Prognostic analysis of disulfidptosis-based ferroptosis-related genes in gastric cancer

doi:10.21203/rs.3.rs-3234593/v1

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Prognostic analysis of disulfidptosis-based ferroptosis-related genes in gastric cancer

https://doi.org/10.21203/rs.3.rs-3234593/v1

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Purpose Gastric cancer is one of the highly prevalent malignant tumours of the digestive tract in China and is highly lethal. Several studies have confirmed that Ferroptosis is closely related to the development of gastric cancer. disulfidptosis is a new type of cell death, which is a rapid death mode caused by disulfide stress induced by excessive cystine accumulation in the cell. Currently there are fewer studies on the discovery of disulfidptosis-related genes and their role in cancer development, and this study was used to explore the prognostic analysis of gastric cancer based on the disulfidptosis-related Ferroptosis genes.

Methods The mRNA expression profiles of gastric cancer patients and corresponding clinical information were downloaded from the TCGA public database. Ferroptosis, disulfidptosis related gene ensembles were obtained from FerrDb database and published literature search. Differential expression analysis of genes was done on gastric cancer tissues and paracancerous tissues using the R package to screen out the disulfidptosis-related Ferroptosis-based genes with prognostic value. A prognostic model was constructed using LASSO Cox regression analysis, and the RiskScore of the corresponding genes was calculated, and the patients were classified into high-risk and low-risk groups according to the median value of the RiskScore. The predictive ability of the prognostic model for 1-year, 3-year, and 5-year survival was assessed by ROC curves. Multifactorial independent prognostic analysis was used to analyse the correlation between genes in the predictive model and clinicopathological features. The degree of immune infiltration of DEGs in gastric cancer tissues was analysed by Timer database.

Results In the TCGA cohort, disulfidptosis-related Ferroptosis genes were differentially expressed between tumours and adjacent normal tissues. In this study, we constructed relevant prognostic models, including DUSP1, TSC22D3, AKR1C2, HNF4A, HELLS, SLC1A5, and BID, and divided the patients into two populations, high and low risk, according to the score of risk, and the overall survival rate of the high-risk group was significantly lower compared with that of the low-risk group (p < 0.001). In multifactorial Cox regression analysis, the risk score could be used as an independent prognostic factor (p < 0.05). The results of immune infiltration analysis showed that the expression level of each DEGs was significantly correlated with the level of immune cell infiltration in gastric cancer (p<0.05).

Conclusion In this study, the disulfidptosis-related Ferroptosis gene prognostic model was constructed and identified as an independent prognostic factor, which provides a reference for the treatment and prognosis of gastric cancer patients.

Prognostic analysis

Disulfidptosis

Ferroptosis

Gastric cancer

Gastric cancer is a common malignant tumor of the digestive system, with the fifth and third highest morbidity and mortality rates in the world^[1]. STAD is the most common histological type of gastric cancer, which is characterized by rapid progression, invasiveness, and high degree of malignancy^[2]. Due to the lack of typical clinical manifestations and specific diagnostic markers in the early stage, most of them are already in the advanced stage when diagnosed, and even though molecular diagnostic techniques, surgical and adjuvant therapeutic techniques are improving, the 5-year survival rate of most patients with advanced gastric cancer is < 10%^[3]. Therefore, a better understanding of the molecular mechanisms of gastric carcinogenesis and the identification of new biomarkers are essential to improve the early diagnosis and treatment of gastric cancer.

On February 6, 2023, an article published by the team of Professors Bo-Yi Gan and Jun-Jie Chen identified a new type of cell death-disulfidptosis, a rapid mode of death caused by disulfide stress induced by the accumulation of excess cystine in the cells^[4]. Ferroptosis is an iron-dependent programmed cell death characterized by lipid peroxidation with accumulation of reactive oxygen species^[5]. In recent years, Ferroptosis has emerged as a highly promising treatment for inducing cancer cell death^[6]. Studies have shown that Ferroptosis is associated with tumor prognosis, and Ferroptosis-associated prognostic gene models have been established in hepatocellular carcinoma, lung cancer, and breast cancer^[7–9].

The process of disulfidptosis and ferroptosis is complex and consists of a wide range of metabolites and biomolecules, and many genes have been identified as markers of Ferroptosis involved in the process of tumorigenesis and progression, however, fewer studies have been conducted related to disulfidptosis, the relationship between disulfidptosis-related ferroptosis genes and the prognosis of gastric cancer patients deserves further investigation. Therefore, in this study, we constructed a prognostic model based on the differential expression of disulfidptosis-related ferroptosis genes based on the TCGA database, and also performed tumor immune microenvironmental analyses in order to explore the underlying mechanisms.

