A novel pyroptosis-related gene signature correlated to C6orf99 for predicting the prognosis of breast cancer

A novel pyroptosis-related gene signature correlated to C6orf99 for predicting the prognosis of 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A novel pyroptosis-related gene signature correlated to C6orf99 for predicting the prognosis of breast cancer Hong Gao, ZhaoHua Gui, ShiKai Hong, ZhengZhi Zhu, DaKe Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4258362/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 The relationship between the pyroptosis-related long non-coding RNA (PRlncRNA) and the prognosis of breast cancer has not been clearly studied. According to relevant studies, the expression of the lncRNA C6orf99 is associated with poor prognosis of breast cancer. In our study, we demonstrated that high expression of C6orf99 is adverse for the prognosis of breast cancer using the TCGA dataset. In addition, there were five pyroptosis related genes (PRGs) ( PJVK , CASP4 , IL18 , ELANE and TIRAP ) associated with the expression of C6orf99 and survival outcomes of breast cancer. According to the expression of PJVK , CASP4 , IL18 , and ELANE , breast cancer samples were divided into two different subclusters (C1 and C2) using clustering analysis. Furthermore, a new C6orf99-related PRGs risk model was constructed to predict the prognosis of breast cancer. The high-risk group had worse survival outcomes and lower immune infiltration level, whereas the low-risk group had better survival outcomes and higher immune infiltration level. We further found that the higher the expression intensity of CASP4 and IL18 in 66 breast tumor tissues by immunohistochemical staining, the earlier the clinical stage, the smaller the tumor, the better the survival outcome. Our finding of C6orf99-related PRGs suggest a new idea for the treatment of breast cancer cases, and may become a new molecular target for targeted therapy. Breast Cancer C6orff99 Pyroptosis Long non-coding RNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction According to the newest literature reports [ 1 ] , breast cancer is the second most common cancer in women after lung cancer. It is the fifth leading cause of cancer mortality in women, which seriously threatens women's health. In recent years, with the rapid development of medical imaging and genomics technologies and the improvement of patients' awareness of self-health management, the overall survival rate of most patients has been improved through early diagnosis and treatment, but tumor-related death caused by breast cancer is still the most common cause of death. Therefore, it is particularly important to study the pathogenesis of breast cancer and seek more effective diagnosis and prognostic indicators. Breast cancer is a heterogeneous disease involving multiple genes, and the mechanisms involved in the occurrence and development of breast cancer are diverse under the same pathological type. Therefore, there is still a large space for research on the mechanism of breast cancer occurrence, development and metastasis. The research on the mechanism of breast cancer occurrence and development will provide a more rigorous scientific basis for the development of new targeted drugs. Pyroptosis is a new form of programmed cell death, proposed by Cookson and Brennan in 2001, which has become a hot research topic in recent years [ 2 ] . It is usually performed by the caspase protein family and gasdermin protein family [ 3 ] . Therefore, Shao Feng's [ 4 ] team redefined the concept of pyroptosis as gasdermin-mediated programmed cell death, opening up a new field of research on cell death and inflammatory immunity. Recent studies on pyroptosis have found that it may be closely related to malignant tumors and play a crucial role both in anti-tumor effects and in cancerization, progression, and drug resistance. Long non-coding RNAs (lncRNAs) are a class of RNAs that are longer than 200 nucleotide sequences and lack the ability to code for proteins [ 5 ] . LncRNAs were initially thought to be genomes without any biological function. Recently, researchers began to pay attention to the role of lncRNAs and found that lncRNAs exist in the whole cell, not only in the nucleus, but also in the cytoplasm and mitochondria. RNA-protein, RNA-RNA, DNA-RNA interactions can be associated with lncRNAs to form different functional complexes. Due to their ability to regulate mRNA stability, translation, and cell signaling pathways, lncRNAs can perform multiple functions within cells. LncRNAs can influence chromatin structure and gene expression through interactions with epigenetic remodelers, transcription factors, and spliceosomes in the nucleus, thereby regulating gene expression at multiple levels, including epigenetic, transcriptional, and post-transcriptional regulation. According to literature reports [ 6 ] , the expression of lncRNA C6orf99 is associated with poor prognosis of breast cancer. However, there are few studies on the prognostic effects of pyroptosis genes associated with lncRNAs, especially C6orf99, in breast cancer. Therefore, the construction of prognostic models of C6orf99-related PRGs is a necessary means to investigate the function of BC-related genes, tumor microenvironment and their relationship with immunotherapy. In this study, we firstly analyzed the correlation between C6orf99 expression and BC prognosis, and comprehensively compared the clinical and molecular features between high and low C6orf99 expression groups. Next, we clustered two subgroups based on the expressions of four C6orf99-related PRGs and conducted pathway enrichment analysis based on differential expression genes between these two subclusters. Furthermore, we constructed a risk model predicting the prognosis for breast cancer patients and validated its applicability. We further compared the immune infiltration levels between high and low risk groups in breast cancer. Finally, we validated the correlations of the expression levels of C6orf99-related PRGs with clinicopathological features and survival outcomes in breast cancer patients. 2 Materials and methods 2.1 Data source and processing We downloaded the gene expression profiles (RSEM normalized and batch effects adjusted) and clinical information of 1110 breast cancer patients from the Cancer Genome Atlas (TCGA) data set ( https://portal.gdc.cancer.gov/ ). We also downloaded RNA-seq data of 179 breast normal tissues from the GTEx data portal website ( https://www.gtexportal.org/home/datasets ). Besides, an RNA sequencing dataset (GSE9893 [ 7 ] ) and its clinical information were downloaded from the NCBI gene expression omnibus (GEO) ( https://www.ncbi.nlm.nih.gov/geo/ ). 2.2 Identification of pyroptosis-related genes correlated to C6orf99 To identify the pyroptosis-related genes correlated to C6orf99, we firstly performed the Pearson correlation analysis among 33 pyroptosis-related genes and C6orf99 in TCGA cohort with a screening condition that P < 0.05. Univariate COX regression analysis was applied to identify the prognosis-related PRGs in TCGA cohort ( P < 0.1). 2.3 Clustering analysis We used hierarchical clustering to identify breast cancer subclusters based on PRGs’ gene expression values, which were correlated with the expression of C6orf99. This analysis was conducted using the R package “hclust” with the parameters: method = “ward. D2” and members = NULL. Prior to clustering, the data of gene expression values were transformed by z-score and translated into distance matrices by the “dist()” function with the parameter: method = “Euclidean”. 2.4 Identification of differential expression genes To identify marker genes for C6orf99-related PRGs subclusters, we identified the upregulated genes in each subcluster using the threshold of Student’s t test adjusted P value 1.5. 2.5 Pathway Enrichment Analysis In the TCGA cohort, we utilized the R package “ClusterProfiler” to explore the Gene Oncology (GO) pathways based on the upregulated genes in each subcluster. GO categories are composed of cellular components, molecular function, and biological process. 2.6 Establishment and Validation of C6orf99-related pyroptosis prognostic signature The Elastic-net regression (alpha = 0.98, 10-fold cross validation) was performed to screen the potential prognosis signature of C6orf99-related PRGs in TCGA cohort. We established the prognosis signature of breast cancer based on C6orf99-related PRGs that was identified using elastic-net regression and multivariate cox regression. Using candidate PRGs, we calculated the risk score of each patient in TCGA cohort. Using the R packages “Survival”, “survminer” and “timeROC”, we performed the sensitivity and specificity of the prognostic model. We also used the GEO cohort (GSE9893) to validate this prognostic model. In addition, we used the R package “rms” to construct the nomogram, which prominently predict the 1-,3-,5-year overall survival (OS) by evaluating various variables (age, sex, tumor stage, risk score). 2.7 Evaluation of genomic instability scores and immune signature levels Copy number alterations (CNAs) and tumor mutation burden (TMB) reflect genomic instability. We obtained CNA scores of the breast cancer from the publication by Knijnenburg et al [ 8 ] . We calculated TMB using the total number of somatic mutations in the tumor. Besides, we evaluated intratumor heterogeneity (ITH) in tumors by conducting the DEPTH algorithm [ 9 ] at mRNA level. In addition, to explore immune infiltration levels within tumors, we calculated the proportion of immune cells in each sample using the Cibersort algorithm [ 10 ] by inputting of gene expression matrix. 2.8 Survival analysis We performed the Kaplan–Meier survival analysis to compare the OS, disease-free survival (DFS) or progression-free survival (PFS) between different subgroups of breast cancer patients by utilizing R packages “survival” and “survminer”. Log-rank tests were used to evaluate the significance of their differences. Besides, the univariate Cox regression analysis was conducted by the Cox proportional hazards model in the R package “survival” to respectively assess the correlations of the expression of each PRG with survival prognosis in breast cancer. 