GSDMS are potential therapeutic targets and prognostic biomarkers in breast cancer

GSDMS are potential therapeutic targets and prognostic biomarkers in breast cancer | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Article GSDMS are potential therapeutic targets and prognostic biomarkers in breast cancer Xiaoying Huang, Yaxiang Han, Li Na, Zhaoyi Yue, Rongrong Ma, Ligang Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3910767/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 Pyroptosis is a new type of programmed death recently discovered,the GSDM family plays a crucial role in pyroptotic processes. Numerous studies have reported that GSDM family can either inhibit or promote tumor development; However, the mechanism of action in breast cancer is unclear. In this study, we employed diverse online databases to investigate the differential expression of GSDMS in breast cancer compared to para-cancer and transcriptional variations among molecular subtypes. Furthermore, we explored the association between GSDMS and clinical characteristics, assessed its prognostic value, examined gene alterations, investigated the relationship between subtypes and RFS, evaluated immune cell infiltration level, and analyzed the level of GSDMS methylation in breast cancer. We observed a significant upregulation of GSDMD in breast cancer, while GSDMB-E and PJVK exhibited low expression levels in this malignancy. GSDMB and GSDMC were highly expressed in HER2-positive breast cancer but showed low expression in triple-negative and luminal subtypes. GSDMD, GSDME, and PJVK displayed high expression specifically in the luminal phenotype of breast cancer while being downregulated in Her2+ and triple-negative subtypes. The expression pattern of GSDMS was closely associated with distinct clinical features. Missense mutation, deletion mutation, amplification, and deep deletion primarily accounted for alterations observed in the genes encoding GSDMA-E and PJVK with change rates ranging from 0.9% to 19%. Survival analysis revealed that elevated expressions of both GSDMD and PJVK were correlated with improved prognosis among breast cancer patients. Furthermore, the members of the GSDM family demonstrated strong associations with immune-infiltrating cells within the of breast cancer. DNA methylation analysis indicated lower levels along with decreased methylation patterns for GSDMC, GSDMD, and PJVK in breast cancer tissues; conversely increased methylation was observed for both GSDMA and GSDME. Our study systematically elucidates the expression and prognostic significance of GSDMS in breast cancer, as well as the potential prognostic implications of GSDMD and PJVK in breast cancer. These findings provide valuable guidance for clinicians regarding drug utilization. Health sciences/Medical research/Biomarkers Health sciences/Medical research Health sciences/Oncology Health sciences/Oncology/Cancer Biological sciences/Cancer/Breast cancer Breast cancer GSDMS expression profiles prognosis immune infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction In February 2021, the International Agency for Research on Cancer (IARC) statistics reported that in 2020, the incidence of breast cancer in women globally has surpassed that of lung cancer. It has become the first cancer with the highest incidence rate in the world [1]. Breast cancer is mainly treated by surgery, chemotherapy, radiotherapy immunotherapy, etc., but recurrence and metastasis of breast cancer are still the main reasons for poor prognosis [2]. Therefore, the discovery of new biomarkers is necessary for the prediction of breast cancer prognosis. Gasdermin (GSDM) is a family of pore-forming proteins, which in humans mainly includes GSDMA, GSDMB, GSDMC, GSDMD, GSDME, and Pejvakin (PJVK) [3]. Due to the diversity of the GSDM family, it functions as a multifunctional family of proteins expressed in a wide range of cells and tissues [4-6]. Structurally similar except for PJVK proteins, the remaining members of the GSDM family contain both a C-terminal structural domain and an N-terminal structural domain [7-9]. The presence of specific structural domains allows most gasdermin (GSDM) proteins to play important roles by mediating pyroptosis processes that affect the onset, progression, and prognosis of various cancers [10]. The expression patterns of the GSDM protein family differ among different tumors and can either promote or suppress tumor growth. Peng et al. discovered up-regulated expression levels of GSDMA in lung adenocarcinoma compared to normal tissues [11]. High expression levels of GSDMB are observed in breast cancer, cervical cancer, uterine cancer, and gastric cancer [12]. Overexpressed GSDMB was found to interact with STAT3 leading to increased phosphorylation levels that regulate glucose metabolism and ultimately contribute to bladder cancer progression [13]. Increased expression of GSDMC promotes colon cancer and pancreatic cancer development; thus serving as a potential target for treatment strategies against these types of cancers [14-15]. Dasatinib inhibits non-small cell lung cancer development by inducing cleavage-mediated pyroptosis through targeting GSDMD[16]. High expression levels of GSDME are associated with renal clear cell carcinoma and lung adenocarcinoma along with poor prognoses for these diseases[11,17] . It has been reported that low expression levels PJVK are observed in ovarian cancer which correlates with survival rates[18].All of the aforementioned studies indicate that GSDMS holds potential as a prognostic or post-treatment marker in various tumor types. Therefore, this study aimed to conduct an investigation into the expression and prognostic role of GSDMS in breast cancer using different online databases. The online searched databases provided us with a large amount of genetic information, such as the GEO database [19] and the Cancer Genome Atlas (TCGA) database [20].Additionally, we utilized the UALCAN database to compare GSDMS expression levels between breast cancer tissues and adjacent non-cancerous tissues [21]. Furthermore, bc-GenExMiner v5.0 [22,23] was employed to explore the association between GSDMS family members and clinicopathological features specific to breast cancer. Genetic alterations such as mutations and putative copy number alterations (CNAs) were analyzed for GSDMs family members using cBioPortal for GSDMS family members [24]. Moreover, Kaplan-Meier mapper database analysis was conducted to assess the relationship between GSDMS gene expression and clinical outcomes in breast cancer patients with different subtypes [25]. The infiltration of GSDMS in immune cells within breast cancer was evaluated through TIMER (Tumor Immune Estimation Resource) database analysis [26]. Finally, DiseaseMeth 3.0 database analysis was performed to investigate GSDMS methylation levels in breast cancer[27]. Methods and materials 2.1 Analysis of GSDM family expression in breast cancer and adjacent tissues using the UALCAN database.The UALCAN database provides gene transcriptome data from TCGA [21]. In this study, we utilized UALCAN to analyze the differential expression of GSDMS genes in cancer and adjacent non-cancerous tissues, as well as the expression correlation among members of the GSDMS family in different breast cancer subtypes. 2.2. The relationship between GSDMS and clinicopathological features in breast cancer was analyzed using the BC-GenExMiner database.The BcGenExMiner v5.0 (http://bcgenex.centregauducheau.fr/BCGEM) is an online analysis tool for evaluating the correlation between GSDMS family members and clinicopathological characteristics in breast cancer transcriptomes, encompassing age, lymph node status, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, triple-negative tumors, P53 status (sequence), and lymph node involvement. 2.3 Genetic variation was analyzed using oncopprint database.The oncoprint feature has been integrated into the open-source web application cbiopportal, facilitating comprehensive exploration and analysis of cancer genome datasets (http://www.cbioportal.org/) [27,28]. In this study, a thorough examination of genetic alterations, encompassing mutations and putative copy number alterations (CNAs), was performed on members belonging to the GSDM family. 2.4 The prognostic value of GSDMS in patients with breast cancer was analyzed using the Kaplan-Meier online website. The Kaplan-Meier mapper is a database that predicts how gene expression profiles impact cancer patient survival, mainly derived from GEO, EGA (European Genome-Phenotype Archive), and TCGA. We used K-M survival plots to visualize overall survival/recurrence-free survival (OS/RFS) in breast cancer patients, including risk ratios (HR), 95% confidence intervals (CI), and logrank P values calculation. We compared prognosis across all cohorts using Kaplan-Meier survival plots. 