol kit. RNA sequencing was performed by Shanghai Meiji Biomedical Technology Co., Ltd. on the Illumina NovaSeq 6000 platform. Differentially expressed genes (DEGs) were identified using the criteria of P < 0.05 and absolute log₂ fold change > 1.

Animal experiments

Sixteen 4-week-old male BALB/c nude mice, weighing 18-22 g, were purchased from Shanghai Lingshang Biotechnology Co., Ltd. and acclimatized for one week after arrival. Each mouse was subcutaneously inoculated with 2 × 10⁶ MG63 cells into the right axillary region. Tumor volume was measured every 5 days starting from day 10 post-inoculation, calculated using the formula: tumor volume = length × width²/2. All mice were housed under specific pathogen-free (SPF) conditions, with controlled temperature, humidity, light-dark cycles, and free access to food and water. Due to the minor trauma associated with the procedure, no analgesics were administered during the experiment, and no symptoms such as trembling or huddling were observed in the mice. Their health status was monitored daily. All mice survived until the predetermined experimental endpoint (day 40) and were euthanized by cervical dislocation under pentobarbital sodium anesthesia (30-90 mg/kg). No criteria for humane endpoints (e.g., body weight loss exceeding 20%, tumor ulceration, or severe distress) were observed during the experiment. Death was confirmed by cessation of heartbeat and respiration. All animal carcasses were centrally processed by the Laboratory Animal Center according to institutional hazardous waste disposal protocols. All animal procedures were approved by the Nantong University Laboratory Animal Ethics Committee (NTULAC, No. P20230228-093), and the study was conducted in accordance with the ARRIVE guidelines and NIH standards for tumor burden control.

Immunohistochemistry

The fresh tissue was soaked in 4% paraformaldehyde for 24 h, cut to a suitable size, and completely soaked in paraffin solution. After the paraffin solidified, the wax block was cut into 4 µm slices, baked, and immobilized on the slide to form a paraffin section. The slices were then dewaxed and rehydrated with xylene and a series of graded alcohols, followed by EDTA antigen retrieval, 10 min of incubation with 3% H₂O₂ at room temperature after two PBS rinsings, and 30 min of blocking with 5% silk milk after three PBS washing. The section was incubated at 4°C overnight with primary antibodies (anti-p-mTOR (Abclonal; cat. no. AP0094; 1:200) and ki-67 (Abclonal; cat. no. A2094; 1:800)). After rinsing with PBS three times, the secondary antibody (Abclonal; cat. no. AS014; 1:100) was introduced and incubated at 37°C for 30 min. Then DAB (ServiceBio; cat. no. G1212) was employed for color development and hematoxylin (ServiceBio; cat. no. G1004) was for re-staining. the section was dehydrated, transparent, and sealed with neutral resin. The slide was observed and photographed under an optical microscope (Nikon; Ci-S).

Statistical analysis

We constructed a LASSO regression model and evaluated its predictive performance using ROC curves. The relationship between gene expression and patient prognosis was analyzed using Kaplan-Meier survival curves and the Log-rank test, implemented with the R packages survival (3.3-0) and survminer (0.4.8). Immune infiltration levels were quantified using single-sample gene set enrichment analysis (ssGSEA). Cellular experimental data were organized and analyzed using GraphPad Prism 8.0. Each experiment was independently repeated at least three times, and results are presented as mean ± standard deviation (mean ± SD). Statistical analysis methods were selected according to the experimental design: comparisons between two groups were performed using unpaired t-tests, comparisons among multiple groups were performed using one-way ANOVA, and experiments involving multiple factors were analyzed using repeated measures ANOVA. A value of P < 0.05 was considered statistically significant.

Results

Analysis of SLC7A5 overexpression in sarcoma and OS

The investigation commenced with an extensive analysis of SLC7A5 mRNA expression across various cancer types using TCGA datasets. Within the cohort of 33 cancer types, the expression of SLC7A5 showed distinct patterns. SLC7A5 exhibited a substantial upregulation in several cancers, including breast invasive carcinoma, cervical and endocervical carcinoma, cholangiocarcinoma, colon adenocarcinoma, esophageal carcinoma, head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, pheochromocytoma and paraganglioma, rectum adenocarcinoma, stomach adenocarcinoma, thyroid carcinoma and uterine corpus endometrial carcinoma (Figure 1A). In three specific cancer types: Kidney chromophobe, kidney renal papillary cell carcinoma and prostate adenocarcinoma, a slight downregulation trend in SLC7A5 expression was observed.