2.9 Tissue samples and immunohistochemical experiments A total of 66 cases of breast cancer (invasive breast cancer, non-special type) archived specimens (paraffin-embedded) in Anhui Provincial Cancer Hospital (Hefei, China) from January 2014 to December 2019 were collected. Immunohistochemical method was used to detect and analyze 66 cases of breast cancer tissues. Among them, CASP4 and IL18 protein expression were detected. 2.10 Statistical analysis To compare the values of two groups, we utilized Mann–Whitney U tests for non-normally distributed data or Student’s t tests for normally distributed data. For contingency tables, the Fisher’s exact tests or Chi-square tests were performed. We utilized the Pearson method to evaluate the correlations between two groups of normally distributed data. Besides, we calculated the false discovery rate (FDR) to adjust P values in multiple tests by utilizing the Benjamini-Hochberg method [ 11 ] . All of these statistical analyses were performed through the R programming environment (version 4.2.2). 3 Results 3.1 Correlation between C6orf99 expression and BC patient OS in the TCGA cohort Firstly, we analyzed and compared the expression levels of lncRNA C6orf99 in 179 normal tissues from GTEx dataset and 1096 tumor tissues from the TCGA cohort. We found that the expression level of C6orf99 lncRNA in breast cancer tissue was significantly higher than that in normal breast tissue ( P < 0.001) (Fig. 1 A). According to the median value of the expression of lncRNA C6orf99, two subgroups were divided, namely the low expression (low-exp) group and the high expression (high-exp) group. The survival difference between these two groups was analyzed by Kalan-Meier analysis curve, which showed that the survival of lncRNA C6orf99 low-exp group was significantly better than that of high-exp group ( P = 0.04) (Fig. 1 B). 3.2 Correlation between C6orf99 expression and clinical and molecular features According to pathological stage, BC patients were divided into early-stage group and late-stage group. There were 373 cases of early-stage BC and 151 cases of late-stage BC in the group with high C6orf99 expression, whereas 426 cases of early-stage BC and 116 cases of late-stage BC in the group with low C6orf99 expression. The Sankey diagram of clinical factors related to C6orf99 expression was constructed (clinical factors: pTNM stage, BRCA subtypes, OS status) (Fig. 1 C). From the pathological TNM stage, we found that the high-exp group had a higher proportion in late-stage tumors, while the low-exp group had a higher proportion in early-stage tumors (Fisher’s exact test, P = 0.006, OR = 1.486), indicating the higher expression of C6orf99 was positively correlated with worse clinical tumor stage (Fig. 1 D). To explore the associations of C6orf99 expression with genomic instability, we compared the CNA scores, TMB, and ITH scores between high-exp and low-exp groups. We found that the CNA scores, TMB and ITH scores of high-exp group were markedly higher than those of low-exp group (Fig. 1 E). 3.3 Identification of C6orf99-related PRGs in the TCGA cohort A total of 33 PRGs were selected based on the previously published literature [ 12 ] . We used the Pearson method to analyze the correlation of C6orf99 expression with 33 PRGs. We found that 22 PRGs were related to C6orf99 expression ( P < 0.05). To identify the C6orf99-related PRGs associated with prognosis, we used univariate Cox regression model and found 8 PRGs related to prognosis. Four PRGs ( CASP4,1 ELANE, IL18, PJVK ) were negatively correlated with C6orf99 expression and were favorable for prognosis, while one PRG ( TIRAP ) was positively correlated with C6orf99 expression and was adverse for prognosis (Fig. 2 A and 2 B). Furthermore, we performed hierarchical clustering to identify two risk subclusters based on the expressions of 4 PRGs ( CASP4, ELANE, IL18, PJVK ). We termed the high expression subcluster as C1 and the low expression subcluster as C2 (Fig. 2 C). We compared 10-year OS between the two subclusters and found that the C1 group survived better than the C2 group ( P < 0.001), suggesting that the high expressions of the four PRGs were positively with BC OS (Fig. 2 D). In order to explore the differences in gene function and biological pathway between C1 and C2, we performed differential expression gene (DEG) analysis and GO analysis. Based on DEG analysis, 2062 DEGs were identified between C1 and C2 groups in the TCGA cohort. Among them, 1903 genes were upregulated in C1 group, while 159 genes were downregulated (Fig. 2 E and Supplementary Table S1 ). GO enrichment analysis showed that the upregulated DEGs were mainly correlated with immune activities, including leukocyte cell-cell adhesion, regulation of T cell activation, external side of plasma membrane, MHC protein complex, immune receptor activity, and cytokine activity. However, the downregulated DEGs were enriched in mammary gland and neuronal activities, such as mammary gland alveolus development, reproductive structure development, neuronal cell body, synaptic membrane, sodium ion transmembrane transporter activity, and gated channel activity (Fig. 2 F). 3.4 The construction of a prognostic risk model associated with C6orf99 expression Based on the 22 C6orf99-related PRGs, we constructed a risk model using Elastic-net Cox regression in the TCGA cohort. Five PRGs with the largest absolute regression coefficients were selected to construct the risk model (Fig. 3 A and 3 B). The formula was as follows: risk score = (-0.154) * CASP8 +(-0.150) * IL18 + (0.079) * GSDMC + (0.160) * NLRC4 + (0.210) * TIRAP . Breast cancer samples in the TCGA cohort were divided into high-risk group and low-risk group according to the median risk score. Through survival analysis, the 10-year OS and DFS of the high-risk group were significantly worse than that of the low-risk group (OS: P < 0.001, DFS: P = 0.02) (Fig. 3 C- 3 G). To evaluate the sensitivity and specificity of the prognostic model, we employed time-dependent receiver operating characteristic (ROC) curve analysis. The time-dependent ROC curve displayed the area under the curve (AUC) of the prognosis model at 1-, 3- and 5-year, which were 0.71, 0.63 and 0.63, respectively (Fig. 3 H). 3.5 Validation of the risk model predicting breast cancer OS in the GEO cohort BC patients from the GEO cohort (GSE9893) were utilized to validate the prognostic risk model. Similar to the TCGA cohort, we classified the BC patients into high-risk and low-risk groups based on the formula and the median risk score (Fig. 4 A and 4 B). Furthermore, we analyzed the 8-year OS in the GEO cohort, which showed that the tumors with higher risk scores (> median) displayed worse outcomes than those with lower risk scores (< median) ( P = 0.0786) (Fig. 4 C). These findings further supported the applicability of the risk model in breast cancer. 3.6 Nomogram construction for breast cancer OS prediction in the TCGA cohort To analyze the correlation between risk model and clinicopathological parameters in BC patients, we further developed a nomogram based on the risk model (risk scores) and various clinicopathological parameters (age, sex, pTNM_stage) in the TCGA cohort. The nomogram was constructed by using these independent prognostic variables determined by multivariate Cox regression analysis to predict 1-, 3-, and 5-year OS in BC patients (C-index = 0.764, P < 0.001) (Fig. 5 ). 3.7 Correlation between the risk model and immune infiltration level in the TCGA cohort Previous studies have shown that the occurrence of pyroptosis further activates the anti-tumor immune response and inhibits tumor growth [ 13 ] . Therefore, to assess the immune infiltration level in breast cancer, we used the Cibersort algorithm to compare the proportion of immune cells between the high-risk group and the low-risk group in the TCGA cohort. We found that the proportions of naive B cells, CD8 T cells, follicular helper T cells, regulatory T cells, activated NK cells, and M1 macrophages in the low-risk group were significantly higher than those in the high-risk group ( P < 0.05) (Fig. 6 ). Conversely, the proportions of M0 macrophages, eosinophils, and neutrophils in the high-risk group were significantly higher than those in the low-risk group ( P < 0.05) (Fig. 6 ). These findings indicated that the low-risk group might have positive correlation with anti-tumor immune response. 3.8 The clinicopathologic and survival analysis of C6orf99-related CASP4 and IL18 By immunohistochemical staining in 66 cases of breast cancer tissues, we analyzed the expression of CASP4 and IL18 , which were PRGs and were negatively correlated with C6orf99 expression (Fig. 7 A). We first examined the expression of CASP4 and IL18 in 66 BC patients to validate the findings from the bioinformatics analyses. As shown in Table 1, among the 66 breast cancer patients, 22 of them had high expression of CASP4 (positive), 44 of them had low or no expression of CASP4 (negative). Similarly, 24 patients had high expression of IL18 (positive), and 42 patients had low or no expression of IL18 (negative). The expression level of CASP4 or IL18 was related to tumor size, axillary lymph node metastasis and pathological stage, but was not correlated with age, BMI and pathological subtypes of patients (Fisher’s exact test, P < 0.1). We then analyzed the correlations of CASP4 and IL18 expression levels with OS and PFS in 66 breast cancer patients, respectively. We found that there were no significant differences between high and low expression of these two genes in OS probably because of the short follow-up time and small sample size ( P > 0.05) (Fig. 7 B). However, patients with high expression of CASP4 and IL18 showed significantly better PFS than patients with low or no expression of CASP4 and IL18 ( P < 0.05) (Fig. 7 C). 