Prognostic Significance of Disulfidptosis-Related Gene DSTN in Kidney Renal Clear Cell Carcinoma: Correlation with Immune Cell Infiltration and Cancer Stemness

Prognostic Significance of Disulfidptosis-Related Gene DSTN in Kidney Renal Clear Cell Carcinoma: Correlation with Immune Cell Infiltration and Cancer Stemness | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prognostic Significance of Disulfidptosis-Related Gene DSTN in Kidney Renal Clear Cell Carcinoma: Correlation with Immune Cell Infiltration and Cancer Stemness Zuifei Shangguan, Yimin Yao, Jiale Chen, Ping chen, Na Shi, Xue Ying, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3908062/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Backgrounds Kidney renal clear cell carcinoma (KIRC) is a highly metastatic cancer that shows resistance to traditional chemoradiotherapy. Disulfidptosis, a newly discovered mechanism of cell death in malignancies, involves the accumulation of intracellular disulfides, leading to rapid cell demise. Identifying disulfidptosis-related genes (DRGs) in KIRC can provide novel treatment strategies for patients with this disease. Methods The 15 DRGs and differentially expressed genes (DEGs) obtained from the KIRC-TCGA database were intersected to identify overlapping genes, and a prognostic model was constructed using Lasso regression analysis. Univariate and multivariate Cox regression analysis were conducted to identify independent prognostic factors associated with disulfidptosis. Kaplan-Meier (KM) survival curve was used for prognostic analysis. Co-expression analysis was performed between the screened DRGs and other DRGs to investigate their correlation. The samples in KIRC-TCGA were grouped based on the selected DRGs, and Gene Set Enrichment Analysis (GSEA) as well as immune infiltration analysis were performed. Tumor stemness analysis was conducted using the OCLR algorithm, and correlation analysis between the independent prognostic DRGs and the inhibitory concentration 50% (IC50) of Pazopanib and Sorafenib was performed using ridge regression. Results Univariate and multivariate regression analysis indicated that DSTN and FLNA may serve as independent prognostic DRGs for KIRC. In the KIRC-TCGA, FLNA expression was higher in tumor tissues compared with adjacent tissues, whereas DSTN expression was lower in tumor tissues than in adjacent tissues ( P < 0.05). KM survival curve demonstrated that high expression of DSTN and FLNA correlated with a higher survival rate. Co-expression analysis revealed positive correlations between DSTN and the expression of FLNA, MYH9, TLN1, MYL6, MYH10, IQGAP1, and CD2AP. Immune infiltration analysis showed that DSTN was positively correlated with endothelial cell infiltration. High expression of DSTN and endothelial cell marker genes were associated with a longer survival period. Correlation analysis revealed a negative correlation between DSTN expression and stemness scores. Additionally, the IC50 values of Pazopanib and Sorafenib showed a high negative correlation with DSTN expression (0.5≤|ρSpearman|<0.8). Conclusions DSTN, as a DRG, had been identified as an independent prognostic biomarker in patients with KIRC. Its expression was closely linked to tumor cell stemness and also correlated with the IC50 of commonly used chemotherapy drugs in KIRC. DSTN holded promise as a meaningful prognostic marker and potential therapeutic target for KIRC. Biological sciences/Cancer/Tumour biomarkers Biological sciences/Computational biology and bioinformatics Disulfidptosis-related gene DSTN FLNA immune infiltration stemness scores inhibitory concentration 50% Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Kidney renal clear cell carcinoma (KIRC) is the most prevalent form of kidney cancer, accounting for approximately 75% of all cases[ 1 ]. Its incidence has been steadily increasing in recent years, posing a significant global public health challenge. KIRC is characterized by its propensity to metastasize and resistance to conventional treatments, making effective management particularly challenging[ 2 ]. Furthermore, the heterogeneity within this disease contributes to variations in patient outcomes and treatment response. Disulfidptosis, a newly discovered form of programmed cell death, has gained substantial attention in the field of cancer research. Under conditions of glucose limitation, rapid depletion of NADPH occurs in SLC7A11 high cells, resulting in abnormal accumulation of disulfides such as cystine[ 3 ]. This accumulation induces disulfide stress and triggers rapid cell death. Disulfidptosis plays a crucial role in regulating fundamental cellular processes, including apoptosis, proliferation, and migration[ 4 ]. Dysregulation of disulfidptosis has been implicated in various malignancies, including lung, breast, and prostate cancer[ 5 ]. However, its association with KIRC remains poorly understood. Understanding the relationship between disulfidptosis and KIRC could provide valuable insights into the pathogenesis and progression of this aggressive cancer. Moreover, the identification of prognostic biomarkers that are associated with disulfidptosis-related genes (DRGs) could potentially enhance the ability to predict KIRC patient outcomes and guide personalized treatment approaches. In this study, we analyzed the KIRC datasets to explore whether DRGs were associated with KIRC prognosis. We constructed prognostic models and performed risk regression analysis. Ultimately, we identified the DSTN as an independent prognostic molecule for KIRC. Through an analysis of DSTN expression, tumor immune cell infiltration, and stemness indices, we observed a positive correlation between DSTN and endothelial cell infiltration, while noting a negative correlation with tumor stemness indices. High DSTN expression was associated with improved survival rates, suggesting a favorable prognosis for KIRC. In addition, we analyzed the association between DSTN and the inhibitory concentration 50% (IC50) of therapeutic drugs, namely Pazopanib and Sorafenib, for KIRC. Our findings demonstrated a negative correlation, suggesting a possible connection between DSTN expression levels and drug sensitivity. Based on our findings, we hypothesized that the expression levels of DSTN could potentially serve as a prognostic biomarker for KIRC. These expression levels may reflect immune cell infiltration patterns and stemness indices. By further investigating these relationships, our aim was to enhance the understanding of KIRC biology and explore new therapeutic targets. Materials and methods Data sources The KIRC-TCGA database, obtained from The Cancer Genome Atlas (TCGA) on May 3, 2022 ( https://portal.gdc.com ), was used for this study. This database provided clinical information and processed RNA-sequencing expression data at level 3. To validate the identified key genes, we retrieved the GSE36895 and GSE53757 datasets from the Gene Expression Omnibus (GEO) database. The GSE36895 dataset contained 76 samples, including 52 human samples and 24 mouse samples. Among them, 52 human samples were divided into two groups, including 29 primary KIRC tumor tissues and 23 normal renal cortex samples. In the GSE53757 dataset, there were 144 samples, including 72 KIRC tumor tissues and 72 normal kidney tissues. Construction of a prognostic model through LASSO regression analysis The DEGs in the KIRC-TCGA were intersected with 15 DRGs (FLNA, FLNB, MYH9, TLN1, ACTB, MYL6, MYH10, CAPZB, DSTN, IQGAP1, ACTN4, PDLIM1, CD2AP, INF2, and SLC7A11) to obtain overlapping genes[ 6 ]. LASSO regression analysis was performed using the "glmnet" R package to reduce dimensionality and construct a prognostic model or signature. A 10-fold cross-validation approach was utilized. The prognostic model was assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC) using the “ggplot2” and the “timeROC” R packages. The survival analysis of KIRC was conducted using the “survminer” and the “survival” R packages. Univariate and multivariate Cox regression analysis Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors associated with KIRC. The P-value, hazard ratio (HR), and 95% confidence interval (CI) for each variable were presented using the 'forestplot' R package. The univariate Cox regression analysis revealed significant differences that were deemed relevant for prognosis. Furthermore, the multivariate analysis also demonstrated significant differences, indicating the presence of independent prognostic factors. Validation of gene expression in GEO database Gene expression profiles for DRGs were obtained from GSE36895 and GSE53757 datasets. Boxplots were created using the “ggplot2” R package to compare the differences, and the WilCoxon rank sum test was used to assess the disparities. The The University of ALabama at Birmingham Cancer data analysis Portal database (UALCAN, https://ualcan.path.uab.edu ) was used to evaluate the differential expression of DSTN between tumor tissue and normal tissue. The Human protein atlas database (HPA, https://www.proteinatlas.org)wa s used to evaluate the expression of DSTN in renal cancer tissue and normal renal tissue. Kaplan-Meier (KM) survival curve analysis KM analysis was a non-parametric statistical technique used to estimate the survival probability or survival rate over time. The P -values and HR with 95% CI were calculated using log-rank tests and univariate Cox proportional hazards regression. The “survival” R package was used for testing and fitting the proportional risk hypothesis. The results of survival regression were visualized using the “survminer” and “ggplot2” R packages. Correlation between disulfidptosis-related independent prognostic molecules and other DRGs The selected disulfidptosis-related molecule was identified as an independent prognostic marker. Single gene co-expression analysis was performed on the RNA-seq data of TCGA-KIRC using the “ggplot2” R package and Spearman statistical method. Gene Set Enrichment Analysis (GSEA) The samples were divided into high and low expression groups based on the expression levels of disulfidptosis-related independent prognostic molecules. The “DESeq2” R package was employed to conduct differential analysis using the original Counts matrix. The “clusterProfiler” R package was utilized for performing GSEA analysis. The “ggplot2” R package was used to visualization. Tumor immune infiltration analysis The KIRC samples from KIRC-TCGA were categorized into two groups based on the expression of a disulfidptosis-related independent prognostic molecule. The immune scores were evaluated using the “immunedeconv” R package. The results were visualized using the “ggplot2” and “pheatmap” R packages. Further survival analysis of immune infiltrating cells was performed using TIMER2.0 ( http://timer.cistrome.org ), a web server specifically designed for comprehensive analysis of tumor-infiltrating immune cells. RNA-sequencing expression profiles (level 3) and corresponding clinical information for KIRC were obtained from the TCGA database. The count data was converted to transcripts per million (TPM) and then normalized by applying log2(TPM + 1). The “immunedeconv” R package was used to examine the correlation between the expression of the independent prognostic molecule and immune scores. The results were analyzed and visualized using the “ggClusterNet” R package. Correlation between endothelial cell marker genes and survival rate The OS rate was chosen as the prognostic variable, and Cox regression was used as the statistical method. Based on the expression of independent prognostic genes related to disulfidptosis, the KIRC samples from the TCGA database and GSE53757 dataset were divided into high and low expression groups. The high expression group had an expression level above the median, while the low expression group had an expression level below the median. The expression differences of immune cell marker genes between these two groups were compared using the “ggplot2” and “pheatmap” R packages. Additionally, differential analysis of the expression of immune cell marker genes was performed based on different M-stage groupings of KIRC samples in the TCGA database. This analysis was conducted using the “ggplot2” and “pheatmap” R packages. Tumor stemness indices and the IC50 of KIRC chemotherapy drug The OCLR algorithm developed by Malta et al. was utilized to calculate the mRNAsi of two groups: high expression and low expression of independent prognostic genes[ 7 ]. The mRNA expression features, consisting of a gene expression profile with 11,774 genes, were analyzed using Spearman correlation. Subsequently, the stemness index was mapped to a range of [0,1] through a linear transformation, achieved by subtracting the minimum value and dividing by the maximum value. The chemotherapy response of high and low expression groups of independent prognostic genes to targeted drugs was predicted using the largest publicly available pharmacogenomics database, Cancer Drug Sensitivity Genomics (GDSC, https://www.cancerrxgene.org/ ). The prediction process was carried out using the R package “pRRophytics”, where the half maximum IC50 of the sample was estimated through ridge regression, with all parameters set to default values. Batch effects of combo and tissue types were considered, and the expression of duplicate genes was summarized as the average value. Statistical analysis Spearman’s correlation analysis was conducted to examine the correlation between non-normally distributed quantitative variables. The analysis methods and R packages were implemented using R (Foundation for Statistical Computing, 2020) version 4.0.3. A significance level of P < 0.05 was considered statistically significant. Results Constructing a prognostic model through LASSO regression analysis To explore whether DRGs can serve as independent prognostic indicators for KIRC, we obtained 8962 differential molecules from KIRC-TCGA database for analysis. These molecules were then intersected with 15 known DRGs (FLNA, FLNB, MYH9, TLN1, ACTB, MYL6, MYH10, CAPZB, DSTN, IQGAP1, ACTN4, PDLIM1, CD2AP, INF2, and SLC7A11) to obtain overlapping genes. We observed that 12 genes (FLNA, FLNB, MYH9, TLN1, MYH10, DSTN, IQGAP1, ACTN4, PDLIM1, CD2AP, INF2, and SLC7A11) were present in this intersection (Fig. 1 A). LASSO regression analysis was employed to construct a prognostic model and fitted the overall survival rate of KIRC patients using 12 selected DRGs. The prognostic model was built based on the key genes that showed a non-zero coefficient. Finally, a prognostic scoring formula was derived: Riskscore = (0.4592) * FLNA + (-0.0463) * FLNB + (-0.2556) * TLN1 + (-0.0401) * MYH10 + (-0.2795) * DSTN + (-0.0169) * IQGAP1 + (-0.3716) * ACTN4 + (-0.1016) * PDLIM1. The eight genes included in the model were assigned weights, where negative numbers represented protective genes and positive numbers indicated risk genes. Based on our analysis, FLNA was identified as a risk factor, while the other seven genes were identified as protective factors. The LASSO variable trajectory diagram showed that the selection method using L1 norm resulted in the identification of variables with corresponding non-zero coefficients at the position of 8 (Fig. 1 B). The LASSO coefficient screening diagram indicated that when lambda.min was 8, the partial likelihood deviance was minimized, suggesting that this model was the most appropriate (Fig. 1 C). To visualize the prognostic risk factors, we plotted the riskscore, survival time, and survival status for KIRC patients from the TCGA database. The top graph displayed a scatter plot of the riskscore ranging from low to high. Meanwhile, the middle figure illustrated the scatter diagram showing the distribution of survival time and survival state corresponding to different riskscore values. We observed a higher number of deceased patients in the high-risk area compared to the low-risk area. The bottom figure presented an expression heat map of eight molecules, namely FLNA, FLNB, TLN1, MYH10, DSTN, IQGAP1, ACTN4, and PDLIM1, within the signature (Fig. 1 D). In the prognostic model, there was a significant difference in the survival probability between the high-risk group and the low-risk group. This observation was confirmed through the log-rank test using the KM survival curve [P = 7.14e − 12, HR = 3.24, 95%CL (2.315, 4.535)]. The AUC values for this prognostic model at 1, 3, and 5 years were 0.665, 0.670, and 0.720, respectively. These results indicated that DRGs (FLNA, FLNB, TLN1, MYH10, DSTN, IQGAP1, ACTN4, and PDLIM1) served as predictive prognostic models with good accuracy (Fig. 1 E). Independent Prognostic Molecules Associated with Disulfidoptosis Univariate and multivariate Cox regression analysis were employed to assess hazard ratios and identify independent prognostic factors. The obtained results were presented in a forest plot. Notably, DSTN and FLNA exhibited significant variations in both univariate and multivariate analysis, suggesting that these variables were independent of other clinical factors (Fig. 2 A, B). FLNA expression validation and KM curve analysis According to univariate and multivariate Cox regression analysis, we concluded that FLNA was an independent prognostic factor of KIRC related to prognosis. In KIRC-TCGA database, the expression of FLNA was increased in tumor tissues compared with adjacent tissues, ( P < 0.05) (Fig. 3 A, B), but survival curve showing significantly longer survival in the FLNA-high group (Fig. 3 C). DSTN expression validation In the KIRC-TCGA database, the expression of DSTN was found to be decreased in tumor tissues compared with adjacent tissues ( P < 0.05) (Fig. 4 A, B). This finding was also verified in GSE36895 and GSE53757 datasets, where the expression of DSTN was lower in tumor tissues compared with normal renal tissues ( P < 0.05) (Fig. 4 C, D). Consistent with the mRNA analysis, UALCAN database showed that NP_006861_DSTN_S24 and NP_001011546_DSTN_S7 expression levels were lower in renal tumor tissues than in adjacent tissues ( P < 0.05) (Fig. 4 F, H). Further analysis of DSTN expression in Pan cancer using the TCGA database revealed that DSTN expression was lower in tumor tissues compared with normal tissues in Kidney Chromophobe (KICH), Kidney renal clear cell carcinoma (KIRC), Kidney renal papillary cell carcinoma (KIRP), Lung Adenocarcinoma (LUAD), Lung Squamous cell carcinoma (LUSC), Rectum adenocarcinoma (READ), Thyroid carcinoma (THCA), and Uterine Corpus Endometrial Carcinoma (UCEC) ( P < 0.05). However, DSTN expression in Cholangiocarcinoma (CHOL), Head and Neck squamous cell carcinoma (HNSC), and Liver hepatocellular carcinoma (LIHC) was higher than in normal tissues ( P < 0.05) (Fig. 4 E, G). Expression analysis of DSTN in KIRC in HPA database The expression of DSTN was assessed using immunohistochemistry (IHC) in two KIRC tissues and two corresponding normal renal tissues in HPA database. Our analysis revealed a significant downregulation of DSTN expression in tumor tissues compared with the normal tissues (Fig. 5 ). Coexpression analysis of DSTN and other DRGs The coexpression heatmap analysis of single gene expression in KIRC revealed a strong positive correlation between DSTN and 14 other DRGs (Fig. 6 A). Notably, DSTN showed significant positive correlations with FLNA, MYH9, TLN1, MYL6, MYH10, IQGAP1, and CD2AP. The co-expression analysis revealed that, except for a negative correlation between INF2 and SLC7A11 expression, the other DRGs exhibited positive correlations with SLC7A11. (Fig. 6 B). Furthermore, the expression of SLC7A11 was found to be higher in tumor tissues compared with normal tissues in KIRC (Fig. 6 C, D). GSEA and immune infiltration analysis Through GSEA of DEGs in DSTN-high and DSTN-low groups, several critical pathways were identified. These pathways included RHO_GTPASES_ACTIVATE_PAKS, FCGR_ACTIVATION, CREATION_OF_C4_AND_C2_ACTIVATORS, CD22_MEDIATED_BCR_REGULATION, and SCAVENGING_OF_HEME_FROM_PLASMA (Fig. 7 A). Furthermore, the correlation between DSTN and immune infiltration was investigated. In the KIRC-TCGA database, the abundance of CD4 + T cells and endothelial cells was found to be lower in the DSTN-low group compared with the DSTN-high group ( P < 0.05). Conversely, B cells, macrophages, and NK cells showed higher infiltration in the DSTN-low group compared with the DSTN-high group (Fig. 7 B). Additionally, KM survival analysis demonstrated that low expression of DSTN and decreased endothelial cell infiltration were associated with poor cumulative survival in KIRC (Fig. 7 C). DSTN expression was positively correlated with infiltrating endothelial cell The expression of DSTN was significantly positively correlated with endothelial cell infiltration, as determined by the EPIC algorithm (Fig. 8 A). Additionally, the expression of DSTN gene demonstrates a positive correlation with CD4 + T cells and endothelial cell scores, while displaying a negative correlation with B cells, CD8 + T cells, macrophages, and NK cells scores (Fig. 8 B). Further analysis was conducted to examine the correlation between DSTN and endothelial cell marker genes. The results showed positive associations with PECAM1 (R = 0.557), CLDN5 (R = 0.370), KDR (R = 0.532), PLVAP (R = 0.416), PTPRB (R = 0.537), SLC14A1 (R = 0.506), and AQP1 (R = 0.329) (Fig. 8 C-I). Correlation between endothelial cell marker genes and prognosis KM survival curve analysis revealed that a low expression of PECAM1, CLDN5, KDR, PLVAP, PTPRB, SLC14A1, and AQP1 was associated with poor OS (Fig. 9 A-H). In the KIRC-TCGA database, we investigated the correlation between endothelial cell marker genes and M stage. The infiltration of endothelial cells was found to be lower in KIRC M1 stage compared to M0 stage (Fig. 9 I). Additionally, the expression levels of PECAM1, CLDN5, KDR, PLVAP, PTPRB, SLC14A1, and AQP1 were lower in M1 stage compared to M0 stage (Fig. 9 J). To further analyze the relationship between DSTN and endothelial cell marker genes, we divided the KIRC-TCGA database and GSE57757 into high expression and low expression groups based on DSTN. Among the selected seven endothelial cell marker genes, the expression levels of PECAM1, CLDN5, KDR, PLVAP, PTPRB, SLC14A1, and AQP1 were lower in the DSTN-low group compared with the DSTN-high group in the KIRC-TCGA database (P < 0.05) (Fig. 9 K). Similarly, in GSE57757, the expression levels of PECAM1, CLDN5, KDR, PTPRB and SLC14A1 were lower in the DSTN-low group compared to the DSTN-high group ( P < 0.05) (Fig. 9 L). Correlation between DSTN and stemness scores in KIRC The correlation analysis revealed a negative correlation between DSTN and mRNAsi (Fig. 10 A). The mRNAsi score was higher in the KIRC group compared with the Normal group, and it was also higher in the M1 stage compared with the M0 stage (Fig. 10 B, C). In addition, the DSTN-low group in KIRC, KIRP, and renal cell carcinoma (RCC) patients exhibited a higher stemness scores compared with the DSTN-high group (Fig. 10 D-F). Correlations between the IC50 of Pazopanib and Sorafenib and DSTN Expression The relationship between the IC50 scores of Pazopanib and Sorafenib and DSTN expression were demonstrated through correlation plots. It was found that Pazopanib and Sorafenib IC50 scores were negatively correlated with DSTN expression (Fig. 11 A, C). The IC50 scores of Pazopanib and Sorafenib were higher in DSTN-high group compared with the DSTN-low group in KIRC-TCGA (Fig. 11 B, D). Disscusion KIRC is the most prevalent type of malignant tumor in the kidney, accounting for approximately 70–80% of all renal cancer cases[ 8 ]. It originates from the renal tubular epithelial cells and is characterized by high heterogeneity and invasiveness[ 9 ]. Unfortunately, advanced KIRC patients do not respond well to chemotherapy and radiotherapy, and surgical outcomes are often unsatisfactory[ 10 ]. As a result, targeted therapy has emerged as the primary treatment approach for KIRC[ 11 ]. However, the lack of precise targets due to the diverse nature and heterogeneity of KIRC results in varying sensitivity to targeted treatment among different patients[ 12 ]. Therefore, it is crucial to identify key prognostic genes for KIRC, as they could serve as potential therapeutic targets and bring hope for advanced patient treatment. Disulfidptosis is a newly identified form of non-apoptotic cell death that is characterized by the rapid accumulation of excess cysteine, leading to disulfide stress[ 4 ]. In cancer cells with high expression of SLC7A11, glucose deprivation leads to the formation of abnormal disulfide bonds in the actin cytoskeleton protein[ 13 , 14 ]. This ultimately causes the collapse of the cytoskeleton and subsequent cell death. Malignant tumors are characterized by their ability to undergo metabolic reprogramming and evade cell death, often displaying resistance to therapies that induce apoptosis[ 15 ]. As a result, exploring DRGs has important clinical implications in the treatment of tumors and the development of novel anticancer drugs, offering new strategies for guidance. In this study, we conducted an analysis of DRGs from the KIRC-TCGA database to identify potential prognostic genes for KIRC. Using LASSO regression analysis, we developed a prognostic model and further evaluated the 12 identified genes based on their weights in the model. Our findings indicated that eight DRGs (FLNA, FLNB, TLN1, MYH10, DSTN, IQGAP1, ACTN4, and PDLIM1) may influence the prognosis of KIRC. The LASSO prognostic model, built on these eight genes, revealed lower survival rates in the high-risk group compared with the low-risk group ( P < 0.05). Moreover, the AUC for the 5-year survival rate was 0.72, suggesting a certain predictive value of these model for KIRC prognosis. Overall, our research suggested that these identified DRGs have potential as prognostic markers for KIRC. The Lasso prognostic model, based on these genes, demonstrates their predictive value, highlighting their significance in guiding future treatments and the development of novel therapies for KIRC. According to the constructed LASSO model, FLNA was found to have a positive weight, indicating a potential detrimental effect on KIRC prognosis. On the other hand, the remaining seven genes showed negative weights, suggesting that these genes may act as protective factors for KIRC prognosis. Through univariate and multivariate regression analysis, we have identified tumor pathological stage (M stage), age, FLNA, and DSTN as independent risk factors for the prognosis of KIRC. We then proceeded to analyze the expression levels of FLNA and DSTN in tumors and their corresponding survival curves. The findings revealed that FLNA expression was higher in tumors compared to adjacent normal tissues. Surprisingly, the high expression group of FLNA exhibited better survival rates than the low expression group, which contradicts the notion that FLNA is a risk factor for KIRC. This inconsistency could be explained by the fact that RNA-seq data represents bulk sequencing, encompassing different cell types found in tumor tissues, including immune cells. The elevated expression of FLNA in tumor tissues detected through RNA-seq may be attributed to the high expression of immune cells within the tumor tissue. Conversely, DSTN expression was found to be lower in KIRC tumors compared to adjacent normal tissues. Furthermore, low DSTN expression was observed in most analyzed cancers. In KIRC, we examined the co-expression of DSTN with other DRGs and identified a positive correlation between DSTN and genes such as FLNA, MYH9, TLN1, MYL6, MYH10, IQGAP1, and CD2AP. This indicated that DSTN may be involved in the regulation of tumor cell apoptosis in KIRC through disulfide stress, along with other DRGs. SLC7A11 has been shown to be highly expressed in various solid tumors[ 16 , 17 ]. Tumor cells increase the expression of SLC7A11 to sustain elevated levels of glutathione, which helps counteract the heightened oxidative stress caused by accelerated metabolism. This increased expression of SLC7A11 has been found to be positively associated with tumor progression[ 18 ]. Studies by Boyi Gan et al. demonstrated that SLC7A11-overexpressing tumor cells accumulate abnormal disulfides, such as cysteine, under glucose starvation conditions, inducing disulfide stress[ 19 ]. According to studies conducted by Boyi Gan et al., it was found that tumor cells that overexpress SLC7A11 accumulate abnormal disulfides, including cysteine, when subjected to glucose starvation conditions. This accumulation of abnormal disulfides leads to disulfide stress. As a result, there is an increase in disulfide bonds within the cytoskeleton, which is regulated by actin. This increase in disulfide bonds causes significant contraction of the cytoskeleton and detachment from the cell membrane, ultimately leading to cell death. Dong Zhang et al. also found that KIRC tumor cells with high SLC7A11 expression exhibit a malignant phenotype.We analyzed the co-expression of SLC7A11 with 15 other disulfide stress-related genes in tumor tissues[ 20 ]. Except for INF2, all the other 14 genes demonstrated a positive correlation with SLC7A11 expression. The INF2 gene encodes a protein that plays a role in the assembly and remodeling of the cytoskeleton, influencing the dynamics of the actin cytoskeleton by regulating the polymerization and depolymerization of actin fibers [ 21 ]. While SLC7A11-overexpressing tumor cells experience cytoskeletal disruption, decreased expression of INF2 further affects cytoskeletal assembly and remodeling, exacerbating disulfide stress-induced cell death under glucose-starved conditions. Through immune infiltration analysis, we observed a significant positive correlation between DSTN expression and the endothelial cell infiltration score. This finding suggests a strong association between DSTN gene expression levels and the degree of endothelial cell infiltration in tumor tissues. DSTN showed a positive correlation with endothelial cell marker genes (PECAM1, CLDN5, KDR, PLVAP, PTPRB, SCL14A1, and AQP1), further supporting the link between DSTN and endothelial cells. This suggested that DSTN may play a potential role in regulating endothelial cell function, metabolism, or development. Survival analysis was conducted by grouping patients based on DSTN expression and endothelial cell infiltration scores. The results demonstrated that the group with low DSTN expression and low endothelial cell infiltration scores exhibited the poorest survival rates. These findings provide additional evidence to suggest that high DSTN expression might serve as a protective factor influencing the prognosis of KIRC. Such a discovery could potentially contribute to the utilization of DSTN as a biomarker for assessing KIRC prognosis and developing treatment strategies. Additionally, we conducted a separate analysis of the correlation between endothelial cell marker genes and survival rates. Through KM survival curves, we observed a correlation between lower expression of endothelial cell marker genes and poorer survival. This suggests that reduced expression of these genes may be associated with an adverse prognosis in tumors. In the analysis of KIRC-TCGA data, it was observed that the endothelial cell infiltration score was lower in the M1 stage compared to the M0 stage. Additionally, the expression of PECAM1, CLDN5, KDR, PLVAP, PTPRB, SLC14A1, and AQP1 was also lower in the M1 stage compared to the M0 stage. These findings may suggest the significant involvement of endothelial cell infiltration and these specific endothelial cell marker genes in the process of tumor metastasis in KIRC. The expression levels of PECAM1, CLDN5, KDR, PLVAP, PTPRB, SLC14A1, and AQP1 were lower in the low-DSTN group compared to the high-DSTN group. This indicated a potential interaction or co-regulation between DSTN and these endothelial cell marker genes. OCLR scores, which stand for Oncogenic Cell Lineage Representation scores, are indicators used to measure the extent of stem cell characteristics present in tumor samples[ 22 ]. By analyzing and calculating a series of gene expression levels related to stem cells, a comprehensive stem cell feature score can be obtained. Higher stem cell feature scores are usually associated with tumor invasiveness, drug resistance, metastatic tendencies, and poor prognosis[ 23 ]. When comparing the KIRC group with the normal group, it was observed that the KIRC group exhibited higher scores for stem cell features, indicating an increased presence of stem cell characteristics in KIRC patients. These characteristics are known to be associated with tumor invasiveness and poor prognosis. Furthermore, a correlation analysis between DSTN expression and stem cell feature scores revealed a negative correlation, suggesting that high DSTN expression may play a role in inhibiting or regulating tumor stem cell properties. Therefore, it is important to investigate the mechanisms by which DSTN regulates tumor stem cell characteristics, as well as its potential value in assessing prognosis and developing treatment strategies. Pazopanib and Sorafenib are commonly used drugs in the clinical treatment of renal clear cell carcinoma[ 24 ]. We conducted an analysis to examine the correlation between the IC50 values of Pazopanib and Sorafenib and DSTN expression. The findings demonstrated a negative correlation between these IC50 values and DSTN expression. Specifically, we observed higher IC50 values for Pazopanib and Sorafenib in tumor samples with low DSTN expression. These results highlight the significance of DSTN in the treatment of KIRC and offer insights for personalized therapy and prognosis assessment. However, our study had some limitations. We relied on public databases for our data sources and did not have our own clinical data verification. In future research, it would be beneficial to analyze the effectiveness of DSTN as an independent prognostic factor for KIRC using large clinical samples. In conclusion, KIRC is a prevalent malignant tumor of the kidney, and targeted therapy is the primary treatment approach. However, due to the diversity and heterogeneity of tumors and the lack of precise targets, patient sensitivity to targeted treatments can vary. Therefore, it is crucial to identify key genes that influence the prognosis of KIRC. Disulfidptosis, a novel form of non-apoptotic cell death, has been associated with tumor progression. In our study, we conducted an analysis of DRGs and identified eight DRGs that could potentially affect the prognosis of KIRC. Further analysis revealed that FLNA and DSTN were independent prognostic risk factors, and DSTN may play a role in regulating tumor stem cell characteristics. Additionally, we found a correlation between the expression level of DSTN and the sensitivity of drugs such as Pazopanib and Sorafenib. These findings offer potential biomarkers and insights for prognostic assessment and personalized treatment of RCC. Declarations Funding Statement This work was supported by the Zhejiang Province Traditional Chinese Medicine Science and Technology Plan (2022ZA057, 2023ZL398 and 2024ZL055). Ethical Compliance All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Data Availability All data generated or analysed during this study are included in this published article [and its supplementary information files]. Conflict of Interest declaration The authors declare that they have no affiliations with or involvement in any organization or entity with any financial interest in the subject matter or materials discussed in this manuscript. Author contributions SGZF and CP were primarily responsible for manuscript writing. YYM and CJL were primarily responsible for result analysis, SN and YX were mainly responsible for mapping the results of bioinformatics. CTT was responsible for project design. All authors reviewed the manuscript. 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Cell, 2018. 