Figure 1.

High expression of SLC7A5 in multiple cancers, including OS, correlates with poor survival in sarcoma. A. Determination of SLC7A5 expression in multiple tumors and corresponding normal tissues from the TCGA database (red, tumor group; blue, normal group; purple, metastatic tumor group; *P<0.05). B. OE of SLC7A5 in OS tissues and cells compared with control confirmed in GSE19276, GSE87624, and GSE99671 datasets. C. OE of SLC7A5 in multifocal sarcomas compared with non-multifocal sarcomas in TCGA-SARC cohorts. D. OE of SLC7A5 in metastatic sarcomas compared with non-metastatic sarcomas in TCGA-SARC cohorts. E. Kaplan-Meier survival curves show the correlation between overall survival and SLC7A5 expression. SLC7A5, solute carrier family 7 member 5; TCGA, The Cancer Genome Atlas; SARC, sarcoma; OE, overexpression; OS, osteosarcoma.

To further verify whether SLC7A5 is aberrantly expressed in osteosarcoma, we additionally analyzed data from the GEO database. By analyzing the GSE19276 dataset comprising 23 OS samples and five non-malignant bone samples, GSE87624 consisting of 44 OS samples and five normal bone samples and GSE99671 encompassing 18 tumoral bone samples and 18 non-tumoral paired samples, significantly elevated expression of SLC7A5 in OS tissues compared with the non-tumor bone tissues were consistently observed (Figure 1B). Following this, patients with sarcoma were stratified into two cohorts based on the median expression level of SLC7A5: i) SLC7A5 high (top 50%); and ii) SLC7A5 low (bottom 50%). The association between SLC7A5 expression and various clinical characteristics was subsequently investigated (Table 1). The expression of SLC7A5 in sarcoma samples with tumor multifocality was significantly higher (Figure 1C), and a similar trend was observed in samples with metastasis (Figure 1D). Additionally, a comparison of survival curves revealed that a high mRNA level of the SLC7A5 gene was associated with poorer overall survival in patients with sarcoma [Figure 1E; hazard ratio (HR), 1.5; P = 0.0001].

Table 1.

Correlation among SLC7A5 expression and clinical characteristics of patients with sarcoma according to the TCGA database

Characteristics	SLC7A5 expression		P value	Statistic	Method
	Low	High
n	131	132
Gender, n (%)			0.2416	1.3713	Chi-square
Female	67 (25.5%)	77 (29.3%)
Male	64 (24.3%)	55 (20.9%)
Age, n (%)			0.9514	0.0037	Chi-square
≤ 60	65 (24.7%)	65 (24.7%)
> 60	66 (25.1%)	67 (25.5%)
Tumor multifocal, n (%)			0.0042*	8.1777	Chi-square
No	109 (45.6%)	90 (37.7%)
Yes	12 (5%)	28 (11.7%)
Residual tumor, n (%)			0.7336	0.6197	Yates’s correction
R0	79 (33.6%)	78 (33.2%)
R1	38 (16.2%)	31 (13.2%)
R2	4 (1.7%)	5 (2.1%)
Tumor necrosis, n (%)			0.1491	5.330	Chi-square
No necrosis	35 (19.1%)	36 (19.7%)
Focal necrosis	19 (10.4%)	19 (10.4%)
Moderate necrosis	25 (13.7%)	37 (20.2%)
Extensive necrosis	2 (1.1%)	10 (5.5%)
Tumor depth, n (%)			0.2406	1.377	Chi-square
Superficial	13 (6.2%)	8 (3.8%)
Deep	91 (43.5%)	97 (46.4%)
Metastasis, n (%)			0.0038*	8.3782	Chi-square
No	62 (34.6%)	58 (32.4%)
Yes	17 (9.5%)	42 (23.5%)

^*P<0.05.

