2.1. Cell culture

The human RB cell line Y79 (HTB-18) was purchased from the American Type Culture Collection (ATCC). The CBP-resistant subline Y79-R was generated by continuous exposure of the parental Y79 cells to stepwise increasing concentrations of carboplatin (starting from 0.1 μM up to a maintenance concentration) over a period of 6 months, and was maintained under this selective pressure. Resistance was validated by a significantly elevated IC₅₀ for carboplatin compared to the parental line. All cell lines were authenticated by short tandem repeat (STR) profiling and confirmed to be free of mycoplasma contamination prior to use. The cells were routinely cultured in suspension using RPMI-1640 complete medium (12633020, ThermoFisher, USA; containing 10% fetal bovine serum, 100 U/mL penicillin, and 100 μg/mL streptomycin) in a saturated humidity incubator at 37°C with 5% CO₂. The cells were passaged at a 1:2 ratio every 2–3 days, and cells in the logarithmic growth phase were used for experiments.

2.2. Dataset collection and differential expression analysis

The normalized gene expression matrix of the GSE58780 [22] dataset was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Bioinformatics analysis of genes in the GSE58780 dataset was performed using the Hg-U133 Plus 2.0 Affymetrix gene array. Differentially expressed genes (DEGs) were identified using the “Limma” R package, with the criteria of |log₂ fold change (FC)| > 1.5 and false discovery rate (FDR) < 0.05 after Benjamini–Hochberg multiple testing correction. Volcano plots of the DEGs were generated using the “ggplot2” R package. To explore the biological functions of the genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the “ClusterProfiler” package in R software, with a p-value threshold of < 0.05. The results were visualized using the “ggplot2” R package. Specifically, the DEGs between the RB group and the control group in the dataset were identified, and GO and KEGG analyses were conducted based on these DEGs.

2.3. Inference of SALL1 regulon and Two-tail GSEA

Based on the R package RTN [23], we utilized gene expression data to construct a regulatory network that links SALL1 to all its potential target genes. These target genes were referred to as the SALL1 regulon. The network could be simplified or “filtered” by applying data processing inequality functions with increasingly stringent thresholds, thereby increasing the likelihood that the target genes assigned to SALL1 were direct targets. Gene Set Enrichment Analysis (GSEA) was employed to assess the skewed distribution of a selected gene set within a gene list ranked according to a specific phenotype. In this study, GSEA was used to identify changes in the expression levels of the SALL1 regulon induced by cancer. The two-tail GSEA method was based on the Connectivity Map procedure [24]. The regulon was divided into a positive target set (P) and a negative target set (N) based on the Pearson correlation coefficient. The gene phenotype was ranked using Log2(Foldchange). Subsequently, the distribution of P and N in the ranked phenotype was tested using GSEA statistics, generating independent enrichment scores (ES) for each subgroup. The differential enrichment score was calculated as dES = ESpos - ESneg. The two-tail GSEA analysis was implemented using the “tni,gsea2” function in the RTN R package (permutation = 1000).

2.4. Analysis of key genes related to immune cell infiltration and construction of functional networks

Based on published evidence of immune infiltration in RB, we applied the LM22 signature matrix to deconvolve immune cell proportions from ocular tumor transcriptomic data [25]. Functional networks were constructed using Cytoscape. The CIBERSORT algorithm was applied to analyze the microarray expression data of the samples, infer the relative proportions of 22 types of immune-infiltrating cells, and perform Pearson correlation analysis between MGST1 expression and immune cell content. The deconvolution P‑values and root mean squared error (RMSE) for each immune cell type across all samples are provided in Supplementary Table 4. The “psych” R package was used to calculate the Pearson correlation between MGST1 expression and the expression levels of other genes in the data, and the genes were ranked accordingly. Next, the fast gene set enrichment analysis (fgsea) algorithm in the “ClusterProfiler” R package was used to perform GSEA on the pre-ranked gene list. The number of permutations was set to 1000. A false discovery rate (FDR) < 0.05 was considered statistically significant for biological functions.