2.5 Immunohistochemical analysis of GSDMS in breast cancer.TIMER (Tumor Immune Estimation Resource)[26] database was utilized for analyzing immune cell infiltration levels in tumor tissues based on high-throughput sequencing data (RNA-Seq expression profiling). It provides information about B cells, CD4+ T cells, CD8+ T cells, neutrophils macrophages and dendritic cells infiltration levels. 2.6 The methylation of GSDMS in breast cancer. Diseasemeth3.0 is an online database for analyzing human gene methylation status across various diseases [27]. We used this database specifically to analyze GSDMS methylation levels in breast cancer. Results 3.1 Expression of the GSDM family in breast cancer: we examined the mRNA levels of the GSDM family in breast and paracancerous tissues using the UALCAN database. The results revealed that, with the exception of GSDMA, which exhibited no significant difference in expression between breast and paracancerous (P=0.499), the other members of the GSDMS family displayed differential expression in both tissue types. Specifically, GSDMD was highly expressed in breast cancer while GSDMB, GSDMC,GSDME(DFNA5) and PJVK(DFNB59) were all expressed at low levels (Figure 1). 3.2 Transcriptional differences among molecular subtypes of breast cancer: To further investigate potential variations in mRNA expression levels among different molecular subtypes of breast cancer within the various members of the GSDM family, we analyzed their transcript levels using data from the UALCAN database (Figure 2). The expression levels of GSDMB and GSDMC are significantly upregulated in HER2-positive breast cancer, while they are lower in triple-negative and luminal breast cancer. (Figures 2b,c). The expression levels of GSDMD, GSDME (DFNA5), and PJVK (DFNB59) were significantly elevated in the luminal phenotype of breast cancer compared to Her2+ and triple-negative breast cancer subtypes (Figure 2d, 2e, 2f). These findings suggest a potential correlation between the expression patterns of the GSDMS family and molecular subtypes in breast cancer patients. 3.3 The expression of the GSDM family was analyzed in relation to clinical characteristics of breast cancer patients: In order to analyze the expression of GSDM family in clinical case characteristics of breast cancer patients, we summarized the results based on age (with a cut-off at 51 years old), presence or absence of lymph node metastasis, ER and PR expression status, Her2 status, occurrence of triple-negative breast cancer, and presence or absence of P53 mutation. Among these factors, GSDMA showed significantly higher expression in patients aged 51 years old or younger (p=0.0207). Similarly, GSDMB and GSDMC also showed significantly higher expression in this age group (p=0.0181 and p<0.0001 respectively) [Table 1]. There was no significant association between age and the expression levels of other members within the GSDM family. The expression levels of GSDMB (P<0.0001), GSDMC (P=0.0011), GSDMD (P=0.0113), and GSDME (P=0.0388) were significantly upregulated in breast cancer patients with lymph node metastasis, while PJVK showed high expression in breast cancer patients without lymph node metastasis (p<0.0001). Additionally, the expression of GSDMA remained unchanged regardless of the patient's lymph node status (P=0.1862). Moreover,In patients positive for ER and PR antibodies, the expression levels of GSDMD (P<0.0001) and PJVK (P<0.0001) were significantly upregulated. GSDMA (ER-P=0.0128, PR-P<0.0001), GSDMB (P<0.0001), GSDMC (P<0.0001), and GSDME (P<0.0001) exhibited elevated expression in ER and PR antibody-positive individuals.The expression levels of GSDMA (P=0.0034), GSDMB (P< 0.0001), and GSDMC (P< 0.0001) were significantly higher in HER2+ positive patients, whereas the expression levels of GSDMD (P< 0.0001) and PJVK (P< 0.0001) were markedly elevated in HER2- patients. No significant association was observed between the expression level of GSDME and HER2 status (P=0.3308).The expression of GSDMC (P<0.0001) and GSDME (P<0.0001) exhibited a positive correlation in patients with triple-negative breast cancer, whereas the expression of GSDMD (P<0.0001) and PJVK (P<0.0001) demonstrated an inverse association with triple-negative breast cancer. However, no significant correlation was observed between the expression of GSDMA (P=0.6576), GSDMB (P=0.6184), and triple-negative breast cancer.. Furthermore, patients harboring P53 mutations exhibited significantly elevated expressions of GSDMB and GSDMC (P<0.0001). In contrast, pronounced expression of PJVK was specifically observed in patients with wild-type P53. No significant association between other members of the GSDM family and P53 status was found. 3.4 Genetic alterations of GSDMS in patients with breast cancer:To explore the underlying factors contributing to differential expression of GSDMS among breast cancer patients, we employed cBioPortal (http://www.cbioportal.org), an online database, for analyzing genetic alterations associated with GSDMS. The findings are presented in Figure 3, demonstrating a predominant occurrence of missense mutations, deletion mutations, amplifications, and deep deletions within GSDMA-E and PJVK genes at alteration rates of approximately 10%, 11%, 19%, and 15% respectively. 3.5 Prognostic value of GSDM family mRNA expression in patients with breast cancer:We employed Kaplan-Meier analysis to evaluate the prognostic significance of each member within the GSDMS family in breast cancer patients, encompassing overall survival (OS) and disease-free survival (RFS). Results: As shown in Figure 4A,high expressionlevels of GSDMB and GSDMC were negatively correlated with overall survival (OS) among breast cancer patients. Conversely, high expression levels of GSDMD and PJVK demonstrated a positive association with overall survival (OS), while no significant correlation was observed between GSDMA or GSDME expression and overall survival. As illustrated in Figure 4B, high expressions of GSDMB, GSDMC, GSDMD, and PJVK were positively associated with the recurrence-free survival rate (RFS) in breast cancer patients. On the other hand, high levels of expressed GSDME displayed a negative correlation with the disease-free survival rate among breast cancer patients; however, no significant association was found between RFS and the expression level of GSDMA. The aforementioned data indicate that elevated expressions of both GSDMD and PJVK are positively associated with both OS and RFS in breast cancer patients. Therefore, these two markers may serve as valuable prognostic indicators for assessing the prognosis of individuals diagnosed with breast cancer. 3.6 RFS prognostic value of GSDM family mRNA expression levels in breast cancer subtypes:In order to investigate the association between mRNA expression levels of GSDMS family and RFS prognostic value in breast cancer patients with different subtypes, we further analyzed the corresponding survival curve. The subtypes were classified based on the 2011 St. Gallen criteria [28], including basal-like, luminal A, luminal B, and HER2+. As shown in Table 2, within these subclasses, high expressions of GSDMB, GSDMD, and PJVK were positively correlated with improved RFS prognosis. Specifically in Luminal A subtype, high expressions of GSDMB, GSDMC and PJVK showed a positive correlation with RFS prognosis. In Luminal B subtype, high expressions of GSDMC,GSDME,and PJVK were also associated with better RFS prognosis; however,in HER2+ subtype,the high expressions of GSDMB and PJVK indicated a positive correlation with RFS prognosis while higher expression level of GSDMC was negatively correlated. 3.7 The relationship between the GSDM family and immune cells in breast cancer patients has been investigated in several studies [29,30]. In this study, we utilized the Timer2.0 online database to further analyze this relationship. Our findings revealed a positive correlation between the expression of GSDMA and B cells, macrophages, dendritic cells, and neutrophils (Figure 5A). Similarly, GSDMB expression showed a positive correlation with CD4+ T cells, B cells, and dendritic cells but a negative correlation with CD8+ T cells and macrophages (Figure 5B). Additionally, GSDMC expression demonstrated a positive correlation with CD4+ T cells, dendritic cells, and neutrophils while exhibiting a negative correlation with B cells (Figure 5C);The expression of GSDMD exhibited a positive correlation with CD4+ T cells, B cells, and dendritic cells, while it demonstrated a negative correlation with CD8+ T cells, macrophages, and neutrophils (Figure 5D). The expression of GSDME showed a positive correlation with CD4+ T cells, CD8+ T cells, macrophages, dendritic cells, and neutrophils; however, it displayed a negative correlation with B cells (Figure 5E). PJVK expression was positively associated with CD4+ T cells but negatively associated with B cells and dendritic cells (Figure 5F). 