Identification of SLC7A5-related genes in TCGA-SARC

The development of cancer is the consequence of the co-regulation of numerous genes. Based on the median expression levels of SLC7A5 in TCGA-SARC cohorts, samples were categorized into two groups: i) High SLC7A5 group; and ii) low SLC7A5 group, each comprising 132 samples. Using the DESeq2 method, DEGs were identified applying the criteria of |log2FC| > 1 and FDR < 0.05. A total of 479 downregulated genes and 635 upregulated genes were selected and presented in Figure 2A. Ranked by FC, Figure 2B and Table S1 display the top 10 upregulated genes and top 10 downregulated genes, respectively. To evaluate the co-expression patterns with SLC7A5, Spearman’s correlation analysis was conducted, and several genes were identified, although the correlation coefficients were not modest. Figure 2C and Table S2 illustrate the top seven positively correlated genes and the top seven negatively correlated genes, respectively. The results showed that the genes positively correlated with the expression of SLC7A5 were PSAT1, RELT, RCC2, AUNIP, HMGB3, TUBA1C, and TPM3, whereas the expression of THRB, CPO, NENF, METTL7A, PDE1B, AOC3, and ACBD4 was negatively correlated with SLC7A5. These co-expressed genes may have mutual mechanisms of action working together in the progression of OS.

Figure 2.

Identification of differentially expressed genes associated with SLC7A5 expression in TCGA-SARC cohorts. A. Volcano plots show upregulated and downregulated genes between SLC7A5-high and SLC7A5-low sarcoma samples. B. The heatmap illustrates the top 10 upregulated and downregulated genes. C. The heatmap shows the top seven positively correlated genes and the top seven negatively correlated genes from Spearman’s correlation analysis. SLC7A5, solute carrier family 7 member 5; TCGA, The Cancer Genome Atlas; SARC, sarcoma.

Establishment of an SLC7A5-derived genomic model for sarcoma prognosis

SLC7A5 expression in the TCGA database can predict survival outcomes in osteosarcoma patients. However, due to the relatively low area under the curve (AUC) (Figure S1), the time-dependent ROC curve for SLC7A5 alone has limitations. To more accurately identify prognostic factors, the top 30 upregulated genes in SLC7A5-high sarcoma samples were used as input for a random survival forest analysis (Figure 3A). The relative importance of these prognostic genes was calculated and ranked. Based on their importance values, six genes-SLC7A5, CERS1, TRAF3IP3, RFX6, RGPD1, and CSMD3-were selected (Figure 3B), and the risk score was calculated as follows: Risk score = (SLC7A5 × 0.1146814) + (CERS1 × 0.04358834) - (TRAF3IP3 × 0.12706772) + (RFX6 × 0.02177245) + (RGPD1 × 1.18127866) + (CSMD3 × 0.03530255). Using the mean risk score, OS patients were stratified into two subgroups: i) high-risk and ii) low-risk. Although only SLC7A5 and TRAF3IP3 were independent survival-related factors (Figure 3C-G), the combination of these six genes significantly stratified patients according to overall survival, as indicated by a log-rank P value of 0.00016 (Figure 3H). ROC curve analysis confirmed that this SLC7A5-based prognostic model has strong potential for predicting patient outcomes (Figure 3I).

Figure 3.

SLC7A5-derived genomic model better predicts the sarcoma prognosis. A. Cross-validation for tuning the parameter selection in the LASSO regression. B. Relative importance of selected genes shown during the Lasso analysis. C-G. Kaplan-Meier survival curves show the correlation between overall survival and the expression level of CERS1, TRAF3IP3, RFX8, RGPD1, and CDMD3. H. Kaplan-Meier survival curves show the correlation between overall survival and risk score calculated by LASSO analysis. I. Time-dependent ROC curves and AUC analyses depict the predictive efficiency of the risk score in TCGA-SARC cohorts. ROC, receiver operating characteristic; AUC, area under the curve; SLC7A5, solute carrier family 7 member 5; TCGA, The Cancer Genome Atlas; SARC, sarcoma; LASSO, least absolute shrinkage and selection operator; CERS1, ceramide synthase 1; TRAF3IP3, tumor necrosis factor receptor-associated factor 3-interacting protein 3; RFX6, regulatory factor X6; RGPD1, RANBP2 like and GRIP domain containing 1; CSMD3, CUB and Sushi multiple domains 3.