4 Discussion Breast cancer is currently the main cause of cancer-specific death in women [ 1 ] . In China, the annual incidence of breast cancer continues to increase by about 1.2%, and it is still increasing every year [ 1 ] . In recent years, researchers have proposed a variety of effective treatment options for breast cancer, but some patients still suffer from metastatic recurrence, leading to death. Therefore, we need to continue to seek more effective methods to further improve the disease-free survival rate and overall survival rate of patients, to improve their life quality, and to prolong their lifespan. Biomarkers, which play an important role in the emergence and development of this disease, have naturally become the current hotspot of research in this field. Pyroptosis is a "new" type of programmed cell death discovered and confirmed in recent years. It is characterized by cell swelling and membrane rupture. The oligomerization of effector molecules, Gasdermins, perforates the membrane and destroys the ion homeostasis of the cell, eventually leading to osmotic disintegration of the cell. In this process, a large number of inflammatory cytokines and danger signaling molecules are released to activate the immune system, which plays an important role in regulating the anti-infection immune response and anti-tumor immune response [ 4 , 14 – 16 ] . The Gasdermin family includes GSDMA, GSDMB, GSDMC, GSDMD, GSDME (also known as DFNA5) and DFNB59. Our study found that GSDMC is a prognostic risk factor for breast cancer, which may be due to the altered structural domains of GSDMC that activate the associated inflammatory response. The Gasdermin protein family has both N-terminal and C-terminal structural domains, and the active N-terminal structural domain is released when the caspase protease specifically cleaves the connecting site between the two structural domains of pyrodehydic proteins. The N-terminal domain recognises and binds to phospholipid molecules in the cell membrane, which accumulate and pore, leading to extracellular and internal fluid reflux and osmotic pressure changes, ultimately leading to swelling and lysis of the cell membrane, and the release of large amounts of intracellular contents, which activates a strong inflammatory response and leads to the development of pyroptosis [ 17 – 18 ] . The clinical manifestations and clinical benefit rates of breast cancer patients vary greatly due to tumor heterogeneity. With the development of sequencing technology, the establishment of corresponding tumor data is becoming more perfect, which greatly accelerates tumor classification and provides a new direction for precise cancer prevention and treatment [ 19 – 24 ] . Although an increasing number of breast cancer tumor markers have been mined, a single prediction model cannot accurately predict the prognosis of patients, so the multi-genes prediction model combining multiple markers has become the mainstream trend [ 25 ] . C6orf99 is a member of lncRNAs. Some studies have found that C6orf99 is related to pyroptosis of breast cancer, and other studies have confirmed that C6orf99 is related to the prognosis of breast cancer and may be used as a potential diagnostic or prognostic marker. In order to better understand the relationship among C6orf99, pyroptosis, and prognosis in breast cancer, we identified 22 pyroptosis-related genes associated with lncRNA C6orf99 in TCGA dataset. Eight genes significantly related to survival were screened out from the above 22 PRGs by univariate Cox analysis, including CASP4 , CASP8 , ELANE , GPX4 , GSDMC , IL18 , PJVK and TIRAP . Among them, CASP4 , ELANE , IL18 , and PJVK were negatively correlated with C6orf99 expression and favorable for prognosis, while TIRAP was positively correlated with C6orf99 expression and unfavorable for prognosis. DEG analysis and GO analysis were performed on the above four positively correlated genes. Based on DEG analysis, 1903 genes were up-regulated and 159 genes were down-regulated. GO enrichment analysis showed that the up-regulated DEGs were mainly related to immune activity, including leukocyte-cell adhesion, regulation of T cell activation, outside of plasma membrane, MHC protein complex, immune receptor activity, and cytokine activity. The down-regulated DEGs were enriched in mammary gland and neuronal activities, such as mammary alveolar development, reproductive structure development, neuronal cell body, synaptic membrane, sodium transmembrane transport activity and gated channel activity. To further predict the prognostic impact of these PRGs related to lncRNA C6orf99, a risk model was constructed by Elastic-net Cox regression with five predictors, namely CASP8 , IL18 , GSDMC , NLRC4 , and TIRAP . The applicability of the risk model in breast cancer was verified by BC patients in the GEO cohort (GSE9893). The independent prognostic variables identified by multivariate Cox regression analysis were used to construct a nomogram to predict the 1 -, 3 -, and 5-year OS of breast cancer patients, and the correlation between the risk model and clinicopathological parameters of breast cancer patients was further verified. We further compared the proportions of immune cells in the high-risk and low-risk groups in the TCGA cohort to assess the immune infiltration level in breast cancer. We found that the low-risk group had significantly higher proportions of naive B cells, CD8 T cells, follicular helper T cells, regulatory T cells, activated NK cells, and M1-type macrophages than the high-risk group. In contrast, the proportion of M0 macrophages, eosinophils, and neutrophils in the high-risk group was significantly higher than that in the low-risk group. This result also confirms that the onset of pyroptosis is associated with the dynamics of pro- and anti-inflammatory responses. In low-risk situations, it is associated with anti-tumor immunity, thus inhibiting tumor growth. Caspases (CASP) are a group of highly conserved intracellular proteolytic enzymes with an active site of cysteine residues, which can specifically break polypeptide bonds after aspartic acid residues [ 26 ] . So far, 14 CASPs have been identified, which are involved in the regulation of various biological processes such as apoptosis, cell growth, differentiation, proliferation and motility. As a key molecule in noncanonical pyroptosis, CASP4 comes from the CASP family and is the core component of noncanonical inflammasome [ 27 – 28 ] . It mainly mediates the maturation of specific cytokines IL-1β and IL18 and their precursors to form active IL-1β and IL18 , which participate in pyroptosis and play an important role in pyroptosis. We found that the higher the expression intensity of CASP4 in breast adenocarcinoma tissues, the smaller the tumor size, the less lymph node metastasis and the earlier the clinical stage ( P < 0.1) (Table 1). Interleukin-18 ( IL18 , also known as interferon-γ inducible factor), a member of the C-X-C chemokine superfamily, which is mainly produced by macrophages. The protein encoded by IL18 is a proinflammatory cytokine. IL18 has both pro-tumor and anti-tumor effects, and can protect the body by repairing the epithelial barrier in colitis-associated colorectal cancer [ 29 ] . However, in hepatocellular carcinoma, it can drive the metastasis of cancer cells and cause poor prognosis [ 30 ] . In this study, we found that IL18 favors breast cancer prognosis and the upregulation of IL18 expression in the risk prediction model may be associated with anti-tumor immunity. The results of immunohistochemical staining further indicated that the higher the expression of IL18 in breast cancer tissues, the smaller the tumor size, the less lymph node metastasis and the lower the clinical stage ( P < 0.05) (Table 1). This study reports for the first time the related genes involved in the regulation of pyroptosis by lncRNA C6orf99. It is also confirmed that the role of pyroptosis in tumors is like a double-edged sword. We need to continue to study how to use pyroptosis to exert its anti-tumor effect, while avoiding long-term chronic inflammatory stimulation caused by pyroptosis in normal tissues as much as possible. This is a challenge for us and an opportunity at the same time. The expression of CASP4 , CASP8 , ELANE , GPX4 , GSDMC , IL18 , PJVK , TIRAP and NLRC4 related to lncRNAC6orf99 may affect the development and prognosis of breast cancer patients. It may suggest a new idea for the treatment of breast cancer cases, and may become a new molecular target for targeted therapy. Declarations Author Contribution HG wrote the main manuscript textJLLprepared figures 1-6DKH and ZHG prepared figures 7ZZZ 、SKH and SYW modified the manuscript text References Han B, Zheng R et al (2022) Cancer incidence and mortality in China. Journal of the National Cancer Center. 2024. 4(1) Cooksonbt B (2001) Pro-inflammatory programmed cell death. Trends Microbiol 9(3):113–114 Kovacs SB, Miao EA (2017) Gasdermins: effectors of pyroptosis. Trends Cell Biol 27:673–684 SHI J, ZHAO Y, WANG K et al (2015) Cleavage of GSDMD by inflammatory caspases determines pyroptotic cell death. 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Clin Cancer ༲es 21(7):1688–1698 Sotiriou C, Wriapati P, Loi S et al (2006) Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 98(4):262–272 Tomczak K, Czerwińska P, Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn) 19:68–77 Alnemri ES et al (1996) Human ICE/CED-3 protease nomenclature. Cell 87(2):171 Shi Y Mechanisms of caspase activation and inhibition during apoptosis. Mol cell 2002, 9 (3), 459–470 Shi J et al (2014) Inflammatory caspases are innate immune receptors for intracellular LPS. Nature 514(7521):187–192 Karki R, Man SM,Kanneganti TD Inflammasomes and Cancer. Cancer Immunol Res 2017, 5(2):94–99 Van Gorp H, Lamkanfi M (2019) The emerging roles of inflamma-some-dependent cytokines in cancer development. EMBO Rep 20(6):e47575 Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.pdf SupplementaryTableS1.