YY1: a key regulator inhibits gastric cancer ferroptosis and mediating Apatinib-resistance

doi:10.21203/rs.3.rs-2993818/v1

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Research Article

YY1: a key regulator inhibits gastric cancer ferroptosis and mediating Apatinib-resistance

https://doi.org/10.21203/rs.3.rs-2993818/v1

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Background

Gastric cancer (GC) is one of the most common and lethal cancers worldwide. Apatinib, a preoperative neoadjuvant therapy, has limited efficacy in GC patients with high YY1 expression. YY1 is associated with poor prognosis, drug resistance and metastasis in GC, but its mechanisms are unclear.

Results

We observed that higher YY1 expression levels were associated with lower postoperative progression-free survival (PFS) and disease-specific survival (DSS) rates based on the TCGA analysis. Moreover, YY1 expression was found to be an independent indicator of DSS in GC patients. Additionally, YY1 expression was significantly higher in tumor tissues compared to adjacent normal tissues. Bioinformatics analysis revealed significant differentially expressed genes (DEGs), transcriptional targets, transcription factors, and co-expressed genes related to YY1. Through LASSO Cox analysis, we identified Transferrin as a potential key protein regulated by YY1, and its high expression was correlated with adverse DSS and PFS outcomes. Further analysis showed that YY1 regulated the activity of the p53 signaling pathway and ferroptosis in GC cells. The study also revealed that YY1 overexpression suppressed immune cell infiltration in GC tumors. Furthermore, the study demonstrated that YY1 overexpression inhibited GC cell ferroptosis and mediated Apatinib resistance through the p53 signaling pathway. IFN-a reverses Apatinib resistance and immune suppression in GC tissues.

Conclusions

Our findings suggest that YY1 plays a crucial role in GC progression and prognosis and may serve as a potential therapeutic target for improving patient outcomes.

Gastric cancer

YY1

ferroptosis

Apatinib

p53

Gastric cancer (GC) is a common malignant tumor with high incidence and poor prognosis. According to the latest epidemiological research statistics, GC ranks sixth in the world in incidence and third in mortality^[1]. Additionally, GC has an insidious onset and a high morbidity rate. About half of GC patients lose the opportunity for radical surgery due to locally advanced or distant metastasis at the time of treatment, thereby resulting in a high mortality rate. The survival data for GC in different regions have shown that, with tumor-stage progression, the 5-year survival rate of GC gradually decreases, and the downward trend is more clear after stage IIIb^[2]. Besides improving the early diagnosis rate of GC using early gastroscopy screening, the remaining first-diagnosed advanced GC patients can only improve their prognosis through multidisciplinary comprehensive treatments^[3].

In our previous clinical practice and research, we have found that YY1 is significantly overexpressed in GC patients with poor regression under Apatinib as preoperative neoadjuvant therapy. Additionally, even when Apatinib was used in combination with XELOX, about 30% of patients still presented drug resistance (JGCA TRG 0-Ia).

As a small molecule inhibitor of tyrosine kinase, Apatinib can reduce VEGF-mediated endothelial cell migration and proliferation and inhibit tumor micro-angiogenesis by binding and suppressing the activation of the vascular endothelial growth factor receptor 2 (VEGFR-2) with high selectivity. Apatinib can also improve the resistance of chemotherapeutic drugs^[4], and sensitize immune checkpoint inhibitors to a certain extent^[5–7], comprehending the world's first safe and effective small-molecule anti-vascular drug for advanced GC treatment. Moreover, Apatinib, has greatly benefited domestic patients. However, drug resistance and acquired drug resistance to targeted drugs are still concerning problems. Additionally, different patients can present diverse reactions due to their high heterogeneity. For example, the objective response rate (ORR) of Apatinib is only 1.7% (compared to 0% in the placebo group), and the disease control rate is 31.82% (compared to 10.99% in the placebo group)^[8]. Finally, drug resistance can also be acquired after long-term treatment, which seriously restricts the prognosis of GC patients in China.