SLC7A5-associated immune infiltration landscape in sarcoma

We performed KEGG and GO enrichment analysis using the upregulated and downregulated differentially expressed genes (DEGs) as input. Through this analysis, we were able to identify activated and inactivated pathways associated with SLC7A5. The top four pathways with the lowest p-values in the BP, CC, MF, and KEGG categories are shown in Figure 4A and 4B, with detailed information provided in Table S3. Notably, in the SLC7A5 high-expression group, both cytokine activity and cytokine-cytokine receptor interaction were identified as activated pathways, suggesting that SLC7A5 may play an important role in regulating the tumor microenvironment.

Figure 4.

GO, KEGG, and GSEA analyses for DEGs associated with SLC7A5 expression. A. Activated pathways enriched by GO and KEGG using upregulated DEGs in SLC7A5-high TCGA-SARC samples. B. Inactivated pathways enriched by GO and KEGG using downregulated DEGs in SLC7A5-high TCGA-SARC samples. C. Immune-related pathways associated with SLC7A5 expression selected by GSEA. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes; GSEA, Gene Set Enrichment Analysis; SLC7A5, solute carrier family 7 member 5; TCGA, The Cancer Genome Atlas; SARC, sarcoma.

Furthermore, a GSEA was conducted to further confirm the functional implications of SLC7A5. The analysis revealed the activation of several pathways, including the CD8-T cell receptor (TCR) downstream pathway, interleukin 10 signaling, hedgehog signaling pathway, antigen processing and presentation, PD-1 signaling, NK cell-mediated cytotoxicity, interactions of NK cells in pancreatic cancer, cancer immunotherapy by PD1 blockade, and TCR signaling pathway. The corresponding normalized enrichment score, P-value, and FDR are presented in Figure 4C and Table S4.

Liang et al. found that there was significant immune cell infiltration in the adjacent tissues of osteosarcoma (OS). This infiltration formed a complex immune microenvironment, which might promote the proliferation and survival of tumor cells within the bone under immunosuppressive conditions [33,34]. To analyze the immune infiltration characteristics associated with SLC7A5 in sarcoma, we applied single-sample gene set enrichment analysis (ssGSEA) to evaluate the infiltration levels of different immune cell subsets. Through this approach, we systematically compared immune cell infiltration patterns across different sarcoma types in relation to SLC7A5 expression levels. Sarcoma cases with high SLC7A5 expression showed increased infiltration of various immune cells, including T helper (Th) 2 cells, macrophages, neutrophils, TFH cells, Tregs, and NK CD56dim cells (Figure S2A). These immune cell types are generally known to have immune-suppressing effects during tumorigenesis. Conversely, there was a negative correlation between SLC7A5 expression and infiltration of NK cells, plasmacytoid dendritic cells (DCs), mast cells, TGD cells, and DCs (Figure S2B). Most of these cell types are actively involved in the processes of antigen presentation, tumor recognition, and elimination. Spearman’s correlation analysis further confirmed the correlation between SLC7A5 and dysregulated immune cells in each sample of TCGA-SARC cohorts (Figure S2C), providing additional support for the immune-suppressing role of SLC7A5 in the sarcoma microenvironment.

SLC7A5 modulates proliferation, apoptosis, migration and invasion in OS cells

We compared SLC7A5 expression in osteosarcoma (OS) cell lines MG63, HOS, and U2OS with that in normal osteoblasts. SLC7A5 was significantly upregulated in all OS cell lines compared with osteoblasts (Figure 5A). Following lentiviral transduction, SLC7A5 expression was markedly higher than in untreated cells and the empty vector control group, with consistent results at both the mRNA and protein levels (Figure 5B, 5C and Figure S2A, S2B). CCK-8 assays showed that SLC7A5 overexpression significantly promoted proliferation of MG63 and HOS cells, whereas SLC7A5 silencing markedly inhibited cell proliferation (Figure 5D and Figure S2C). Colony formation assay results were consistent with the CCK-8 findings (Figure 5E). Apoptosis was assessed by flow cytometry, revealing a significant increase in apoptosis in MG63 cells, while no obvious change was observed in HOS cells (Figure 5F). Further evaluation of cell migration and invasion yielded results consistent with the colony formation assays (Figure 6). These findings suggest that SLC7A5 may serve as a potential therapeutic target for inhibiting osteosarcoma metastasis (Figure S3).