3.8 Methylation levels of GSDM family in breast cancer patients: The negative correlation between gene expression and DNA hypermethylation levels has been demonstrated [31,32]. To investigate the methylation level of GSDMS in breast cancer patients, we utilized the DiseaseMeth database online for analyzing the methylation status of GSDMS in breast cancer . The results are presented in Figure 6. Notably, the methylation levels of GSDMC, GSDMD, and PJVK were lower in breast cancer compared to paracancerous; conversely, higher methylation levels were observed for GSDMA and GSDME in breast cancer when compared to normal tissues. Our previous findings have demonstrated a significant upregulation of GSDMD in breast cancer, while GSDME exhibits low expression levels in these tissues. The observed methylation patterns for both GSDMD and GSDME are consistent with their respective expression trends, suggesting a potential influence of methylation on the regulation of thei rexpressionlevels. Discussion Pyroptosis is a newly discovered form of programmed cell death, primarily dependent on caspase-mediated pore formation. It leads to a pressure gradient across the cell membrane, cellular swelling, and release of intracellular contents, thereby inducing an inflammatory response [33]. Numerous studies have reported that pyroptosis can either inhibit or promote tumor development; however, the precise mechanisms underlying its actions remain unclear. Additionally, it has been associated with drug resistance during tumor chemotherapy [34]. The GSDMS family plays a crucial role in pyroptotic processes, with GSDMB, GSDMC, GSDMD, and GSDME mediating enhanced immune responses against certain cancers [35]. Notably, GSDMD-mediated pyroptotic response is well-documented [36]. Existing literature suggests that GSDMS can synergistically contribute to the initiation of pyroptosis. Rogers et al. demonstrated that GSDMD-mediated pyroptosis concurrently triggers the release of GSDME which further intensifies inflammation and enhances immune responses [37]. In our study, we utilized online database to analyze the differential expression of GSDM family mRNAs in breast cancer and paracancer. Furthermore, we investigated the expression of GSDMS in molecular subtypes of breast cancer and explored its association with clinical characteristics. Our findings revealed that GSDMD exhibited high expression levels in breast cancer, while GSDMB-E and PJVK showed low expression levels. There was no significant difference in GSDMA expression between breast cancer and paracancer.Additionally, GSDMB demonstrated high expression in both overall breast cancer cases and specifically in HER2+ subtype cases, which is consistent with previous studies [38-40]. Moreover, Kaplan-Meier curves were employed to assess the overall survival (OS) and disease-free survival (RFS) rates among breast cancer patients belonging to different subtypes within the GSDM family. The results indicated a negative correlation between high expressions of GSDMB and GSDMC with OS rates; conversely, high expressions of GSDMD and PJVKd were positively correlated with higher OS rates. Similarly, elevated expressions of all four genes (GSDMB, GSDMC, GSDMD, PJVK) exhibited a positive correlation with RFS among breast cancer patients; However, increased expression levels of GSDME were linked to poorer RFS rates. These data collectively suggest that elevated levels of GDSM、PJVK may serve as a valuable prognostic marker for predicting outcomes in breast cancer patients. Furthermore, positive correlations observed between RFS rates across various subtypes indicate that members within the GSDM family could potentially be utilized as prognostic markers for distinct subsets of individuals diagnosed with breast cancer. Multiple alterations in genes play an important role in the development of breast cancer [41], here we confirmed the mutation of GSDM family mRNAs during breast cancer using Timer3.0 online database, which may be the reason why GSDMS plays an important role in the development of breast cancer. Numerous studies have demonstrated the crucial role of immune cell infiltration in the tumor microenvironment during tumor development, which significantly impacts patient treatment and prognostic outcomes [42-44]. Additionally, tumor-infiltrating lymphocytes have been identified as independent predictors of immunotherapy response and prognosis across various cancer types [45,46]. Previous reports have highlighted that GSDME expression in tumors enhances phagocytosis by tumor-associated macrophages, augments the quantity and functionality of infiltrating natural killer lymphocytes and CD8+ T lymphocytes, thereby acting as a tumor suppressor to bolster anti-tumor immunity through juxtaposition activation [47]. Our study reveals a close association between members of the GSDMs family and distinct immune cell populations in breast cancer, suggesting their potential utility as diagnostic biomarkers while also implicating their involvement in immune regulation. Methylation plays a crucial role in tumorigenesis and tumor development. Therefore, we conducted an analysis of the methylation level of the GSDMS family in breast cancer. Our results revealed differential expression of GSDMS family methylation in breast cancer. Interestingly, previous reports have suggested that GSDME methylation could serve as a potential marker for detecting both breast and colorectal cancers [48,49]. Conversely, other studies have indicated that GSDME methylation leads to reduced expression levels, thereby inhibiting focal responses and promoting tumorigenesis [50-52]. Furthermore, GSDME methylation has been associated with distant metastasis in tumors [53]. Notably, our study demonstrated a consistent correlation between the methylation status of GSDMD and GSDME with their respective expression trends. These findings suggest that the expression levels of GSDMD and GSDME may be influenced by their corresponding methylation levels. However, our study has certain limitations. Firstly, we solely analyzed the mRNA level relationship between the GSDM family and breast cancer, lacking validation of its corresponding functional proteins which may not fully represent the protein expression of the GSDMS family. Therefore, future studies should focus on increasing validation of functional protein expression. Secondly, due to database constraints, we were unable to perform multivariate analysis between clinicopathological characteristics. Lastly, there is currently no literature reporting on the mechanism of PJVK in breast cancer. Although this study reveals a close association between GSDMS and breast cancer progression based on online database analysis alone, further verification through experimental and clinical data is necessary to elucidate the unclear role of GSDM family in breast cancer development Finally, by conducting comprehensive analysis of diverse online databases, we have identified a significant correlation between elevated expression levels of GSDMD and PJVK and improved prognosis in breast cancer patients. These findings suggest that GSDMD and PJVK hold promise as potential prognostic targets for breast cancer, providing valuable guidance to clinicians regarding drug utilization Declarations Funding The R&D projects of significant importance in the Ningxia Hui Autonomous Region. Availability of data The experimental data that support the findings of this study are openly available in UALCAN(https://ualcan.path.uab.edu/),BC-GenExMine(http://bcgenex.centregauducheau.fr/BCGEM),oncopprint(http://www.cbioportal.org/),TIMER(https://cistrome.shinyapps.io/timer/),Diseasemeth2.0(http://bio-bigdata.hrbmu.edu.cn/diseasemeth/) ,Kaplan-Meier( https://kmplot.com/analysis/ ) online website. Ethics approval and consent to participate Ethical approval are not required for the current study due to the nature of the current study. Consent for publication Written informed consent was obtained from the patient for the publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal. Competing interests The authors declare that they have no competing interests. Authors’ contributions Data curation ： Yaxiang Han Formal analysis: Zhaoyi Yue , Rongrong Ma Investigation: Ligang Wu Methodology: Li Na Software: Xiaoying Huang Supervision: Ligang Wu Validation: Qilun Liu. Visualization: Yaxiang Han Writing – original draft: Xiaoying Huang Writing – review & editing: Qilun Liu References SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021. Epub ahead of print. D. A. Berry, K. A. Cronin, S. K. Plevritis et al., “Effect of screen-ing and adjuvant therapy on mortality from breast cancer,”The New England Journal of Medicine, vol. 353, no. 17,pp. 1784–1792, 2005。 Tamura M, Tanaka S, Fujii T, Aoki A, Komiyama H, Ezawa K, Sumiyama K, Sagai T, Shiroishi T. 