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4258362","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290640268,"identity":"1ce43645-1ed8-42f0-a3e4-6612f225f449","order_by":0,"name":"Hong Gao","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Gao","suffix":""},{"id":290640270,"identity":"c07f7913-5766-4dfa-8eb2-356b0f54a506","order_by":1,"name":"ZhaoHua Gui","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"ZhaoHua","middleName":"","lastName":"Gui","suffix":""},{"id":290640273,"identity":"745502bd-03bb-4534-9677-19e31ad16478","order_by":2,"name":"ShiKai Hong","email":"","orcid":"","institution":"West District of The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China - Hefei","correspondingAuthor":false,"prefix":"","firstName":"ShiKai","middleName":"","lastName":"Hong","suffix":""},{"id":290640274,"identity":"f3288476-d38a-4442-97f6-fa53002e46d1","order_by":3,"name":"ZhengZhi Zhu","email":"","orcid":"","institution":"West District of The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China - Hefei","correspondingAuthor":false,"prefix":"","firstName":"ZhengZhi","middleName":"","lastName":"Zhu","suffix":""},{"id":290640275,"identity":"dd947d3c-0ff1-4647-8993-6307f5e69f2c","order_by":4,"name":"DaKe Huang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"DaKe","middleName":"","lastName":"Huang","suffix":""},{"id":290640276,"identity":"2b50f484-9eed-4b43-8344-5b398b2bb3b8","order_by":5,"name":"JiaLi Lei","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"JiaLi","middleName":"","lastName":"Lei","suffix":""},{"id":290640277,"identity":"f865adf3-0426-4f86-aa5e-efc9e36fe206","order_by":6,"name":"ShengYing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYLACxgYYWSEhx8befoAULWcsjPl4ziQQqwXEaKlInCfhYIBXtcHxs4df/txhkycfkdz28GuDRHqbBEMCw4+Kbbi1nMlLs+Y9k1ZseCOx3Vh2h0Rum3TjAcaeM7dxajE7kGNmzNh2OHHjjMQ2ackzQC0yBxKYGdvwaDn/xszwZ9t/qJY2iXQ2iQQD/Fpu5Bg/4G07kDhfIrFN8mObRAJBLfY33pgx87YlJ27gedgmzXBGwrANGMgH8flFsj/H+OPPNrvE+e3pzyR/VNTJy7e3H3zwowK3FiBgkwCRBhcSGJh5oEIH8KkHAuYPIFK+/wAD4w8CSkfBKBgFo2BkAgDwDmBEigFegQAAAABJRU5ErkJggg==","orcid":"","institution":"West District of The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China - Hefei","correspondingAuthor":true,"prefix":"","firstName":"ShengYing","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-04-12 14:27:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4258362/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4258362/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54861961,"identity":"b5fe2146-4d49-4ee5-8b8a-b7c6badf8661","added_by":"auto","created_at":"2024-04-17 20:00:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1415526,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between C6orf99 expression and prognostic, clinical and molecular features of breast cancer patient in the TCGA cohort. A. \u003c/strong\u003eComparison of the expression levels of lncRNA C6orf99 in 179 normal tissues from GTEx dataset and 1096 tumor tissues from the TCGA cohort. The one-tailed Student’s \u003cem\u003et\u003c/em\u003e tests \u003cem\u003eP\u003c/em\u003e value is shown. \u003cstrong\u003eB. \u003c/strong\u003eKaplan–Meier curves show that the tumors with high C6orf99 expression levels (\u0026gt; median) have worse OS than the tumors with low C6orf99 expression levels (\u0026lt; median) in the TCGA cohort. The log-rank test \u003cem\u003eP\u003c/em\u003e value is shown. OS: overall survival. \u003cstrong\u003eC.\u003c/strong\u003e The Sankey diagram shows C6orf99 expression profile in BC tumor tissues categorized by different clinical factors (pTNM stage, BRCA subtypes, OS status). \u003cstrong\u003eD.\u003c/strong\u003e The bar charts show the proportions of pathological TNM stage (early-stage and late-stage) in the high and low C6orf99 expression groups. The Fisher’s exact test \u003cem\u003eP\u003c/em\u003e value and the odds ratio (OR) are shown. \u003cstrong\u003eE.\u003c/strong\u003e Comparisons of CNA scores(left), TMB (medium), and ITH scores (right) between the high and low C6orf99 expression groups. The Kruskal-Wallis test \u003cem\u003eP \u003c/em\u003evalues are shown.\u003c/p\u003e","description":"","filename":"Figure171.png","url":"https://assets-eu.researchsquare.com/files/rs-4258362/v1/d71bd2d5d78ebb5941eb5f5f.png"},{"id":54862381,"identity":"4d0996e6-7c7e-4880-a479-476e00435e50","added_by":"auto","created_at":"2024-04-17 20:08:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1224805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of C6orf99-related PRGs in the TCGA cohort. A. \u003c/strong\u003eCorrelations of C6orf99 expression levels with the expression levels of \u003cem\u003eCASP4,1 ELANE, IL18, PJVK, \u003c/em\u003eand\u003cem\u003e TIRAP. \u003c/em\u003eThe Pearson correlation coefficients (\u003cem\u003eρ\u003c/em\u003e) and \u003cem\u003eP\u003c/em\u003e values are shown. \u003cstrong\u003eB.\u003c/strong\u003e Forest plots analyzed by univariate Cox regression model show that the expression of \u003cem\u003eCASP4,1 ELANE, IL18, PJVK \u003c/em\u003eare favorable for prognosis, but the expression of \u003cem\u003eTIRAP \u003c/em\u003eis adverse for prognosis in BC in the TCGA cohort. \u003cstrong\u003eC.\u003c/strong\u003e Heatmap shows that hierarchical clustering identifies two risk subclusters (C1 and C2) of BC patients in the TCGA cohort (TCGA-BRCA) based on the expression levels of \u003cem\u003eCASP4, ELANE, IL18,\u003c/em\u003e and \u003cem\u003ePJVK\u003c/em\u003e. \u003cstrong\u003eD.\u003c/strong\u003e Kaplan–Meier curves show that C1 group has better OS than C2 group in the TCGA cohort. The log-rank test \u003cem\u003eP\u003c/em\u003e value is shown. OS: overall survival. \u003cstrong\u003eE.\u003c/strong\u003e The volcano plot (1.5-fold change, \u003cem\u003eP\u003c/em\u003evalue \u0026lt; 0.05) shows differential expression genes between C1 and C2 groups. The red dots indicate upregulated genes. The blue dots indicate down-regulated genes. The grey dots indicate no significant genes. \u003cstrong\u003eF.\u003c/strong\u003e The GO pathways upregulated and downregulated in C1 versus C2 group in the TCGA cohort. BP: biological process, CC: cellular component, MF: molecular function.\u003c/p\u003e","description":"","filename":"Figure172.png","url":"https://assets-eu.researchsquare.com/files/rs-4258362/v1/755eda2c880163ea16751b35.png"},{"id":54861962,"identity":"7d2ef64e-eea8-449b-9fec-635d3fed81a0","added_by":"auto","created_at":"2024-04-17 20:00:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":524025,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe construction of a prognostic risk model associated with C6orf99 expression in the TCGA cohort. A. \u003c/strong\u003eThe coefficient profiles of 22 C6orf99-related PRGs are shown by lambda (λ) parameter. The abscissa represents the lambda value, and the ordinate represents the coefficients of the independent variable. \u003cstrong\u003eB.\u003c/strong\u003e Partial likelihood deviations are plotted against the log (λ) using the Elastic-net Cox regression model. \u003cstrong\u003eC. \u003c/strong\u003eThe scatterplot shows the risk scores (from low to high) in the TCGA cohort. Each group is represented by a different color. \u003cstrong\u003eD. \u003c/strong\u003eThe survival time and survival status of the risk groups. The scatter plot distribution represents the risk score of different samples corresponding to the survival time and status. \u003cstrong\u003eE. \u003c/strong\u003eHeatmap of the expression of the genes in the risk model. \u003cstrong\u003eF, G. \u003c/strong\u003eKaplan–Meier curves show that the low-risk group has better OS (\u003cstrong\u003eF\u003c/strong\u003e) and DFS (\u003cstrong\u003eG\u003c/strong\u003e) than the high-risk group in the TCGA cohort. The log-rank test \u003cem\u003eP\u003c/em\u003e values are shown. \u003cstrong\u003eH.\u003c/strong\u003e Time-dependent ROC analysis of the risk model. High values of area under the curve (AUC) indicate high predictive power. ROC: receiver operating characteristic.\u003c/p\u003e","description":"","filename":"Figure173.png","url":"https://assets-eu.researchsquare.com/files/rs-4258362/v1/4d9d39a9a2ae3d6904dbc4b5.png"},{"id":54861965,"identity":"06d3aca6-4a7b-48df-85d1-8b0b45435d7d","added_by":"auto","created_at":"2024-04-17 20:00:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":212409,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of the risk model predicting breast cancer OS in the GEO cohort. A. \u003c/strong\u003eThe scatterplot shows the risk scores (from low to high) in the GEO cohort. Each group is represented by a different color. \u003cstrong\u003eB. \u003c/strong\u003eThe survival time and survival status of the risk groups. The scatter plot distribution represents the risk score of different samples corresponding to the survival time and status. \u003cstrong\u003eC.\u003c/strong\u003e Kaplan–Meier curves show that the low-risk group has better OS than the high-risk group in the GEO cohort. The log-rank test \u003cem\u003eP\u003c/em\u003e value is shown.\u003c/p\u003e","description":"","filename":"Figure174.png","url":"https://assets-eu.researchsquare.com/files/rs-4258362/v1/4129c07b3382f093933cf7f7.png"},{"id":54861963,"identity":"52184027-4d0e-4610-96eb-54a464f2b85f","added_by":"auto","created_at":"2024-04-17 20:00:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":139051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram construction for breast cancer OS prediction in the TCGA cohort. \u003c/strong\u003eNomogram based on the risk model in the TCGA cohort. Nomogram can predict the 1-year, 3-year and 5-year overall survival (OS) of BC patients. The concordance index (C-index) and \u003cem\u003eP\u003c/em\u003e value are shown.\u003c/p\u003e","description":"","filename":"Figure175.png","url":"https://assets-eu.researchsquare.com/files/rs-4258362/v1/caf95cd0b3a560a72d2f5e06.png"},{"id":54861964,"identity":"fe73b703-eeff-4490-94b2-190e76d279a1","added_by":"auto","created_at":"2024-04-17 20:00:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":190935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between the risk model and immune infiltration level in the TCGA cohort. \u003c/strong\u003eThe boxplots show the comparison of the proportion of immune cells between the high-risk group and the low-risk group in the TCGA cohort. The one-tailed Mann–Whitney \u003cem\u003eU\u003c/em\u003e test \u003cem\u003eP\u003c/em\u003e values are shown. *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003ens\u003c/sup\u003e not significant.