Figure 5.

SLC7A5 influences the proliferation and apoptosis of OS cells. A. RT-qPCR analysis of SLC7A5 mRNA expression in OS cell lines versus osteoblasts. B. RT-qPCR detection of SLC7A5 mRNA expression in MG63 and HOS cells following lentivirus-mediated modulation. C. Western blotting of SLC7A5 protein levels in MG63 and HOS cells. D. Cell viability assessed by CCK-8 assay in OS cells with modulated SLC7A5 expression. E. Colony-formation assay of OS cells with differential SLC7A5 expression. F. Apoptosis assay of OS cells with differential SLC7A5 expression. Data are presented as mean ± SD from three independent experiments (n = 3). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 analyzed with Students’ t-test. ##P < 0.01, ####P < 0.0001 analyzed with repeated measures ANOVA.

Figure 6.

SLC7A5 regulates the migratory and invasive capacities of OS cells. A. Wound healing assay of OS cells with differential SLC7A5 expression. The wound closure area was quantified 24 h after scratching (scale bar = 500 µm). B. Transwell invasion assay of OS cells with differential SLC7A5 expression (scale bar = 100 µm). The number of invasive cells was quantified 24 h after seeding. Data are presented as mean ± SD from three independent experiments (n = 3). **P < 0.01, ***P < 0.001, ****P < 0.0001.

SLC7A5 activates mTOR phosphorylation signaling

To investigate the molecular mechanism by which SLC7A5 regulates osteosarcoma progression, transcriptome sequencing was performed on SLC7A5-knockdown MG63 cells and SLC7A5-overexpressing HOS cells. Differentially expressed genes were identified using the criteria of |fold change| > 2 and P < 0.05, and visualized using volcano plots (Figure 7A, 7B). KEGG pathway enrichment analysis revealed that upon SLC7A5 knockdown, We analyzed the enrichment of differentially expressed genes and found that following SLC7A5 knockdown, the affected pathways mainly involved “nucleotide sugar biosynthesis”, “amino sugar and nucleotide sugar metabolism”, and the “mTOR signaling pathway”. Interestingly, after SLC7A5 overexpression, the enriched pathways shifted to “oxidative phosphorylation” and “glycosaminoglycan degradation”, with the mTOR signaling pathway appearing again (Figure 7C and 7D). Given that the mTOR pathway was significantly enriched in both conditions, we further verified its activity via Western blot. The results showed that SLC7A5 knockdown suppressed the phosphorylation levels of mTOR and its downstream effector S6, with little effect on total protein expression. Conversely, SLC7A5 overexpression markedly enhanced the phosphorylation of mTOR and S6 (Figure 7E and 7F).

Figure 7.

RNA-seq analysis of SLC7A5-mediated transcriptional changes in OS cells. A. Volcano plot of DEGs in MG63 cells after SLC7A5 knockdown. Upregulated genes are shown in red and downregulated genes in blue. B. Volcano plot of DEGs in HOS cells following SLC7A5 overexpression. C. KEGG pathway enrichment analysis of DEGs after SLC7A5 knockdown in MG63 cells. D. KEGG pathway enrichment analysis of DEGs after SLC7A5 knockdown in HOS cells. E. Western blot analysis of the mTOR signaling pathway in OS cells with altered SLC7A5 expression. F. Quantification of phosphorylated mTOR and S6 levels under different SLC7A5 expression conditions. Data are expressed as mean ± SD from three independent experiments (n = 3). ***P < 0.001, ****P < 0.0001.

