Determining new disulfidptosis-associated lncRNA signatures pertinent to breast cancer prognosis and immunological microenvironment

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Despite this, the identification of disulfidptosis-related lncRNAs in BC remains has not been reported. Therefore, this study aimed to examine the prognostic potential of disulfidptosis-associated lncRNAs in BC. Methods: RNA-seq data and clinical information of BC patients were obtained from the TCGA database. Co-expression analysis was performed to identify disulfidptosis-associated lncRNAs. Subsequently, a risk signature was developed through univariate Cox and LASSO analyses, and its predictive ability was validated. Additionally, the association between the risk signature and immune cell infiltration, immune function, tumor mutational burden (TMB）, and immune checkpoints was investigated. Finally, potential anticancer drugs associated with the risk signatures were predicted. Results: A 10-lncRNA signature was established to stratify BC patients into high-risk and low-risk groups, where the high-risk group showed an unfavorable prognosis. This signature served as an independent prognostic factor in BC patients. Notably, the two subgroups displayed distinct mutation gene profiles, and the risk score exhibited a significant correlation with TMB. Furthermore, ssGSEA and immune checkpoint analysis revealed a significant association between the predictive signature and the immune status of BC patients. Finally, 55 potential anticancer drugs associated with the signature were identified. CONCLUSIONS: We successfully established an independent prognostic signature of disulfidptosis-related lncRNAs in BC patients. This signature provides a solid basis for future investigations into the functional significance of disulfidptosis-associated lncRNAs in breast cancer. breast cancer disulfidptosis lncRNA immunity risk signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Breast cancer is the most common malignancy worldwide, threatening women's health and longevity(Sung et al., 2021 ). Contrary to other solid tumors, the prognosis of many breast cancer patients can be improved with multiple treatments, such as radical surgery, chemotherapy, radiotherapy, and targeted therapy, but some breast cancer patients still experience unfavorable outcomes due to the variable heterogeneity of breast cancer(Promny et al., 2022 ; L. Wang et al., 2017 ). At the same time, several clinical trials have shown that the development of tumor resistance often leads to a poor prognosis. The etiology of breast cancer is greatly influenced by the tumor microenvironment (TME). The tumor immunological microenvironment and related immune escape mechanisms have a substantial influence on the course of breast cancer(Yang et al., 2022 ). TME, essential for the proliferation and development of neoplastic cells, is made of different cellular constituents such as endothelial cells, immune cells, and lymphocytes, in addition to non-cellular components(Arneth, 2019 ). M2 macrophages, which make up the majority of tumor-associated macrophages (TAMs), secrete cytokines such as matrix metalloproteinase (MMP), vascular endothelial growth factor-A (VEGF-A), CCL18, and IL-10 to advance tumors(Qiu et al., 2018 ). In addition, regulatory T cells (Tregs) promote tumor progression, angiogenesis and metastasis by suppressing anti-tumor responses(H.-R. Lan et al., 2021 ). In addition to suppressing immune cell function by producing various cytokines or metabolites that promote tumor progression, invasion and metastasis, tumor-associated fibroblasts (CAFs) also have the ability to shape the extra-tumoral stroma, creating a blockage to the penetration of drugs or therapeutic immune cells. This may inhibit immune cells and medications from penetrating tumor tissue deeply, decreasing the effectiveness of tumor therapy(Fernández-Nogueira et al., 2020 ; Hu et al., 2022 ). Disulfidptosis represents a novel mode of cell death that operates independently from recognized pathways such as apoptosis, ferroptosis, and cuproptosis. In sugar-free SLC7A11-high UMRC6 cells, the accumulation of a large number of disulfide bonds led to abnormal disulfide bond cross-linking between actin cytoskeletal proteins, contraction of the cytoskeleton, disruption of its organisation, and ultimately destruction of actin networks and cell death, according to the study. Although the study of disulfidptosis in their tumors is still ongoing at this time, it remains a prospective novel method for the treatment of tumors(Liu et al., 2023 ). Long non-coding RNAs (lncRNAs) are molecules of non-coding RNA that are longer than 200 nucleotides. They are capable of modulating gene expression at various stages, including epigenetic, transcriptional, and post-transcriptional regulation. There is mounting evidence that lncRNAs play key roles in regulating tumorigenesis and metastasis. For example, OCC1 lncRNA promotes HuR protein ubiquitinated degradation, thereby inhibiting the proliferation of colonic carcinoma cells(Y. Lan et al., 2018 , p. 1). In addition, some lncRNA that can be detected in the plasma and urine of tumor patients due to their high stability, such as PCA3, PCGEM1 and PCAT-1, are expected to be potential tumor biomarkers(Bhan et al., 2017 ; Crea et al., 2014 ). Therefore, it is of utmost importance to investigate the relationship between lncRNAs and the mechanism of breast cancer formation, to develop new treatment options for breast cancer, and to provide benefits to patients. The purpose of this research was to create a prediction signature using lncRNAs linked to disulfidptosis and then test its reliability using internal validation. This signature was then analysed to see if it could be used to predict prognosis, tumour immune cell infiltration, and tumour immune function in patients with breast cancer. Potential anti-cancer drugs associated with this signature have also been predicted. Materials and Methods Data collection and Processing. 1098 breast cancer patients' clinical information and RNA-seq data were extracted from the The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) database. After data cleaning, data from 1059 BC patients were used for subsequent analysis. Fifteen disulfidptosis-related genes were extracted from previously published literature(Liu et al., 2023 ). Figure 1 illustrates the flowchart of the data analysis process. Construction of the risk signature. We used the "limma" package in R to calculate the correlation between disulfidptosis-associated genes and lncRNAs. A total of 172 disulfide-related lncRNAs were identified using correlation coefficient (|R2|>0.3) with a p-value < 0.05 as a screening condition. Using the R package 'survival', a univariate Cox regression analysis was conducted to identify lncRNAs associated with disulfidptosis prognosis in BC patients. The data set was subsequently divided into training and testing cohorts using a 7:3 random assignment ratio. The training cohort was subjected to Lasso analysis in order to generate a risk signature. The risk score was calculated as: risk score = (Expi × βi), where Exp represents the expression level of the model gene and β denotes the risk coefficient of the signature lncRNAs. Independent prognostic analysis and establishment of a predictive nomogram Using R package 'survival', univariate and multivariate Cox regression analyses identified independent prognostic factors. Subsequently, we constructed a nomogram utilizing the 'rms' and 'survminer' R packages, based on the multivariate regression analysis results. The concordance index (C-index) and calibration curves were plotted to assess the one, three, and five-year overall survival probabilities. Tumor mutation burden analyses Tumor mutation burden (TMB) files, which contained information about somatic mutations, were retrieved from the TCGA database. Using the 'maftools,' 'ggpubr,' and 'ggplot2' R packages, we were able to estimate and visualise the differences in TMB levels between