\u003c/p\u003e","description":"","filename":"Figure176.png","url":"https://assets-eu.researchsquare.com/files/rs-4258362/v1/4bb13840c039ea4a0ef22a02.png"},{"id":54861966,"identity":"9d4a3c98-c9c1-41e8-a967-db38854e4b90","added_by":"auto","created_at":"2024-04-17 20:00:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2192041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe clinicopathologic and survival analysis of C6orf99-related \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCASP4\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIL18. \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eA. \u003c/strong\u003eRepresentative images showing protein levels of \u003cem\u003eCASP4\u003c/em\u003e (negative: top left; positive: top right), \u003cem\u003eIL18\u003c/em\u003e (negative: bottom left; positive: bottom right) in BC tumor tissue (×400 magnification). \u003cstrong\u003eB.\u003c/strong\u003e Kaplan–Meier curves show that the expression levels of \u003cem\u003eCASP4\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e were not correlated with OS in 66 BC patients. The log-rank test \u003cem\u003eP\u003c/em\u003e values are shown. \u003cstrong\u003eC.\u003c/strong\u003eKaplan–Meier curves show that the high expression of \u003cem\u003eCASP4 \u003c/em\u003e(left) and \u003cem\u003eIL18\u003c/em\u003e(right) has better PFS than the low expression group in 66 BC patients. The log-rank test \u003cem\u003eP\u003c/em\u003e values are shown.\u003c/p\u003e","description":"","filename":"Figure177.png","url":"https://assets-eu.researchsquare.com/files/rs-4258362/v1/3af3982f3564f6652ba52a4a.png"},{"id":56096579,"identity":"10a9aac6-14f0-4cc6-87d8-72fb091f1b62","added_by":"auto","created_at":"2024-05-08 13:53:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2712676,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4258362/v1/93633dae-8ef0-4f6a-9569-3a12c2d7143f.pdf"},{"id":54861959,"identity":"15ee08ea-6dba-431b-a637-ac683421bb8f","added_by":"auto","created_at":"2024-04-17 20:00:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":108927,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4258362/v1/cbd9e320612af055a2fd209f.pdf"},{"id":54862382,"identity":"5109f480-31ad-4b07-bed8-76185f0dc95a","added_by":"auto","created_at":"2024-04-17 20:08:15","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":166167,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4258362/v1/edcfe5c264cc6c6076872c35.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A novel pyroptosis-related gene signature correlated to C6orf99 for predicting the prognosis of breast cancer","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAccording to the newest literature reports \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, breast cancer is the second most common cancer in women after lung cancer. It is the fifth leading cause of cancer mortality in women, which seriously threatens women's health. In recent years, with the rapid development of medical imaging and genomics technologies and the improvement of patients' awareness of self-health management, the overall survival rate of most patients has been improved through early diagnosis and treatment, but tumor-related death caused by breast cancer is still the most common cause of death. Therefore, it is particularly important to study the pathogenesis of breast cancer and seek more effective diagnosis and prognostic indicators. Breast cancer is a heterogeneous disease involving multiple genes, and the mechanisms involved in the occurrence and development of breast cancer are diverse under the same pathological type. Therefore, there is still a large space for research on the mechanism of breast cancer occurrence, development and metastasis. The research on the mechanism of breast cancer occurrence and development will provide a more rigorous scientific basis for the development of new targeted drugs.\u003c/p\u003e \u003cp\u003ePyroptosis is a new form of programmed cell death, proposed by Cookson and Brennan in 2001, which has become a hot research topic in recent years \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It is usually performed by the caspase protein family and gasdermin protein family \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Therefore, Shao Feng's \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e team redefined the concept of pyroptosis as gasdermin-mediated programmed cell death, opening up a new field of research on cell death and inflammatory immunity. Recent studies on pyroptosis have found that it may be closely related to malignant tumors and play a crucial role both in anti-tumor effects and in cancerization, progression, and drug resistance.\u003c/p\u003e \u003cp\u003eLong non-coding RNAs (lncRNAs) are a class of RNAs that are longer than 200 nucleotide sequences and lack the ability to code for proteins \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. LncRNAs were initially thought to be genomes without any biological function. Recently, researchers began to pay attention to the role of lncRNAs and found that lncRNAs exist in the whole cell, not only in the nucleus, but also in the cytoplasm and mitochondria. RNA-protein, RNA-RNA, DNA-RNA interactions can be associated with lncRNAs to form different functional complexes. Due to their ability to regulate mRNA stability, translation, and cell signaling pathways, lncRNAs can perform multiple functions within cells. LncRNAs can influence chromatin structure and gene expression through interactions with epigenetic remodelers, transcription factors, and spliceosomes in the nucleus, thereby regulating gene expression at multiple levels, including epigenetic, transcriptional, and post-transcriptional regulation. According to literature reports \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, the expression of lncRNA C6orf99 is associated with poor prognosis of breast cancer. However, there are few studies on the prognostic effects of pyroptosis genes associated with lncRNAs, especially C6orf99, in breast cancer. Therefore, the construction of prognostic models of C6orf99-related PRGs is a necessary means to investigate the function of BC-related genes, tumor microenvironment and their relationship with immunotherapy.\u003c/p\u003e \u003cp\u003eIn this study, we firstly analyzed the correlation between C6orf99 expression and BC prognosis, and comprehensively compared the clinical and molecular features between high and low C6orf99 expression groups. Next, we clustered two subgroups based on the expressions of four C6orf99-related PRGs and conducted pathway enrichment analysis based on differential expression genes between these two subclusters. Furthermore, we constructed a risk model predicting the prognosis for breast cancer patients and validated its applicability. We further compared the immune infiltration levels between high and low risk groups in breast cancer. Finally, we validated the correlations of the expression levels of C6orf99-related PRGs with clinicopathological features and survival outcomes in breast cancer patients.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source and processing\u003c/h2\u003e \u003cp\u003eWe downloaded the gene expression profiles (RSEM normalized and batch effects adjusted) and clinical information of 1110 breast cancer patients from the Cancer Genome Atlas (TCGA) data set (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We also downloaded RNA-seq data of 179 breast normal tissues from the GTEx data portal website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/home/datasets\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/home/datasets\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Besides, an RNA sequencing dataset (GSE9893 \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e) and its clinical information were downloaded from the NCBI gene expression omnibus (GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Identification of pyroptosis-related genes correlated to C6orf99\u003c/h2\u003e \u003cp\u003eTo identify the pyroptosis-related genes correlated to C6orf99, we firstly performed the Pearson correlation analysis among 33 pyroptosis-related genes and C6orf99 in TCGA cohort with a screening condition that \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Univariate COX regression analysis was applied to identify the prognosis-related PRGs in TCGA cohort (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 Clustering analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe used hierarchical clustering to identify breast cancer subclusters based on PRGs\u0026rsquo; gene expression values, which were correlated with the expression of C6orf99. This analysis was conducted using the R package \u0026ldquo;hclust\u0026rdquo; with the parameters: method = \u0026ldquo;ward. D2\u0026rdquo; and members\u0026thinsp;=\u0026thinsp;NULL. Prior to clustering, the data of gene expression values were transformed by z-score and translated into distance matrices by the \u0026ldquo;dist()\u0026rdquo; function with the parameter: method = \u0026ldquo;Euclidean\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Identification of differential expression genes\u003c/h2\u003e \u003cp\u003eTo identify marker genes for C6orf99-related PRGs subclusters, we identified the upregulated genes in each subcluster using the threshold of Student\u0026rsquo;s t test adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute mean expression fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Pathway Enrichment Analysis\u003c/h2\u003e \u003cp\u003eIn the TCGA cohort, we utilized the R package \u0026ldquo;ClusterProfiler\u0026rdquo; to explore the Gene Oncology (GO) pathways based on the upregulated genes in each subcluster. GO categories are composed of cellular components, molecular function, and biological process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Establishment and Validation of C6orf99-related pyroptosis prognostic signature\u003c/h2\u003e \u003cp\u003eThe Elastic-net regression (alpha\u0026thinsp;=\u0026thinsp;0.98, 10-fold cross validation) was performed to screen the potential prognosis signature of C6orf99-related PRGs in TCGA cohort. We established the prognosis signature of breast cancer based on C6orf99-related PRGs that was identified using elastic-net regression and multivariate cox regression. Using candidate PRGs, we calculated the risk score of each patient in TCGA cohort. Using the R packages \u0026ldquo;Survival\u0026rdquo;, \u0026ldquo;survminer\u0026rdquo; and \u0026ldquo;timeROC\u0026rdquo;, we performed the sensitivity and specificity of the prognostic model. We also used the GEO cohort (GSE9893) to validate this prognostic model. In addition, we used the R package \u0026ldquo;rms\u0026rdquo; to construct the nomogram, which prominently predict the 1-,3-,5-year overall survival (OS) by evaluating various variables (age, sex, tumor stage, risk score).