2.1 TCGA transcriptome and clinical data download and pre-organization

Extraction of mRNA expression information from 36 normal gastric tissues and 412 gastric cancer tissues in the STAD dataset from TCGA (https://portal.gdc.cancer.gov) as well as patients' clinical and pathological information (age, gender, TNM, stage, grade, survival status and survival time, etc.). FerrDb (http://www.zhounan.org/ferrdb/) aims to provide up-to-date data on ferroptosis markers, ferroptosis-regulating molecules and ferroptosis-associated diseases, from which 259 ferroptosis-associated genes were acquired^[10]. 23 disulfidptosis-related genes from the currently published literature. Using the "Limma" package of R software, DEGs (differential expression genes) were obtained from the mRNA dataset based on the analysis of the disulfidptosis related ferroptosis genes under the limitations of | logFC | > 1 and adjusted P < 0.05 for subsequent research. Both TCGA and FerrDb data are publicly available, therefore, our study did not require approval from the local ethics committee. In addition, this study followed the TCGA and FerrDb guidelines for data access and use.

2.2 Construction of prognostic models

Use LASSO Cox regression analysis and run R packages "glmnet" and "survival" to construct prognostic models^[11]. LASSO Cox regression analysis uses the expression of the intersecting genes as the independent variable and the overall survival and status of STAD patients as the dependent variable to derive the genes for the best model, extract the gene expression in that model, and output the associated gene coefficients, followed by the calculation of the RiskScore for the corresponding genes. According to the median value of RiskScore, patients were divided into high-risk and low-risk groups, and the R package "pheatmap" was loaded to plot the risk curves of high-risk and low-risk groups as well as the survival status maps. In the survival analysis, Kaplan-Meier survival curves were plotted using the R package "survival, survminer" according to the high and low risk groups. ROC curve analysis was performed using the R package "timeROC" to assess the predictive power of the prognostic model for 1-, 3-, and 5-year survival. Finally, we used Cox regression analysis to combine risk scores with clinical information to assess the relationship with patient prognosis and identify independent prognostic factors.

2.3 Independent prognostic analysis

A multifactorial independent prognostic analysis was performed by applying the "survival" package in the R software to derive the HR (Hazard Ratio) and 95% CI (confidence interval) to assess the prognostic value of risk scores and various clinicopathological characteristics (age, gender, Stage), analyze the correlation between genes in the prediction model and clinicopathological characteristics, and then finally, draw a forest plot to complete the visualization of the results.

2.4 Correlation analysis with immune infiltration

The TIMER database (https://cistrome.shin-yapps.io/timer/) was used to study the expression of disulfidptosis-based Ferroptosis-associated DEGs in STAD and the level of gastric cancer immune cells (CD4⁺ T cells, CD8⁺ T cells, B cells, neutrophils, dendritic cells and macrophages) infiltration levels, Cor represents the correlation coefficient, and P < 0.05 indicates a statistically significant difference. Checkpoints are a series of molecules expressed on immune cells that regulate the degree of immune activation, and they play an important role in preventing the onset of autoimmune effects. 47 checkpoint genes were extracted from the literature^[12]. Analysis of checkpoint was performed using R software to extract significantly different genes, and high-risk and low-risk samples were analyzed for differences in checkpoint content and filtered according to the criterion P < 0.05.

3.1 Expression of disulfidptosis-related ferroptosis genes in STAD

Based on the data related to gastric cancer obtained from TCGA database, the differential expression analysis of genes in gastric cancer tissues and paracancerous tissues based on the expression of disulfidptosis-related ferroptosis genes was extracted by the "Limma" package of R software. DGEs with disulfidptosis-related ferroptosis genes were screened under the limits of | logFC | > 1 and adjusted P < 0.05. The "pheatmap" and " ggplot2" software packages were used to plot the heatmaps and volcano maps of the obtained DEGs, respectively (Fig. 1a, b). Univariate Cox analyses of overall survival using the R "survival" package yielded nine differential genes associated with prognosis for overall survival, including DUSP1, TSC22D3, PLIN4, RRM2, HNF4A, AKR1C2, HELLS, SLC1A5, and BID. Among them, RRM2, HNF4A, HELLS, SLC1A5, and BID were up-regulated in STAD compared with paracancerous tissues, DUSP1, TSC22D3, PLIN4, and AKR1C2 were down-regulated, and these nine genes were included in the subsequent prognostic analyses, and all of them had an FDR value of less than 0. 05 (Fig. 1c).