2.5. SALL1 gene knockdown and overexpression

To reduce SALL1 expression, specific siRNAs against SALL1 (AM16708, Thermo Fisher Scientific, USA) and a negative control siRNA (4390843, Thermo Fisher Scientific, USA) were used. These siRNAs were transfected into Y79 cells at a final concentration of 50 nM using Lipofectamine 3000 transfection reagent (L3000001, Thermo Fisher Scientific, USA) when the cells reached 70–80% confluence. SALL1 expression levels were verified using qPCR and Western Blot.

A commercial lentiviral system was used to construct an SALL1-overexpressing Y79 cell model. The lentiviral vector carrying the human SALL1 gene (OE-SALL1) and the empty vector (EV) were purchased from Tsingke (China). Y79 cells in the logarithmic growth phase were seeded into 24-well plates at a density of 1 × 10⁵ cells per well. The viral solution was added at a multiplicity of infection (MOI) = 20, along with 8 μg/mL Polybrene. After 12 hours, the medium was replaced with fresh complete medium, and the cells were cultured for an additional 48 hours. Subsequently, the cells were cultured in medium containing 1.0 μg/mL puromycin for 7 days to select stable cell lines. SALL1 expression levels were verified using qPCR and Western Blot.

2.6. Real-Time Fluorescent Quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR)

Total RNA was extracted from the cells using Trizol (15596026CN, Thermo Fisher Scientific, USA). The integrity, quantity, and purity of the RNA were examined using a spectrophotometer. The experimental system was prepared using the BeyoFast™ SYBR Green One-Step qRT-PCR Kit 171 (D7268M, Beyotime) and amplified using the SLAN Automatic Medical PCR Analyzer. The primer sequences are provided in Supplementary Table 1. The reaction conditions were set as follows: 5 minutes at 37°C, 10 minutes at 50°C, followed by 2 minutes at 94°C, and then 40 cycles of 15 seconds at 94°C and 30 seconds at 60°C. Data analysis was performed using the 2^−ΔΔCt method, and corresponding statistical analyses were conducted.

2.7. Western blot

The cells were lysed in RIPA lysis buffer (89901, Thermo Fisher Scientific, USA) containing protease inhibitors, followed by sonication to further promote cell lysis. The protein concentration was determined using a BCA protein assay kit (A55864, Thermo Fisher Scientific, USA). The protein samples were separated by SDS-PAGE and then transferred onto PVDF membranes. The membranes were blocked in 5% skim milk for 1 hour and then incubated overnight with primary antibodies at 4°C (SALL1, PA5–115864, Invitrogen, USA; MGST1, ab131059, Abcam, USA; GPX4, ab125066, abcam, USA; SLC7A11, ab307601, abcam, USA; β-actin, ab8227, Abcam, USA; diluted 1:1000). After washing three times with PBST (15 minutes each), the membranes were incubated with diluted secondary antibodies (Goat Anti-Rabbit IgG H&L (HRP), ab6721, Abcam, USA; diluted 1:1000) at room temperature for 1 hour. After washing four times with PBST, enhanced chemiluminescence was performed using ECL luminescent solution (P0018M, Beyotime, China). Density analysis of each band was performed using ImageJ software, and quantitative statistical analysis was conducted.

2.8. Chromatin immunoprecipitation-quantitative PCR (ChIP-qPCR)

To verify the direct binding of the transcription factor SALL1 to the promoter region of MGST1, a ChIP-qPCR experiment was conducted. Y79 cells were cross-linked and then sonicated to fragment the chromatin into 200–500 bp fragments. Immunoprecipitation was performed using a specific antibody against SALL1 (PA5–115864, Invitrogen, USA), with normal IgG antibody serving as the negative control and the Input group as the positive control. The DNA fragments obtained from the precipitation were purified, and quantitative PCR was used to analyze specific fragments in the promoter region of MGST1 where the predicted SALL1 binding sites were located (primer sequences are provided in Supplementary Table 2). The data were calculated and analyzed using the relative Input percentage method.The percentage of Input for the SALL1-bound MGST1 promoter fragment was approximately 2%, confirming specific enrichment. The experiment was independently repeated three times.