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Evaluation of genomic instability scores and immune signature levels\u003c/h2\u003e \u003cp\u003eCopy number alterations (CNAs) and tumor mutation burden (TMB) reflect genomic instability. We obtained CNA scores of the breast cancer from the publication by Knijnenburg et al\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. We calculated TMB using the total number of somatic mutations in the tumor. Besides, we evaluated intratumor heterogeneity (ITH) in tumors by conducting the DEPTH algorithm \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e at mRNA level. In addition, to explore immune infiltration levels within tumors, we calculated the proportion of immune cells in each sample using the Cibersort algorithm \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e by inputting of gene expression matrix.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Survival analysis\u003c/h2\u003e \u003cp\u003eWe performed the Kaplan\u0026ndash;Meier survival analysis to compare the OS, disease-free survival (DFS) or progression-free survival (PFS) between different subgroups of breast cancer patients by utilizing R packages \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer\u0026rdquo;. Log-rank tests were used to evaluate the significance of their differences. Besides, the univariate Cox regression analysis was conducted by the Cox proportional hazards model in the R package \u0026ldquo;survival\u0026rdquo; to respectively assess the correlations of the expression of each PRG with survival prognosis in breast cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Tissue samples and immunohistochemical experiments\u003c/h2\u003e \u003cp\u003eA total of 66 cases of breast cancer (invasive breast cancer, non-special type) archived specimens (paraffin-embedded) in Anhui Provincial Cancer Hospital (Hefei, China) from January 2014 to December 2019 were collected. Immunohistochemical method was used to detect and analyze 66 cases of breast cancer tissues. Among them, \u003cem\u003eCASP4\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e protein expression were detected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo compare the values of two groups, we utilized Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e tests for non-normally distributed data or Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e tests for normally distributed data. For contingency tables, the Fisher\u0026rsquo;s exact tests or Chi-square tests were performed. We utilized the Pearson method to evaluate the correlations between two groups of normally distributed data. Besides, we calculated the false discovery rate (FDR) to adjust \u003cem\u003eP\u003c/em\u003e values in multiple tests by utilizing the Benjamini-Hochberg method \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. All of these statistical analyses were performed through the R programming environment (version 4.2.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":" \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Correlation between C6orf99 expression and BC patient OS in the TCGA cohort\u003c/h2\u003e \u003cp\u003eFirstly, we analyzed and compared the expression levels of lncRNA C6orf99 in 179 normal tissues from GTEx dataset and 1096 tumor tissues from the TCGA cohort. We found that the expression level of C6orf99 lncRNA in breast cancer tissue was significantly higher than that in normal breast tissue (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the median value of the expression of lncRNA C6orf99, two subgroups were divided, namely the low expression (low-exp) group and the high expression (high-exp) group. The survival difference between these two groups was analyzed by Kalan-Meier analysis curve, which showed that the survival of lncRNA C6orf99 low-exp group was significantly better than that of high-exp group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation between C6orf99 expression and clinical and molecular features\u003c/h2\u003e \u003cp\u003eAccording to pathological stage, BC patients were divided into early-stage group and late-stage group. There were 373 cases of early-stage BC and 151 cases of late-stage BC in the group with high C6orf99 expression, whereas 426 cases of early-stage BC and 116 cases of late-stage BC in the group with low C6orf99 expression. The Sankey diagram of clinical factors related to C6orf99 expression was constructed (clinical factors: pTNM stage, BRCA subtypes, OS status) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). From the pathological TNM stage, we found that the high-exp group had a higher proportion in late-stage tumors, while the low-exp group had a higher proportion in early-stage tumors (Fisher\u0026rsquo;s exact test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, OR\u0026thinsp;=\u0026thinsp;1.486), indicating the higher expression of C6orf99 was positively correlated with worse clinical tumor stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo explore the associations of C6orf99 expression with genomic instability, we compared the CNA scores, TMB, and ITH scores between high-exp and low-exp groups. We found that the CNA scores, TMB and ITH scores of high-exp group were markedly higher than those of low-exp group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of C6orf99-related PRGs in the TCGA cohort\u003c/h2\u003e \u003cp\u003eA total of 33 PRGs were selected based on the previously published literature \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. We used the Pearson method to analyze the correlation of C6orf99 expression with 33 PRGs. We found that 22 PRGs were related to C6orf99 expression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To identify the C6orf99-related PRGs associated with prognosis, we used univariate Cox regression model and found 8 PRGs related to prognosis. Four PRGs (\u003cem\u003eCASP4,1 ELANE, IL18, PJVK\u003c/em\u003e) were negatively correlated with C6orf99 expression and were favorable for prognosis, while one PRG (\u003cem\u003eTIRAP\u003c/em\u003e) was positively correlated with C6orf99 expression and was adverse for prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we performed hierarchical clustering to identify two risk subclusters based on the expressions of 4 PRGs (\u003cem\u003eCASP4, ELANE, IL18, PJVK\u003c/em\u003e). We termed the high expression subcluster as C1 and the low expression subcluster as C2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). We compared 10-year OS between the two subclusters and found that the C1 group survived better than the C2 group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that the high expressions of the four PRGs were positively with BC OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eIn order to explore the differences in gene function and biological pathway between C1 and C2, we performed differential expression gene (DEG) analysis and GO analysis. Based on DEG analysis, 2062 DEGs were identified between C1 and C2 groups in the TCGA cohort. Among them, 1903 genes were upregulated in C1 group, while 159 genes were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). GO enrichment analysis showed that the upregulated DEGs were mainly correlated with immune activities, including leukocyte cell-cell adhesion, regulation of T cell activation, external side of plasma membrane, MHC protein complex, immune receptor activity, and cytokine activity. However, the downregulated DEGs were enriched in mammary gland and neuronal activities, such as mammary gland alveolus development, reproductive structure development, neuronal cell body, synaptic membrane, sodium ion transmembrane transporter activity, and gated channel activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The construction of a prognostic risk model associated with C6orf99 expression\u003c/h2\u003e \u003cp\u003eBased on the 22 C6orf99-related PRGs, we constructed a risk model using Elastic-net Cox regression in the TCGA cohort. Five PRGs with the largest absolute regression coefficients were selected to construct the risk model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The formula was as follows: risk score = (-0.154) *\u003cem\u003eCASP8\u003c/em\u003e +(-0.150) *\u003cem\u003eIL18\u003c/em\u003e + (0.079) *\u003cem\u003eGSDMC\u003c/em\u003e + (0.160) *\u003cem\u003eNLRC4\u003c/em\u003e + (0.210) *\u003cem\u003eTIRAP\u003c/em\u003e. Breast cancer samples in the TCGA cohort were divided into high-risk group and low-risk group according to the median risk score. Through survival analysis, the 10-year OS and DFS of the high-risk group were significantly worse than that of the low-risk group (OS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, DFS: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). To evaluate the sensitivity and specificity of the prognostic model, we employed time-dependent receiver operating characteristic (ROC) curve analysis. The time-dependent ROC curve displayed the area under the curve (AUC) of the prognosis model at 1-, 3- and 