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Tables Table1 Relationship between GSDMS expression and clinicopathological features of breast cancer patients Parameters GSDMA GSDMB GSDMC GSDMD GSDME PJVK Age (years) >51 ≤51 0.0207 0.0181 <0.0001 0.0565 0.1841 0.6574 Nodal status Negative Positive 0.1862 <0.0001 0.0011 0.0113 0.0388 <0.0001 ER (IHC) Negative Positive 0.0128 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 PR (IHC) Negative Positive <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 HER2 (IHC) Negative Positive 0.0034 <0.0001 <0.0001 <0.0001 0.3308 <0.0001 Triple-negative status Not TNBC 0.6576 0.6184 <0.0001 <0.0001 <0.0001 <0.0001 P53 sequence Wild type Mutated 0.6048 <0.0001 <0.0001 0.0804 0.2250 <0.0001 Table2 Prognostic values of GSDMS expression for RFS in different BrCa intrinsic subtypes. Subclasses N HR P GSDMA Subclasses 134 0.76 0.56 Luminal A 408 0.8 0.52 Luminal B 188 1.2 0.76 HER2 positive 61 0.15 0.087 GSDMB Subclasses 846 0.62 <0.0001 Luminal A 2277 0.69 <0.0001 Luminal B 1491 1.15 0.18 HER2 positive 325 0.67 0.033 GSDMC Subclasses 417 0.89 0.48 Luminal A 952 0.53 <0.0001 Luminal B 565 0.61 0.0018 HER2 positive 198 1.73 0.02 GSDMD Subclasses 846 0.59 <0.0001 Luminal A 794 0.88 0.41 Luminal B 515 1.25 0.27 HER2 positive 116 1.47 0.23 GSDME Subclasses 404 1.29 0.19 Luminal A 794 1.19 0.28 Luminal B 1491 1.23 0.04 HER2 positive 315 0.78 0.17 PJVK Subclasses 417 0.69 0.029 Luminal A 952 0.46 <0.0001 Luminal B 465 0.43 <0.0001 HER2 positive 198 1.64 0.035 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-3910767","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":275227630,"identity":"adc85c86-67c2-4e57-9f91-694763bf48a7","order_by":0,"name":"Xiaoying Huang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Huang","suffix":""},{"id":275227631,"identity":"318ef055-3955-43f2-9e4e-533d7507f034","order_by":1,"name":"Yaxiang Han","email":"","orcid":"","institution":"Department of Cardiovascular Medicine, Ning Xia Medical University Affiliated General Hospital, Yinchuan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yaxiang","middleName":"","lastName":"Han","suffix":""},{"id":275227632,"identity":"bc2deb6b-f1db-4e21-a41d-9977e15dab89","order_by":2,"name":"Li Na","email":"","orcid":"","institution":"Biobank, Ning Xia Medical University Affiliated General Hospital, Yinchuan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Na","suffix":""},{"id":275227633,"identity":"56b6c24b-9f70-469f-9aea-2e7b40fd432e","order_by":3,"name":"Zhaoyi Yue","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhaoyi","middleName":"","lastName":"Yue","suffix":""},{"id":275227634,"identity":"97186774-3b91-45e0-aa16-763703831e0e","order_by":4,"name":"Rongrong Ma","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rongrong","middleName":"","lastName":"Ma","suffix":""},{"id":275227635,"identity":"592a08f3-8be1-4473-a921-0cba787d8144","order_by":5,"name":"Ligang Wu","email":"","orcid":"","institution":"Department of Oncology, Ning Xia Medical University Affiliated General Hospital, Yinchuan,","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ligang","middleName":"","lastName":"Wu","suffix":""},{"id":275227636,"identity":"4f9c6bac-e517-41ab-b416-734eeb11b66c","order_by":6,"name":"Qilun liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPgbmxocfDCR42NgbgQxitLAxMDYbSxRYyPDxHAYyiNTSJsDzocJGTiIdyCBKC3tiG4MEyGGSD4EMBjs53QZCWngetj0oAGmRTgQyGJKNzQ4Q0iKR2G4gAdECZDAcSNxGhJY2CR6www4CGaRpkWAkVgvPQ2DYgrTwJIIYRPiFnz354MMPf+rs5duPP3z4ocJOjqAWBoYEZI4BQeUYWkbBKBgFo2AUYAEABZA4JNfKjxcAAAAASUVORK5CYII=","orcid":"","institution":"Department of Oncology, Ning Xia Medical University Affiliated General Hospital, Yinchuan,","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qilun","middleName":"","lastName":"liu","suffix":""}],"badges":[],"createdAt":"2024-01-30 13:52:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3910767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3910767/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51835177,"identity":"07b0331a-6769-4d57-bef3-7fc6ae8944e5","added_by":"auto","created_at":"2024-02-29 20:31:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71904,"visible":true,"origin":"","legend":"\u003cp\u003eThe UALCAN database was used to analyze the expression levels of the GSDM family in breast cancer and paracancerous:The expression of GSDMD was significantly higher in breast cancer tissues, while GSDMB, GSDMC, GSDME (DFNA5), and PJVK (DFNB59) showed comparatively lower expression levels in breast cance. There was no significant difference observed in the transcriptional level of GSDMA between breast cancer and para-cancer tissues.\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-3910767/v1/047f5d4d3de71965ef2f7387.png"},{"id":51834959,"identity":"9f7a01c7-8925-4519-9198-1958a2b4c96b","added_by":"auto","created_at":"2024-02-29 20:23:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109843,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of GSDMS in molecular subtypes of breast cancer: GSDMB and GSDMC are significantly upregulated in HER2-positive breast cancer, while they are lower in triple-negative and luminal breast cancer. (Figures 2b,c). GSDMD, GSDME (DFNA5), and PJVK (DFNB59) were highly expressed in lumina phenotype of breast cancer, but were low in Her2+ and triple-negative breast cancer (Figure 2d,2e,2f).\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-3910767/v1/532d62d943c36000c5ff77e9.png"},{"id":51834958,"identity":"e00ce178-e409-4522-9c3b-cb249075d560","added_by":"auto","created_at":"2024-02-29 20:23:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51394,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic alternatives and correlation analysis of GSDM family in breast cancer: Summary of observed variation rates among the GSDM family in breast cancer\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-3910767/v1/54afd0d8a3493cd269a72287.png"},{"id":51834963,"identity":"54838b56-b1b1-4869-9999-296b6d83e385","added_by":"auto","created_at":"2024-02-29 20:23:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":218805,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic value of GSDMS in patients with breast cancer:A High expression levels of GSDMB and GSDMC were negatively correlated with overall survival, high expression levels of GSDMD and PJVK demonstrated a positive association with overall survival (OS);B High expression levels of GSDMD and PJVK demonstrated a positive association with RFS,high levels of expressed GSDME displayed a negative correlation with the RFS among breast cancer patients.GSDMA were not significantly correlated with OS and RFS.\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-3910767/v1/9a85fa02ebfa39bff525b3e4.png"},{"id":51835178,"identity":"a89e3ddc-3a0e-4b38-ba54-9aa31fb9fd44","added_by":"auto","created_at":"2024-02-29 20:31:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":373034,"visible":true,"origin":"","legend":"\u003cp\u003eThe associations between differentially expressed members of the GSDM family and immune cell infiltration:The impact of GSDMS on the infiltration of immune cells in breast cancer was evaluated using Timer 3.0.\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-3910767/v1/c374304680c638647305ed32.png"},{"id":51834961,"identity":"92cd83b6-b66e-419f-929f-9e80fdd13214","added_by":"auto","created_at":"2024-02-29 20:23:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":51097,"visible":true,"origin":"","legend":"\u003cp\u003eMethylation levels of GSDMS in breast cancer：DiseaseMeth database online for analyzing the methylation status of GSDMS in breast cancer\u003c/p\u003e","description":"","filename":"F6.png","url":"https://assets-eu.researchsquare.com/files/rs-3910767/v1/1582508a48208427705822c0.png"},{"id":56788786,"identity":"4177a475-2daa-4a62-8f65-923127b95345","added_by":"auto","created_at":"2024-05-20 13:17:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1322356,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3910767/v1/9e365b05-9b2b-48ef-acc9-d2cc8158a83c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GSDMS are potential therapeutic targets and prognostic biomarkers in breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn February 2021, the International Agency for Research on Cancer (IARC) statistics reported that in 2020, the incidence of breast cancer in women globally has surpassed that of lung cancer. It has become the first cancer with the highest incidence rate in the world [1].\u0026nbsp;Breast cancer is mainly treated by surgery, chemotherapy, radiotherapy immunotherapy, etc., but recurrence and metastasis of breast cancer are still the main reasons for poor prognosis [2]. Therefore, the discovery of new biomarkers is necessary for the prediction of breast cancer prognosis.