Many cancer-related cells, such as B and follicular lymphoid tumor, acute myeloid leukemia, gastric, osteosarcoma, cervical, brain, prostate, colon, ovarian, breast, and lung cancer cells, have a high expression of YY1^[9]. Meanwhile, it has been shown that the expression of YY1 is significantly associated with poor prognosis, drug resistance, and cancer metastasis. Additionally, YY1 is significantly involved in the progression of many tumors. However, the mechanisms in which YY1 exerts its promotive effect on tumor growth vary for different tumors. On the other hand, YY1 can present a dual function on transcriptional regulation and tumor growth, acting both as an activator and a suppressor^[10]. Therefore, the in-depth understanding of the mechanisms underlying the roles of YY1 as a tumor promoter and suppressor is of great interest for the design of novel drug candidates to target YY1 function in tumor therapy.

Access to TCGA datasets, data normalization, and analysis of differentially expressed genes (DEGs)

First, we retrieved the expression profiles and downloaded the v22 version ( (https://www.gencodegenes.org/human/release_22/gencode.v22.annotation.gff3.gz) file and the v33 version (http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_33/gencode.v33.annotation.gff3.gz) of the gff3 files from GENCODE http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human)^[11]. Then, we extracted the mapping information of GeneSymbol and ENSG_ID, using the map ENSG_ID to GeneSymbol function. When multiple matches were detected, the median was retrieved, and the converted expression profile was finally obtained.

To identify the DEGs between control and different comparison groups, differential expression analysis was conducted using the “limma” R package^[12]. Specifically, for the expression profile dataset obtained, the proportion with an expression value of 0 greater than 50% of the genes was removed, and the “voom” function was used for data transformation. Next, we used the “lmFit” function for multiple linear regression, and the “eBays” function to calculate the moderated t- and F-statistics. We also used the log-odds of differential expression by empirical Bayes moderation of the standard errors towards a common value, resulting in a significant difference for each gene. Finally, we obtained the significant difference of each gene, resulting in the DEGs between high and low YY1 expression groups.

Functional annotation and pathway enrichment analyses

The transcriptional targets and transcription factors related to YY1 were obtained from TRRUST v2^[13] (https://www.grnpedia.org/trrust/). We used GO annotations via the “org.Hs.eg.db” (version 3.1.0) R package. The latest gene annotation of KEGG pathways, used as a background, was obtained using the KEGG rest API (https://www.kegg.jp/kegg/rest/keggapi.html). Then, the “clusterProfiler” R package was used to map the genes into the background set, and the enrichment analysis was conducted to evaluate the enrichment of the gene set. The gene set number was defined between 5 to 5000, and significant differences were defined by an FDR < 0.25 and p < 0.05. To explore the function of DEGs, we used GENEMANIA^[14] and constructed a gene-gene interaction network. Next, we performed gene set enrichment analysis (GSEA) using the GSEA software (version 3.0; http://software.broadinstitute.org/gsea/index.jsp). We predefined the gene ranking and used the c2.all.v7.4.symbols.gmt sub-collection to assess related pathways and molecular mechanisms. Based on a predetermined gene ranking, the gene set number was defined between 5 to 5000 and one thousand resamplings were performed. Finally, significant differences were defined by an FDR < 0.25 and p < 0.05.

Evaluation of immune cell infiltration

Furthermore, we calculated the stromal for each patient in each tumor group based on the gene expression profile using the “ESTIMATE” R package^[15]. The infiltration of immune cells was analyzed using ssGSEA. Further, to evaluate the correlation coefficients between immune cell infiltration and YY1 expression, we used the Tumor IMmune Estimation Resource (TIMER 2.0)^[16–18].

Construction of YY1-overexpressed and SLC7A11-knockdown cells and transfection

The YY1-overexpressed and SLC7A11-knockdown human HGC-27 GC and mice MFC GC cells were purchased from Merdobio Co. Ltd. and the Cell Bank of the Institute of Biochemistry and Cell Biology, respectively. Cells were cultured in FBS (10%, Biotime)-supplemented DMEM medium (Biotime)^[19]. Lentivirus packaging was performed by Merdobio Co. Ltd. (Shanghai) and cells were transfected with Lipofectamine (Life) as previously described. Briefly, target cells were infected with filtered lentivirus plus 6 µg/mL polybrene to generate stable cell lines. Then, MFC and HGC-27 cells were infected with viruses, and puromycin was added for selection.