5-year, which were 0.71, 0.63 and 0.63, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.5 Validation of the risk model predicting breast cancer OS in the GEO cohort\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eBC patients from the GEO cohort (GSE9893) were utilized to validate the prognostic risk model. Similar to the TCGA cohort, we classified the BC patients into high-risk and low-risk groups based on the formula and the median risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, we analyzed the 8-year OS in the GEO cohort, which showed that the tumors with higher risk scores (\u0026gt;\u0026thinsp;median) displayed worse outcomes than those with lower risk scores (\u0026lt;\u0026thinsp;median) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0786) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These findings further supported the applicability of the risk model in breast cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.6 Nomogram construction for breast cancer OS prediction in the TCGA cohort\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo analyze the correlation between risk model and clinicopathological parameters in BC patients, we further developed a nomogram based on the risk model (risk scores) and various clinicopathological parameters (age, sex, pTNM_stage) in the TCGA cohort. The nomogram was constructed by using these independent prognostic variables determined by multivariate Cox regression analysis to predict 1-, 3-, and 5-year OS in BC patients (C-index\u0026thinsp;=\u0026thinsp;0.764, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Correlation between the risk model and immune infiltration level in the TCGA cohort\u003c/h2\u003e \u003cp\u003ePrevious studies have shown that the occurrence of pyroptosis further activates the anti-tumor immune response and inhibits tumor growth \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Therefore, to assess the immune infiltration level in breast cancer, we used the Cibersort algorithm to compare the proportion of immune cells between the high-risk group and the low-risk group in the TCGA cohort. We found that the proportions of naive B cells, CD8 T cells, follicular helper T cells, regulatory T cells, activated NK cells, and M1 macrophages in the low-risk group were significantly higher than those in the high-risk group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Conversely, the proportions of M0 macrophages, eosinophils, and neutrophils in the high-risk group were significantly higher than those in the low-risk group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These findings indicated that the low-risk group might have positive correlation with anti-tumor immune response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 The clinicopathologic and survival analysis of C6orf99-related \u003cem\u003eCASP4\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eBy immunohistochemical staining in 66 cases of breast cancer tissues, we analyzed the expression of \u003cem\u003eCASP4\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e, which were PRGs and were negatively correlated with C6orf99 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). We first examined the expression of \u003cem\u003eCASP4\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e in 66 BC patients to validate the findings from the bioinformatics analyses. As shown in Table\u0026nbsp;1, among the 66 breast cancer patients, 22 of them had high expression of \u003cem\u003eCASP4\u003c/em\u003e (positive), 44 of them had low or no expression of \u003cem\u003eCASP4\u003c/em\u003e (negative). Similarly, 24 patients had high expression of \u003cem\u003eIL18\u003c/em\u003e (positive), and 42 patients had low or no expression of \u003cem\u003eIL18\u003c/em\u003e (negative). The expression level of \u003cem\u003eCASP4\u003c/em\u003e or \u003cem\u003eIL18\u003c/em\u003e was related to tumor size, axillary lymph node metastasis and pathological stage, but was not correlated with age, BMI and pathological subtypes of patients (Fisher\u0026rsquo;s exact test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1). We then analyzed the correlations of \u003cem\u003eCASP4\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e expression levels with OS and PFS in 66 breast cancer patients, respectively. We found that there were no significant differences between high and low expression of these two genes in OS probably because of the short follow-up time and small sample size (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). However, patients with high expression of \u003cem\u003eCASP4\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e showed significantly better PFS than patients with low or no expression of \u003cem\u003eCASP4\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eBreast cancer is currently the main cause of cancer-specific death in women \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In China, the annual incidence of breast cancer continues to increase by about 1.2%, and it is still increasing every year \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In recent years, researchers have proposed a variety of effective treatment options for breast cancer, but some patients still suffer from metastatic recurrence, leading to death. Therefore, we need to continue to seek more effective methods to further improve the disease-free survival rate and overall survival rate of patients, to improve their life quality, and to prolong their lifespan. Biomarkers, which play an important role in the emergence and development of this disease, have naturally become the current hotspot of research in this field.\u003c/p\u003e \u003cp\u003ePyroptosis is a \"new\" type of programmed cell death discovered and confirmed in recent years. It is characterized by cell swelling and membrane rupture. The oligomerization of effector molecules, Gasdermins, perforates the membrane and destroys the ion homeostasis of the cell, eventually leading to osmotic disintegration of the cell. In this process, a large number of inflammatory cytokines and danger signaling molecules are released to activate the immune system, which plays an important role in regulating the anti-infection immune response and anti-tumor immune response \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The Gasdermin family includes GSDMA, GSDMB, GSDMC, GSDMD, GSDME (also known as DFNA5) and DFNB59. Our study found that GSDMC is a prognostic risk factor for breast cancer, which may be due to the altered structural domains of GSDMC that activate the associated inflammatory response. The Gasdermin protein family has both N-terminal and C-terminal structural domains, and the active N-terminal structural domain is released when the caspase protease specifically cleaves the connecting site between the two structural domains of pyrodehydic proteins. The N-terminal domain recognises and binds to phospholipid molecules in the cell membrane, which accumulate and pore, leading to extracellular and internal fluid reflux and osmotic pressure changes, ultimately leading to swelling and lysis of the cell membrane, and the release of large amounts of intracellular contents, which activates a strong inflammatory response and leads to the development of pyroptosis \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe clinical manifestations and clinical benefit rates of breast cancer patients vary greatly due to tumor heterogeneity. With the development of sequencing technology, the establishment of corresponding tumor data is becoming more perfect, which greatly accelerates tumor classification and provides a new direction for precise cancer prevention and treatment \u003csup\u003e[\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Although an increasing number of breast cancer tumor markers have been mined, a single prediction model cannot accurately predict the prognosis of patients, so the multi-genes prediction model combining multiple markers has become the mainstream trend \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eC6orf99 is a member of lncRNAs. Some studies have found that C6orf99 is related to pyroptosis of breast cancer, and other studies have confirmed that C6orf99 is related to the prognosis of breast cancer and may be used as a potential diagnostic or prognostic marker. In order to better understand the relationship among C6orf99, pyroptosis, and prognosis in breast cancer, we identified 22 pyroptosis-related genes associated with lncRNA C6orf99 in TCGA dataset. Eight genes significantly related to survival were screened out from the above 22 PRGs by univariate Cox analysis, including \u003cem\u003eCASP4\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, \u003cem\u003eELANE\u003c/em\u003e, \u003cem\u003eGPX4\u003c/em\u003e, \u003cem\u003eGSDMC\u003c/em\u003e, \u003cem\u003eIL18\u003c/em\u003e, \u003cem\u003ePJVK\u003c/em\u003e and \u003cem\u003eTIRAP\u003c/em\u003e. Among them, \u003cem\u003eCASP4\u003c/em\u003e, \u003cem\u003eELANE\u003c/em\u003e, \u003cem\u003eIL18\u003c/em\u003e, and \u003cem\u003ePJVK\u003c/em\u003e were negatively correlated with C6orf99 expression and favorable for prognosis, while \u003cem\u003eTIRAP\u003c/em\u003e was positively correlated with C6orf99 expression and unfavorable for prognosis. DEG analysis and GO analysis were performed on the above four positively correlated genes. Based on DEG analysis, 1903 genes were up-regulated and 159 genes were down-regulated. GO enrichment analysis showed that the up-regulated DEGs were mainly related to immune activity, including leukocyte-cell adhesion, regulation of T cell activation, outside of plasma membrane, MHC protein complex, immune receptor activity, and cytokine activity. The down-regulated DEGs were enriched in mammary gland and neuronal activities, such as mammary alveolar development, reproductive structure development, neuronal cell body, synaptic membrane, sodium transmembrane transport activity and gated channel activity. To further predict the prognostic impact of these PRGs related to lncRNA C6orf99, a risk model was constructed by Elastic-net Cox regression with five predictors, namely \u003cem\u003eCASP8\u003c/em\u003e, \u003cem\u003eIL18\u003c/em\u003e, \u003cem\u003eGSDMC\u003c/em\u003e, \u003cem\u003eNLRC4\u003c/em\u003e, and \u003cem\u003eTIRAP\u003c/em\u003e. The applicability of the risk model in breast cancer was verified by BC patients in the GEO cohort (GSE9893). The independent prognostic variables identified by multivariate Cox regression analysis were used to construct a nomogram to predict the 1 -, 3 -, and 5-year OS of breast cancer patients, and the correlation between the risk model and clinicopathological parameters of breast cancer patients was further verified. We further compared the proportions of immune cells in the high-risk and low-risk groups in the TCGA cohort to assess the immune infiltration level in breast cancer. We found that the low-risk group had significantly higher proportions of naive B cells, CD8 T cells, follicular helper T cells, regulatory T cells, activated NK cells, and M1-type macrophages than the high-risk group. In contrast, the proportion of M0 macrophages, eosinophils, and neutrophils in the high-risk group was significantly higher than that in the low-risk group. This result also confirms that the onset of pyroptosis is associated with the dynamics of pro- and anti-inflammatory responses. In low-risk situations, it is associated with anti-tumor immunity, thus inhibiting tumor growth.