173(2): p. 338–354.e15. Yamana, K., R. Ohashi, and Y. Tomita, Contemporary Drug Therapy for Renal Cell Carcinoma- Evidence Accumulation and Histological Implications in Treatment Strategy . Biomedicines, 2022. 10(11). Schiavoni, V., et al., Recent Advances in the Management of Clear Cell Renal Cell Carcinoma: Novel Biomarkers and Targeted Therapies . Cancers (Basel), 2023. 15(12). Graham, J., S. Dudani, and D.Y.C. Heng, Prognostication in Kidney Cancer: Recent Advances and Future Directions . J Clin Oncol, 2018: p. Jco2018790147. Yuan, Z.X., et al., Targeting Strategies for Renal Cell Carcinoma: From Renal Cancer Cells to Renal Cancer Stem Cells . Front Pharmacol, 2016. 7: p. 423. Zhang, D., et al., Spatial heterogeneity of tumor microenvironment influences the prognosis of clear cell renal cell carcinoma . J Transl Med, 2023. 21(1): p. 489. Liu, X., L. Zhuang, and B. Gan, Disulfidptosis: disulfide stress-induced cell death . Trends Cell Biol, 2023. Liu, T., et al., Exploring the role of the disulfidptosis-related gene SLC7A11 in adrenocortical carcinoma: implications for prognosis, immune infiltration, and therapeutic strategies . Cancer Cell Int, 2023. 23(1): p. 259. Wen, G.M., X.Y. Xu, and P. Xia, Metabolism in Cancer Stem Cells: Targets for Clinical Treatment. Cells, 2022. 11(23). He, J., et al., The amino acid transporter SLC7A11-mediated crosstalk implicated in cancer therapy and the tumor microenvironment . Biochem Pharmacol, 2022. 205: p. 115241. He, J., et al., Intra-Tumoral Expression of SLC7A11 Is Associated with Immune Microenvironment, Drug Resistance, and Prognosis in Cancers: A Pan-Cancer Analysis . Front Genet, 2021. 12: p. 770857. Tang, X., et al., Research progress on SLC7A11 in the regulation of cystine/cysteine metabolism in tumors . Oncol Lett, 2022. 23(2): p. 47. Yan, Y., et al., SLC7A11 expression level dictates differential responses to oxidative stress in cancer cells . Nat Commun, 2023. 14(1): p. 3673. Zhang, D., et al., An integrative multi-omics analysis based on disulfidptosis-related prognostic signature and distinct subtypes of clear cell renal cell carcinoma . Front Oncol, 2023. 13: p. 1207068. A, M., et al., Lysine acetylation of cytoskeletal proteins: Emergence of an actin code . J Cell Biol, 2020. 219(12). Lian, H., et al., Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma . Mol Oncol, 2019. 13(10): p. 2227–2245. Ponomarev, A., et al., Intrinsic and Extrinsic Factors Impacting Cancer Stemness and Tumor Progression . Cancers (Basel), 2022. 14(4). Santoni, M., et al., Sunitinib, pazopanib or sorafenib for the treatment of patients with late relapsing metastatic renal cell carcinoma . J Urol, 2015. 193(1): p. 41–7. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3908062","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":274308988,"identity":"17841974-e82d-441c-9c93-2a16f210c1c2","order_by":0,"name":"Zuifei Shangguan","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zuifei","middleName":"","lastName":"Shangguan","suffix":""},{"id":274308989,"identity":"975c7a13-e848-48c3-ae47-d864bf45943c","order_by":1,"name":"Yimin Yao","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yimin","middleName":"","lastName":"Yao","suffix":""},{"id":274308990,"identity":"c1460986-8f90-42f6-bf4b-72b392148639","order_by":2,"name":"Jiale Chen","email":"","orcid":"","institution":"The Third Clinical Medical College, Zhejiang Chinese Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiale","middleName":"","lastName":"Chen","suffix":""},{"id":274308991,"identity":"26d2beac-25f8-49f9-b146-f733514717b2","order_by":3,"name":"Ping chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"chen","suffix":""},{"id":274308992,"identity":"c9f85f0e-16db-47ce-862b-e69dbef0b092","order_by":4,"name":"Na Shi","email":"","orcid":"","institution":"Second Sanatorium of Air Force Healthcare Center for Special Services","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Shi","suffix":""},{"id":274308993,"identity":"d526769c-ee7a-4993-91db-68b49051eae6","order_by":5,"name":"Xue Ying","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Ying","suffix":""},{"id":274308994,"identity":"11c0186f-c80d-4f5b-823f-cd6a837e2218","order_by":6,"name":"Tingting Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYHAC5gcJP9jkYBwiNLAxsxl87OEzJkkLg+QMNrnEBqK18M/vP2DMw2OWPr/9dJoEQ4V1YgP72QN4tUgcY2Z4zGORltvYk7tNguFMemIDT14CXi0GQIcBbTmW2yzBu02Cse1wYoMEjwFBLdI8bP/T2cBa/hGpBeh9tgQesJYGIrRIHEs2AwYym+EMntzNFgnH0o3beHLwa+FvPvgYFJXy8u1nN974UGMt289+Br8WVJAAxGwkqB8Fo2AUjIJRgAMAAOeVOnhU0s7eAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-01-29 05:35:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3908062/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3908062/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51562830,"identity":"8ef6d301-553b-40e4-9e1f-96a8dd646b90","added_by":"auto","created_at":"2024-02-23 18:33:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2888608,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic model established through LASSO regression analysis. A) Wayne diagram of DRGs and those DEGs obtained from KIRC-TCGA. B) The LASSO variable trajectory diagram. C) The LASSO coefficient screening diagram. D) Risk Assessment Model. E) KM survival curve.\u003c/p\u003e\n\u003cp\u003eDEGs: differentially expressed genes; DRGs: disulfidptosis-related genes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/996aeceb5ef4c232d217b038.png"},{"id":51562828,"identity":"eba03996-6b7c-461f-88fe-2fd23bdd4347","added_by":"auto","created_at":"2024-02-23 18:33:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1232564,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate and multivariate Cox regression analysis. A) Univariate Cox regression analysis. B) Multivariate Cox regression analysis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/e6621e6a514ef9a12781acd1.png"},{"id":51562831,"identity":"eee7e961-fc40-4a0a-82b0-c5b2dce8597d","added_by":"auto","created_at":"2024-02-23 18:33:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":869345,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and survival analysis of FLNA in KIRC. A) Non paired comparative analysis of FLNA in tumor and adjacent tissues in the KIRC-TCGA database. B) Paired comparative analysis of FLNA in tumor and adjacent tissues in the KIRC-TCGA database. C) Comparative analysis of KM survival curves between FLNA-low group and FLNA-high group.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/c5ab803175d70e65e35ad42d.png"},{"id":51562981,"identity":"0ba4a53a-91f6-46ab-bbc7-6e8e5e3b8a91","added_by":"auto","created_at":"2024-02-23 18:41:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2808547,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of DSTN expression between tumor tissue and adjacent tissues. A) Non paired comparative analysis of DSTN in tumor and adjacent tissues in the KIRC-TCGA database. B) Paired comparative analysis of DSTN in tumor and adjacent tissues in the KIRC-TCGA database. C) Non paired comparative analysis of DSTN in tumor and adjacent tissues in GSE36895. D) Non paired comparative analysis of DSTN in tumor and adjacent tissues in GSE53757. E, G) Differential analysis of DSTN expression in pan cancer. F, H) Protein expression of DSTN in RCC.\u003c/p\u003e\n\u003cp\u003eKICH: Kidney Chromophobe. KIRC: Kidney renal clear cell carcinoma. KIRP: Kidney renal papillary cell carcinoma. LUAD: Lung Adenocarcinoma. LUSC: Lung Squamous cell carcinoma. READ: Rectum adenocarcinoma. THCA: Thyroid carcinoma. UCEC: Uterine Corpus Endometrial Carcinoma. CHOL: Cholangiocarcinoma. HNSC: Head and Neck squamous cell carcinoma. LIHC: Liver hepatocellular carcinoma.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/98c1dbef1e043c9f0e12f3bd.png"},{"id":51562836,"identity":"d8c92b57-b8c6-495d-b751-d9d3b55bf265","added_by":"auto","created_at":"2024-02-23 18:33:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17802953,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of DSTN in renal cancer tissues and normal renal tissues in HPA database. A, B) DSTN expression in normal renal tissues. C, D) DSTN expression in renal cancer tissues.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/e46b9d8b56c788fe6f9a9c92.png"},{"id":51562835,"identity":"2ba288d6-3ad0-497b-b044-27e35556f4ec","added_by":"auto","created_at":"2024-02-23 18:33:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3699113,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis of DRGs. A) Coexpression analysis of DSTN and 14 other DRGs. B) Coexpression analysis of 15 DRGs. C) Non paired comparative analysis of SLC7A11 in tumor and adjacent tissues in the KIRC-TCGA database. D) Paired comparative analysis of SLC7A11 in tumor and adjacent tissues in the KIRC-TCGA database.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/ace87ccce5e8d5e9aa64b6ca.png"},{"id":51562834,"identity":"e96bbc4a-5d99-4540-8308-b8962fd77b31","added_by":"auto","created_at":"2024-02-23 18:33:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1681321,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA and immune infiltration analysis. A) GSEA of DEGs in DSTN-high and DSTN-low groups. B) Analysis of immune infiltration in DSTN-low and DSTN-high groups. C) KM survival analysis based on DSTN expression and endothelial cell infiltration score.\u003c/p\u003e\n\u003cp\u003eGSEA: Gene Set Enrichment Analysis. DEGs: differentially expressed genes.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/729627cdf93826f5bd8140bf.png"},{"id":51562838,"identity":"6ecf9f89-0027-4753-a7c8-ac23e1f34e5b","added_by":"auto","created_at":"2024-02-23 18:33:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4831646,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between DSTN and immune cell infiltration. A) Correlation between DSTN and endothelial cell infiltration. B) Correlation between DSTN and immune cell infiltration. C-I) Correlation between DSTN and endothelial cell marker genes.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/1799bc8173d63de69fc81c71.png"},{"id":51562982,"identity":"d25448a7-329c-4f77-ac72-b803c6f9162a","added_by":"auto","created_at":"2024-02-23 18:41:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2129433,"visible":true,"origin":"","legend":"\u003cp\u003eKM survival curve analysis and differential analysis of endothelial cell infiltration between different tumor groups. A-H) KM survival curves analysis between high and low expression groups of endothelial cell marker genes. I) Differential analysis of infiltrating endothelial cells in M0 and M1 groups in the KIRC-TCGA database. J) Differential expression of endothelial cell marker genes in M0 and M1 groups in the KIRC-TCGA database. K) Differential expression of endothelial cell marker genes between DSTN-high and DSTN-low groups in the KIRC-TCGA database. L) Differential expression of endothelial cell marker genes between DSTN-high and DSTN-low groups in GSE53757.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/da1eca3776d4d9ec3cf372ed.png"},{"id":51562839,"identity":"7bd9e2ff-ec85-4ba5-a79c-fc75e84da4a7","added_by":"auto","created_at":"2024-02-23 18:33:32","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1651191,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between DSTN and stemness scores in KIRC. A) Correlation between DSTN and mRNAsi. B) Differences in mRNAsi scores between KIRC and normal group. C) Differences in mRNAsi scores between M1 and M0 groups in KIRC. D) Differences in mRNAsi scores between DSTN-low and DSTN-high groups in KIRC. E) Differences in mRNAsi scores between DSTN-low and DSTN-high groups in KIRP. F) Differences in mRNAsi scores between DSTN-low and DSTN-high groups in RCC.\u003c/p\u003e\n\u003cp\u003eKIRC: Kidney renal clear cell carcinoma. KIRP: Kidney renal papillary cell carcinoma. RCC: Renal cell carcinoma.