the two risk groupings. R packages 'limma,' 'ggpubr,' 'ggplot2,' and 'ggExtra' were used to analyse and visualize the relationship between risk scores and TMB scores. Kaplan-Meier analysis, performed using the 'survival' and 'survminer' R packages, was used to compare the survival rates of patients with varied TMB levels and risk statuses. Analysis of tumor immune microenvironment Single-sample gene set enrichment analysis (ssGSEA) was used to assess immune infiltrating cell activity and immune-related functions. This method made use of the "GSVA" R packages. Five immunological checkpoints were evaluated for expression levels across the two groups. Functional Enrichment Analysis Differentially expressed genes (DEGs) were identified between the high-risk and low-risk groups using the screening criteria of false discovery rate (FDR) 0.05 and log2 fold change > 1 or log2 fold change < -1. Then we performed GO and KEGG enrichment analysis for DEGs and mapping using "clusterProfiler", "ggplot2", and "circlize" software packages. Immunotherapy and Drug Sensitivity Prediction Methods Use of the TIDE ( http://tide.dfci.harvard.edu/ ) online tool to evaluate the ability of immune escape in high- and low-risk groups within the risk signature. Using the gene expression profile of tumor samples, the framework assesses the potential for immunological escape from tumors(Chi et al., 2022 ). Use the “oncoPredict” package to predict and screen potential drugs associated with the model, with a p-value less than 0.001 as a screening condition(Maeser et al., 2021 ). Statistical analysis Differences in percentages were analysed using either a Chi-square test (parametric) or a Wilcoxon test (non-parametric). The R statistical programming language (version 4.1.2) was used for all studies. Statistical significance was assumed at a p-value of less than 0.05. Result Construction of the risk signature on disulfidptosis-related lncRNA Differential analysis revealed a substantial upregulation of SLC7A11 expression in breast cancer tissues compared to adjacent normal tissues (Fig. 2 A). We identified 172 lncRNA associated with disulfidptosis. In the training cohort, univariate Cox analysis was performed and eleven lncRNAs were identified that were significantly associated with BC prognosis (Table 1 ). In addition, a risk signature is constructed by LASSO analysis (Fig. 2 B-C). The risk signature consists of ten lncRNA and the risk score per each patient was calculated as: risk score = (0.207 * expression level of LINC01589) − (0.020 * expression level of LINC01087 ) − (0.085 * expression level of AL137847.1 ) − (0.066 * expression level of ELOVL2-AS1 ) − (2.626 * expression level of AL137856.1 ) − (0.072 * expression level of LINC01948 ) + (0.104 * expression level of AP003031.1 ) + (0.443 * expression level of AP001922.3 ) − (0.231 * expression level of MAPT-IT1 ) − (0.208 * expression level of AC004967.2 ) (Table 2 ). LINC01589, AP003031.1, AP001922.3 were risk factors. LINC01087, AL137847.1, ELOVL2-AS1, AL137856.1, LINC01948, MAPT-IT1, and AC004967.2 were protective factors. BC patients were separated into a high-risk group and a low-risk group based on the median value. PCA analysis revealed that the risk signature's 10 lncRNAs involved with disulfidptosis were very effective at telling the two groups apart. (Fig. 2 D-G). Table 1 Results of univariate COX prognostic analysis lncRNA HR lower 95%CI upper 95%CI pvalue LINC01087 0.884 0.795 0.983 0.023 AL137847.1 0.418 0.199 0.876 0.021 ELOVL2-AS1 0.809 0.677 0.967 0.02 AL137856.1 0.044 0.006 0.344 0.003 LINC01589 1.319 1.026 1.695 0.031 LINC01948 0.791 0.636 0.983 0.034 AP003031.1 1.472 1.12 1.934 0.006 AP001922.3 1.816 1.178 2.798 0.007 MAPT-IT1 0.655 0.498 0.861 0.002 AC004967.2 0.608 0.372 0.993 0.047 AC018398.2 0 0 0.847 0.046 Table 2 Prognostic lncRNAs generated by LASSO analysis. LncRNA Coef LINC01087 -0.020078 AL137847.1 -0.0849331 ELOVL2-AS1 -0.0660437 AL137856.1 -2.62639 LINC01589 0.20721027 LINC01948 -0.0719397 AP003031.1 0.10402237 AP001922.3 0.44334307 MAPT-IT1 -0.2313379 AC004967.2 -0.2081916 Survival Analysis and Risk Signature Verification Using the established risk signature, we classified the samples into two distinct subgroups: high-risk and low-risk (Figs. 3 A, B, G, H, M, and N). Heatmaps showed downregulation of LINC01087, AL137847.1, ELOVL2-AS1, AL137856.1, LINC01948, and AC004967.2 in high-risk groups. LINC01589, AP003031.1, and AP001922.3 were upregulated (Figs. 3 C, I, and O). In the high-risk subgroup, Kaplan-Meier (KM) analysis showed an unfavorable prognosis (Fig. 3 D). Consistently, similar prognostic patterns were observed in both the testing and entire sets (Figs. 3 J and P). The reliability of the risk signature was assessed by evaluating the area under the curve (AUC) of the training set (Fig. 3 E), testing set (Fig. 3 K), and entire sets (Fig. 3 Q) for 1, 3, and 5 years, with respective AUCs of 0.761/0.725/0.677, 0.733/0.718/0.700 and 0.748/0.723/0.681. Figure 3 F, L and R depict receiver operating characteristic (ROC) curves, illustrating the performance of clinical and risk signature. These findings provide further evidence to support the robustness and validity of our risk profile in accurately forecasting overall survival outcomes in breast cancer patients. Independent prognostic analysis and establishment of a predictive nomogram We analyzed the clinical correlation of risk signature and showed that the risk signature created were significantly correlated with pathological T-stage (pT), pathological M-stage (pM), and pathological stage (Fig. 4 A-C and Supplement 1). The results of the univariate Cox analysis, integrating clinical factors, revealed that Age, Gender, pT, pN, pM, Stage, and the risk score exhibited independent predictive predictors (Fig. 5 A). Moreover, the multivariate Cox analysis demonstrated that Age, Stage, and the risk score remained as independent predictors of the outcome (Fig. 5 B). A nomogram was developed for the prediction of survival of breast cancer patients at 1, 3, and 5 years on the basis of the results of multivariate Cox analysis (Fig. 5 C) and calibration graphs were drawn for 1-year, 3-year, and 5-year OS probabilities (Fig. 5 D). The C-index of the nomogram was 0.783. The C-index plot learns that the predictive power of this risk profile is superior (Fig. 5 E). The calibration curves demonstrated a close alignment between the predicted values of 1-year, 3-year, and 5-year survival times and the corresponding true survival times. This observation underscores the high predictive accuracy and reliability of the developed nomogram. Tumor mutation burden analyses The TMB was statistically greater in the high-risk group compared to the low-risk group (Fig. 6 A), and the TMB is positively and substantially correlated with the risk score (Fig. 6 B). The fifteen genes with the highest mutation rates also differed between the two groups (Fig. 6 C-D). PI3KCA and CDH1, which are considered breast cancer oncogenes, and GATA, which is considered a potential breast cancer biomarker, were mutated with increased frequency in the low-risk group(Fang et al., 2009 ; Ge et al., 2017 ; Guo et al., 2017 ; Jiang et al., 2014 ; Xu et al., 2022 ). The high-risk group demonstrated an elevated frequency of mutations in TP53 and TTN genes, both of which are known to be associated with breast cancer development(Badve & Gökmen-Polar, 2019 ; Q.