Cell proliferation, viability, and invasion assays

First, the CCK-8 solution (20 µL, Dojindo) was added to each well of cells (1000 cells/100 µL medium) at the indicated time points to evaluate cell growth and viability. Then, 10⁶ GC cells were added into the upper chamber membrane pre-coated with Matrix Gel, and 10% FBS-containing DMEM was added into the lower chamber, as previously described^[20]. After 48 h of incubation, the invaded cells were fixed, stained, and counted to evaluate the invasion capacity.

Measurement of iron concentration and oxidative stress

The concentrations of total iron and intro cellular Fe²⁺ and GSH were detected using an iron assay kit from Merdo Bio Inc. (Shanghai, China) and Abcam. To detect changes in the oxidative stress of cells, the malondialdehyde concentration was determined using commercial kits (Nanjing Jiancheng).

Western blot and qRT-PCR

After extraction using Trizol and quality control, RNA was reversely transcribed to cDNA using the PrimeScript™ RT reagent Kit (Takara). Then, the qRT-PCR was conducted using the SYBR® Premix Ex Taq™ II (Takara) to quantify the expression of the target gene. The protein level changes were determined by Western blot, as previously described^[21].

Animal assays

Male nude mice (6 weeks) were obtained from the SLAC ANIMAL (Shanghai, China) and housed in specific-pathogen-free (SPF) conditions^[22]. During experiments, food and water were freely accessed by the animals. All procedures, operations, and protocols were carefully reviewed by The Research Ethics Committee of Zhongshan Hospital and approved. For the murine xenograft model, 3×10⁶ lentivirus-infected cells were subcutaneously injected into mice. After 5 weeks of injection, the mice were killed and tumor tissues were collected and resected for further analysis. For tumor therapy, mIFN-α formulation (1x10⁴ IU, Miltenyi Biotec; #130-093-130) or anti-PD-1 antibody (10 mg/kg, Bio X Cell, BE0061, RRID: AB_1125541) were intraperitoneally injected every day. The isotype control, mAb (BioXCell, BE0089), was used as a negative control. After 6 weeks of tumor inoculation, the tumor tissues were collected for further histological analysis^{[23, 24]}.

Immunohistochemistry (IHC) detection

The IHC was performed as previously described^[25]. The antibodies used for IHC were anti-CD27 (Abcam Cat# ab214043, 1:100), anti-CD8 (Abcam Cat# ab109228, 1:100), anti-CD19 (Abcam Cat# ab245235, 1:100), anti-PD-L1 (Abcam Cat# ab213480, 1:100), and anti-YY1 (Abcam Cat# ab109228, 1:250). The scores of the IHC results were blindly accessed by two pathologists. Briefly, we defined the negative staining as score 0; weak staining (ex. light yellow) as score 1; moderate staining (ex. yellow-brown) as score 2; and strong staining (ex. brown) as score 3. The percentage of positively stained cells was defined as the proportion between 0–100%.

Statistical analyses

All data were processed using SPSS 21.0 (SPSS Inc.). The median was selected as the cut-off value for expression. Spearman correlation analyses were carried out to evaluate the correlations. The Fisher exact or χ² tests were used to analyze categorical variables. Meanwhile, continuous variables were analyzed using the Wilcoxon rank-sum, t-tests or ANOVA. The Kaplan-Meier curves were used to analyze survival outcomes, and the survival from indicated groups was compared using the log-rank test, and multivariate analysis based on the Cox proportional hazards method. Results were visualized using the “ggplot2” (3.3.3) R package. All statistical analyses were 2-sided and a p < 0.05 was considered statistically significant^[26].

The bioinformatics analysis of TCGA STAD dataset indicated that a high expression of YY1is related to poor GC prognosis

First, we analyzed the expression of YY1in GC tumors and adjacent normal tissues and divided all enrolled patients into two groups based on their YY1 expression. The basic characteristics of patients are summarized in Table 1.