\u003c/p\u003e\n\u003cp\u003eGasdermin (GSDM) is a family of pore-forming proteins, which in humans mainly includes GSDMA, GSDMB, GSDMC, GSDMD, GSDME, and Pejvakin (PJVK) [3]. Due to the diversity of the GSDM family, it functions as a multifunctional family of proteins expressed in a wide range of cells and tissues [4-6]. Structurally similar except for PJVK proteins, the remaining members of the GSDM family contain both a C-terminal structural domain and an N-terminal structural domain [7-9]. The presence of specific structural domains allows most gasdermin (GSDM) proteins to play important roles by mediating pyroptosis processes that affect the onset, progression, and prognosis of various cancers [10]. The expression patterns of the GSDM protein family differ among different tumors and can either promote or suppress tumor growth. Peng et al. discovered up-regulated expression levels of GSDMA in lung adenocarcinoma compared to normal tissues [11]. High expression levels of GSDMB are observed in breast cancer, cervical cancer, uterine cancer, and gastric cancer [12]. Overexpressed GSDMB was found to interact with STAT3 leading to increased phosphorylation levels that regulate glucose metabolism and ultimately contribute to bladder cancer progression [13]. Increased expression of GSDMC promotes colon cancer and pancreatic cancer development; thus serving as a potential target for treatment strategies against these types of cancers [14-15]. Dasatinib inhibits non-small cell lung cancer development by inducing cleavage-mediated pyroptosis through targeting GSDMD[16]. High expression levels of GSDME are associated with renal clear cell carcinoma and lung adenocarcinoma along with poor prognoses for these diseases[11,17] . It has been reported that low expression levels PJVK are observed in ovarian cancer which correlates with survival rates[18].All of the aforementioned studies indicate that GSDMS holds potential as a prognostic or post-treatment marker in various tumor types. Therefore, this study aimed to conduct an investigation into the expression and prognostic role of GSDMS in breast cancer using different online databases.\u003c/p\u003e\n\u003cp\u003eThe online searched databases provided us with a large amount of genetic information, such as the GEO database [19] and the Cancer Genome Atlas (TCGA) database [20].Additionally, we utilized the UALCAN database to compare GSDMS expression levels between breast cancer tissues and adjacent non-cancerous tissues [21]. Furthermore, bc-GenExMiner v5.0 [22,23] was employed to explore the association between GSDMS family members and clinicopathological features specific to breast cancer. Genetic alterations such as mutations and putative copy number alterations (CNAs) were analyzed for GSDMs family members using cBioPortal for GSDMS family members [24]. Moreover, Kaplan-Meier mapper database analysis was conducted to assess the relationship between GSDMS gene expression and clinical outcomes in breast cancer patients with different subtypes [25]. The infiltration of GSDMS in immune cells within breast cancer was evaluated through TIMER (Tumor Immune Estimation Resource) database analysis [26]. Finally, DiseaseMeth 3.0 database analysis was performed to investigate GSDMS methylation levels in breast cancer[27].\u0026nbsp;\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cp\u003e2.1 Analysis of GSDM family expression in breast cancer and adjacent tissues using the UALCAN database.The UALCAN database provides gene transcriptome data from TCGA [21]. In this study, we utilized UALCAN to analyze the differential expression of GSDMS genes in cancer and adjacent non-cancerous tissues, as well as the expression correlation among members of the GSDMS family in different breast cancer subtypes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2. The relationship between GSDMS and clinicopathological features in breast cancer was analyzed using the BC-GenExMiner database.The BcGenExMiner v5.0 (http://bcgenex.centregauducheau.fr/BCGEM) is an online analysis tool \u0026nbsp;for evaluating the correlation between GSDMS family members and clinicopathological characteristics in breast cancer transcriptomes, encompassing age, lymph node status, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, triple-negative tumors, P53 status (sequence), and lymph node involvement.\u003c/p\u003e\n\u003cp\u003e2.3 Genetic variation was analyzed using oncopprint database.The oncoprint feature has been integrated into the open-source web application cbiopportal, facilitating comprehensive exploration and analysis of cancer genome datasets (http://www.cbioportal.org/) [27,28]. In this study, a thorough examination of genetic alterations, encompassing mutations and putative copy number alterations (CNAs), was performed on members belonging to the GSDM family.\u003c/p\u003e\n\u003cp\u003e2.4 The prognostic value of GSDMS in patients with breast cancer was analyzed using the Kaplan-Meier online website. The Kaplan-Meier mapper is a database that predicts how gene expression profiles impact cancer patient survival, mainly derived from GEO, EGA (European Genome-Phenotype Archive), and TCGA. We used K-M survival plots to visualize overall survival/recurrence-free survival (OS/RFS) in breast cancer patients, including risk ratios (HR), 95% confidence intervals (CI), and logrank P values calculation. We compared prognosis across all cohorts using Kaplan-Meier survival plots.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.5 Immunohistochemical analysis of GSDMS in breast cancer.TIMER (Tumor Immune Estimation Resource)[26] database was utilized for analyzing immune cell infiltration levels in tumor tissues based on high-throughput sequencing data (RNA-Seq expression profiling). It provides information about B cells, CD4+ T cells, CD8+ T cells, neutrophils macrophages and dendritic cells infiltration levels.\u003c/p\u003e\n\u003cp\u003e2.6 The methylation of GSDMS in breast cancer. Diseasemeth3.0 is an online database for analyzing human gene methylation status across various diseases [27]. We used this database specifically to analyze GSDMS methylation levels in breast cancer.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e3.1 Expression of the GSDM family in breast cancer: we examined the mRNA levels of the GSDM family in breast and paracancerous tissues using the UALCAN database. The results revealed that, with the exception of GSDMA, which exhibited no significant difference in expression between breast and paracancerous (P=0.499), the other members of the GSDMS family displayed differential expression in both tissue types. Specifically, GSDMD was highly expressed in breast cancer while GSDMB, GSDMC,GSDME(DFNA5) and PJVK(DFNB59) were all expressed at low levels (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2 Transcriptional differences among molecular subtypes of breast cancer: To further investigate potential variations in mRNA expression levels among different molecular subtypes of breast cancer within the various members of the GSDM family, we analyzed their transcript levels using data from the UALCAN database (Figure 2). The expression levels of GSDMB and GSDMC are significantly upregulated in HER2-positive breast cancer, while they are lower in triple-negative and luminal breast cancer. (Figures 2b,c). The expression levels of GSDMD, GSDME (DFNA5), and PJVK (DFNB59) were significantly elevated in the luminal phenotype of breast cancer compared to Her2+ and triple-negative breast cancer subtypes (Figure 2d, 2e, 2f). These findings suggest a potential correlation between the expression patterns of the GSDMS family and molecular subtypes in breast cancer patients.\u003c/p\u003e\n\u003cp\u003e3.3 \u0026nbsp;The expression of the GSDM family was analyzed in relation to clinical characteristics of breast cancer patients: In order to analyze the expression of GSDM family in clinical case characteristics of breast cancer patients, we summarized the results based on age (with a cut-off at 51 years old), presence or absence of lymph node metastasis, ER and PR expression status, Her2 status, occurrence of triple-negative breast cancer, and presence or absence of P53 mutation. Among these factors, GSDMA showed significantly higher expression in patients aged 51 years old or younger (p=0.0207). Similarly, GSDMB and GSDMC also showed significantly higher expression in this age group (p=0.0181 and p\u0026lt;0.0001 respectively) [Table 1]. There was no significant association between age and the expression levels of other members within the GSDM family. The expression levels of GSDMB (P\u0026lt;0.0001), GSDMC (P=0.0011), GSDMD (P=0.0113), and GSDME (P=0.0388) were significantly upregulated in breast cancer patients with lymph node metastasis, while PJVK showed high expression in breast cancer patients without lymph node metastasis (p\u0026lt;0.0001). Additionally, the expression of GSDMA remained unchanged regardless of the patient\u0026apos;s lymph node status (P=0.1862). Moreover,In patients positive for ER and PR antibodies, the expression levels of GSDMD (P\u0026lt;0.0001) and PJVK (P\u0026lt;0.0001) were significantly upregulated. GSDMA (ER-P=0.0128, PR-P\u0026lt;0.0001), GSDMB (P\u0026lt;0.0001), GSDMC (P\u0026lt;0.0001), and GSDME (P\u0026lt;0.0001) exhibited elevated expression in ER and PR antibody-positive individuals.The expression levels of GSDMA (P=0.0034), GSDMB (P\u0026lt; 0.0001), and GSDMC (P\u0026lt; 0.0001) were significantly higher in HER2+ positive patients, whereas the expression levels of GSDMD (P\u0026lt; 0.0001) and PJVK (P\u0026lt; 0.0001) were markedly elevated in HER2- patients. No significant association was observed between the expression level of GSDME and HER2 status (P=0.3308).The expression of GSDMC (P\u0026lt;0.0001) and GSDME (P\u0026lt;0.0001) exhibited a positive correlation in patients with triple-negative breast cancer, whereas the expression of GSDMD (P\u0026lt;0.0001) and PJVK (P\u0026lt;0.0001) demonstrated an inverse association with triple-negative breast cancer. However, no significant correlation was observed between the expression of GSDMA (P=0.6576), GSDMB (P=0.6184), and triple-negative breast cancer.. Furthermore, patients harboring P53 mutations exhibited significantly elevated expressions of GSDMB and GSDMC (P\u0026lt;0.0001). In contrast, pronounced expression of PJVK was specifically observed in patients with wild-type P53. No significant association between other members of the GSDM family and P53 status was found.\u003c/p\u003e\n\u003cp\u003e3.4 Genetic alterations of GSDMS in patients with breast cancer:To explore the underlying factors contributing to differential expression of GSDMS among breast cancer patients, we employed cBioPortal (http://www.cbioportal.org), an online database, for analyzing genetic alterations associated with GSDMS. The findings are presented in Figure 3, demonstrating a predominant occurrence of missense mutations, deletion mutations, amplifications, and deep deletions within GSDMA-E and PJVK genes at alteration rates of approximately 10%, 11%, 19%, and 15% respectively.\u003c/p\u003e\n\u003cp\u003e3.5 Prognostic value of GSDM family mRNA expression in patients with breast cancer:We employed Kaplan-Meier analysis to evaluate the prognostic significance of each member within the GSDMS family in breast cancer patients, encompassing overall survival (OS) and disease-free survival (RFS). Results: As shown in Figure 4A,high expressionlevels of GSDMB and GSDMC were negatively correlated with overall survival (OS) among breast cancer patients. Conversely, high expression levels of GSDMD and PJVK demonstrated a positive association with overall survival (OS), while no significant correlation was observed between GSDMA or GSDME expression and overall survival. As illustrated in Figure 4B, high expressions of GSDMB, GSDMC, GSDMD, and PJVK were positively associated with the recurrence-free survival rate (RFS) in breast cancer patients. On the other hand, high levels of expressed GSDME displayed a negative correlation with the disease-free survival rate among breast cancer patients; however, no significant association was found between RFS and the expression level of GSDMA. The aforementioned data indicate that elevated expressions of both GSDMD and PJVK are positively associated with both OS and RFS in breast cancer patients. Therefore, these two markers may serve as valuable prognostic indicators for assessing the prognosis of individuals diagnosed with breast cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.6 \u0026nbsp;RFS prognostic value of GSDM family mRNA expression levels in breast cancer subtypes:In order to investigate the association between mRNA expression levels of GSDMS family and RFS prognostic value in breast cancer patients with different subtypes, we further analyzed the corresponding survival curve. The subtypes were classified based on the 2011 St. Gallen criteria [28], including basal-like, luminal A, luminal B, and HER2+. As shown in Table 2, within these subclasses, high expressions of GSDMB, GSDMD, and PJVK were positively correlated with improved RFS prognosis. Specifically in Luminal A subtype, high expressions of GSDMB, GSDMC and PJVK showed a positive correlation with RFS prognosis. In Luminal B subtype, high expressions of GSDMC,GSDME,and PJVK were also associated with better RFS prognosis; \u0026nbsp; \u0026nbsp; however,in HER2+ subtype,the high expressions of GSDMB and PJVK indicated a positive correlation with RFS prognosis while higher expression level of GSDMC was negatively correlated.\u003c/p\u003e\n\u003cp\u003e3.7 The relationship between the GSDM family and immune cells in breast cancer patients has been investigated in several studies [29,30]. In this study, we utilized the Timer2.0 online database to further analyze this relationship. Our findings revealed a positive correlation between the expression of GSDMA and B cells, macrophages, dendritic cells, and neutrophils (Figure 5A). Similarly, GSDMB expression showed a positive correlation with CD4+ T cells, B cells, and dendritic cells but a negative correlation with CD8+ T cells and macrophages (Figure 5B). Additionally, GSDMC expression demonstrated a positive correlation with CD4+ T cells, dendritic cells, and neutrophils while exhibiting a negative correlation with B cells (Figure 5C);The expression of GSDMD exhibited a positive correlation with CD4+ T cells, B cells, and dendritic cells, while it demonstrated a negative correlation with CD8+ T cells, macrophages, and neutrophils (Figure 5D). The expression of GSDME showed a positive correlation with CD4+ T cells, CD8+ T cells, macrophages, dendritic cells, and neutrophils; however, it displayed a negative correlation with B cells (Figure 5E). \u0026nbsp; PJVK expression was positively associated with CD4+ T cells but negatively associated with B cells and dendritic cells (Figure 5F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.8 Methylation levels of GSDM family in breast cancer patients: The negative correlation between gene expression and DNA hypermethylation levels has been demonstrated [31,32]. To investigate the methylation level of GSDMS in breast cancer patients, we utilized the DiseaseMeth database online for analyzing the methylation status of GSDMS in breast cancer . The results are presented in Figure 6. Notably, the methylation levels of GSDMC, GSDMD, and PJVK were lower in breast cancer compared to paracancerous; conversely, higher methylation levels were observed for GSDMA and GSDME in breast cancer \u0026nbsp; when compared to normal tissues. Our previous findings have demonstrated a significant upregulation of GSDMD in breast cancer, while GSDME exhibits low expression levels in these tissues. The observed methylation patterns for both GSDMD and GSDME are consistent with their respective expression trends, suggesting a potential influence of methylation on the regulation of thei rexpressionlevels. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePyroptosis is a newly discovered form of programmed cell death, primarily dependent on caspase-mediated pore formation. It leads to a pressure gradient across the cell membrane, cellular swelling, and release of intracellular contents, thereby inducing an inflammatory response [33]. Numerous studies have reported that pyroptosis can either inhibit or promote tumor development; however, the precise mechanisms underlying its actions remain unclear. Additionally, it has been associated with drug resistance during tumor chemotherapy [34]. The GSDMS family plays a crucial role in pyroptotic processes, with GSDMB, GSDMC, GSDMD, and GSDME mediating enhanced immune responses against certain cancers [35]. Notably, GSDMD-mediated pyroptotic response is well-documented [36]. Existing literature suggests that GSDMS can synergistically contribute to the initiation of pyroptosis. Rogers et al. demonstrated that GSDMD-mediated pyroptosis concurrently triggers the release of GSDME which further intensifies inflammation and enhances immune responses [37].\u003c/p\u003e\n\u003cp\u003eIn our study, we utilized online database to analyze the differential expression of GSDM family mRNAs in breast cancer and paracancer. Furthermore, we investigated the expression of GSDMS in molecular subtypes of breast cancer and explored its association with clinical characteristics. Our findings revealed that GSDMD exhibited high expression levels in breast cancer, while GSDMB-E and PJVK showed low expression levels. There was no significant difference in GSDMA expression between breast cancer and paracancer.Additionally, GSDMB demonstrated high expression in both overall breast cancer cases and specifically in HER2+ subtype cases, which is consistent with previous studies [38-40]. Moreover, Kaplan-Meier curves were employed to assess the overall survival (OS) and disease-free survival (RFS) rates among breast cancer patients belonging to different subtypes within the GSDM family. The results indicated a negative correlation between high expressions of GSDMB and GSDMC with OS rates; conversely, high expressions of GSDMD and PJVKd were positively correlated with higher OS rates. Similarly, elevated expressions of all four genes (GSDMB, GSDMC, GSDMD, PJVK) exhibited a positive correlation with RFS among breast cancer patients; However, increased expression levels of GSDME were linked to poorer RFS rates. These data collectively suggest that elevated levels of GDSM、PJVK may serve as a valuable prognostic marker for predicting outcomes in breast cancer patients. Furthermore, positive correlations observed between RFS rates across various subtypes indicate that members within the GSDM family could potentially be utilized as prognostic markers for distinct subsets of individuals diagnosed with breast cancer.