2.9. Luciferase reporter assay

To verify the transcriptional activation of the MGST1 promoter by SALL1, a luciferase reporter system was used. The promoter sequence of MGST1 was cloned into the pGL3-Basic vector to construct the reporter plasmid pGL3-MGST1-promoter. Y79 cells were seeded into 24-well plates at a density of 1 × 10⁵ cells per well and co-transfected with the reporter plasmid and pcDNA3.1-SALL1 or the empty pcDNA3.1 vector using Lipofectamine 3000. After 48 hours of transfection, the cells were collected, and luciferase activity was measured using a dual-luciferase reporter gene assay kit (RG027, Beyotime, China). Renilla luciferase activity was used as an internal control for normalization. The experiment was independently repeated three times.

2.10. Cell Counting Kit-8 (CCK8) assay

Cells were seeded into 96-well plates, with approximately 1000–2000 cells and 100 μL of medium per well. After incubation in a 37°C incubator for 24 hours, 10 μL of CCK-8 reagent (96992, Sigma, USA) was added to each well. The optical density (OD) values of the cells were measured at 0, 24, 48, and 72 hours.

2.11. Colony formation assay

After corresponding transfection treatments, Y79 cells were seeded into 6-well plates at a low density (500 cells per well) and cultured in RPMI-1640 medium containing 10% fetal bovine serum for 14 days. The medium was refreshed every 3 days. At the end of the culture period, the medium was discarded, and the cells were fixed with 4% paraformaldehyde (441244, Sigma, USA) for 15 minutes and stained with 0.1% crystal violet for 30 minutes. The number of cell colonies with a diameter greater than 50 μm was counted, and the colony formation rate was compared among the groups. The experiment was independently repeated three times.

2.12. Lipid peroxidation assay

The lipid peroxidation level in Y79 cells was detected using a lipid peroxidation detection kit (BODIPY 581/591, C11S0043S, Beyotime, China) according to the manufacturer’s instructions. The reduced form of BODIPY 581/591 C11 has a maximum excitation wavelength of 581 nm and a maximum emission wavelength of 591 nm, predominantly emitting red fluorescence, resulting in a relatively large red-to-green fluorescence ratio. After oxidation by lipid hydroperoxides, the maximum excitation and emission wavelengths shift to approximately 488/510 nm, predominantly emitting green fluorescence, which reduces the red-to-green fluorescence ratio. The degree of lipid peroxidation can be determined based on this ratio. In this experiment, the positive control reagent LpoUp provided in the kit was not used.

2.13. Malondialdehyde (MDA) content assay

To evaluate the lipid peroxidation level in Y79 cells, the thiobarbituric acid (TBA) assay was used to detect the intracellular MDA content. Cells from different treatment groups were collected, washed with pre-cooled PBS, and lysed in cell lysis buffer on ice for 30 minutes. After centrifugation at 12,000 rpm for 10 minutes, the supernatant was collected. The MDA detection kit (BC0025, Solarbio, China) was used strictly according to the instructions: an appropriate amount of supernatant was mixed thoroughly with the TBA working solution, heated in a 95°C water bath for 40 minutes, cooled, and the absorbance was measured at 532 nm. The MDA concentration in each sample was calculated based on the standard curve and normalized to the total protein concentration. The experiment was independently repeated three times.

2.14. GSH content assay

To detect the intracellular GSH level in Y79 cells, a GSH and GSSG detection kit (S0053, Beyotime, China) was used. The following steps were briefly performed: approximately 1 × 10⁶ cells from different treatment groups were collected, washed with pre-cooled PBS, and mixed with the protein removal reagent provided in the kit. The mixture was vortexed on ice and allowed to stand for 10 minutes, followed by centrifugation at 10,000 rpm at 4°C for 10 minutes. The supernatant was collected as the sample to be tested. According to the kit instructions, the sample was mixed with the chromogenic agent and incubated at 37°C in the dark for 20 minutes, and the absorbance was measured at 420 nm. The absolute GSH concentration in the sample was calculated based on the standard curve and normalized to the total protein concentration determined by the BCA method.