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/5ae0c31f786f24b2e303913e.png"},{"id":51562832,"identity":"fb0a9141-9229-48b5-9ebb-956b079f049f","added_by":"auto","created_at":"2024-02-23 18:33:31","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1467326,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between the IC50 of Pazopanib and Sorafenib and DSTN Expression. A) Correlation between Pazopanib IC50 and DSTN expression. B) Analysis of differences in Pazopanib IC50 between DSTN-low group and DSTN-high group. C) Correlation between Sorafenib IC50 and DSTN expression. D) Analysis of differences in Sorafenib IC50 between DSTN-low group and DSTN-high group.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/d1cb4ffbc2b556cc2e9860fc.png"},{"id":53622180,"identity":"b7a21e4a-46fd-41a1-95a1-eacbb5694683","added_by":"auto","created_at":"2024-03-28 08:02:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4629079,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/3449f9f2-4853-43fe-8d0a-4d98bf8318da.pdf"},{"id":51562840,"identity":"86e67e01-2aba-4579-9a35-f45c74d498d4","added_by":"auto","created_at":"2024-02-23 18:33:36","extension":"zip","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":55699371,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-3908062/v1/2433a6c9dde19c737e981ac8.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Significance of Disulfidptosis-Related Gene DSTN in Kidney Renal Clear Cell Carcinoma: Correlation with Immune Cell Infiltration and Cancer Stemness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKidney renal clear cell carcinoma (KIRC) is the most prevalent form of kidney cancer, accounting for approximately 75% of all cases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its incidence has been steadily increasing in recent years, posing a significant global public health challenge. KIRC is characterized by its propensity to metastasize and resistance to conventional treatments, making effective management particularly challenging[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, the heterogeneity within this disease contributes to variations in patient outcomes and treatment response.\u003c/p\u003e \u003cp\u003eDisulfidptosis, a newly discovered form of programmed cell death, has gained substantial attention in the field of cancer research. Under conditions of glucose limitation, rapid depletion of NADPH occurs in SLC7A11\u003csup\u003ehigh\u003c/sup\u003e cells, resulting in abnormal accumulation of disulfides such as cystine[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This accumulation induces disulfide stress and triggers rapid cell death. Disulfidptosis plays a crucial role in regulating fundamental cellular processes, including apoptosis, proliferation, and migration[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Dysregulation of disulfidptosis has been implicated in various malignancies, including lung, breast, and prostate cancer[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, its association with KIRC remains poorly understood. Understanding the relationship between disulfidptosis and KIRC could provide valuable insights into the pathogenesis and progression of this aggressive cancer. Moreover, the identification of prognostic biomarkers that are associated with disulfidptosis-related genes (DRGs) could potentially enhance the ability to predict KIRC patient outcomes and guide personalized treatment approaches.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the KIRC datasets to explore whether DRGs were associated with KIRC prognosis. We constructed prognostic models and performed risk regression analysis. Ultimately, we identified the DSTN as an independent prognostic molecule for KIRC. Through an analysis of DSTN expression, tumor immune cell infiltration, and stemness indices, we observed a positive correlation between DSTN and endothelial cell infiltration, while noting a negative correlation with tumor stemness indices. High DSTN expression was associated with improved survival rates, suggesting a favorable prognosis for KIRC. In addition, we analyzed the association between DSTN and the inhibitory concentration 50% (IC50) of therapeutic drugs, namely Pazopanib and Sorafenib, for KIRC. Our findings demonstrated a negative correlation, suggesting a possible connection between DSTN expression levels and drug sensitivity. Based on our findings, we hypothesized that the expression levels of DSTN could potentially serve as a prognostic biomarker for KIRC. These expression levels may reflect immune cell infiltration patterns and stemness indices. By further investigating these relationships, our aim was to enhance the understanding of KIRC biology and explore new therapeutic targets.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThe KIRC-TCGA database, obtained from The Cancer Genome Atlas (TCGA) on May 3, 2022 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.com\u003c/span\u003e\u003cspan address=\"https://portal.gdc.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was used for this study. This database provided clinical information and processed RNA-sequencing expression data at level 3. To validate the identified key genes, we retrieved the GSE36895 and GSE53757 datasets from the Gene Expression Omnibus (GEO) database.\u003c/p\u003e \u003cp\u003eThe GSE36895 dataset contained 76 samples, including 52 human samples and 24 mouse samples. Among them, 52 human samples were divided into two groups, including 29 primary KIRC tumor tissues and 23 normal renal cortex samples. In the GSE53757 dataset, there were 144 samples, including 72 KIRC tumor tissues and 72 normal kidney tissues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a prognostic model through LASSO regression analysis\u003c/h2\u003e \u003cp\u003eThe DEGs in the KIRC-TCGA were intersected with 15 DRGs (FLNA, FLNB, MYH9, TLN1, ACTB, MYL6, MYH10, CAPZB, DSTN, IQGAP1, ACTN4, PDLIM1, CD2AP, INF2, and SLC7A11) to obtain overlapping genes[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. LASSO regression analysis was performed using the \"glmnet\" R package to reduce dimensionality and construct a prognostic model or signature. A 10-fold cross-validation approach was utilized. The prognostic model was assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC) using the \u0026ldquo;ggplot2\u0026rdquo; and the \u0026ldquo;timeROC\u0026rdquo; R packages. The survival analysis of KIRC was conducted using the \u0026ldquo;survminer\u0026rdquo; and the \u0026ldquo;survival\u0026rdquo; R packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and multivariate Cox regression analysis\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors associated with KIRC. The P-value, hazard ratio (HR), and 95% confidence interval (CI) for each variable were presented using the 'forestplot' R package. The univariate Cox regression analysis revealed significant differences that were deemed relevant for prognosis. Furthermore, the multivariate analysis also demonstrated significant differences, indicating the presence of independent prognostic factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eValidation of gene expression in GEO database\u003c/h2\u003e \u003cp\u003eGene expression profiles for DRGs were obtained from GSE36895 and GSE53757 datasets. Boxplots were created using the \u0026ldquo;ggplot2\u0026rdquo; R package to compare the differences, and the WilCoxon rank sum test was used to assess the disparities. The The University of ALabama at Birmingham Cancer data analysis Portal database (UALCAN, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to evaluate the differential expression of DSTN between tumor tissue and normal tissue. The Human protein atlas database (HPA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org)wa\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org)wa\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003es used to evaluate the expression of DSTN in renal cancer tissue and normal renal tissue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eKaplan-Meier (KM) survival curve analysis\u003c/h2\u003e \u003cp\u003eKM analysis was a non-parametric statistical technique used to estimate the survival probability or survival rate over time. The \u003cem\u003eP\u003c/em\u003e-values and HR with 95% CI were calculated using log-rank tests and univariate Cox proportional hazards regression. The \u0026ldquo;survival\u0026rdquo; R package was used for testing and fitting the proportional risk hypothesis. The results of survival regression were visualized using the \u0026ldquo;survminer\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo; R packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between disulfidptosis-related independent prognostic molecules and other DRGs\u003c/h2\u003e \u003cp\u003eThe selected disulfidptosis-related molecule was identified as an independent prognostic marker. Single gene co-expression analysis was performed on the RNA-seq data of TCGA-KIRC using the \u0026ldquo;ggplot2\u0026rdquo; R package and Spearman statistical method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Enrichment Analysis (GSEA)\u003c/h2\u003e \u003cp\u003eThe samples were divided into high and low expression groups based on the expression levels of disulfidptosis-related independent prognostic molecules. The \u0026ldquo;DESeq2\u0026rdquo; R package was employed to conduct differential analysis using the original Counts matrix. The \u0026ldquo;clusterProfiler\u0026rdquo; R package was utilized for performing GSEA analysis. The \u0026ldquo;ggplot2\u0026rdquo; R package was used to visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eTumor immune infiltration analysis\u003c/h2\u003e \u003cp\u003eThe KIRC samples from KIRC-TCGA were categorized into two groups based on the expression of a disulfidptosis-related independent prognostic molecule. The immune scores were evaluated using the \u0026ldquo;immunedeconv\u0026rdquo; R package. The results were visualized using the \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;pheatmap\u0026rdquo; R packages. Further survival analysis of immune infiltrating cells was performed using TIMER2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a web server specifically designed for comprehensive analysis of tumor-infiltrating immune cells. RNA-sequencing expression profiles (level 3) and corresponding clinical information for KIRC were obtained from the TCGA database. The count data was converted to transcripts per million (TPM) and then normalized by applying log2(TPM\u0026thinsp;+\u0026thinsp;1). The \u0026ldquo;immunedeconv\u0026rdquo; R package was used to examine the correlation between the expression of the independent prognostic molecule and immune scores. The results were analyzed and visualized using the \u0026ldquo;ggClusterNet\u0026rdquo; R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between endothelial cell marker genes and survival rate\u003c/h2\u003e \u003cp\u003eThe OS rate was chosen as the prognostic variable, and Cox regression was used as the statistical method. Based on the expression of independent prognostic genes related to disulfidptosis, the KIRC samples from the TCGA database and GSE53757 dataset were divided into high and low expression groups. The high expression group had an expression level above the median, while the low expression group had an expression level below the median. The expression differences of immune cell marker genes between these two groups were compared using the \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;pheatmap\u0026rdquo; R packages. Additionally, differential analysis of the expression of immune cell marker genes was performed based on different M-stage groupings of KIRC samples in the TCGA database. This analysis was conducted using the \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;pheatmap\u0026rdquo; R packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTumor stemness indices and the IC50 of KIRC chemotherapy drug\u003c/h2\u003e \u003cp\u003eThe OCLR algorithm developed by Malta et al. was utilized to calculate the mRNAsi of two groups: high expression and low expression of independent prognostic genes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The mRNA expression features, consisting of a gene expression profile with 11,774 genes, were analyzed using Spearman correlation. Subsequently, the stemness index was mapped to a range of [0,1] through a linear transformation, achieved by subtracting the minimum value and dividing by the maximum value.