3.2 Construction of Risk-Scoring Model

Based on the Lasso regression analysis of the 9 DEGs with prognostic value obtained from the preliminary analysis, an iterative analysis was performed by the cross-validation method to construct a prognostic model containing 7 mRNAs (DUSP1, TSC22D3, AKR1C2, HNF4A, HELLS, SLC1A5, BID) and their corresponding correlation coefficients (Fig. 2a, b). Patients were categorized into a high-risk group (128 patients) and a low-risk group (129 patients) based on the median value of the RiskScore (Fig. 2c). As the RiskScore increases, the patient's risk of death increases (Fig. 2d). When survival analysis was performed, Kaplan-Meier curves showed a significantly lower survival rate in the high-risk group than in the low-risk group, and P < 0. 001 (Fig. 2e). The predictive performance of the overall survival risk score was assessed using ROC curves, and the area under the curve (AUC) was 0.683 at 1 year, 0.62 at 3 years, and 0.658 at 5 years (Fig. 2f). It meant that the model we constructed had great diagnostic efficacy in GC. Finally, we analyzed whether the prognostic model could be used to determine survival in the presence of different clinical characteristics, stratified by age (≤ 65 and > 65), sex (male and female), and pathological stage (Stage I-II and Stage III-IV). We found that there was a significant difference in survival between males, clinical staging groups in the high-risk and low-risk groups (P < 0.05) (Fig. 2g, h, i).

3.3 Independent prognostic analysis

We performed multivariate Cox analyses of available clinical indicators to explore whether risk score could be an independent prognostic predictor of overall survival. After correcting for other confounders, multivariate Cox analyses showed that clinical stage and risk score (HR = 4.9904, 95% CI = 1.2280-20.2804, P < 0.05) were independent risk factors for OS in STAD patients (Fig. 3).

3.4 Correlation between the expression of DEGs in STAD and the level of immune infiltration

The level of tumor immune cell infiltration is closely related to cancer proliferation and metastasis, and is an important predictor of survival prognosis in cancer patients, and it is unclear whether disulfidptosis-related ferroptosis DEGs in STAD affect immune cell recruitment in the tumor microenvironment, and thus the prognosis of STAD. This study has analyzed 7-mRNA prognostic models: HNF4A, HELLS, SLC1A5, and BID expression was upregulated in STAD, and DUSP1, TSC22D3, and AKR1C2 expression was downregulated in STAD. Based on the analysis of TIMER2.0 database, it was found that the expression level of HNF4A gene was significantly negatively correlated with the infiltration level of CD8⁺ T Cell, macrophage, neutrophil, and dendritic cell, the expression level of HELLS gene was significantly negatively correlated with the infiltration level of CD4⁺ T Cell and macrophage, the expression level of SLC1A5 gene was significantly negatively correlated with the infiltration level of all immune cells, BID gene expression level was significantly negatively correlated with the infiltration level of B Cells, CD4⁺ T Cells, and macrophages, DUSP1 and TSC22D3 gene expression levels were significantly positively correlated with the infiltration level of all immune cells, and AKR1C2 gene expression level was significantly positively correlated with the infiltration level of B Cells, CD8⁺ T Cells, Neutrophils, and Dendritic Cells (P < 0.05). These results suggest that DEGs may be involved in the regulation of tumor immune infiltration in gastric cancer and play an important role in increasing immune cell infiltration in the gastric cancer microenvironment. Expression levels of immune checkpoints or their ligands may constitute predictive biomarkers for immune checkpoint blockade therapy, and we further explored immune checkpoint expression between high- and low-risk groups in a prognostic model. In this study, we found differences in TNF (tumor necrosis factor) superfamily (CD40LG, CD27, etc.), B7-CD28 family (PDCD1LG2, CD28, CD86, CD276, etc.) and other immune checkpoints (CD40LG, CD209, VTCN1, PVR, PDCD1LG2, TNFRSF14, etc.) between the two groups, and most of them, especially in the high-risk group, were up-regulated, which suggests that the patients with a high-risk score may have a better response to immunotherapy.