Table 1

Basic characteristics of patients
Characteristic	Low YY1(n = 187)	High YY1(n = 188)	p
Age, meidan (IQR)	66 (58, 73)	68 (59, 73)	0.522
Gender, n (%)			0.718
Female	69 (18.4%)	65 (17.3%)
Male	118 (31.5%)	123 (32.8%)
T stage, n (%)			0.066
T1	12 (3.3%)	7 (1.9%)
T2	46 (12.5%)	34 (9.3%)
T3	86 (23.4%)	82 (22.3%)
T4	40 (10.9%)	60 (16.3%)
N stage, n (%)			0.159
N0	64 (17.9%)	47 (13.2%)
N1	48 (13.4%)	49 (13.7%)
N2	34 (9.5%)	41 (11.5%)
N3	31 (8.7%)	43 (12%)
M stage, n (%)			0.398
M0	168 (47.3%)	162 (45.6%)
M1	10 (2.8%)	15 (4.2%)
Pathologic stage, n (%)			0.189
Stage I	30 (8.5%)	23 (6.5%)
Stage II	59 (16.8%)	52 (14.8%)
Stage III	69 (19.6%)	81 (23%)
Stage IV	14 (4%)	24 (6.8%)
Histological type, n (%)			0.387
Diffuse Type	36 (9.6%)	27 (7.2%)
Mucinous Type	12 (3.2%)	7 (1.9%)
Not Otherwise Specified	102 (27.3%)	105 (28.1%)
Papillary Type	1 (0.3%)	4 (1.1%)
Signet Ring Type	5 (1.3%)	6 (1.6%)
Tubular Type	31 (8.3%)	38 (10.2%)
Histologic grade, n (%)			0.897
G1	6 (1.6%)	4 (1.1%)
G2	69 (18.9%)	68 (18.6%)
G3	109 (29.8%)	110 (30.1%)
Anatomic neoplasm subdivision, n (%)			0.543
Antrum/Distal	70 (19.4%)	68 (18.8%)
Cardia/Proximal	28 (7.8%)	20 (5.5%)
Fundus/Body	58 (16.1%)	72 (19.9%)
Gastroesophageal Junction	22 (6.1%)	19 (5.3%)

Furthermore, TCGA data showed that patients with higher levels of YY1 presented significant lower postoperative PFS [HR = 1.39 (0.98 − 1.98), p = 0.065] and DSS [HR = 1.99 (1.31 − 3.02), ** p < 0.01] rates (Fig. 1A-B). We also showed that the DSS was associated with YY1 expression and that it could be used as an independent DSS indicator [HR = 1.84 (1.16, 2.91), **p < 0.01]. Hence, these results indicated the prognostic value of YY1 as a GC biomarker (Fig. 1C-D). Additionally, compared to adjacent normal tissues, the tumor tissues exhibited significantly higher YY1 expression. (t = 4.042, ***p < 0.001, Fig. 2A). Next, we performed bioinformatics analysis on YY1 using TCGA STAD dataset to clarify the underlying promotive mechanisms of YY1 on the invasion, metastasis, and drug resistance of GC cells. We detected 3286 significant DEGs between patients with high and low YY1 expression. Additionally, 105 transcriptional targets and 11 transcription factors related to YY1 were predicted using the TRRUST v2 database. Then, 4741 YY1 co-expression genes were retrieved from the cbioportal (Fig. 2B-2D). Finally, 30 genes were identified in the intersection analysis between DEGs, co-expressed genes, regulators, and targets of YY1 (Venn diagram - Fig. 2E).