2.15. IC50 assay

Cells in the logarithmic growth phase were seeded into 96-well plates at a density of 5 × 10³ cells per well. Blank zero-adjustment wells and different concentration groups of CBP (0–1000 μM, C2538, Sigma, USA) treatment groups were set up, with 5 replicate wells per group [26–28]. The cells were cultured at 37°C with 5% CO₂ for 48 hours. Then, 10 μL of CCK-8 solution was added to each well, and the cells were incubated for an additional 2 hours. The absorbance of each well was measured at 450 nm using a microplate reader. GraphPad Prism 9 was used to perform non-linear regression fitting, with the CBP as the x-axis (logarithmically transformed) and the cell survival rate of Y79 and Y79-R cells as the y-axis. The half‑maximal inhibitory concentration (IC₅₀) values, along with the Hill slope and 95% confidence intervals (95% CI), were derived from the fitted curves. The experiment was independently repeated three times.

2.16. Live/Dead cell staining assay

Cell viability and death were assessed using a Live/Dead Cell Double-Staining Kit (04511, Sigma, USA). Briefly, after treatment, cells were collected and washed twice with pre-warmed PBS. The cells were then resuspended in 1 mL of staining buffer containing 2 μM Calcein-AM and 4.5 μM Propidium Iodide (PI) and incubated at 37°C in the dark for 15–20 minutes. After incubation, the cells were washed once with PBS and immediately imaged under a fluorescence microscope. Live cells (with intact esterase activity) were stained green by Calcein-AM (excitation/emission: ~ 494/517 nm), while dead cells (with compromised plasma membrane integrity) were stained red by PI (excitation/emission: ~ 535/617 nm). At least three random fields per sample were captured, and the numbers of live (green) and dead (red) cells were counted using ImageJ software.

2.17. Statistical analysis

All statistical analyses of the experimental results were performed using GraphPad Prism 9. The sample size (n) for all experiments represents the number of independent biological replicates. The normality of data distribution was assessed using the Shapiro-Wilk test, and the homogeneity of variances was verified using Levene’s test. For data meeting the assumptions of normality and homogeneity of variances, parametric tests were applied (unpaired two-tailed t-test for two-group comparisons or one-way ANOVA with Tukey’s post hoc test for multiple comparisons). Effect sizes (Cohen’s d for t‑tests, partial η² for ANOVA) are reported where applicable. The data were presented as the mean ± standard deviation (mean ± SD). A p-value < 0.05 was considered statistically significant.

3.1. Differential gene analysis reveals upregulation of SALL1 in RB

Differential analysis based on the GSE58780 dataset revealed 337 upregulated genes and 374 downregulated genes between the RB group and the control group (Fig 1a). The top 10 most significantly upregulated and downregulated genes were selected to generate a heatmap (Fig 1b). It was found that among the upregulated genes in RB tissues, the gene encoding the transcription factor SALL1 exhibited the most significant upregulation, demonstrating both the largest log2 fold change (logFC) and the most significant p-value. This expression profile suggests a positive correlation between SALL1 and the disease progression of RB. The coordinated upregulation of SALL1 along with genes such as FOXM1 and PRAME may collectively promote malignant proliferation and immune evasion of tumor cells. In contrast, the significant downregulation of differentiation-related genes such as MEIS1 and ISL1 may indicate impaired cell differentiation and loss of retinal function. GO and KEGG enrichment analyses revealed that the DEGs were significantly enriched in multiple biological processes. In biological processes (BP) (Fig 1c), they were mainly enriched in “mitotic cell cycle phase transition,” indicating typical cell cycle checkpoint dysregulation and abnormal proliferation in RB. Additionally, the significant enrichment of the “eye development” process suggests a close relationship between tumor development and the reprogramming and differentiation blockade of retinal-specific developmental programs. In cellular components (CC), genes were enriched in mitotic structures such as the spindle and kinetochore (Fig 1d). In molecular functions (MF), they were primarily enriched in ATP-dependent DNA binding activity and microtubule binding activity (Fig 1e). KEGG analysis showed significant enrichment of DEGs in the cell cycle and DNA replication pathways, revealing the core mechanisms underlying uncontrolled proliferation and genomic instability in RB. The activation of the PI3K-Akt signaling pathway indicates significant survival advantages and apoptotic evasion in tumor cells (Fig 1f). These results collectively suggest the presence of cell cycle dysregulation, genomic instability, and an abnormal proliferation-differentiation balance in RB, providing a basis for core mechanism research and targeted therapy.