STAD is one of the common malignant tumours in China today. Despite the continuous development of modern medicine, there are various modalities such as surgery, radiotherapy, targeted therapy and immunotherapy that can be used to treat and improve the survival and prognosis of patients with STAD, the prognosis of advanced refractory patients who are inoperable and intolerant of radiotherapy is still extremely poor^[1]. As a new form of regulated cell death, ferroptosis has come into the limelight, and numerous researchers have shown great interest in ferroptosis-mediated anti-tumour effects. A large number of studies have demonstrated that ferroptosis is involved in the regulation of molecular biological processes in tumours, including hepatocellular carcinoma, lung carcinoma, pancreatic carcinoma, and breast carcinoma^[8]. Disulfidptosis as a novel mode of cell death, independent of existing programmed cell deaths such as apoptosis, ferroptosis, cuproptosis, etc., is caused by excessive disulfide formation in the actin cytoskeleton and the collapse of actin filaments, highlighting promising anticancer strategies for targeting disulfides^[13]. In the present study, we constructed a prognostic model based on the Disulfidptosis-related ferroptosis gene based on the TCGA database and investigated the role of immune cell infiltration in the tumour microenvironment in the prognosis of STAD patients.

Differential analysis of gene rows between gastric cancer tissues and paracancerous tissues was performed by R software, and Univariate Cox analyses of the differential genes yielded DEGs that were associated with overall survival and prognosis, suggesting that Disulfidptosis-related ferroptosis genes play a potential regulatory role in the development of STAD. Based on the above intersection genes, a novel prognostic model integrating 7 genes (HNF4A, HELLS, SLC1A5, BID, DUSP1, TSC22D3 and AKR1C2) was firstly constructed. HNF4A (Hepatocyte Nuclear Factor 4 Alpha) is not only a core member and central regulator of glucose, fatty acid and lipid metabolism, but also has the ability to regulate the expression of multiple components within the three major regions of pontine, adherens junctions and tight junctions. Studies have shown that overexpression of HNF4A controls cell ferroptosis and enhances cellular proliferation, and may act as an oncogene to promote gastrointestinal adenocarcinoma development^{[14, 15]}. HELLS (Helicase Eymphoid-Specific) is an ATP-dependent chromatin remodelling factor, which is mainly involved in the regulation of DNA methylation during cell proliferation. Relevant studies have shown that HELLS is involved in aerobic glycolysis in gastric cancer cells, and also exerts a cancer-promoting effect in a variety of tumours, such as pancreatic, colon and renal cancers^{[16, 17]}. Glutamine transport mediated by SLC1A5 (Solute Carrier Family 1 Member 5) plays a key role in tumour cell metabolism, proliferation and ferroptosis, and is an up-regulated gene that is associated with ferroptosis in GC and correlates with patient prognosis, and correlates with tumour size, depth of tumour infiltration, lymph node metastasis and TNM staging^[18–20]. Published articles have suggested that in clinical GC specimens not treated with chemotherapy or targeted agents, protein levels of DUSP1 (Dual Specificity Phosphatase-1) are significantly higher in paraneoplastic tissues than in cancerous tissues, and furthermore, increased expression of DUSP1 is associated with cancer progression, drug resistance and poor prognosis^[21]. GILZ / TSC22D3 (Glucocorticoid-Induced Leucine Zipper) can either promote or inhibit tumour growth, depending on the type of tumour and its microenvironment. TSC22D3 plays a dual role in tumours: it not only promotes tumours by affecting the immune system and the tumour microenvironment, but also inhibits tumour growth by inducing apoptosis or inhibiting the proliferation of cancer cells^[22]. There are fewer articles on the role of TSC22D3 in gastric cancer. The expression of AKR1C2 (Aldo-Keto Reductases Family 1 Member C2) has been associated with the prognosis of a variety of diseases including thyroid cancer, acute myeloid leukaemia (AML) and esophageal squamous cell carcinoma (ESCC)^[23–25]. Some articles have now shown that the expression of AKR1C2 is associated with immunostimulants, immunosuppressants, chemokines and receptors, and that as a ferroptosis-related gene, the high expression of AKR1C2 correlates with a good prognosis in GC, which could potentially serve as a promising prognostic biomarker for GC patients^[26]. The BID protein is a member of the Bcl-2 family that activates apoptosis and integrates both the two major apoptotic pathways connecting membranes (external) and mitochondria (internal). However, its role in cancer pathogenesis remains poorly elucidated, and it has also been suggested that BID expression lacks significant differences in different histopathological forms of cancer^[27].