Transferrin is a potential key protein regulated by YY1

To identify key genes potentially regulated by, or significantly correlated to YY1 and that presented the most important interactions with YY1, we performed a LASSO Cox analysis based on the 30 hub genes and YY1. The LASSO results led to YY1 and 5 YY1-related hub genes (Fig. 3A-B). The receiver operating characteristic (ROC) curve was used to predict the DSS of 1, 3, and 5 years and showed that this gene group might be used to predict the DSS in the cohort from TCGA (AUC of 1, 3, 5 years were 0.746, 0.747, and 0.701, respectively; Fig. 3C). Moreover, the expressions of VEGFB, DNAJB4, CXCR4, and Transferrin were positively correlated with YY1, while COX7C was negatively correlated with YY1 (Fig. 3D). The Kaplan-Meier analysis of TCGA STAD indicated that patients with high Transferrin expression were the most correlated to adverse DSS [^***p < 0.01, HR = 2.17 (1.40–3.37)] and PFS [^**p < 0.01, HR = 1.84 (1.28–2.65)] prognoses after surgery (Fig. 3E). A previous study has also suggested that Transferrin participates in systemic iron homeostasis. Our current results indicated that Transferrin is a potential key protein regulated by YY1.

Overexpression of YY1 regulates the activity of the p53 signaling pathway and GC cell ferroptosis.

Next, we used GeneMANIA to predict the biological network integration for gene prioritization and performed a KEGG enrichment analysis using the 30 hub genes. The KEGG analysis identified that the YY1-regulated ferroptosis was highly enriched in GC tumors, and transferrin was involved in the ferroptosis process (Fig. 2F-G). Furthermore, the GSEA showed that the TP53_ACTIVITY_THROUGH_PHOSPHORYLATION, SIGNALING_BY_TGFB_FAMILY_MEMBERS, and GLYCEROPHOSPHOLIPID_BIOSYNTHESIS were significantly enriched pathways regulated by YY1(Fig. 2H). Altogether, these results indicated that the regulatory effect of YY1 on ferroptosis might be exerted through the p53 signaling pathway.

Overexpression of YY1 might suppress the infiltration of immune cells in GC

We also explored the significance of the relationship between YY1 expression and the tumor immune microenvironment via the Pearson's correlation coefficients between YY1 and immune infiltration. The expression of YY1 was negatively correlated with IMMUNE (r = -0.220, p < 0.001), STROMAL (r = -0.095, p = 0.065), and ESTIMATE (r = -0.175, p < 0.001) scores (Fig. 4A). The CIBERSORT analysis for TCGA STAD data showed that the expression of YY1 significantly suppressed immune cells infiltration. Finally, TIMER 2.0 verified that the infiltration of CD8 + T cells (Rho=-0.136, p < 0.001), B memory cells (Rho = -0.201, p < 0.001), active NK cells (Rho = -0.165, p < 0.001), and monocytes (Rho = -0.113, p < 0.001) was significantly reduced in tumor tissues, while the infiltration of NK resting cells (Rho = 0.15, p < 0.001) enhanced (Fig. 4B-C).

Overexpression of YY1 inhibits GC cell ferroptosis and mediates Apatinib-resistance via the p53 signaling pathway

The bioinformatics results showed that Transferrin was a potential target regulated by YY1. A previous study has also suggested that Transferrin plays a role in systemic iron homeostasis. Our current GSEA showed that TP53_ACTIVITY_THROUGH_PHOSPHORYLATION was a significantly enriched pathway regulated by YY1. Further, we verified the functionality of the YY1/Transferrin axis on GC cells. The Western blot showed that YY1 overexpression directly upregulated Transferrin, inhibited p53 expression thus upregulated SLC2A11 (Fig. 5A), which might constitute the mechanism of ferroptosis inhibition after YY1 overexpression.

To demonstrate the basis for YY1 in mediating ferroptosis and Apatinib resistance, YY1-overexpressed HGC-27 and MFC cells were treated with Fer-1, Erastin, Apatinib in vitro, cell death rate and relative MDA levels were also measured. First, YY1-overexpressed and YY1-overexpressed with SLC7A11 knock down HGC-27 and MFC cells were treated with Erastin in vitro. The cell death rate and relative MDA levels showed that YY1 overexpression could inhibit GC cell ferroptosis. Meanwhile, Erastin could reverse the inhibition effect (Fig. 5B-C). What’s more, loss of SLC7A11 could block the inhibition effect (Fig. 5B-5C, *p < 0.05, ANOVA). Considering the effect of Transferrin in maintaining systemic iron homeostasis ferroptosis^[27–29], we tested relative intro-cellular levels of Fe²⁺ and GSH. Relative GSH level in YY1-overexpressed HGC-27 and MFC cells was elevated (Fig. 5D, *p < 0.05, ANOVA). However, Fe²⁺ maintains low level in YY1-overexpressed cell line and relative low level in YY1-overexpressed with SLC7A11 knock down cell line (Fig. 5E, *p < 0.05, ANOVA). Which shows Transferrin and p53 might both contribute to the inhibition effect of YY1. Hence, the overexpression of YY1 could induce Apatinib drug resistance (IC50 of HGC-27: 80.94 vs 20.74 ug/ml, IC50 of MFC: 27.09 vs 10.52 ug/ml, Nonlin-Fit). These results indicated that the inhibition of ferroptosis after YY1 overexpression via p53 signaling pathway and related to elevating Transferrin.