Download:

• PNG
  larger image
• TIFF
  original image

Fig 1. Differential Gene Analysis Reveals Upregulation of SALL1 in RB.

(a) Volcano plot showing the distribution of DEGs between the RB group and the control group. (b) Heatmap showing the distribution of DEGs between the RB group and the control group. (c-e) GO analysis of DEGs between the RB group and the control group. (f) KEGG analysis of DEGs between the RB group and the control group.

https://doi.org/10.1371/journal.pone.0353000.g001

3.2. Construction of the SALL1 regulatory network and functional analysis in RB

To elucidate the regulatory role of the transcription factor SALL1 in RB, this study constructed an SALL1-centered transcriptional regulatory network (SALL1 regulon) using the R package RTN based on gene expression data. The network inferred target gene relationships through mutual information algorithms and validated their statistical significance using permutation tests and bootstrap methods (Fig 2a–c). The analysis revealed highly significant changes in SALL1 regulatory activity, with the regulon comprising 59 positively regulated targets and 104 negatively regulated targets. In RB tissues, two-tailed GSEA analysis indicated significant changes in SALL1 regulon activity (dES = 1.48, p = 0.0029). Functional enrichment analysis of the SALL1 regulon using ClusterProfiler revealed significant enrichment in pathways such as “sensory organ development” and “excitatory synapse assembly” (Fig 2d). The former suggests a critical role of SALL1 in the differentiation and maintenance of retinal photoreceptor cells, with its dysfunction potentially closely related to the cellular origin of RB. The latter may affect inter-neuronal signal transmission and participate in the abnormal behavior of tumor cells. Further sample expression correlation analysis validated the regulatory relationship between SALL1 and its target genes (Fig 2e–f). Among them, MGST1 showed a significant positive correlation with SALL1 expression, indicating that MGST1 is a direct downstream target of SALL1. MGST1, a membrane-bound enzyme localized in the endoplasmic reticulum and mitochondrial outer membrane, reduces phospholipid hydroperoxides through its glutathione peroxidase activity, inhibiting lipid peroxidation. It is a key inhibitor of ferroptosis and a potential therapeutic target for cancer [29]. These findings suggest that SALL1 may enhance the antioxidant and detoxification capabilities of tumor cells by upregulating MGST1, accelerate drug efflux from tumor cells, leading to drug inactivation and resistance. Additionally, SALL1 may reduce tumor cell sensitivity to ferroptosis by upregulating MGST1, thereby decreasing tumor cell death.

Download:

• PNG
  larger image
• TIFF
  original image

Fig 2. Construction of the SALL1 Regulatory Network and Functional Analysis in RB.