\u003c/p\u003e\n\u003cp\u003eMultiple alterations in genes play an important role in the development of breast cancer [41], here we confirmed the mutation of GSDM family mRNAs during breast cancer using Timer3.0 online database, which may be the reason why GSDMS plays an important role in the development of breast cancer.\u003c/p\u003e\n\u003cp\u003eNumerous studies have demonstrated the crucial role of immune cell infiltration in the tumor microenvironment during tumor development, which significantly impacts patient treatment and prognostic outcomes [42-44]. Additionally, tumor-infiltrating lymphocytes have been identified as independent predictors of immunotherapy response and prognosis across various cancer types [45,46]. Previous reports have highlighted that GSDME expression in tumors enhances phagocytosis by tumor-associated macrophages, augments the quantity and functionality of infiltrating natural killer lymphocytes and CD8+ T lymphocytes, thereby acting as a tumor suppressor to bolster anti-tumor immunity through juxtaposition activation [47]. Our study reveals a close association between members of the GSDMs family and distinct immune cell populations in breast cancer, suggesting their potential utility as diagnostic biomarkers while also implicating their involvement in immune regulation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethylation plays a crucial role in tumorigenesis and tumor development. Therefore, we conducted an analysis of the methylation level of the GSDMS family in breast cancer. Our results revealed differential expression of GSDMS family methylation in breast cancer. Interestingly, previous reports have suggested that GSDME methylation could serve as a potential marker for detecting both breast and colorectal cancers [48,49]. Conversely, other studies have indicated that GSDME methylation leads to reduced expression levels, thereby inhibiting focal responses and promoting tumorigenesis [50-52]. Furthermore, GSDME methylation has been associated with distant metastasis in tumors [53]. Notably, our study demonstrated a consistent correlation between the methylation status of GSDMD and GSDME with their respective expression trends. These findings suggest that the expression levels of GSDMD and GSDME may be influenced by their corresponding methylation levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, our study has certain limitations. Firstly, we solely analyzed the mRNA level relationship between the GSDM family and breast cancer, lacking validation of its corresponding functional proteins which may not fully represent the protein expression of the GSDMS family. Therefore, future studies should focus on increasing validation of functional protein expression. Secondly, due to database constraints, we were unable to perform multivariate analysis between clinicopathological characteristics. Lastly, there is currently no literature reporting on the mechanism of PJVK in breast cancer. Although this study reveals a close association between GSDMS and breast cancer progression based on online database analysis alone, further verification through experimental and clinical data is necessary to elucidate the unclear role of GSDM family in breast cancer development\u003c/p\u003e\n\u003cp\u003eFinally, by conducting comprehensive analysis of diverse online databases, we have identified a significant correlation between elevated expression levels of GSDMD and PJVK and improved prognosis in breast cancer patients. These findings suggest that GSDMD and PJVK hold promise as potential prognostic targets for breast cancer, providing valuable guidance to clinicians regarding drug utilization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R\u0026amp;D projects of significant importance in the Ningxia Hui Autonomous Region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental data that support the findings of this study are openly available in \u0026nbsp;UALCAN(https://ualcan.path.uab.edu/),BC-GenExMine(http://bcgenex.centregauducheau.fr/BCGEM),oncopprint(http://www.cbioportal.org/),TIMER(https://cistrome.shinyapps.io/timer/),Diseasemeth2.0(http://bio-bigdata.hrbmu.edu.cn/diseasemeth/) ,Kaplan-Meier( https://kmplot.com/analysis/ ) online website.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval are not required for the current study due to the nature of the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from the patient for the publication of this case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData curation\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eYaxiang Han\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFormal analysis:\u003c/strong\u003eZhaoyi Yue\u003cstrong\u003e,\u003c/strong\u003e Rongrong Ma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigation:\u0026nbsp;\u003c/strong\u003eLigang Wu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003eLi Na\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware:\u003c/strong\u003eXiaoying Huang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupervision:\u0026nbsp;\u003c/strong\u003eLigang Wu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation:\u0026nbsp;\u003c/strong\u003eQilun Liu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVisualization:\u0026nbsp;\u003c/strong\u003eYaxiang Han\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026ndash; original draft:\u0026nbsp;\u003c/strong\u003eXiaoying Huang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026ndash; review \u0026amp; editing:\u003c/strong\u003eQilun Liu\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSUNG H, FERLAY J, SIEGEL R L, et al. 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Ann Otol Rhinol Laryngol 121: 197‑207, 2012.\u003c/li\u003e\n\u003cli\u003eHergueta‑Redondo M, Sarrio D, Molina‑Crespo A, Vicario R, Bernad\u0026oacute;‑Morales C, Mart\u0026iacute;nez L, Rojo‑Sebasti\u0026aacute;n A, Serra‑Musach J, Mota A, Mart\u0026iacute;nez‑Ram\u0026iacute;rez \u0026Aacute;, et al: Gasdermin B expression predicts poor clinical outcome in HER2‑positive breast cancer. Oncotarget 7: 56295‑56308, 2016.\u003c/li\u003e\n\u003cli\u003eHallajzadeh J, Maleki Dana P, Mobini M, Asemi Z, Mansournia MA, Sharifi M, Yousefi B. Targeting of oncogenic signaling pathways by berberine for treatment of colorectal cancer. Med Oncol. 2020; 37:49.\u003c/li\u003e\n\u003cli\u003eBinnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI, OstrandRosenberg S, Hedrick CC, Vonderheide RH, Pittet MJ, Jain RK, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018; 24:541\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eXie F, Zhou X, Fang M, Li H, Su P, Tu Y, Zhang L, Zhou F. Extracellular Vesicles in Cancer Immune Microenvironment and Cancer Immunotherapy. Adv Sci (Weinh). 2019; 6:1901779.\u003c/li\u003e\n\u003cli\u003eVarn FS, Wang Y, Mullins DW, Fiering S, Cheng C. Systematic Pan-Cancer Analysis Reveals Immune Cell Interactions in the Tumor Microenvironment. Cancer Res. 2017; 77:1271\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eOhtani H. Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human colorectal cancer. Cancer Immun. 2007; 7:4.\u003c/li\u003e\n\u003cli\u003eAzimi F, Scolyer RA, Rumcheva P, Moncrieff M, Murali R, McCarthy SW, Saw RP, Thompson JF. Tumorinfiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol. 2012; 30:2678\u0026ndash;83.\u003c/li\u003e\n\u003cli\u003eZhang Z, Zhang Y, Xia S, Kong Q, Li S, Liu X, Junqueira C, Meza-Sosa KF, Mok TMY, Ansara J, Sengupta S, Yao Y, Wu H, Lieberman J. Gasdermin E suppresses tumour growth by activating anti-tumour immunity. Nature. 2020 Mar;579(7799):415-420. doi: 10.1038/s41586-020-2071-9. Epub 2020 Mar 11.\u003c/li\u003e\n\u003cli\u003eIbrahim J, Op de Beeck K, Fransen E, Peeters M, Van Camp G. The Gasdermin E Gene Has Potential as a Pan-Cancer Biomarker, While Discriminating between Different Tumor Types. Cancers (Basel). 2019; 11:1810\u003c/li\u003e\n\u003cli\u003eIbrahim J, Op de Beeck K, Fransen E, Croes L, Beyens M, Suls A, Vanden Berghe W, Peeters M, Van Camp G. Methylation analysis of Gasdermin E shows great promise as a biomarker for colorectal cancer. Cancer Med. 2019; 8:2133\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eKim MS, Chang X, Yamashita K, Nagpal JK, Baek JH, Wu G, Trink B, Ratovitski EA, Mori M, Sidransky D. Aberrant promoter methylation and tumor suppressive activity of the DFNA5 gene in colorectal carcinoma. Oncogene. 