Compared to normal control GC cells, YY1-overexpressed GC cells presented significantly enhanced growth, migration, and invasion. Thus, these results indicated the promotive effect of YY1 on GC cell growth, invasion, and metastasis (Fig. 6A-6B).

Our previous study demonstrated that Interferon-α could remodel the hepatocellular microenvironment and potentiates anti-PD-1 efficacy^[25]. These findings suggested that IFN-α might be an effective treatment for Apatinib and mPD-1-resistant GC cells.

First, to verify the promotive effect of YY1 on tumor growth in vivo, we injected YY1-overexpressed GC cells and measured the tumor progression. We found that overexpression of YY1 promoted tumor progression in vivo (Fig. 6D). Additionally, we analyzed the phenotypes of GC cells with YY1 overexpression after IFN-α treatment. The IFN-α treatment did not directly lead to decreased tumor growth in vivo and did not significantly reverse both Apatinib and mPD-1 antibody resistance in YY1-overexpressed subcutaneous tumors (Fig. 6D). Finally, the IHC analysis demonstrated that, after treatment with mIFN-α, both CD8 and CD27 were significantly upregulated in GC tissues, thereby indicating an improvement in the immune microenvironment of GC tissues. However, CD19 and PD-L1 has no significant different between the two groups (Fig. 6E).

YY1 is a zinc finger protein that belongs to the Gli-Krüppel family^[30]. It can act as an activator or repressor of gene transcription depending on the intracellular physiological state and microenvironment, as well as the presence of repression and activation domains at the C- and N-terminus, respectively. YY1 has been implicated in various biological processes, such as development, differentiation, cell cycle regulation, DNA repair, and apoptosis respectively, YY1 can act as an activator or repressor of gene transcription^[31].

Previous studies have demonstrated the close association between YY1 expression and the prognosis of various cancers^[32]. Additionally, YY1 can promote the proliferation and metastasis of GC via multiple cancer-related pathways^[33–38]. YY1 can also induce immune therapy resistance through p53, miR34a, STAT3, NF-kB, PI3K/AKT/mTOR, c-Myc, and COX-2^[39]. Moreover, YY1 mRNA stabilization induced by HnRNP L can promote the transcription of PD-L1 in prostate cancer cell lines^[40]. However, the role of YY1 in regulating PD-L1 expression and immune evasion in GC remains unclear^[39].

In this study, we aimed to elucidate the molecular mechanisms and functional implications of YY1 in GC. We found that YY1 expression was significantly higher in GC tissues compared to adjacent normal tissues, and high YY1 expression was associated with poor disease-specific survival (DSS), indicating that YY1 may serve as an independent risk indicator for GC prognosis. Through bioinformatics analysis, we identified genes co-expressed with YY1 and pathways involved in YY1-mediated GC progression.

KEGG and GSEA analysis revealed a negative correlation between YY1 and ferroptosis-related genes in GC tissues. Ferroptosis is a form of regulated cell death that is dependent on intracellular iron concentrations and is associated with tumor growth and drug resistance. Our findings suggest that YY1 expression might inhibit GC ferroptosis, thereby mediating Apatinib resistance and immune suppression.