(a) Significance of transcription factor (TF) activity changes, with colors representing the fold change in TF expression between groups. (b) Double-tailed GSEA analysis of SALL1. (c) Regulatory network of SALL1. (d) Enriched pathways of SALL1 target genes. (e-f) Bar charts and scatter plots showing the correlation between SALL1 scores and target gene scores.

https://doi.org/10.1371/journal.pone.0353000.g002

3.3. Immune microenvironment regulation and functional enrichment analysis of MGST1

This study used the CIBERSORT algorithm to calculate the infiltration proportions of 22 immune cell types in the samples (Fig 3a). Correlation analysis revealed that MGST1 expression was significantly positively associated with resting dendritic cells and activated mast cells, was significantly negatively associated with Regulatory T Cells (Tregs), naive B cells, and CD8⁺ T cells (Fig 3b). This suggests that high MGST1 expression may be associated with the formation of an immunosuppressive microenvironment and weakened anti-tumor immune responses. To further explore the function of MGST1, we analyzed its correlation with other genes (Fig 3c). MGST1 showed a significant positive correlation with the pro-apoptotic gene BAD, the transcriptional repressor ERF, and the receptor tyrosine kinase ALK. Conversely, MGST1 exhibited a significant negative correlation with SMAD9, a key regulator of the BMP signaling pathway; CD24, an immune-regulatory membrane protein; and PTPRG, a tumor suppressor-associated tyrosine phosphatase. These results suggest that MGST1 may be involved in regulating various biological processes, including cell apoptosis, signal transduction, and the immune microenvironment. GSEA enrichment analysis based on genome-wide Spearman correlations (MsigDB C5 GO BP database) revealed a significant positive correlation between MGST1 expression and the “reactive oxygen species metabolic process” (Fig 3d), indicating that MGST1 may play a central role in regulating intracellular redox balance. In RB, high MGST1 expression may enhance cellular metabolic capacity for reactive oxygen species, reduce oxidative stress damage, and thereby inhibit oxidative stress-related cell death pathways such as ferroptosis, which might ultimately promot tumor cell survival and the formation of chemotherapy resistance.

Download:

• PNG
  larger image
• TIFF
  original image

Fig 3. Immune Microenvironment Regulation and Functional Enrichment Analysis of MGST1.

(a) Heatmap showing the distribution of immune cell proportions. (b) Lollipop plot showing the correlation between MGST1 expression levels and immune cell content. (c) Bar chart showing the correlation between MGST1 scores and other gene scores. (d) Biological function network influenced by MGST1, as determined by GSEA.

https://doi.org/10.1371/journal.pone.0353000.g003

3.4. SALL1 binds to the MGST1 promoter and enhances its activity in RB

To elucidate the functional relationship between SALL1 and MGST1 in RB, this study first established SALL1 knockdown and overexpression cell models in Y79 cells. qPCR and Western blot results showed that SALL1 knockdown significantly reduced both mRNA and protein levels of SALL1 and MGST1 (Fig 4a-c). Conversely, SALL1 overexpression markedly upregulated the transcription and translation of both SALL1 and MGST1 (Fig 4d-f). These findings indicate that SALL1 positively regulates MGST1 expression. To investigate whether this regulation occurs at the transcriptional level through promoter binding, we performed ChIP-qPCR experiments. The results showed that SALL1 binds to the promoter region of MGST1 (Fig 4g). Subsequently, luciferase reporter gene experiments confirmed that SALL1 significantly enhances the transcriptional activity of the MGST1 promoter (Fig 4h), further supporting the conclusion that SALL1, as a transcription factor, directly activates MGST1 expression. Functionally, CCK8 assays revealed that SALL1 knockdown significantly inhibited the proliferation of Y79 cells. However, overexpression of MGST1 in this context partially restored the proliferation inhibition phenotype caused by SALL1 knockdown. Conversely, SALL1 overexpression increased cellular proliferation, and this enhanced proliferative capacity could be attenuated by concomitant MGST1 knockdown. (Fig 4i). Similarly, colony formation assays showed that MGST1 overexpression reversed the decline in colony-forming ability induced by SALL1 knockdown (Fig 4j-k).These results suggest that MGST1 is a key effector molecule downstream of SALL1 that promotes proliferation. SALL1 may enhance the proliferative activity of RB cells by positively regulating MGST1 expression.

Download:

• PNG
  larger image
• TIFF
  original image

Fig 4. SALL1 Binds to the MGST1 Promoter and Enhances its Activity in RB.

