The Role of CDCA3 in Disulfidptosis: Implications for Breast Cancer Cell Viability and Treatment

The Role of CDCA3 in Disulfidptosis: Implications for Breast Cancer Cell Viability and Treatment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Role of CDCA3 in Disulfidptosis: Implications for Breast Cancer Cell Viability and Treatment minbin chen, anran wang, Xiaoren Zhu, xaioren zhu, Yuanyuan Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4154876/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 "Disulfidptosis" is a novel concept, primarily characterized by the depletion of intracellular NADPH during glucose deprivation, leading to the accumulation of disulfides. This accumulation triggers disulfide stress, breakdown of the actin cytoskeleton, and ultimately, cell death. Breast cancer, being a highly prevalent cancer type, requires a substantial amount of glucose for its rapid growth and division. Glucose deprivation induces rapid cell death, possibly associated with the process of disulfidptosis. In this study, we developed a novel signature based on 10 machine learning algorithms integration. Compared to the traditional TNM staging, this new predictive model demonstrates superior predictive capabilities. Among the 13 genes included in the signature, CDCA3, a cell cycle-regulating protein, is expressed at higher levels in breast cancer. Our experiments indicate that breast cancer cells (SKBR-3 and MCF-7 lines) with downregulated expression of CDCA3 through shRNA exhibit reduced proliferation, migration, and invasion capabilities compared to the WT cells. Conversely, breast cancer cells with forced overexpression of CDCA3 show enhanced growth and mobility. Moreover, observations of primary and immortalized breast cancer cells in a glucose-deprived environment reveal that overexpressing CDCA3 can slow down the process of cell cytoskeleton contraction under starvation conditions, while knockdown CDCA3 accelerates this change. In conclusion, CDCA3, associated with the process of disulfidptosis, may represent a new target for breast cancer treatment. Biological sciences/Cancer Biological sciences/Cell biology Disulfidptosis CDCA3 Predictive Signature Breast Cancer Glucose Deprivation Metabolic Stress Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Breast cancer stands as the most widespread form of malignancy affecting women worldwide, drawing extensive focus due to its high incidence rates( 1 ). According to statistics from the American Cancer Society, the incidence of female breast cancer exhibited a yearly increase from 2015 to 2019. In 2023, breast cancer is projected to account for 31% of new cancer diagnoses in women( 2 ). This substantial burden of disease exerts significant strain on individuals, communities, and healthcare systems( 3 ).in the era of precision medicine, exploring new prognostic biomarkers and treatment targets is crucial for accurately assessing patients' conditions, seeking improved therapeutic approaches, and enhancing overall prognosis for breast cancer patients( 4 ). In recent years, the regulated cell death pathway has become increasingly important as a target for cancer medication development( 5 ). Disulfidptosis, an emerging mode of cell death, is illustrated by the involvement of Solute Carrier Family 7 Member 11 (xCT), a crucial amino acid transporter embedded in the cell membrane ( 6 ). Unlike ferroptosis or cuproptosis, disulfidptosis is specifically mediated by SLC7A11, facilitating extracellular cystine uptake crucial for cancer cell proliferation via the Xc-transport system (consisting of SLC7A11 and SLC3A2 subunits). However, under glucose-deprived conditions, cells with elevated SLC7A11 expression rapidly deplete their NADPH reservoirs, resulting in an excessive accumulation of disulfide bonds, notably cystine. This buildup induces disulfide stress, leading to aberrant cross-linking of disulfide bonds within cytoskeletal proteins like actin, triggering cytoskeletal contraction and collapse, ultimately culminating in cell demise. Notably, this unique form of cell demise, disulfidptosis, remains resistant to traditional cell death inhibitors and remains unaffected by the knockout of critical genes associated with ferroptosis or apoptosis. Thiols oxidants, such as diamide and dimethyl malonate, significantly exacerbate disulfidptosis, underscoring its unique nomenclature. So far, there has been limited research on the relationship between breast cancer and disulfidptosis. This study aims to unearth innovative therapeutic targets for breast cancer by examining the involvement of disulfidptosis. We scrutinized the gene expression variability of key disulfidptosis-related genes (SLC7A11, SLC3A2, RPN1, NCKAP1) across 32 cancer types. Specifically, we pinpointed genes that not only are associated with disulfidptosis in breast cancer but also participate collaboratively in this mechanism. Using machine learning integration, we constructed a novel risk prognostic signature. Subsequent validation demonstrated the signature's robustness in forecasting clinical outcomes and alignment with tumor mutation patterns. Cell division cycle-associated protein-3 (CDCA3) is one of the key genes regulating the cell cycle, and its mechanism of action involves controlling the process of mitosis( 7 ). While prior research has highlighted the promoting influence of CDCA3 in diverse tumors, investigations into its specific role in breast cancer are still relatively sparse. 