SLC7A5 promotes in vivo progression of osteosarcoma

We selected 4-week-old BALB/c nude mice and established a tumor model by subcutaneous injection in the right axilla. After the mice had acclimated to the experimental environment for one week, Each mouse was inoculated with 2 × 10⁶ cells. Tumor volume was measured every 5 days. On day 40, the mice were euthanized, and the tumors were collected, cleaned, and photographed. The results showed that tumors in the SLC7A5 overexpression group were significantly larger in volume and size and heavier in weight compared with the vector control group (Figure 8A-C). Immunohistochemical staining showed that the number of Ki-67-positive cells increased in the SLC7A5 overexpression group, indicating enhanced cell proliferative activity (Figure 8D). Phosphorylated mTOR (p-mTOR) was also significantly upregulated, consistent with the RNA-seq and molecular experimental results (Figure 8E).

Figure 8.

In vivo overexpression of SLC7A5 accelerates tumor progression in a mouse model. A. Tumor volume was measured over time after injecting 2 × 10⁶ MG63 cells stably transfected with either empty vector (Vector) or SLC7A5-overexpression vector into BALB/c nude mice. B. Representative bright-field image of tumors excised at day 40 post-injection. C. Final tumor weight at endpoint, showing a significant increase in the SLC7A5-overexpression group compared with the vector control group. D. IHC confirming elevated ki67 protein expression in the overexpression group (scale bar = 50 µm). E. IHC analysis revealing increased p-mTOR expression in the overexpression group (scale bar = 50 µm). Data are presented as mean ± SD (n = 8 mice per group). ***P < 0.001, ****P < 0.0001 analyzed with Students’ t-test. ####P < 0.0001 analyzed with repeated measures ANOVA. IHC, immunohistochemistry.

Discussion

By integrating bioinformatics analyses, in vitro experiments, and in vivo animal studies, we systematically investigated the expression patterns, biological functions, and potential therapeutic value of SLC7A5 in osteosarcoma (OS). Analysis of the TCGA database showed that SLC7A5 is significantly upregulated in multiple tumor types; in osteosarcoma, its expression positively correlates with tumor multifocality, distant metastasis, and poor prognosis. A risk score model based on SLC7A5 expression effectively predicted patient survival outcomes. In vitro experiments, including RT-qPCR and Western blotting, confirmed that SLC7A5 expression is markedly higher in MG63, HOS, and U2OS cell lines compared with normal osteoblasts. Functional assays demonstrated that silencing SLC7A5 in MG63 cells significantly inhibited proliferation, colony formation, migration, and invasion, whereas SLC7A5 overexpression produced the opposite effects. In vivo experiments further validated these findings, showing that tumors formed by MG63 cells overexpressing SLC7A5 exhibited significantly increased volume and weight. Collectively, these results indicate that SLC7A5 plays a critical role in osteosarcoma progression and may serve as a novel therapeutic target.

SLC7A5 is a critical amino acid transporter that plays an important regulatory role in cellular metabolism and various biological processes [35]. It is primarily localized to the plasma membrane of cancer cells and is upregulated in multiple malignancies, including breast cancer, head and neck squamous cell carcinoma, and bladder cancer [36-39]. Studies have shown that SLC7A5 is an essential factor for the growth of KRAS-mutant colorectal cancer [40]. Ma and Wu et al. demonstrated that in gastric cancer, SLC7A5 promotes tumor proliferation through interaction with IGF2BP3 [23,41]. Gai et al. indicated that SLC7A5 influences therapeutic responses in non-small cell lung cancer by regulating amino acid metabolism [42]. In tumors such as breast cancer and bladder cancer, SLC7A5 expression is closely associated with immune evasion mechanisms and immunotherapy response, affecting the efficacy of anti-PD-1 immunotherapy by modulating the tumor immune microenvironment [36,43,44]. Jiang and Huang et al. demonstrated that in triple-negative breast cancer, SLC7A5 improves the sensitivity of the tumor to treatment by promoting cross-regulation of amino acid metabolism [43,44]. SLC7A5 plays a role in tumor treatment resistance. Zhou et al. confirmed that in certain cancers, SLC7A5, in conjunction with other molecules, enhances the resistance of the tumor to chemotherapy and radiotherapy [45]. At the functional mechanism level, SLC7A5 mediates the uptake of essential amino acids such as leucine and phenylalanine, and is closely associated with the mTORC1 signaling pathway, thereby regulating protein synthesis and cell growth [40,46,47]. In the present study, RNA-seq analysis of MG63 cells following SLC7A5 knockdown and HOS cells after SLC7A5 overexpression revealed significant enrichment of the mTOR signaling pathway in KEGG analysis of differentially expressed genes. Subsequent western blot experiments demonstrated that SLC7A5 OE promotes phosphorylation of mTOR and its downstream target S6, whereas SLC7A5 knockdown suppresses mTOR phosphorylation.