-X. Zheng et al., 2021 ). To compare the predictive abilities of the risk signature and tumor mutation burden (TMB) on survival outcomes, the sample population was stratified into high-TMB and low-TMB subgroups using the median TMB value. Patients in the high-TMB group and those in the low-TMB group did not have significantly different survival rates (Fig. 6 E). Compared to the high-TMB-low-risk group, the high-TMB-high-risk group had poorer survival rates (Fig. 6 F), indicating that the prediction model is quite accurate in predicting breast cancer survival. Tumor microenvironment analysis The correlation between immune cell infiltration and immune function in the tumor microenvironment of two groups was assessed using the ssGSEA method. Activated CD4 T cell, Activated dendritic cell, CD56bright natural killer cell, Central memory CD4 T cell, Gamma delta T cell, Natural killer T cell, T follicular helper cell, Regulatory T cell, Type 1 T helper cell, Type 17 T helper cell, Type 2 T helper cell, among others, obtained higher ssGSEA scores in the higher group (Fig. 7 A). Meanwhile, the high-risk group exhibited elevated ssGSEA scores for immune function-related genes in the CCR and APC co-stimulation pathways. But Type II IFN Response in the low-risk group obtained higher ssGSEA scores (Fig. 7 B). In addition, we analyzed the expression of five classical immune checkpoint genes in the high-risk and low-risk groups. The expression levels of classical immune checkpoint genes, including CD274, CD47, CTLA-4, and PDCD1, were found to be significantly higher in the low-risk group compared to the high-risk group (Fig. 7 C). Therefore, BC patients in the low-risk group benefited more from immunotherapy. We found no significant difference in TIDE scores between the high and low risk groups, implying that there was no significant difference in patient response to immunotherapy between the high and low risk groups (Fig. 7 D). Functional Enrichment Analysis To explore potential distinctions between the high-risk and low-risk groups, enrichment analysis of differentially expressed genes was conducted. The enriched GO terms for DEGs primarily included epidermis development, skin development, synaptic membrane, intermediate filament cytoskeleton, intermediate filament, signaling receptor activator activity, receptor ligand activity, serine hydrolase activity (Fig. 8 A). KEGG pathway enrichment analysis revealed that the DEGs were primarily involved in Neuroactive ligand − receptor interaction, Salivary secretion and IL − 17 signaling pathway (Fig. 8 B). Screening of sensitive anticancer drugs We identified 55 anticancer drugs whose sensitivities showed a significant correlation with our signature. Among these drugs, 31 drugs showed high sensitivity in the low-risk group, while 21 drugs showed high sensitivity in the high-risk group (Fig. 9 and Supplementary 2). Discussion Although the mortality rate of BC patients has declined due to early detection and improved treatment methods, the incidence rate is still increasing year by year, becoming the cancer with the highest incidence rate in the world. Finding new treatments that benefit patients is one direction scientists are pursuing. Disulfidptosis is a newly discovered way of cell death. In 2020, researchers found that the process of reducing cysteine intake into cysteine mediated by SLC7A11 is highly dependent on reduced nicotinamide adenine dinucleotide phosphate (NADPH) generated by the glucose-pentose phosphate pathway(Liu et al., 2020 ).Therefore, under glucose deficiency, NADPH in highly expressed cells of SLC7A11 is rapidly depleted and cysteine and other disulfides accumulate abnormally, thus inducing disulfide stress and rapid cell death. However, what kind of cell death type this is and the mechanism of disulfide stress triggering cell death are still unknown. Researchers found in subsequent studies that under glucose deficiency, NADPH in SLC7A11 high-expression cells will be rapidly exhausted, cysteine and other disulfides will accumulate abnormally, thereby inducing disulfide stress and rapid cell death (Fig. 10 ). Unlike other cell death mechanisms, disulfidptosis is related to the actin cytoskeleton(Liu et al., 2023 ; P. Zheng et al., 2023 ). Disulfide metabolism is closely associated with cancer development, as it encompasses redox reactions involving the breakdown and formation of disulfide bonds(Zhao et al., 2023 ). The impact of oxidative stress on disulfide metabolism has been observed in numerous cancer cells, leading to disturbances in cellular survival and proliferation(Daly et al., 2004 ; Hogg, 2002 ). Additionally, disulfide metabolism in cancer cells has been implicated in various biological behaviors, including drug resistance, metastasis, and immune evasion(Chen et al., 2021 ; Y. Wang et al., 2021 ). This study established a prognostic model of disulfidptosis-related lncRNA to evaluate the survival rate and death risk of breast cancer patients. We identified disulfidptosis-related lncRNAs from the TCGA database and employed LASSO analysis to develop a prognostic signature comprising 10 lncRNAs. We verified the accuracy and stability of the signature on the inner testing set, and found that the signature has high discriminative and predictive power. The prognostic signature we constructed is not only significantly associated with the survival rate and death risk of breast cancer patients, but also closely related to the immune cell infiltration and tumor mutation burden (TMB) of breast cancer patients. In addition, we also performed potential drug prediction for high and low risk groups and found 55 potential anti-cancer drugs related to the model. Patients in the low-risk group may benefit more from immune checkpoint therapy. Nevertheless, it is important to acknowledge the limitations of our study. Firstly, our internal validation solely relied on data obtained from the TCGA database, thus necessitating external validation using additional databases to thoroughly assess the generalizability and robustness of our prediction model. Secondly, further experimental investigations are warranted to validate the mechanistic involvement of disulfidptosis-related lncRNA in breast cancer. Conclusion In summary, our study has identified a novel lncRNA risk signature associated with disulfidptosis in breast cancer. The disulfidptosis-associated lncRNA signature holds the potential to provide new insights into the mechanisms underlying the occurrence and progression of breast cancer. The identification of potential anticancer drugs associated with these features may offer benefits to breast cancer patients. Abbreviations BC- Breast cancer (BC) OS—Overall Survival DEGs—Differentially Expressed Genes GSVA—Gene Set Variation Analysis ssGESA—single sample Gene Set Enrichment Analysis PCA—Principal Component Analysis GO—Gene Ontology KEGG—Kyoto Encyclopedia of Genes and Genomes Declarations Funding This work was supported by the National Natural Science Foundation of China (No.22074024，21005022), Key scientific research platforms and projects of Guangdong colleges and universities (No.2021ZDZX2043), Natural Science Foundation of Guangdong Province (No.2022A1515012045, No. 2023A1515012573). Declaration of conflicting interest The authors declare that there is no conflict of interest. Ethics Statement Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Data will be made available on request Author Contributions Conception and design: WBH. Development of methodology: YFZ, YFL, YCZ, SJL, YXY and WBH. Acquisition of data: YFZ and WBH. Analysis and interpretation of data (e.g., statistical analysis, bioinformatic, computational analysis): YFZ and WBH. Writing, review, and/or revision of the manuscript: YFZ, YFL, YCZ, SJL, YXY and WBH. Administrative, technical, or material support: YFZ and WBH. Study supervision: YXY. All authors contributed to the article and approved the submitted version. Acknowledgments ： We sincerely acknowledge TCGA database for providing their platforms and contributors for uploading their meaningful datasets. References Arneth, B. (2019). Tumor Microenvironment. Medicina (Kaunas, Lithuania) , 56 (1), 15. https://doi.org/10.3390/medicina56010015 Badve, S. S., & Gökmen-Polar, Y. (2019). TP53 Status and Estrogen Receptor-Beta in Triple-Negative Breast Cancer: Company Matters. 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Oncotarget , 8 (32), 52877–52888. https://doi.org/10.18632/oncotarget.17907 Wang, Y., Jiang, Y., Wei, D., Singh, P., Yu, Y., Lee, T., Zhang, L., Mandl, H. K., Piotrowski-Daspit, A. S., Chen, X., Li, F., Li, X., Cheng, Y., Josowitz, A., Yang, F., Zhao, Y., Wang, F., Zhao, Z., Huttner, A., … Mark Saltzman, W. (2021). Nanoparticle-mediated convection-enhanced delivery of a DNA intercalator to gliomas circumvents temozolomide resistance. Nature Biomedical Engineering , 5 (9), 1048–1058. https://doi.org/10.1038/s41551-021-00728-7 Xu, Q.