2. Material and methods 2.1 Regents The culture medium, antibiotics, fetal bovine serum, Polybrene, puromycin, and fluorescence probes put to use in this experiment were procured from Sigma-Aldrich (St. Louis, Missouri). The fluorescence probe, Actin Stain 555 Phalloidin, and DAPI (4',6-diamidino-2-phenylindole), as well as EdU (5-ethynyl-2'-deoxyuridine), were provided by Thermo-Fisher Invitrogen (Shanghai, China). Primary antibodies used were as follows employed were as listed below: anti-β-tubulin (1:3000; EPR16778, Abcam); anti-CDCA3 (1:800; BM4788, BOSTER, Wuhan, China). 2.2 Cultured cell lines The MCF7 and SKBR3 cell lines, along with MCF10A, were sourced from the Cell Bank of the Shanghai Institute of Biological Science (Shanghai, China). The MCF7 and SKBR3 cell lines, along with MCF10A, are representative cell lines commonly used in breast cancer research. The MCF7 cell line represents the luminal A subtype of breast cancer cells, which is one of the most common subtypes of breast cancer. It is typically used to study hormone-sensitive breast cancer. The SKBR3 cell line represents a subtype of breast cancer cells that overexpress the HER2 protein, leading to a more aggressive subtype with a poorer prognosis. The MCF10A cell line serves as a control or comparison group in breast cancer research. It is derived from human mammary epithelial cells and represents normal breast epithelial cells. It is often used as a model for studying normal breast cell biology and for comparison with breast cancer cell lines.. Prior to acquisition, the vendor confirmed that all cell lines were devoid of mycoplasma contamination. All cells were cultured in the same condition (5% CO2 and 37°C). For experiments involving glucose deprivation, the medium is devoid of glucose, but the serum concentration is consistent with that of normal culture medium (10% FBS) 2.3 Public Database and processing Downloaded gene expression data of tumor cells from the CCLE database ( https://portals.broadinstitute.org/ccle ) for pan-cancer analysis( 8 ). The Cancer Genome Atlas (TCGA) database was the source of complete RNA-seq raw read counts, mutational profiles, and associated clinical data related to breast cancer. Expression profiles from microarray data and clinical information were gathered from three distinct breast cancer datasets (GSE42568, GSE21653, GSE20685) which from the Gene Expression Omnibus (GEO) database, using the Affymetrix GPL570 platform. 2.4 Appropriate screening and processing. The study examined the expression patterns of essential genes (SLC7A11, SLC3A2, RPN1, NCKAP1) associated with the disulfidptosis, across 32 cancer types utilizing data from the CCLE database. On the horizontal axis are represented distinct sample groups, while on the vertical axis is indicated the distribution of mRNA expression. 2.5 The enrichment scores of samples in the disulfidptosis-related gene sets We utilized the single-sample gene set enrichment analysis (ssGSEA) algorithm, implemented via the R package GSVA, to categorize samples in a breast cancer dataset (GSE42568), which consisted of 17 normal and 104 breast cancer specimens. A significance threshold with an adjusted P-value of beneath 0.05 was set to identify statistical validity within the clusters. For each sample-gene set pairing, enrichment scores were computed to quantify the coordinated expression changes of gene sets within individual samples. Specifically, the ssGSEA enrichment score for each sample indicates the collective upregulation or downregulation of genes within a specific gene set. Seven gene sets related to disulfidptosis and containing key genes involved in the process were selected from the Molecular Signatures Database (MSigDB). The analysis results were visualized using the "pheatmap" package to generate a heatmap. Gene sets showed in table 1. 2.6 Candidate genes identification The process of identifying candidate gene using WGCNA and DEGs. WGCNA on the expression profile of GSE42568 (104 tumor and 17 normal samples) is outlined as follows( 9 ): To build a co-expression similarity matrix (sij), the Pearson’s correlation coefficient between any two genes (xi and xj) was calculated. Sij= |cor(xi,xj) | Compute a weighted adjacency matrix (aij) by applying a soft thresholding power (β = 8) to the co-expression similarity matrix (sij). aij = sij β Convert the adjacency matrix into both a topological overlap matrix (TOM) and a dissimilarity matrix based on correlation (1-TOM). This process clusters highly correlated genes into diverse modules (minModuleSize = 30). We identify hub genes for the co-expression network by selecting the top 5% of genes with the highest intra-module connectivity. The limma package was utilized to detect the Differentially Expressed Genes (DEGs) using samples from the TCGA-BRCA dataset within the R software. The following criterions were needed to satisfied: log2 | fold change (FC) | ≥ 0.5 and P-value < 0.05. 