The cancer-specific expression of SLC7A5 provides an important basis for its application in clinical diagnosis. Jin et al. have shown that PET imaging using SLC7A5-specific tracers, such as FAMT, can effectively distinguish malignant tumors from benign inflammatory lesions [48], highlighting its significant potential in precision tumor imaging. In terms of therapy, targeting SLC7A5 shows promising prospects. Preclinical studies have mainly focused on small-molecule inhibitors and SLC7A5-conjugated antitumor drugs. Zhang and Saito et al reported that the SLC7A5-specific inhibitor JPH095 demonstrated significant antitumor activity with minimal toxicity in in vivo experiments [49,50], suggesting it could serve as a potential new strategy for treatment-resistant tumors. Structural studies of SLC7A5, particularly cryo-electron microscopy (cryo-EM) analyses, provide a solid foundation for designing more selective and effective inhibitors [51]. SLC7A5 has also been shown to mediate targeted delivery of antitumor drugs-for example, facilitating the uptake of L-4-boronophenylalanine in boron neutron capture therapy [27]. further expanding its potential applications in precision therapy. Taken together, SLC7A5 represents a highly promising target for both diagnosis and treatment, especially in malignancies that rely on its activity for growth. Treatment of osteosarcoma is gradually advancing into molecular targeted therapy and immunotherapy. Medical progress is increasing the clinical feasibility of personalized treatment strategies. As an important therapeutic target, SLC7A5 can potentially be combined with immune checkpoint inhibitors or CAR-T therapy. Further exploration of its regulatory role in the osteosarcoma immune microenvironment may enhance antitumor immune responses. In addition, incorporating SLC7A5 into prognostic models could improve early diagnosis and enhance the precision of individualized therapy.

Although multiple biomarkers, such as RAMP1 [52], SMARCB1 [53], have been proposed to predict therapeutic responses in osteosarcoma, their clinical application remains limited. Our study indicates that SLC7A5 plays a central role in the occurrence and progression of osteosarcoma. High SLC7A5 expression not only affects tumor behavior and patient prognosis but also regulates the immune microenvironment. Prognostic models based on SLC7A5 show significantly improved predictive accuracy, offering new directions for optimizing osteosarcoma treatment strategies and improving patient outcomes.

However, several limitations remain. The prognostic model was developed solely using the TCGA-SARC retrospective dataset, lacking external validation in an osteosarcoma-specific cohort. Analyses were primarily conducted at the mRNA level, without including protein expression or multicenter clinical samples. While in vitro experiments demonstrated that SLC7A5 overexpression promotes tumor proliferation and immune evasion, the underlying regulatory mechanisms still require verification in vivo. The causal relationship between SLC7A5 expression and immune cell infiltration remains unclear, and inconsistencies regarding the roles of TFH cells and CD56-dim NK cells suggest that the associated immune regulatory network needs further investigation.

Disclosure of conflict of interest

None.

Abbreviations

ANOVA

Analysis of Variance

BCA

Bicinchoninic acid

BPs

biological processes

CCs

cellular components

Cox

Cox proportional hazards model

DESeq2

Differential Expression Analysis for Sequence Count Data

FDR

False discovery rate

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

LAT1

Large neutral amino acid transporter 1

LASSO

Least Absolute Shrinkage and Selection Operator

Log-rank

Log-rank test

MFs

molecular functions

mRNA

Messenger RNA

NES

Normalized enrichment score

NK

natural killer

OS

osteosarcoma

PET

Positron emission tomography

PVDF

Polyvinylidene fluoride

RIPA

Radioimmunoprecipitation assay

ROC

Receiver operating characteristic

SLC7A5

Solute carrier family 7 member 5

ssGSEA

Single-sample Gene Set Enrichment Analysis

TCGA

The Cancer Genome Atlas

TIL

Tumor-Infiltrating Lymphocyte

TPM

Transcripts per million

Treg

Regulatory T cells