2008; 27:3624\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eCroes L, Beyens M, Fransen E, Ibrahim J, Vanden Berghe W, Suls A, Peeters M, Pauwels P, Van Camp G, Op de Beeck K. Large-scale analysis of DFNA5 methylation reveals its potential as biomarker for breast cancer. Clin Epigenetics. 2018; 10:51.\u003c/li\u003e\n\u003cli\u003eWang Y, Yin B, Li D, Wang G, Han X, Sun X. GSDME mediates caspase-3-dependent pyroptosis in gastric cancer. Biochem Biophys Res Commun. 2018; 495:1418\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eKim MS, Lebron C, Nagpal JK, Chae YK, Chang X, Huang Y, Chuang T, Yamashita K, Trink B, Ratovitski EA, Califano JA, Sidransky D. Methylation of the DFNA5 increases risk of lymph node metastasis in human breast cancer. Biochem Biophys Res Commun. 2008; 370:38\u0026ndash;43.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable1\u0026nbsp;Relationship between GSDMS expression and clinicopathological features of breast cancer patients\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.524475524475523%\" valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003eGSDMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003eGSDMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003eGSDMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003eGSDMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003eGSDME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003ePJVK\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.524475524475523%\" valign=\"bottom\"\u003e\n \u003cp\u003eAge (years)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026gt;51\u003c/p\u003e\n \u003cp\u003e\u0026le;51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e0.0565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e0.1841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e0.6574\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.524475524475523%\" valign=\"top\"\u003e\n \u003cp\u003eNodal status\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e0.1862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.0113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.0388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.524475524475523%\" valign=\"top\"\u003e\n \u003cp\u003eER (IHC)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.0128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.524475524475523%\" valign=\"top\"\u003e\n \u003cp\u003ePR (IHC)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.524475524475523%\" valign=\"top\"\u003e\n \u003cp\u003eHER2 (IHC)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.0034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e0.3308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.524475524475523%\" valign=\"top\"\u003e\n \u003cp\u003eTriple-negative status\u003c/p\u003e\n \u003cp\u003eNot\u003c/p\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e0.6576\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e0.6184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.524475524475523%\" valign=\"top\"\u003e\n \u003cp\u003eP53 sequence\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWild type\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMutated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e0.6048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e0.0804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e0.2250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.412587412587413%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable2 \u0026nbsp;Prognostic values of GSDMS expression for RFS in different BrCa intrinsic subtypes.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"528\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eSubclasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eGSDMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eSubclasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eHER2 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eGSDMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eSubclasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e2277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e1491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eHER2\u0026nbsp;positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eGSDMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eSubclasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eHER2 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eGSDMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eSubclasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eHER2 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eGSDME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eSubclasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e1491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eHER2 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003ePJVK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eSubclasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003eLuminal B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.897920604914933%\" valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.3062381852552%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHER2 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"Breast cancer, GSDMS, expression profiles, prognosis, immune infiltration","lastPublishedDoi":"10.21203/rs.3.rs-3910767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3910767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Pyroptosis is a new type of programmed death recently discovered,the GSDM family plays a crucial role in pyroptotic processes. Numerous studies have reported that GSDM family can either inhibit or promote tumor development; However, the mechanism of action in breast cancer is unclear. In this study, we employed diverse online databases to investigate the differential expression of GSDMS in breast cancer compared to para-cancer and transcriptional variations among molecular subtypes. Furthermore, we explored the association between GSDMS and clinical characteristics, assessed its prognostic value, examined gene alterations, investigated the relationship between subtypes and RFS, evaluated immune cell infiltration level, and analyzed the level of GSDMS methylation in breast cancer. We observed a significant upregulation of GSDMD in breast cancer, while GSDMB-E and PJVK exhibited low expression levels in this malignancy. GSDMB and GSDMC were highly expressed in HER2-positive breast cancer but showed low expression in triple-negative and luminal subtypes. GSDMD, GSDME, and PJVK displayed high expression specifically in the luminal phenotype of breast cancer while being downregulated in Her2+ and triple-negative subtypes. The expression pattern of GSDMS was closely associated with distinct clinical features. Missense mutation, deletion mutation, amplification, and deep deletion primarily accounted for alterations observed in the genes encoding GSDMA-E and PJVK with change rates ranging from 0.9% to 19%. Survival analysis revealed that elevated expressions of both GSDMD and PJVK were correlated with improved prognosis among breast cancer patients. Furthermore, the members of the GSDM family demonstrated strong associations with immune-infiltrating cells within the of breast cancer. DNA methylation analysis indicated lower levels along with decreased methylation patterns for GSDMC, GSDMD, and PJVK in breast cancer tissues; conversely increased methylation was observed for both GSDMA and GSDME. Our study systematically elucidates the expression and prognostic significance of GSDMS in breast cancer, as well as the potential prognostic implications of GSDMD and PJVK in breast cancer. These findings provide valuable guidance for clinicians regarding drug utilization.","manuscriptTitle":"GSDMS are potential therapeutic targets and prognostic biomarkers in breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-29 20:23:19","doi":"10.21203/rs.3.rs-3910767/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":"30cbc883-aead-465b-963f-d2960e4af41b","owner":[],"postedDate":"February 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29010489,"name":"Health sciences/Medical research/Biomarkers"},{"id":29010490,"name":"Health sciences/Medical research"},{"id":29010491,"name":"Health sciences/Oncology"},{"id":29010492,"name":"Health sciences/Oncology/Cancer"},{"id":29010493,"name":"Biological sciences/Cancer/Breast cancer"}],"tags":[],"updatedAt":"2024-05-20T13:09:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-29 20:23:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3910767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3910767","identity":"rs-3910767","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}