\u003c/p\u003e \u003cp\u003eCaspases (CASP) are a group of highly conserved intracellular proteolytic enzymes with an active site of cysteine residues, which can specifically break polypeptide bonds after aspartic acid residues \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. So far, 14 CASPs have been identified, which are involved in the regulation of various biological processes such as apoptosis, cell growth, differentiation, proliferation and motility. As a key molecule in noncanonical pyroptosis, \u003cem\u003eCASP4\u003c/em\u003e comes from the CASP family and is the core component of noncanonical inflammasome \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. It mainly mediates the maturation of specific cytokines \u003cem\u003eIL-1β\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e and their precursors to form active \u003cem\u003eIL-1β\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e, which participate in pyroptosis and play an important role in pyroptosis. We found that the higher the expression intensity of \u003cem\u003eCASP4\u003c/em\u003e in breast adenocarcinoma tissues, the smaller the tumor size, the less lymph node metastasis and the earlier the clinical stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eInterleukin-18 (\u003cem\u003eIL18\u003c/em\u003e, also known as interferon-γ inducible factor), a member of the C-X-C chemokine superfamily, which is mainly produced by macrophages. The protein encoded by \u003cem\u003eIL18\u003c/em\u003e is a proinflammatory cytokine. \u003cem\u003eIL18\u003c/em\u003e has both pro-tumor and anti-tumor effects, and can protect the body by repairing the epithelial barrier in colitis-associated colorectal cancer \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. However, in hepatocellular carcinoma, it can drive the metastasis of cancer cells and cause poor prognosis \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In this study, we found that \u003cem\u003eIL18\u003c/em\u003e favors breast cancer prognosis and the upregulation of \u003cem\u003eIL18\u003c/em\u003e expression in the risk prediction model may be associated with anti-tumor immunity. The results of immunohistochemical staining further indicated that the higher the expression of \u003cem\u003eIL18\u003c/em\u003e in breast cancer tissues, the smaller the tumor size, the less lymph node metastasis and the lower the clinical stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThis study reports for the first time the related genes involved in the regulation of pyroptosis by lncRNA C6orf99. It is also confirmed that the role of pyroptosis in tumors is like a double-edged sword. We need to continue to study how to use pyroptosis to exert its anti-tumor effect, while avoiding long-term chronic inflammatory stimulation caused by pyroptosis in normal tissues as much as possible. This is a challenge for us and an opportunity at the same time. The expression of \u003cem\u003eCASP4\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, \u003cem\u003eELANE\u003c/em\u003e, \u003cem\u003eGPX4\u003c/em\u003e, \u003cem\u003eGSDMC\u003c/em\u003e, \u003cem\u003eIL18\u003c/em\u003e, \u003cem\u003ePJVK\u003c/em\u003e, \u003cem\u003eTIRAP\u003c/em\u003e and \u003cem\u003eNLRC4\u003c/em\u003e related to lncRNAC6orf99 may affect the development and prognosis of breast cancer patients. It may suggest a new idea for the treatment of breast cancer cases, and may become a new molecular target for targeted therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHG wrote the main manuscript textJLLprepared figures 1-6DKH and ZHG prepared figures 7ZZZ 、SKH and SYW modified the manuscript text\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHan B, Zheng R et al (2022) Cancer incidence and mortality in China. 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Na Methods, (2015) 12(5): 453\u0026ndash;457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc B 57:289\u0026ndash;300\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe Y, Dai Q, Qi H (2021) A novel defined pyroptosis-related gene signature for predicting the prognosis of ovarian cancer. Cell Death Discov 7(1):71\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavoli T et al (2017) Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355(6322):eaaf8399\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJimenez Fernandez D, Lamkanfi M (2015) Inflammatory caspases: key regulators of inflammation and cell death. Biol Chem 396:193\u0026ndash;203\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcIntire CR, Yeretssian G, Saleh M (2009) Inflammasomes in infection and inflammation. Apoptosis 14:522\u0026ndash;535\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInoue H, Tani K (2014) Multimodal immunogenic cancer cell death as a consequence of anticancer cytotoxic treatments. Cell Death Differ 21:39\u0026ndash;49\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng S, Fox D, Man SM (2018) Mechanisms of Gasdermin Family Members in Inflammasome Signaling and Cell Death. J Mol Biol 430:3068\u0026ndash;3080\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeki N et al (2009) Distinctive expression and function of four GSDM family genes (GSDMA-D) in normal and malignant upper gastrointestinal epithelium. 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Cell 87(2):171\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Y Mechanisms of caspase activation and inhibition during apoptosis. Mol cell 2002, 9 (3), 459\u0026ndash;470\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi J et al (2014) Inflammatory caspases are innate immune receptors for intracellular LPS. Nature 514(7521):187\u0026ndash;192\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarki R, Man SM,Kanneganti TD Inflammasomes and Cancer. Cancer Immunol Res 2017, 5(2):94\u0026ndash;99\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Gorp H, Lamkanfi M (2019) The emerging roles of inflamma-some-dependent cytokines in cancer development. EMBO Rep 20(6):e47575\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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, C6orff99, Pyroptosis, Long non-coding, RNA","lastPublishedDoi":"10.21203/rs.3.rs-4258362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4258362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe relationship between the pyroptosis-related long non-coding RNA (PRlncRNA) and the prognosis of breast cancer has not been clearly studied. According to relevant studies, the expression of the lncRNA C6orf99 is associated with poor prognosis of breast cancer. In our study, we demonstrated that high expression of C6orf99 is adverse for the prognosis of breast cancer using the TCGA dataset. In addition, there were five pyroptosis related genes (PRGs) (\u003cem\u003ePJVK\u003c/em\u003e, \u003cem\u003eCASP4\u003c/em\u003e, \u003cem\u003eIL18\u003c/em\u003e, \u003cem\u003eELANE\u003c/em\u003e and \u003cem\u003eTIRAP\u003c/em\u003e) associated with the expression of C6orf99 and survival outcomes of breast cancer. According to the expression of \u003cem\u003ePJVK\u003c/em\u003e, \u003cem\u003eCASP4\u003c/em\u003e, \u003cem\u003eIL18\u003c/em\u003e, and \u003cem\u003eELANE\u003c/em\u003e, breast cancer samples were divided into two different subclusters (C1 and C2) using clustering analysis. Furthermore, a new C6orf99-related PRGs risk model was constructed to predict the prognosis of breast cancer. The high-risk group had worse survival outcomes and lower immune infiltration level, whereas the low-risk group had better survival outcomes and higher immune infiltration level. We further found that the higher the expression intensity of \u003cem\u003eCASP4\u003c/em\u003e and \u003cem\u003eIL18\u003c/em\u003e in 66 breast tumor tissues by immunohistochemical staining, the earlier the clinical stage, the smaller the tumor, the better the survival outcome. Our finding of C6orf99-related PRGs suggest a new idea for the treatment of breast cancer cases, and may become a new molecular target for targeted therapy.\u003c/p\u003e","manuscriptTitle":"A novel pyroptosis-related gene signature correlated to C6orf99 for predicting the prognosis of breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 20:00:10","doi":"10.21203/rs.3.rs-4258362/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":"ebe62d14-c370-46aa-beb3-58db52de4662","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T12:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-17 20:00:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4258362","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4258362","identity":"rs-4258362","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}