-T., Wang, Z.-W., Cai, M.-Y., Wei, J.-F., & Ding, Q. (2022). A novel cuproptosis-related prognostic 2-lncRNAs signature in breast cancer. Frontiers in Pharmacology , 13 , 1115608. https://doi.org/10.3389/fphar.2022.1115608 Yang, H., Xue, M., Su, P., Zhou, Y., Li, X., Li, Z., Xia, Y., Zhang, C., Fu, M., Zheng, X., Luo, G., Wei, T., Wang, X., Ding, Y., Zhu, J., & Zhuang, T. (2022). RNF31 represses cell progression and immune evasion via YAP/PD-L1 suppression in triple negative breast Cancer. Journal of Experimental & Clinical Cancer Research: CR , 41 (1), 364. https://doi.org/10.1186/s13046-022-02576-y Zhao, S., Wang, L., Ding, W., Ye, B., Cheng, C., Shao, J., Liu, J., & Zhou, H. (2023). Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework. Frontiers in Endocrinology , 14 , 1180404. https://doi.org/10.3389/fendo.2023.1180404 Zheng, P., Zhou, C., Ding, Y., & Duan, S. (2023). Disulfidptosis: A new target for metabolic cancer therapy. Journal of Experimental & Clinical Cancer Research: CR , 42 (1), 103. https://doi.org/10.1186/s13046-023-02675-4 Zheng, Q.-X., Wang, J., Gu, X.-Y., Huang, C.-H., Chen, C., Hong, M., & Chen, Z. (2021). TTN-AS1 as a potential diagnostic and prognostic biomarker for multiple cancers. Biomedicine & Pharmacotherapy = Biomedecine & Pharmacotherapie , 135 , 111169. https://doi.org/10.1016/j.biopha.2020.111169 Additional Declarations No competing interests reported. Supplementary Files Supplement.docx 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 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-3073426","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":213555194,"identity":"d61e4811-d118-4dcd-a636-799a5e9074fb","order_by":0,"name":"Yifan Zheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangdong Pharmaceutical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Zheng","suffix":""},{"id":213555196,"identity":"383cc2cf-b092-486a-a80b-d50c3d540746","order_by":1,"name":"Yufeng Lin","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangdong Pharmaceutical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Lin","suffix":""},{"id":213555198,"identity":"01f41757-b55c-4535-ad51-e148630100be","order_by":2,"name":"Yongcheng Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangdong Pharmaceutical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongcheng","middleName":"","lastName":"Zhang","suffix":""},{"id":213555199,"identity":"545c3f19-06cf-4f7a-8e3e-84a098702a61","order_by":3,"name":"Shangjie Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangdong Pharmaceutical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shangjie","middleName":"","lastName":"Liu","suffix":""},{"id":213555200,"identity":"7625d6a1-bc5e-40af-a50f-54988cee47f0","order_by":4,"name":"Yongxia Yang","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongxia","middleName":"","lastName":"Yang","suffix":""},{"id":213555201,"identity":"0ffe5847-f0cc-4e17-abbf-56ffc5a40ae8","order_by":5,"name":"Wenbin Huang","email":"data:image/png;base64,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","orcid":"","institution":"The First Affiliated Hospital of Guangdong Pharmaceutical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wenbin","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2023-06-16 16:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3073426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3073426/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39330647,"identity":"bc6ef8d3-07af-4876-b0ff-f8439bda3107","added_by":"auto","created_at":"2023-06-29 21:22:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":766699,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of disulfidptosis-associated lncRNA analysis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/c22ef1866ee04abfebbe4dfc.png"},{"id":39330650,"identity":"87114eef-a084-45e1-aee1-74ad0d3111ac","added_by":"auto","created_at":"2023-06-29 21:22:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1824967,"visible":true,"origin":"","legend":"\u003cp\u003e(A)Differential expression of SLC7A11 in breast cancer tissues and adjacent cancer tissues. (B)Correlation between 14 disulfidptosis-associated genes and 172 disulfidptosis-associated lncRNAs. (D-E) \u003cbr\u003e\n A risk signature was constructed utilizing LASSO analysis method. (E-H) PCA of the entire gene set, disulfidptosis-associated genes set, disulfidptosis-associated lncRNAs set, and 10 disulfidptosis-associated lncRNAs in the risk signature.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/b2903fc15f41f092476b63c9.png"},{"id":39330648,"identity":"d17a0f56-9eb4-44cd-af9b-b758935843d8","added_by":"auto","created_at":"2023-06-29 21:22:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2111857,"visible":true,"origin":"","legend":"\u003cp\u003eRisk signature verification. (A, G, M) Risk distribution plots. (B, H, N) Distribution plots of survival time and survival status. (C, I, O) Heat map of expression of 10 lncRNAs. (D, J, P) Plots of KM survival curves. (E, K, Q) Time-dependent ROC curve plots. (F, L, R) ROC curves of clinical features. (A-F) The analysis result of the training cohort. (G-L) The analysis result of the testing cohort. (M-R) The analysis result of the entire cohort.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/2e3b8c6835edd9b41518fe75.png"},{"id":39331173,"identity":"532800a2-5f40-4c8d-af61-1e11f535825b","added_by":"auto","created_at":"2023-06-29 21:30:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":144921,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of risk characteristics with clinical information. (A)Violin plot of pT and risk score. (B) Violin plot of pM and risk score. (C) Violin plot of Stage and risk score.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/7ac562408fd77a0ef835f7d5.png"},{"id":39331873,"identity":"37b0b6f2-1840-42fb-8b31-86dccd4aee61","added_by":"auto","created_at":"2023-06-29 21:38:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":399518,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent prognostic analysis and construction of prognostic nomogram. (A, B) Forest plots of the univariate COX and multivariate COX analyses on the basis of the risk signature and the clinical factors. (C) Nomogram of BC patients. (D) Calibration curves for prediction of 1-year, 3-year and 5-year overall survival in BC patients. (E) C-index curves for risk signature and clinical factors.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/467355e3d4d24b0c780422da.png"},{"id":39331174,"identity":"58cfe6ba-9fb0-467f-a165-824dd6c6fe00","added_by":"auto","created_at":"2023-06-29 21:30:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1515800,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between risk signature and TMB levels. (A) Violin plot of TMB compared between the two groups (B) Correlation between risk scores and TMB. (C, D) Waterfall plots of TMB status for the top 15 genes in both groups. (E) KM survival analysis curves for high and low TMB groups (F) Survival analysis of patients with combined TMB and risk levels.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/764bf1428e913035c6a65012.png"},{"id":39332674,"identity":"c1cb3049-d504-4ba9-ae8c-3ed218f39a3d","added_by":"auto","created_at":"2023-06-29 21:46:58","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1302809,"visible":true,"origin":"","legend":"\u003cp\u003eImmunocorrelation analysis and comparison of immunotherapy response in two subgroups. (A) ssGSEA scores of immune infiltrating cells in the two groups. (B)Heat map of ssGSEA scores of immune function between the high-risk and low-risk groups. (C) Expression levels of immune checkpoints in the two subgroups. (D) TIDE scores.