Unlike autophagy, necrosis, and apoptosis, ferroptosis is dependent on intracellular iron concentrations and significantly associated with tumor growth and drug resistance. Usually, ferroptosis is significantly regulated by glutathione peroxidase 4 (GPX4) ^[41]. In 2015, the association between the inactivation of the p53 pathway and suppression of ferroptosis was observed by Jiang et al.^[42]. Regarding the relationship between Apatinib and ferroptosis, previous studies have demonstrated that, by causing lipid peroxidation via GPX4, Apatinib can negatively regulate GC cell ferroptosis^[43]. Additionally, by suppressing VEGFR2/Nrf2/Keap1 activation and subsequent enhancement of ferroptosis, Apatinib treatment significantly restrains the growth of glioma cells^[44]. Apatinib might also enhance ELOVL6/ACSL4-mediated ferroptosis in CRC cells^[45]. Hence, we hypothesized that YY1 expression might inhibit GC ferroptosis, thereby mediating Apatinib-resistance and immune suppression.

Our further experiments demonstrated that YY1 promoted GC cell growth and metastasis in vitro. YY1 overexpression decreased the protein levels of p53, a tumor suppressor gene, and increased the expression of SLC7A11, a key regulator of ferroptosis, indicating that YY1 inhibits GC cell ferroptosis via the p53 pathway. YY1 overexpression also conferred resistance to Apatinib, a tyrosine kinase inhibitor known to trigger ferroptosis. These findings suggest that YY1 is a negative regulator of GC cell ferroptosis and a potential mediator of Apatinib resistance. Additionally, YY1 overexpression led to increased intracellular levels of glutathione (GSH) and Fe²⁺, further supporting its role in inhibiting ferroptosis.

IFN-α is commonly used for the treatment of some cancer and viral diseases in clinical practice^[46]. Meanwhile, IFN-α is widely used as a cancer therapeutic drug combined with novel strategies^[46]. For instance, increased PD-L1 expression was observed after IFN-α administration in some human cancers, such as melanoma. However, combined with PD-1 blockade, IFN-α can boost powerful antitumor effects in B16 melanoma-bearing mice^[47]. To overcome Apatinib resistance mediated by YY1, we tested the effect of mIFN-α, a type of interferon with anti-tumor and anti-viral activities, on YY1-overexpressed GC cells in vivo. We observed that mIFN-α treatment partially reversed YY1-mediated tumor growth and drug resistance by increasing the expression of CD8 and CD27, markers of T cell activation, in GC tissues. This suggests that YY1 may modulate the immune microenvironment of GC by affecting T cell-mediated immunity.

In conclusion, our study provides new insights into the role of YY1 in regulating ferroptosis and Apatinib resistance in GC. We demonstrate that YY1 acts as a negative regulator of GC cell ferroptosis through the p53 pathway and may contribute to Apatinib resistance. Furthermore, we propose a novel strategy to overcome this resistance and immune suppression by combining mIFN-α with PD-1 blockade. However, further studies are needed to fully understand the detailed molecular mechanisms of YY1 and IFN-α in GC. Additionally, clinical trials are required to evaluate the efficacy, safety, applicability, and dosage of this combination therapy in GC patients.

Data Availability Statement

The data that support the findings of this study are available in

https://www.gencodegenes.org/human/release_22/gencode.v22.annotation.gff3.gz

http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_33/gencode.v33.annotation.gff3.gz

http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human

https://www.grnpedia.org/trrust/

https://www.kegg.jp/kegg/rest/keggapi.html

http://software.broadinstitute.org/gsea/index.jsp

Ethics approval and consent to participate

Experimental protocols were approved by the Ethics Committee of Zhongshan Hospital in accordance with the National Institutes of Health.

Consent for publication

We have obtained consents to publish this paper from all the participants of this study.

Availability of data and material

Data and material were not available.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by Natural Science Foundation of China (82003083).

Authors’ contributions

Yi-Hong Luo, Wen-Zheng Qin and Ping-Hong Zhou conceived the structure of experiments and revised the manuscript; Jun-Xian Du drafted initial manuscript; Yue-Da Chen and Zi-Han Geng performed the experiments, analyzed the data. Yi-Hong Luo read and approved the final manuscript.

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YY1: a key regulator inhibits gastric cancer ferroptosis and mediating Apatinib-resistance

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