\u003c/p\u003e \u003cp\u003eThe chemotherapy response of high and low expression groups of independent prognostic genes to targeted drugs was predicted using the largest publicly available pharmacogenomics database, Cancer Drug Sensitivity Genomics (GDSC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The prediction process was carried out using the R package \u0026ldquo;pRRophytics\u0026rdquo;, where the half maximum IC50 of the sample was estimated through ridge regression, with all parameters set to default values. Batch effects of combo and tissue types were considered, and the expression of duplicate genes was summarized as the average value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation analysis was conducted to examine the correlation between non-normally distributed quantitative variables.\u003c/p\u003e \u003cp\u003eThe analysis methods and R packages were implemented using R (Foundation for Statistical Computing, 2020) version 4.0.3. A significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConstructing a prognostic model through LASSO regression analysis\u003c/h2\u003e \u003cp\u003eTo explore whether DRGs can serve as independent prognostic indicators for KIRC, we obtained 8962 differential molecules from KIRC-TCGA database for analysis. These molecules were then intersected with 15 known DRGs (FLNA, FLNB, MYH9, TLN1, ACTB, MYL6, MYH10, CAPZB, DSTN, IQGAP1, ACTN4, PDLIM1, CD2AP, INF2, and SLC7A11) to obtain overlapping genes. We observed that 12 genes (FLNA, FLNB, MYH9, TLN1, MYH10, DSTN, IQGAP1, ACTN4, PDLIM1, CD2AP, INF2, and SLC7A11) were present in this intersection (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLASSO regression analysis was employed to construct a prognostic model and fitted the overall survival rate of KIRC patients using 12 selected DRGs. The prognostic model was built based on the key genes that showed a non-zero coefficient. Finally, a prognostic scoring formula was derived: Riskscore = (0.4592) * FLNA + (-0.0463) * FLNB + (-0.2556) * TLN1 + (-0.0401) * MYH10 + (-0.2795) * DSTN + (-0.0169) * IQGAP1 + (-0.3716) * ACTN4 + (-0.1016) * PDLIM1. The eight genes included in the model were assigned weights, where negative numbers represented protective genes and positive numbers indicated risk genes. Based on our analysis, FLNA was identified as a risk factor, while the other seven genes were identified as protective factors.\u003c/p\u003e \u003cp\u003eThe LASSO variable trajectory diagram showed that the selection method using L1 norm resulted in the identification of variables with corresponding non-zero coefficients at the position of 8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe LASSO coefficient screening diagram indicated that when lambda.min was 8, the partial likelihood deviance was minimized, suggesting that this model was the most appropriate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo visualize the prognostic risk factors, we plotted the riskscore, survival time, and survival status for KIRC patients from the TCGA database. The top graph displayed a scatter plot of the riskscore ranging from low to high. Meanwhile, the middle figure illustrated the scatter diagram showing the distribution of survival time and survival state corresponding to different riskscore values. We observed a higher number of deceased patients in the high-risk area compared to the low-risk area. The bottom figure presented an expression heat map of eight molecules, namely FLNA, FLNB, TLN1, MYH10, DSTN, IQGAP1, ACTN4, and PDLIM1, within the signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eIn the prognostic model, there was a significant difference in the survival probability between the high-risk group and the low-risk group. This observation was confirmed through the log-rank test using the KM survival curve [P\u0026thinsp;=\u0026thinsp;7.14e\u0026thinsp;\u0026minus;\u0026thinsp;12, HR\u0026thinsp;=\u0026thinsp;3.24, 95%CL (2.315, 4.535)]. The AUC values for this prognostic model at 1, 3, and 5 years were 0.665, 0.670, and 0.720, respectively. These results indicated that DRGs (FLNA, FLNB, TLN1, MYH10, DSTN, IQGAP1, ACTN4, and PDLIM1) served as predictive prognostic models with good accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Prognostic Molecules Associated with Disulfidoptosis\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate Cox regression analysis were employed to assess hazard ratios and identify independent prognostic factors. The obtained results were presented in a forest plot. Notably, DSTN and FLNA exhibited significant variations in both univariate and multivariate analysis, suggesting that these variables were independent of other clinical factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFLNA expression validation and KM curve analysis\u003c/h2\u003e \u003cp\u003eAccording to univariate and multivariate Cox regression analysis, we concluded that FLNA was an independent prognostic factor of KIRC related to prognosis. In KIRC-TCGA database, the expression of FLNA was increased in tumor tissues compared with adjacent tissues, (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B), but survival curve showing significantly longer survival in the FLNA-high group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDSTN expression validation\u003c/h2\u003e \u003cp\u003eIn the KIRC-TCGA database, the expression of DSTN was found to be decreased in tumor tissues compared with adjacent tissues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). This finding was also verified in GSE36895 and GSE53757 datasets, where the expression of DSTN was lower in tumor tissues compared with normal renal tissues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Consistent with the mRNA analysis, UALCAN database showed that NP_006861_DSTN_S24 and NP_001011546_DSTN_S7 expression levels were lower in renal tumor tissues than in adjacent tissues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther analysis of DSTN expression in Pan cancer using the TCGA database revealed that DSTN expression was lower in tumor tissues compared with normal tissues in Kidney Chromophobe (KICH), Kidney renal clear cell carcinoma (KIRC), Kidney renal papillary cell carcinoma (KIRP), Lung Adenocarcinoma (LUAD), Lung Squamous cell carcinoma (LUSC), Rectum adenocarcinoma (READ), Thyroid carcinoma (THCA), and Uterine Corpus Endometrial Carcinoma (UCEC) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, DSTN expression in Cholangiocarcinoma (CHOL), Head and Neck squamous cell carcinoma (HNSC), and Liver hepatocellular carcinoma (LIHC) was higher than in normal tissues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, G).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eExpression analysis of DSTN in KIRC in HPA database\u003c/h2\u003e \u003cp\u003eThe expression of DSTN was assessed using immunohistochemistry (IHC) in two KIRC tissues and two corresponding normal renal tissues in HPA database. Our analysis revealed a significant downregulation of DSTN expression in tumor tissues compared with the normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCoexpression analysis of DSTN and other DRGs\u003c/h2\u003e \u003cp\u003eThe coexpression heatmap analysis of single gene expression in KIRC revealed a strong positive correlation between DSTN and 14 other DRGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Notably, DSTN showed significant positive correlations with FLNA, MYH9, TLN1, MYL6, MYH10, IQGAP1, and CD2AP. The co-expression analysis revealed that, except for a negative correlation between INF2 and SLC7A11 expression, the other DRGs exhibited positive correlations with SLC7A11. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Furthermore, the expression of SLC7A11 was found to be higher in tumor tissues compared with normal tissues in KIRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eGSEA and immune infiltration analysis\u003c/h2\u003e \u003cp\u003eThrough GSEA of DEGs in DSTN-high and DSTN-low groups, several critical pathways were identified. These pathways included RHO_GTPASES_ACTIVATE_PAKS, FCGR_ACTIVATION, CREATION_OF_C4_AND_C2_ACTIVATORS, CD22_MEDIATED_BCR_REGULATION, and SCAVENGING_OF_HEME_FROM_PLASMA (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the correlation between DSTN and immune infiltration was investigated. In the KIRC-TCGA database, the abundance of CD4\u0026thinsp;+\u0026thinsp;T cells and endothelial cells was found to be lower in the DSTN-low group compared with the DSTN-high group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, B cells, macrophages, and NK cells showed higher infiltration in the DSTN-low group compared with the DSTN-high group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Additionally, KM survival analysis demonstrated that low expression of DSTN and decreased endothelial cell infiltration were associated with poor cumulative survival in KIRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDSTN expression was positively correlated with infiltrating endothelial cell\u003c/h2\u003e \u003cp\u003eThe expression of DSTN was significantly positively correlated with endothelial cell infiltration, as determined by the EPIC algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Additionally, the expression of DSTN gene demonstrates a positive correlation with CD4\u0026thinsp;+\u0026thinsp;T cells and endothelial cell scores, while displaying a negative correlation with B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, macrophages, and NK cells scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Further analysis was conducted to examine the correlation between DSTN and endothelial cell marker genes. The results showed positive associations with PECAM1 (R\u0026thinsp;=\u0026thinsp;0.557), CLDN5 (R\u0026thinsp;=\u0026thinsp;0.370), KDR (R\u0026thinsp;=\u0026thinsp;0.532), PLVAP (R\u0026thinsp;=\u0026thinsp;0.416), PTPRB (R\u0026thinsp;=\u0026thinsp;0.537), SLC14A1 (R\u0026thinsp;=\u0026thinsp;0.506), and AQP1 (R\u0026thinsp;=\u0026thinsp;0.329) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-I).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation between endothelial cell marker genes and prognosis\u003c/h2\u003e \u003cp\u003eKM survival curve analysis revealed that a low expression of PECAM1, CLDN5, KDR, PLVAP, PTPRB, SLC14A1, and AQP1 was associated with poor OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-H). In the KIRC-TCGA database, we investigated the correlation between endothelial cell marker genes and M stage. The infiltration of endothelial cells was found to be lower in KIRC M1 stage compared to M0 stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eI). Additionally, the expression levels of PECAM1, CLDN5, KDR, PLVAP, PTPRB, SLC14A1, and AQP1 were lower in M1 stage compared to M0 stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further analyze the relationship between DSTN and endothelial cell marker genes, we divided the KIRC-TCGA database and GSE57757 into high expression and low expression groups based on DSTN. Among the selected seven endothelial cell marker genes, the expression levels of PECAM1, CLDN5, KDR, PLVAP, PTPRB, SLC14A1, and AQP1 were lower in the DSTN-low group compared with the DSTN-high group in the KIRC-TCGA database \u003cem\u003e(P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eK). Similarly, in GSE57757, the expression levels of PECAM1, CLDN5, KDR, PTPRB and SLC14A1 were lower in the DSTN-low group compared to the DSTN-high group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eL).