(a) qPCR results showing that SALL1 knockdown downregulates MGST1 mRNA expression (n = 6). MGST1, Ctrl vs si-SALL1–1, p = 0.01; Ctrl vs si-SALL1–2, p = 0.01. (b-c) Western blot results showing that SALL1 knockdown downregulates MGST1 protein expression (n = 3). MGST1, Ctrl vs OE-SALL1, p = 0.01. (d) qPCR results showing that SALL1 overexpression upregulates MGST1 mRNA expression (n = 6). (e-f) Western blot results showing that SALL1 overexpression upregulates MGST1 protein expression (n = 3). (g) ChIP-qPCR results showing direct binding of SALL1 to the DNA promoter region of MGST1 (n = 3). (h) Luciferase reporter assay results showing that SALL1 increases MGST1 promoter reporter activity (n = 6). (i) CCK8 results showing that MGST1 overexpression rescues the proliferation inhibition of RB cells after SALL1 knockdown, and knockdown of MGST1 reduces the enhanced proliferative capacity of RB cells following SALL1 overexpression (n = 6). (j-k) Colony formation assay results showing that MGST1 overexpression rescues the proliferation inhibition of RB cells after SALL1 knockdown (n = 3). si-SALL1 vs si-SALL1 + OE-MGST1, p = 0.007. The statistical analysis for ChIP-qPCR is Student’s t-test, while one-way ANOVA with Tukey’s multiple comparisons test was used for the other statistical analyses. *P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant. Error bars represent mean ± SD.

https://doi.org/10.1371/journal.pone.0353000.g004

3.5 Targeting the SALL1-MGST1 axis in RB inhibits ferroptosis and reverses tumor drug resistance

To further investigate the functional role of the SALL1-MGST1 axis in RB, we first examined the effect of SALL1 knockdown on lipid peroxidation levels in Y79 cells. Lipid peroxidation assays revealed that SALL1 knockdown significantly increased lipid peroxidation levels in Y79 cells (Fig 5a-b), suggesting enhanced sensitivity to ferroptosis. To further validate the correlation of this phenotype with ferroptosis, we treated the cells with the ferroptosis inhibitor Ferrostatin-1 (Fer-1) and deferoxamine (DFO). CCK-8 assay results showed that treatment with Fer-1 and DFO significantly rescued the proliferation inhibition of Y79 cells caused by SALL1 knockdown (Fig 5c). We further examined ferroptosis-related proteins in Y79 cells by Western blot. The results showed that the expression of both GPX4 and SLC7A11 decreased following SALL1 knockdown, while their expression increased after treatment with Fer-1 and DFO (Fig 5d-e). These results indicate that the proliferation defect induced by SALL1 knockdown is partially dependent on the ferroptosis pathway. Subsequently, we used the ferroptosis inducer Erastin to further intervene in the cells. Erastin treatment alone significantly induced lipid peroxidation in Y79 cells, while combined treatment with SALL1 knockdown further elevated lipid peroxidation levels, showing a synergistic effect (Fig 5f-g). To quantitatively assess the extent of oxidative damage, we measured intracellular MDA content and GSH levels (Fig 5h-i). MDA, a stable end product of lipid peroxidation, reflects the degree of cellular oxidative damage, while GSH, a key intracellular antioxidant molecule, indicates the depletion of the antioxidant defense system. The experimental results showed that in the group with SALL1 knockdown combined with Erastin treatment, MDA content significantly increased, while GSH levels markedly decreased, further confirming a significant enhancement in oxidative stress levels and a decline in antioxidant capacity under these conditions.

Download:

• PNG
  larger image
• TIFF
  original image

Fig 5. Targeting the SALL1-MGST1 Axis in RB Inhibits Ferroptosis and Reverses Tumor Drug Resistance.