In this study, we showed that HNF4A, HELLS, SLC1A5, and BID, which were up-regulated in STAD expression, were negatively correlated with the level of infiltration of a variety of immune cells, and DUSP1, TSC22D3, and AKR1C2, which were down-regulated in STAD expression, were positively correlated with the level of infiltration of a variety of immune cells. In conclusion, the above results indicate that the seven disulfidptosis-related ferroptosis genes correlate with immune cell infiltration, suggesting that this prognostic model may influence the composition of the immune microenvironment of STAD, and become a potential target to inhibit STAD recurrence and metastasis, as well as a potential marker for differential diagnosis and evaluation of tumour biological properties. Continued advances in ICIs (Immune Check Point Inhibitors) therapy in recent years have deepened our understanding of the interactions between the immune system, cancer cells and the tumour microenvironment^[28]. Currently tumour mutational load and immune checkpoint-associated gene expression do not accurately predict response to ICIs therapy. Therefore, the identification of biomarkers that can accurately predict the clinical efficacy of ICIs therapies is important to further advance precision immunotherapy^{[29, 30]}. Among the many immunotherapeutic strategies, ICIs have shown great benefits in the treatment of a range of cancer types, for example the use of CTLA-4 and PD-1 or PD-L1 enhances anti-tumour immunity^[31]. In this study, disulfidptosis-related ferroptosis genes were significantly associated with ICIs therapeutic target genes such as CD40LG, CD27, PDCD1LG2, CD28, CD86, and CD276. These findings suggest that the risk score model in this study has the potential to predict the efficacy of ICIs and will hopefully provide new perspectives for STAD treatment.

In this study, we identified the DGEs associated with disulfidptosis and ferroptosis in STAD by bioinformatics methods, and constructed a risk prediction model that can better classify STAD patients into high-risk and low-risk groups, which can provide clinicians with referable suggestions for evaluating the prognosis and the risk of recurrence of STAD patients, and provide a new way of thinking for designing the therapeutic strategies based on disulfidptosis and ferroptosis. However, there are some limitations in this study. The clinical information source of the prognostic model established in this paper is the TCGA database, and the stability of the model may be limited by factors such as ethnicity, geography, and sample size; in addition, the genes related to ferroptosis and disulfidptosis were obtained from the FerrDb database and from literature searches, and these genes may be continuously updated as the relevant studies of researchers from all walks of life continue to deepen. In addition, the genes related to ferroptosis and disulfidptosis were obtained from the FerrDb database and literature searches, and as researchers from all walks of life continue to deepen their studies, these genes may be updated continuously, so it is necessary to pay close attention to the development of the relevant frontiers and then adjust the design scheme; there is still room for exploring the biological mechanism of disulfidptosis and ferroptosis, and therefore, it is necessary to further experimental verification.

Disulfidptosis and ferroptosis are relatively new forms of cell death and are expected to provide a new avenue for tumour therapy. We screened disulfidptosis-associated ferroptosis biomarkers for STAD prognosis, which provides some theoretical basis for the treatment of STAD. This study has some limitations and further prospective studies are still needed to verify its clinical value.

Data Availability

The data used to support the findings of this study are included in the article.

Conflicts of Interest

According to the authors, the study was conducted without any financial or commercial ties that would be seen as a possible conflict of interest.

Authors’ Contributions

X-HS and C-QY conceived and designed this research. C-XR and X-HS collected and analyzed the data. X-HS, C-XR, Z-YH and Y-WW wrote the manuscript. C-QY , Y-YW, L-LH and C-XJ revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

The research was supported by National Natural Science Foundation of China (81600422) and the Basic Research Program of Shanxi Province (Free exploration category) (202303021211108).

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No competing interests reported.

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Prognostic analysis of disulfidptosis-based ferroptosis-related genes in gastric cancer

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 TCGA transcriptome and clinical data download and pre-organization

2.2 Construction of prognostic models

2.3 Independent prognostic analysis

2.4 Correlation analysis with immune infiltration

3. Results

3.1 Expression of disulfidptosis-related ferroptosis genes in STAD

3.2 Construction of Risk-Scoring Model

3.3 Independent prognostic analysis

3.4 Correlation between the expression of DEGs in STAD and the level of immune infiltration

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

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