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between DSTN and stemness scores in KIRC\u003c/h2\u003e \u003cp\u003eThe correlation analysis revealed a negative correlation between DSTN and mRNAsi (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). The mRNAsi score was higher in the KIRC group compared with the Normal group, and it was also higher in the M1 stage compared with the M0 stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB, C). In addition, the DSTN-low group in KIRC, KIRP, and renal cell carcinoma (RCC) patients exhibited a higher stemness scores compared with the DSTN-high group (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCorrelations between the IC50 of Pazopanib and Sorafenib and DSTN Expression\u003c/h2\u003e \u003cp\u003eThe relationship between the IC50 scores of Pazopanib and Sorafenib and DSTN expression were demonstrated through correlation plots. It was found that Pazopanib and Sorafenib IC50 scores were negatively correlated with DSTN expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA, C). The IC50 scores of Pazopanib and Sorafenib were higher in DSTN-high group compared with the DSTN-low group in KIRC-TCGA (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Disscusion","content":"\u003cp\u003eKIRC is the most prevalent type of malignant tumor in the kidney, accounting for approximately 70\u0026ndash;80% of all renal cancer cases[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It originates from the renal tubular epithelial cells and is characterized by high heterogeneity and invasiveness[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Unfortunately, advanced KIRC patients do not respond well to chemotherapy and radiotherapy, and surgical outcomes are often unsatisfactory[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As a result, targeted therapy has emerged as the primary treatment approach for KIRC[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the lack of precise targets due to the diverse nature and heterogeneity of KIRC results in varying sensitivity to targeted treatment among different patients[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, it is crucial to identify key prognostic genes for KIRC, as they could serve as potential therapeutic targets and bring hope for advanced patient treatment.\u003c/p\u003e \u003cp\u003eDisulfidptosis is a newly identified form of non-apoptotic cell death that is characterized by the rapid accumulation of excess cysteine, leading to disulfide stress[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In cancer cells with high expression of SLC7A11, glucose deprivation leads to the formation of abnormal disulfide bonds in the actin cytoskeleton protein[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This ultimately causes the collapse of the cytoskeleton and subsequent cell death. Malignant tumors are characterized by their ability to undergo metabolic reprogramming and evade cell death, often displaying resistance to therapies that induce apoptosis[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As a result, exploring DRGs has important clinical implications in the treatment of tumors and the development of novel anticancer drugs, offering new strategies for guidance.\u003c/p\u003e \u003cp\u003eIn this study, we conducted an analysis of DRGs from the KIRC-TCGA database to identify potential prognostic genes for KIRC. Using LASSO regression analysis, we developed a prognostic model and further evaluated the 12 identified genes based on their weights in the model. Our findings indicated that eight DRGs (FLNA, FLNB, TLN1, MYH10, DSTN, IQGAP1, ACTN4, and PDLIM1) may influence the prognosis of KIRC. The LASSO prognostic model, built on these eight genes, revealed lower survival rates in the high-risk group compared with the low-risk group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, the AUC for the 5-year survival rate was 0.72, suggesting a certain predictive value of these model for KIRC prognosis. Overall, our research suggested that these identified DRGs have potential as prognostic markers for KIRC. The Lasso prognostic model, based on these genes, demonstrates their predictive value, highlighting their significance in guiding future treatments and the development of novel therapies for KIRC.\u003c/p\u003e \u003cp\u003eAccording to the constructed LASSO model, FLNA was found to have a positive weight, indicating a potential detrimental effect on KIRC prognosis. On the other hand, the remaining seven genes showed negative weights, suggesting that these genes may act as protective factors for KIRC prognosis. Through univariate and multivariate regression analysis, we have identified tumor pathological stage (M stage), age, FLNA, and DSTN as independent risk factors for the prognosis of KIRC. We then proceeded to analyze the expression levels of FLNA and DSTN in tumors and their corresponding survival curves. The findings revealed that FLNA expression was higher in tumors compared to adjacent normal tissues. Surprisingly, the high expression group of FLNA exhibited better survival rates than the low expression group, which contradicts the notion that FLNA is a risk factor for KIRC. This inconsistency could be explained by the fact that RNA-seq data represents bulk sequencing, encompassing different cell types found in tumor tissues, including immune cells. The elevated expression of FLNA in tumor tissues detected through RNA-seq may be attributed to the high expression of immune cells within the tumor tissue. Conversely, DSTN expression was found to be lower in KIRC tumors compared to adjacent normal tissues. Furthermore, low DSTN expression was observed in most analyzed cancers. In KIRC, we examined the co-expression of DSTN with other DRGs and identified a positive correlation between DSTN and genes such as FLNA, MYH9, TLN1, MYL6, MYH10, IQGAP1, and CD2AP. This indicated that DSTN may be involved in the regulation of tumor cell apoptosis in KIRC through disulfide stress, along with other DRGs. SLC7A11 has been shown to be highly expressed in various solid tumors[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Tumor cells increase the expression of SLC7A11 to sustain elevated levels of glutathione, which helps counteract the heightened oxidative stress caused by accelerated metabolism. This increased expression of SLC7A11 has been found to be positively associated with tumor progression[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Studies by Boyi Gan et al. demonstrated that SLC7A11-overexpressing tumor cells accumulate abnormal disulfides, such as cysteine, under glucose starvation conditions, inducing disulfide stress[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. According to studies conducted by Boyi Gan et al., it was found that tumor cells that overexpress SLC7A11 accumulate abnormal disulfides, including cysteine, when subjected to glucose starvation conditions. This accumulation of abnormal disulfides leads to disulfide stress. As a result, there is an increase in disulfide bonds within the cytoskeleton, which is regulated by actin. This increase in disulfide bonds causes significant contraction of the cytoskeleton and detachment from the cell membrane, ultimately leading to cell death. Dong Zhang et al. also found that KIRC tumor cells with high SLC7A11 expression exhibit a malignant phenotype.We analyzed the co-expression of SLC7A11 with 15 other disulfide stress-related genes in tumor tissues[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Except for INF2, all the other 14 genes demonstrated a positive correlation with SLC7A11 expression. The INF2 gene encodes a protein that plays a role in the assembly and remodeling of the cytoskeleton, influencing the dynamics of the actin cytoskeleton by regulating the polymerization and depolymerization of actin fibers [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. While SLC7A11-overexpressing tumor cells experience cytoskeletal disruption, decreased expression of INF2 further affects cytoskeletal assembly and remodeling, exacerbating disulfide stress-induced cell death under glucose-starved conditions.\u003c/p\u003e \u003cp\u003eThrough immune infiltration analysis, we observed a significant positive correlation between DSTN expression and the endothelial cell infiltration score. This finding suggests a strong association between DSTN gene expression levels and the degree of endothelial cell infiltration in tumor tissues. DSTN showed a positive correlation with endothelial cell marker genes (PECAM1, CLDN5, KDR, PLVAP, PTPRB, SCL14A1, and AQP1), further supporting the link between DSTN and endothelial cells. This suggested that DSTN may play a potential role in regulating endothelial cell function, metabolism, or development. Survival analysis was conducted by grouping patients based on DSTN expression and endothelial cell infiltration scores. The results demonstrated that the group with low DSTN expression and low endothelial cell infiltration scores exhibited the poorest survival rates. These findings provide additional evidence to suggest that high DSTN expression might serve as a protective factor influencing the prognosis of KIRC. Such a discovery could potentially contribute to the utilization of DSTN as a biomarker for assessing KIRC prognosis and developing treatment strategies. Additionally, we conducted a separate analysis of the correlation between endothelial cell marker genes and survival rates. Through KM survival curves, we observed a correlation between lower expression of endothelial cell marker genes and poorer survival. This suggests that reduced expression of these genes may be associated with an adverse prognosis in tumors. In the analysis of KIRC-TCGA data, it was observed that the endothelial cell infiltration score was lower in the M1 stage compared to the M0 stage. Additionally, the expression of PECAM1, CLDN5, KDR, PLVAP, PTPRB, SLC14A1, and AQP1 was also lower in the M1 stage compared to the M0 stage. These findings may suggest the significant involvement of endothelial cell infiltration and these specific endothelial cell marker genes in the process of tumor metastasis in KIRC. The expression levels of PECAM1, CLDN5, KDR, PLVAP, PTPRB, SLC14A1, and AQP1 were lower in the low-DSTN group compared to the high-DSTN group. This indicated a potential interaction or co-regulation between DSTN and these endothelial cell marker genes.\u003c/p\u003e \u003cp\u003eOCLR scores, which stand for Oncogenic Cell Lineage Representation scores, are indicators used to measure the extent of stem cell characteristics present in tumor samples[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. By analyzing and calculating a series of gene expression levels related to stem cells, a comprehensive stem cell feature score can be obtained. Higher stem cell feature scores are usually associated with tumor invasiveness, drug resistance, metastatic tendencies, and poor prognosis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. When comparing the KIRC group with the normal group, it was observed that the KIRC group exhibited higher scores for stem cell features, indicating an increased presence of stem cell characteristics in KIRC patients. These characteristics are known to be associated with tumor invasiveness and poor prognosis. Furthermore, a correlation analysis between DSTN expression and stem cell feature scores revealed a negative correlation, suggesting that high DSTN expression may play a role in inhibiting or regulating tumor stem cell properties. Therefore, it is important to investigate the mechanisms by which DSTN regulates tumor stem cell characteristics, as well as its potential value in assessing prognosis and developing treatment strategies. Pazopanib and Sorafenib are commonly used drugs in the clinical treatment of renal clear cell carcinoma[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We conducted an analysis to examine the correlation between the IC50 values of Pazopanib and Sorafenib and DSTN expression. The findings demonstrated a negative correlation between these IC50 values and DSTN expression. Specifically, we observed higher IC50 values for Pazopanib and Sorafenib in tumor samples with low DSTN expression. These results highlight the significance of DSTN in the treatment of KIRC and offer insights for personalized therapy and prognosis assessment.