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/bc724fabda18ebd49afc83ae.png"},{"id":39331175,"identity":"bda9bb4e-3cd0-45de-925b-1d4b8059c28c","added_by":"auto","created_at":"2023-06-29 21:30:58","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":782425,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between risk score and biological mechanism (B) GO analysis showing enrichment of terms (C) KEGG analysis showing enrichment of pathways.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/d16224da7d4d38c8b65cf963.png"},{"id":39330656,"identity":"eacc9098-9c34-4a3d-9ab5-f5db6860df78","added_by":"auto","created_at":"2023-06-29 21:22:58","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":460844,"visible":true,"origin":"","legend":"\u003cp\u003e20 of 55 Potential anti-cancer drugs associated with risk signature.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/46c81c2e61c9c8143527b325.png"},{"id":39330653,"identity":"91a534f5-3b42-4fe8-bd5d-be1dbda75edc","added_by":"auto","created_at":"2023-06-29 21:22:58","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":565538,"visible":true,"origin":"","legend":"\u003cp\u003eHypothetical diagram. NADPH supply in SLC7A11-overexpressing cancer cells is insufficient for cellular reduction of cystine to cysteine, resulting in disulfide stress, induction of actin cytoskeletal protein disulfide bond cross-linking and cytoskeletal contraction, and ultimately disulfidptosis.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/b685e23148417b2584c03bc6.png"},{"id":49659518,"identity":"ff464e01-4597-4e79-879a-2764bda95a72","added_by":"auto","created_at":"2024-01-16 05:07:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4205526,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/8addb19c-113e-4255-8927-128adb6b7e08.pdf"},{"id":39330657,"identity":"d80cefde-813e-432c-b47a-1a57bc983264","added_by":"auto","created_at":"2023-06-29 21:22:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7995002,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-3073426/v1/d676943f1fdc3f138c54a81e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determining new disulfidptosis-associated lncRNA signatures pertinent to breast cancer prognosis and immunological microenvironment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most common malignancy worldwide, threatening women's health and longevity(Sung et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Contrary to other solid tumors, the prognosis of many breast cancer patients can be improved with multiple treatments, such as radical surgery, chemotherapy, radiotherapy, and targeted therapy, but some breast cancer patients still experience unfavorable outcomes due to the variable heterogeneity of breast cancer(Promny et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; L. Wang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). At the same time, several clinical trials have shown that the development of tumor resistance often leads to a poor prognosis. The etiology of breast cancer is greatly influenced by the tumor microenvironment (TME). The tumor immunological microenvironment and related immune escape mechanisms have a substantial influence on the course of breast cancer(Yang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). TME, essential for the proliferation and development of neoplastic cells, is made of different cellular constituents such as endothelial cells, immune cells, and lymphocytes, in addition to non-cellular components(Arneth, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). M2 macrophages, which make up the majority of tumor-associated macrophages (TAMs), secrete cytokines such as matrix metalloproteinase (MMP), vascular endothelial growth factor-A (VEGF-A), CCL18, and IL-10 to advance tumors(Qiu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, regulatory T cells (Tregs) promote tumor progression, angiogenesis and metastasis by suppressing anti-tumor responses(H.-R. Lan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition to suppressing immune cell function by producing various cytokines or metabolites that promote tumor progression, invasion and metastasis, tumor-associated fibroblasts (CAFs) also have the ability to shape the extra-tumoral stroma, creating a blockage to the penetration of drugs or therapeutic immune cells. This may inhibit immune cells and medications from penetrating tumor tissue deeply, decreasing the effectiveness of tumor therapy(Fern\u0026aacute;ndez-Nogueira et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDisulfidptosis represents a novel mode of cell death that operates independently from recognized pathways such as apoptosis, ferroptosis, and cuproptosis. In sugar-free SLC7A11-high UMRC6 cells, the accumulation of a large number of disulfide bonds led to abnormal disulfide bond cross-linking between actin cytoskeletal proteins, contraction of the cytoskeleton, disruption of its organisation, and ultimately destruction of actin networks and cell death, according to the study. Although the study of disulfidptosis in their tumors is still ongoing at this time, it remains a prospective novel method for the treatment of tumors(Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLong non-coding RNAs (lncRNAs) are molecules of non-coding RNA that are longer than 200 nucleotides. They are capable of modulating gene expression at various stages, including epigenetic, transcriptional, and post-transcriptional regulation. There is mounting evidence that lncRNAs play key roles in regulating tumorigenesis and metastasis. For example, OCC1 lncRNA promotes HuR protein ubiquitinated degradation, thereby inhibiting the proliferation of colonic carcinoma cells(Y. Lan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, p. 1). In addition, some lncRNA that can be detected in the plasma and urine of tumor patients due to their high stability, such as PCA3, PCGEM1 and PCAT-1, are expected to be potential tumor biomarkers(Bhan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Crea et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, it is of utmost importance to investigate the relationship between lncRNAs and the mechanism of breast cancer formation, to develop new treatment options for breast cancer, and to provide benefits to patients.\u003c/p\u003e \u003cp\u003eThe purpose of this research was to create a prediction signature using lncRNAs linked to disulfidptosis and then test its reliability using internal validation. This signature was then analysed to see if it could be used to predict prognosis, tumour immune cell infiltration, and tumour immune function in patients with breast cancer. Potential anti-cancer drugs associated with this signature have also been predicted.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eData collection and Processing.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e1098 breast cancer patients' clinical information and RNA-seq data were extracted from the The Cancer Genome Atlas (TCGA, \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) database. After data cleaning, data from 1059 BC patients were used for subsequent analysis. Fifteen disulfidptosis-related genes were extracted from previously published literature(Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the flowchart of the data analysis process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConstruction of the risk signature.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe used the \"limma\" package in R to calculate the correlation between disulfidptosis-associated genes and lncRNAs. A total of 172 disulfide-related lncRNAs were identified using correlation coefficient (|R2|\u0026gt;0.3) with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as a screening condition. Using the R package 'survival', a univariate Cox regression analysis was conducted to identify lncRNAs associated with disulfidptosis prognosis in BC patients. The data set was subsequently divided into training and testing cohorts using a 7:3 random assignment ratio. The training cohort was subjected to Lasso analysis in order to generate a risk signature. The risk score was calculated as: risk score = (Expi\u0026thinsp;\u0026times;\u0026thinsp;βi), where Exp represents the expression level of the model gene and β denotes the risk coefficient of the signature lncRNAs.