(a-b) Lipid peroxidation assay results showing that SALL1 knockdown increases lipid peroxidation levels in Y79 cells. Scale bar = 100μm, n = 3. (c) CCK8 results showing that ferroptosis inhibition alleviates the proliferation inhibition of Y79 cells caused by SALL1 knockdown, while overexpression of SALL1 increases the proliferative capacity of RB cells (n = 6). (d-e) Western blot analysis demonstrated that knockdown of SALL1 decreased the expression of ferroptosis-related proteins, while the addition of Fer-1 and DFO increased their expression (n = 3). GPX4, si-SALL1 vs si-SALL1 + DFO, p = 0.004. (f-g) Lipid peroxidation assay results showing that the ferroptosis inducer increases lipid peroxidation levels in Y79 cells after SALL1 knockdown. Scale bar = 100μm, n = 3.(h) MDA assay results showing that the ferroptosis inducer increases lipid peroxidation levels in Y79 cells after SALL1 knockdown (n = 6). (i) GSH assay results showing that the ferroptosis inducer decreases the antioxidant capacity of Y79 cells after SALL1 knockdown (n = 6). (j) IC50 results showing stronger resistance in Y79-R cells (n = 6). (k) IC50 results showing decreased resistance in Y79-R cells after SALL1 knockdown, whereas overexpression of SALL1 enhanced it. (n = 6). (l-m) Live/dead cell double-staining assay results showing higher sensitivity of Y79-R cells to CBP after SALL1 knockdown. Scale bar = 100μm, n = 3. Statistical analysis is one-way ANOVA with Tukey’s multiple comparisons test. *P < 0.05, **P < 0.01, ***P < 0.001. Error bars represent mean ± SD.

https://doi.org/10.1371/journal.pone.0353000.g005

To investigate the role of the SALL1-MGST1 axis in chemotherapy resistance, we compared the drug sensitivity of Y79 cells and their CBP-resistant strain, Y79-R. IC50 determination results showed that the IC50 value of Y79-R cells for CBP was significantly higher than that of Y79 cells, confirming their resistant phenotype (Fig 5j). Subsequently, SALL1 knockdown in Y79-R cells significantly reduced their IC50 value, indicating partial reversal of resistance. This effect could be rescued by MGST1 overexpression. Conversely, SALL1 overexpression increased the IC50 values of Y79-R cells, an effect attenuated by MGST1 knockdown (Fig 5k). Taken together, these results suggest that SALL1-mediated regulation of drug resistance may depend on MGST1. Finally, we assessed cell mortality using a live/dead cell double-staining assay. In CBP-treated Y79-R cells, SALL1 knockdown significantly increased cell death, while concurrent treatment with Fer-1 or MGST1 overexpression markedly alleviated this death phenotype (Fig 5l-m). These results consistently indicate that SALL1 inhibits ferroptosis by upregulating MGST1 expression, thereby enhancing the resistance of RB cells to CBP.

3.6. Schematic diagram of the mechanism by which targeting the SALL1-MGST1 axis in RB inhibits ferroptosis and reverses tumor drug resistance

As shown in Fig 6, this study revealed the mechanism by which SALL1 regulates CBP sensitivity in RB. SALL1 directly binds to the promoter region of MGST1 and activates its transcription. The upregulated MGST1 inhibits the accumulation of lipid peroxides, thereby blocking ferroptosis and reducing the sensitivity of tumor cells to CBP while promoting proliferation (Fig 6a). After SALL1 knockdown, MGST1 transcription decreases, and its expression declines, leading to increased lipid peroxidation levels and enhanced ferroptosis, ultimately promoting cell apoptosis and significantly improving CBP chemosensitivity (Fig 6b). In summary, the SALL1-MGST1 axis promotes RB proliferation and CBP resistance by inhibiting ferroptosis. This mechanism provides a new perspective for understanding chemotherapy resistance in RB and lays a theoretical foundation for the development of novel therapeutic strategies targeting this pathway.

Download:

• PNG
  larger image
• TIFF
  original image

Fig 6. Schematic Diagram of the Mechanism by Which Targeting the SALL1-MGST1 Axis in RB Inhibits Ferroptosis and Reverses Tumor Drug Resistance.

https://doi.org/10.1371/journal.pone.0353000.g006