\u003c/p\u003e \u003cp\u003eHowever, our study had some limitations. We relied on public databases for our data sources and did not have our own clinical data verification. In future research, it would be beneficial to analyze the effectiveness of DSTN as an independent prognostic factor for KIRC using large clinical samples.\u003c/p\u003e \u003cp\u003eIn conclusion, KIRC is a prevalent malignant tumor of the kidney, and targeted therapy is the primary treatment approach. However, due to the diversity and heterogeneity of tumors and the lack of precise targets, patient sensitivity to targeted treatments can vary. Therefore, it is crucial to identify key genes that influence the prognosis of KIRC. Disulfidptosis, a novel form of non-apoptotic cell death, has been associated with tumor progression. In our study, we conducted an analysis of DRGs and identified eight DRGs that could potentially affect the prognosis of KIRC. Further analysis revealed that FLNA and DSTN were independent prognostic risk factors, and DSTN may play a role in regulating tumor stem cell characteristics. Additionally, we found a correlation between the expression level of DSTN and the sensitivity of drugs such as Pazopanib and Sorafenib. These findings offer potential biomarkers and insights for prognostic assessment and personalized treatment of RCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Zhejiang Province Traditional Chinese Medicine Science and Technology Plan (2022ZA057, 2023ZL398 and 2024ZL055).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Compliance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no affiliations with or involvement in any organization or entity with any financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSGZF and CP were primarily responsible for manuscript writing. YYM and CJL were primarily responsible for result analysis, SN and YX were mainly responsible for mapping the results of bioinformatics. CTT was responsible for project design. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSyafruddin, S.E., et al., \u003cem\u003eA KLF6-driven transcriptional network links lipid homeostasis and tumour growth in renal carcinoma\u003c/em\u003e. Nat Commun, 2019. 10(1): p. 1152.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBallesteros, P., et al., \u003cem\u003eMolecular Mechanisms of Resistance to Immunotherapy and Antiangiogenic Treatments in Clear Cell Renal Cell Carcinoma\u003c/em\u003e. Cancers (Basel), 2021. 13(23).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoppula, P., L. Zhuang, and B. Gan, \u003cem\u003eCystine transporter SLC7A11/xCT in cancer: ferroptosis, nutrient dependency, and cancer therapy\u003c/em\u003e. Protein Cell, 2021. 12(8): p. 599\u0026ndash;620.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, T., et al., \u003cem\u003eDisulfidptosis: a new form of programmed cell death\u003c/em\u003e. J Exp Clin Cancer Res, 2023. 42(1): p. 137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, P., et al., \u003cem\u003eDisulfidptosis: a new target for metabolic cancer therapy\u003c/em\u003e. J Exp Clin Cancer Res, 2023. 42(1): p. 103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, H. and T. Tang, \u003cem\u003ePan-cancer genetic analysis of disulfidptosis-related gene set\u003c/em\u003e. Cancer Genet, 2023. 278\u0026ndash;279: p. 91\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalta, T.M., et al., \u003cem\u003eMachine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation\u003c/em\u003e. Cell, 2018. 173(2): p. 338\u0026ndash;354.e15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamana, K., R. Ohashi, and Y. Tomita, \u003cem\u003eContemporary Drug Therapy for Renal Cell Carcinoma- Evidence Accumulation and Histological Implications in Treatment Strategy\u003c/em\u003e. Biomedicines, 2022. 10(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchiavoni, V., et al., \u003cem\u003eRecent Advances in the Management of Clear Cell Renal Cell Carcinoma: Novel Biomarkers and Targeted Therapies\u003c/em\u003e. Cancers (Basel), 2023. 15(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraham, J., S. Dudani, and D.Y.C. Heng, \u003cem\u003ePrognostication in Kidney Cancer: Recent Advances and Future Directions\u003c/em\u003e. J Clin Oncol, 2018: p. Jco2018790147.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan, Z.X., et al., \u003cem\u003eTargeting Strategies for Renal Cell Carcinoma: From Renal Cancer Cells to Renal Cancer Stem Cells\u003c/em\u003e. Front Pharmacol, 2016. 7: p. 423.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, D., et al., \u003cem\u003eSpatial heterogeneity of tumor microenvironment influences the prognosis of clear cell renal cell carcinoma\u003c/em\u003e. J Transl Med, 2023. 21(1): p. 489.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, X., L. Zhuang, and B. Gan, \u003cem\u003eDisulfidptosis: disulfide stress-induced cell death\u003c/em\u003e. Trends Cell Biol, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, T., et al., \u003cem\u003eExploring the role of the disulfidptosis-related gene SLC7A11 in adrenocortical carcinoma: implications for prognosis, immune infiltration, and therapeutic strategies\u003c/em\u003e. Cancer Cell Int, 2023. 23(1): p. 259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen, G.M., X.Y. Xu, and P. Xia, \u003cem\u003eMetabolism in Cancer Stem Cells: Targets for Clinical Treatment.\u003c/em\u003e Cells, 2022. 11(23).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, J., et al., \u003cem\u003eThe amino acid transporter SLC7A11-mediated crosstalk implicated in cancer therapy and the tumor microenvironment\u003c/em\u003e. Biochem Pharmacol, 2022. 205: p. 115241.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, J., et al., \u003cem\u003eIntra-Tumoral Expression of SLC7A11 Is Associated with Immune Microenvironment, Drug Resistance, and Prognosis in Cancers: A Pan-Cancer Analysis\u003c/em\u003e. Front Genet, 2021. 12: p. 770857.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, X., et al., \u003cem\u003eResearch progress on SLC7A11 in the regulation of cystine/cysteine metabolism in tumors\u003c/em\u003e. Oncol Lett, 2022. 23(2): p. 47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan, Y., et al., \u003cem\u003eSLC7A11 expression level dictates differential responses to oxidative stress in cancer cells\u003c/em\u003e. Nat Commun, 2023. 14(1): p. 3673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, D., et al., \u003cem\u003eAn integrative multi-omics analysis based on disulfidptosis-related prognostic signature and distinct subtypes of clear cell renal cell carcinoma\u003c/em\u003e. Front Oncol, 2023. 13: p. 1207068.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA, M., et al., \u003cem\u003eLysine acetylation of cytoskeletal proteins: Emergence of an actin code\u003c/em\u003e. J Cell Biol, 2020. 219(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLian, H., et al., \u003cem\u003eIntegrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma\u003c/em\u003e. Mol Oncol, 2019. 13(10): p. 2227\u0026ndash;2245.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePonomarev, A., et al., \u003cem\u003eIntrinsic and Extrinsic Factors Impacting Cancer Stemness and Tumor Progression\u003c/em\u003e. Cancers (Basel), 2022. 14(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantoni, M., et al., \u003cem\u003eSunitinib, pazopanib or sorafenib for the treatment of patients with late relapsing metastatic renal cell carcinoma\u003c/em\u003e. J Urol, 2015. 193(1): p. 41\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Disulfidptosis-related gene, DSTN, FLNA, immune infiltration, stemness scores, inhibitory concentration 50%","lastPublishedDoi":"10.21203/rs.3.rs-3908062/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3908062/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackgrounds\u003c/h2\u003e \u003cp\u003eKidney renal clear cell carcinoma (KIRC) is a highly metastatic cancer that shows resistance to traditional chemoradiotherapy. Disulfidptosis, a newly discovered mechanism of cell death in malignancies, involves the accumulation of intracellular disulfides, leading to rapid cell demise. Identifying disulfidptosis-related genes (DRGs) in KIRC can provide novel treatment strategies for patients with this disease.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe 15 DRGs and differentially expressed genes (DEGs) obtained from the KIRC-TCGA database were intersected to identify overlapping genes, and a prognostic model was constructed using Lasso regression analysis. Univariate and multivariate Cox regression analysis were conducted to identify independent prognostic factors associated with disulfidptosis. Kaplan-Meier (KM) survival curve was used for prognostic analysis. Co-expression analysis was performed between the screened DRGs and other DRGs to investigate their correlation. The samples in KIRC-TCGA were grouped based on the selected DRGs, and Gene Set Enrichment Analysis (GSEA) as well as immune infiltration analysis were performed. Tumor stemness analysis was conducted using the OCLR algorithm, and correlation analysis between the independent prognostic DRGs and the inhibitory concentration 50% (IC50) of Pazopanib and Sorafenib was performed using ridge regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate regression analysis indicated that DSTN and FLNA may serve as independent prognostic DRGs for KIRC. In the KIRC-TCGA, FLNA expression was higher in tumor tissues compared with adjacent tissues, whereas DSTN expression was lower in tumor tissues than in adjacent tissues (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). KM survival curve demonstrated that high expression of DSTN and FLNA correlated with a higher survival rate. Co-expression analysis revealed positive correlations between DSTN and the expression of FLNA, MYH9, TLN1, MYL6, MYH10, IQGAP1, and CD2AP. Immune infiltration analysis showed that DSTN was positively correlated with endothelial cell infiltration. High expression of DSTN and endothelial cell marker genes were associated with a longer survival period. Correlation analysis revealed a negative correlation between DSTN expression and stemness scores. Additionally, the IC50 values of Pazopanib and Sorafenib showed a high negative correlation with DSTN expression (0.5\u0026le;|ρSpearman|\u0026lt;0.8).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDSTN, as a DRG, had been identified as an independent prognostic biomarker in patients with KIRC. Its expression was closely linked to tumor cell stemness and also correlated with the IC50 of commonly used chemotherapy drugs in KIRC. DSTN holded promise as a meaningful prognostic marker and potential therapeutic target for KIRC.\u003c/p\u003e","manuscriptTitle":"Prognostic Significance of Disulfidptosis-Related Gene DSTN in Kidney Renal Clear Cell Carcinoma: Correlation with Immune Cell Infiltration and Cancer Stemness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-23 18:33:26","doi":"10.21203/rs.3.rs-3908062/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d9fc837c-51cf-4158-9701-b92e6fb4f241","owner":[],"postedDate":"February 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28916074,"name":"Biological sciences/Cancer/Tumour biomarkers"},{"id":28916075,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2024-03-28T07:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-23 18:33:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3908062","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3908062","identity":"rs-3908062","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}