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIndependent prognostic analysis and establishment of a predictive nomogram\u003c/h2\u003e \u003cp\u003eUsing R package 'survival', univariate and multivariate Cox regression analyses identified independent prognostic factors. Subsequently, we constructed a nomogram utilizing the 'rms' and 'survminer' R packages, based on the multivariate regression analysis results. The concordance index (C-index) and calibration curves were plotted to assess the one, three, and five-year overall survival probabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTumor mutation burden analyses\u003c/h2\u003e \u003cp\u003eTumor mutation burden (TMB) files, which contained information about somatic mutations, were retrieved from the TCGA database. Using the 'maftools,' 'ggpubr,' and 'ggplot2' R packages, we were able to estimate and visualise the differences in TMB levels between the two risk groupings. R packages 'limma,' 'ggpubr,' 'ggplot2,' and 'ggExtra' were used to analyse and visualize the relationship between risk scores and TMB scores. Kaplan-Meier analysis, performed using the 'survival' and 'survminer' R packages, was used to compare the survival rates of patients with varied TMB levels and risk statuses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of tumor immune microenvironment\u003c/h2\u003e \u003cp\u003eSingle-sample gene set enrichment analysis (ssGSEA) was used to assess immune infiltrating cell activity and immune-related functions. This method made use of the \"GSVA\" R packages. Five immunological checkpoints were evaluated for expression levels across the two groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEGs) were identified between the high-risk and low-risk groups using the screening criteria of false discovery rate (FDR) 0.05 and log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;1 or log2 fold change \u0026lt; -1. Then we performed GO and KEGG enrichment analysis for DEGs and mapping using \"clusterProfiler\", \"ggplot2\", and \"circlize\" software packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmunotherapy and Drug Sensitivity Prediction Methods\u003c/h2\u003e \u003cp\u003eUse of the TIDE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) online tool to evaluate the ability of immune escape in high- and low-risk groups within the risk signature. Using the gene expression profile of tumor samples, the framework assesses the potential for immunological escape from tumors(Chi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Use the \u0026ldquo;oncoPredict\u0026rdquo; package to predict and screen potential drugs associated with the model, with a p-value less than 0.001 as a screening condition(Maeser et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDifferences in percentages were analysed using either a Chi-square test (parametric) or a Wilcoxon test (non-parametric). The R statistical programming language (version 4.1.2) was used for all studies. Statistical significance was assumed at a p-value of less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the risk signature on disulfidptosis-related lncRNA\u003c/h2\u003e \u003cp\u003eDifferential analysis revealed a substantial upregulation of SLC7A11 expression in breast cancer tissues compared to adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We identified 172 lncRNA associated with disulfidptosis. In the training cohort, univariate Cox analysis was performed and eleven lncRNAs were identified that were significantly associated with BC prognosis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, a risk signature is constructed by LASSO analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). The risk signature consists of ten lncRNA and the risk score per each patient was calculated as: risk score = (0.207 * expression level of LINC01589) \u0026minus; (0.020 * expression level of \u003cem\u003eLINC01087\u003c/em\u003e) \u0026minus; (0.085 * expression level of \u003cem\u003eAL137847.1\u003c/em\u003e) \u0026minus; (0.066 * expression level of \u003cem\u003eELOVL2-AS1\u003c/em\u003e) \u0026minus; (2.626 * expression level of \u003cem\u003eAL137856.1\u003c/em\u003e) \u0026minus; (0.072 * expression level of \u003cem\u003eLINC01948\u003c/em\u003e) + (0.104 * expression level of \u003cem\u003eAP003031.1\u003c/em\u003e) + (0.443 * expression level of \u003cem\u003eAP001922.3\u003c/em\u003e) \u0026minus; (0.231 * expression level of \u003cem\u003eMAPT-IT1\u003c/em\u003e) \u0026minus; (0.208 * expression level of \u003cem\u003eAC004967.2\u003c/em\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). LINC01589, AP003031.1, AP001922.3 were risk factors. LINC01087, AL137847.1, ELOVL2-AS1, AL137856.1, LINC01948, MAPT-IT1, and AC004967.2 were protective factors. BC patients were separated into a high-risk group and a low-risk group based on the median value. PCA analysis revealed that the risk signature's 10 lncRNAs involved with disulfidptosis were very effective at telling the two groups apart. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of univariate COX prognostic analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elower 95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eupper 95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL137847.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELOVL2-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL137856.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP003031.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP001922.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAPT-IT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC004967.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC018398.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrognostic lncRNAs generated by LASSO analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLncRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.020078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL137847.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0849331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELOVL2-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0660437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL137856.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.62639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20721027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0719397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP003031.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10402237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP001922.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44334307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAPT-IT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.2313379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC004967.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.2081916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis and Risk Signature Verification\u003c/h2\u003e \u003cp\u003eUsing the established risk signature, we classified the samples into two distinct subgroups: high-risk and low-risk (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B, G, H, M, and N). Heatmaps showed downregulation of LINC01087, AL137847.1, ELOVL2-AS1, AL137856.1, LINC01948, and AC004967.2 in high-risk groups. LINC01589, AP003031.1, and AP001922.3 were upregulated (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, I, and O). In the high-risk subgroup, Kaplan-Meier (KM) analysis showed an unfavorable prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Consistently, similar prognostic patterns were observed in both the testing and entire sets (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ and P). The reliability of the risk signature was assessed by evaluating the area under the curve (AUC) of the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), testing set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK), and entire sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eQ) for 1, 3, and 5 years, with respective AUCs of 0.761/0.725/0.677, 0.733/0.718/0.700 and 0.748/0.723/0.681. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, L and R depict receiver operating characteristic (ROC) curves, illustrating the performance of clinical and risk signature. These findings provide further evidence to support the robustness and validity of our risk profile in accurately forecasting overall survival outcomes in breast cancer patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIndependent prognostic analysis and establishment of a predictive nomogram\u003c/h2\u003e \u003cp\u003eWe analyzed the clinical correlation of risk signature and showed that the risk signature created were significantly correlated with pathological T-stage (pT), pathological M-stage (pM), and pathological stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C and Supplement 1). The results of the univariate Cox analysis, integrating clinical factors, revealed that Age, Gender, pT, pN, pM, Stage, and the risk score exhibited independent predictive predictors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Moreover, the multivariate Cox analysis demonstrated that Age, Stage, and the risk score remained as independent predictors of the outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). A nomogram was developed for the prediction of survival of breast cancer patients at 1, 3, and 5 years on the basis of the results of multivariate Cox analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and calibration graphs were drawn for 1-year, 3-year, and 5-year OS probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The C-index of the nomogram was 0.783. The C-index plot learns that the predictive power of this risk profile is superior (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). The calibration curves demonstrated a close alignment between the predicted values of 1-year, 3-year, and 5-year survival times and the corresponding true survival times. This observation underscores the high predictive accuracy and reliability of the developed nomogram.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTumor mutation burden analyses\u003c/h2\u003e \u003cp\u003eThe TMB was statistically greater in the high-risk group compared to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), and the TMB is positively and substantially correlated with the risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The fifteen genes with the highest mutation rates also differed between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). PI3KCA and CDH1, which are considered breast cancer oncogenes, and GATA, which is considered a potential breast cancer biomarker, were mutated with increased frequency in the low-risk group(Fang et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ge et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The high-risk group demonstrated an elevated frequency of mutations in TP53 and TTN genes, both of which are known to be associated with breast cancer development(Badve \u0026amp; G\u0026ouml;kmen-Polar, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Q.-X. Zheng et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To compare the predictive abilities of the risk signature and tumor mutation burden (TMB) on survival outcomes, the sample population was stratified into high-TMB and low-TMB subgroups using the median TMB value. Patients in the high-TMB group and those in the low-TMB group did not have significantly different survival rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Compared to the high-TMB-low-risk group, the high-TMB-high-risk group had poorer survival rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF), indicating that the prediction model is quite accurate in predicting breast cancer survival.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTumor microenvironment analysis\u003c/h2\u003e \u003cp\u003eThe correlation between immune cell infiltration and immune function in the tumor microenvironment of two groups was assessed using the ssGSEA method. Activated CD4 T cell, Activated dendritic cell, CD56bright natural killer cell, Central memory CD4 T cell, Gamma delta T cell, Natural killer T cell, T follicular helper cell, Regulatory T cell, Type 1 T helper cell, Type 17 T helper cell, Type 2 T helper cell, among others, obtained higher ssGSEA scores in the higher group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Meanwhile, the high-risk group exhibited elevated ssGSEA scores for immune function-related genes in the CCR and APC co-stimulation pathways. But Type II IFN Response in the low-risk group obtained higher ssGSEA scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, we analyzed the expression of five classical immune checkpoint genes in the high-risk and low-risk groups. The expression levels of classical immune checkpoint genes, including CD274, CD47, CTLA-4, and PDCD1, were found to be significantly higher in the low-risk group compared to the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Therefore, BC patients in the low-risk group benefited more from immunotherapy. We found no significant difference in TIDE scores between the high and low risk groups, implying that there was no significant difference in patient response to immunotherapy between the high and low risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eTo explore potential distinctions between the high-risk and low-risk groups, enrichment analysis of differentially expressed genes was conducted. The enriched GO terms for DEGs primarily included epidermis development, skin development, synaptic membrane, intermediate filament cytoskeleton, intermediate filament, signaling receptor activator activity, receptor ligand activity, serine hydrolase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). KEGG pathway enrichment analysis revealed that the DEGs were primarily involved in Neuroactive ligand\u0026thinsp;\u0026minus;\u0026thinsp;receptor interaction, Salivary secretion and IL\u0026thinsp;\u0026minus;\u0026thinsp;17 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eScreening of sensitive anticancer drugs\u003c/h2\u003e \u003cp\u003eWe identified 55 anticancer drugs whose sensitivities showed a significant correlation with our signature. Among these drugs, 31 drugs showed high sensitivity in the low-risk group, while 21 drugs showed high sensitivity in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and Supplementary 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough the mortality rate of BC patients has declined due to early detection and improved treatment methods, the incidence rate is still increasing year by year, becoming the cancer with the highest incidence rate in the world. Finding new treatments that benefit patients is one direction scientists are pursuing. Disulfidptosis is a newly discovered way of cell death. In 2020, researchers found that the process of reducing cysteine intake into cysteine mediated by SLC7A11 is highly dependent on reduced nicotinamide adenine dinucleotide phosphate (NADPH) generated by the glucose-pentose phosphate pathway(Liu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).Therefore, under glucose deficiency, NADPH in highly expressed cells of SLC7A11 is rapidly depleted and cysteine and other disulfides accumulate abnormally, thus inducing disulfide stress and rapid cell death. However, what kind of cell death type this is and the mechanism of disulfide stress triggering cell death are still unknown. Researchers found in subsequent studies that under glucose deficiency, NADPH in SLC7A11 high-expression cells will be rapidly exhausted, cysteine and other disulfides will accumulate abnormally, thereby inducing disulfide stress and rapid cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Unlike other cell death mechanisms, disulfidptosis is related to the actin cytoskeleton(Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; P. Zheng et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDisulfide metabolism is closely associated with cancer development, as it encompasses redox reactions involving the breakdown and formation of disulfide bonds(Zhao et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The impact of oxidative stress on disulfide metabolism has been observed in numerous cancer cells, leading to disturbances in cellular survival and proliferation(Daly et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Hogg, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Additionally, disulfide metabolism in cancer cells has been implicated in various biological behaviors, including drug resistance, metastasis, and immune evasion(Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Y. Wang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study established a prognostic model of disulfidptosis-related lncRNA to evaluate the survival rate and death risk of breast cancer patients. We identified disulfidptosis-related lncRNAs from the TCGA database and employed LASSO analysis to develop a prognostic signature comprising 10 lncRNAs. We verified the accuracy and stability of the signature on the inner testing set, and found that the signature has high discriminative and predictive power. The prognostic signature we constructed is not only significantly associated with the survival rate and death risk of breast cancer patients, but also closely related to the immune cell infiltration and tumor mutation burden (TMB) of breast cancer patients. In addition, we also performed potential drug prediction for high and low risk groups and found 55 potential anti-cancer drugs related to the model. Patients in the low-risk group may benefit more from immune checkpoint therapy.\u003c/p\u003e \u003cp\u003eNevertheless, it is important to acknowledge the limitations of our study. Firstly, our internal validation solely relied on data obtained from the TCGA database, thus necessitating external validation using additional databases to thoroughly assess the generalizability and robustness of our prediction model. Secondly, further experimental investigations are warranted to validate the mechanistic involvement of disulfidptosis-related lncRNA in breast cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study has identified a novel lncRNA risk signature associated with disulfidptosis in breast cancer. The disulfidptosis-associated lncRNA signature holds the potential to provide new insights into the mechanisms underlying the occurrence and progression of breast cancer. The identification of potential anticancer drugs associated with these features may offer benefits to breast cancer patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBC-\u0026nbsp;Breast cancer (BC)\u003c/p\u003e\n\u003cp\u003eOS\u0026mdash;Overall Survival\u003c/p\u003e\n\u003cp\u003eDEGs\u0026mdash;Differentially Expressed Genes\u003c/p\u003e\n\u003cp\u003eGSVA\u0026mdash;Gene Set Variation Analysis\u003c/p\u003e\n\u003cp\u003essGESA\u0026mdash;single sample Gene Set Enrichment Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA\u0026mdash;Principal Component Analysis\u003c/p\u003e\n\u003cp\u003eGO\u0026mdash;Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG\u0026mdash;Kyoto Encyclopedia of Genes and Genomes\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No.22074024，21005022), Key scientific research platforms and projects of Guangdong colleges and universities (No.2021ZDZX2043), Natural Science Foundation of Guangdong Province (No.2022A1515012045, No. 2023A1515012573).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDeclaration of conflicting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Data will be made available on request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: WBH. Development of methodology:\u0026nbsp;YFZ, YFL, YCZ, SJL, YXY and WBH. Acquisition of data: YFZ and WBH. Analysis and interpretation of data (e.g., statistical analysis, bioinformatic, computational analysis): YFZ and WBH. Writing, review, and/or revision of the manuscript: YFZ, YFL, YCZ, SJL, YXY and WBH. Administrative, technical, or material support: YFZ and WBH. Study supervision: YXY. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely acknowledge TCGA database for providing their platforms and contributors for uploading their meaningful datasets.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArneth, B. (2019). Tumor Microenvironment. \u003cem\u003eMedicina (Kaunas, Lithuania)\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(1), 15. https://doi.org/10.3390/medicina56010015\u003c/li\u003e\n\u003cli\u003eBadve, S. S., \u0026amp; G\u0026ouml;kmen-Polar, Y. (2019). 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TTN-AS1 as a potential diagnostic and prognostic biomarker for multiple cancers. \u003cem\u003eBiomedicine \u0026amp; Pharmacotherapy = Biomedecine \u0026amp; Pharmacotherapie\u003c/em\u003e, \u003cem\u003e135\u003c/em\u003e, 111169. https://doi.org/10.1016/j.biopha.2020.111169\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, disulfidptosis, lncRNA, immunity, risk signature","lastPublishedDoi":"10.21203/rs.3.rs-3073426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3073426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eDisulfidptosis, a novel form of cell death triggered by disulfide stress, could have significant implications in breast cancer (BC) pathogenesis. Despite this, the identification of disulfidptosis-related lncRNAs in BC remains has not been reported. Therefore, this study aimed to examine the prognostic potential of disulfidptosis-associated lncRNAs in BC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eRNA-seq data and clinical information of BC patients were obtained from the TCGA database. Co-expression analysis was performed to identify disulfidptosis-associated lncRNAs. Subsequently, a risk signature was developed through univariate Cox and LASSO analyses, and its predictive ability was validated. Additionally, the association between the risk signature and immune cell infiltration, immune function, tumor mutational burden (TMB）, and immune checkpoints was investigated. Finally, potential anticancer drugs associated with the risk signatures were predicted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA 10-lncRNA signature was established to stratify BC patients into high-risk and low-risk groups, where the high-risk group showed an unfavorable prognosis. This signature served as an independent prognostic factor in BC patients. Notably, the two subgroups displayed distinct mutation gene profiles, and the risk score exhibited a significant correlation with TMB. Furthermore, ssGSEA and immune checkpoint analysis revealed a significant association between the predictive signature and the immune status of BC patients. Finally, 55 potential anticancer drugs associated with the signature were identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS: \u003c/strong\u003eWe successfully established an independent prognostic signature of disulfidptosis-related lncRNAs in BC patients. This signature provides a solid basis for future investigations into the functional significance of disulfidptosis-associated lncRNAs in breast cancer.\u003c/p\u003e","manuscriptTitle":"Determining new disulfidptosis-associated lncRNA signatures pertinent to breast cancer prognosis and immunological microenvironment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-29 21:22:53","doi":"10.21203/rs.3.rs-3073426/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":"f7e723b5-bf41-4522-a725-ea98f16b2bfe","owner":[],"postedDate":"June 29th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-16T04:59:18+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-29 21:22:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3073426","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3073426","identity":"rs-3073426","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}