2.7 Integrative establishment of prognostic signature in breast cancer In this study, gene expression profiles linked to disulfidptosis were utilized to identify prognostic genes through univariate Cox regression analysis within the TCGA-BRCA dataset. This study introduced a more sophisticated prediction approach, which involved constructing prediction signature using 10-fold cross-validation to assess 117 combinations of 10 different machine learning algorithms. These algorithms comprised Elastic Net (Enet), Lasso, Ridge, Random Survival Forest (RSF), Partial Least Squares Regression for Cox (plsRcox), Survival Support Vector Machine (Survival-SVM), CoxBoost, Stepwise Cox, Generalized Boosted Regression Modeling (GBM), Supervised Principal Components (SuperPC). GSE20685 dataset served as the training cohort, with GSE21653 and GSE42568 employed as validation sets. The concordance index (C-index) of each model was tallied for validating the accuracy of signature prediction. Kaplan-Meier survival analysis was performed, followed by the plotting of time-dependent ROC curves. The area under the curve (AUC) was then calculated at different time points ( 10 ). 2.8 Tumor mutational burden (TMB), clinical correlation analysis, and target gene mutations For TMB calculation( 11 ), somatic mutation information from 991 breast cancer patients from TCGA-BRCA was downloaded using R, and the "maftools" package was utilized to map the mutation profiles. Subsequently, clinical information was correlated with mutation data, and patients were stratified according to the expression levels of genes within the prognostic signature. The differences in mutation profiles were then compared. Moreover, we conducted an analysis of mutations in target genes within breast cancer samples and visualized the results. 2.9 Validation of the expression patterns of prognostic signature The mRNA expression profiles of prognostic-associated genes were validated via the Gene Expression Profiling Interactive Analysis (GEPIA) platform( http://gepia.cancer-pku.cn/ ), and further quantified by real-time quantitative PCR (RT-qPCR). The RNA-Quick purification kit (YiShan Biotechnology, Shanghai, China) was used to extracted and purified mRNA from two breast cancer cell lines (MCF7 and SKBR3) and one normal breast cell line (MCF10A). Subsequently, cDNA was synthesized via reverse transcription of the mRNA using EasyQuick RT MasterMix (CWBIO, China). RT-qPCR was carried out by the UltraSYBR One Step RT-qPCR Kit, with GAPDH serving as the reference gene for normalization. Each cDNA amplification was repeated at least three times during the RT-qPCR analysis. The primer sequences utilized for detection are provided in Table 2. 2.10 CDCA3 shRNA or overexpressing Read from the instructions supplied by the manufacturer, constructs containing shRNA ("CDCA3-sh-S1/S2/S3", with 3 different verified sequences, Genechem) or CDCA3 cDNA were transfected into 293T packaging cells using GV248 vectors. CDCA3 shRNA lentiviral particles or CDCA3 overexpression lentiviral particles were obtained 72 hours later. These viral particles were then used to transfect cells in six-well plates. After 24 hours of transfection, puromycin was applied to select for stable transfectants. Control groups were treated with scramble shRNA lentiviral particles ("c-sh") or empty vector lentiviral particles ("Vec"). The efficacy of transfection was confirmed by PCR and Western blot analysis. 2.11 Immunofluorescence Staining Immunofluorescence staining was performed after subjecting cells to glucose deprivation in a specialized culture dish (150680; Thermo Fisher Scientific). Subsequently, the cells were first fixed with 3.7% paraformaldehyde in PBS at room temperature for 15 minutes. Next, they were permeabilized using 0.5% Triton X-100 in PBS for 5 minutes. After another 3 washes with PBS, the cells were subjected to immunofluorescence staining with 100 nM Actin Stain 555 Phalloidin (A30106; Thermo Fisher Scientific) for cell cytoskeleton labeling( 12 ). The staining procedure was undertaken in a light-protected environment at ambient temperature for a duration of 30 minutes. Following two washes with PBS, the specimens were fixed using an Antifade solution containing DAPI (beyotime, P0131, Shanghai, China). All fluorescence images were captured using a confocal microscope. 2.12 Statistic analysis Statistical analysis was performed on experimental data that were confirmed to follow a normal distribution and are presented as mean ± standard deviation (SD). Image data were analyzed with GraphPad Prism software. Two-tailed Student's t-tests were employed for comparisons between two groups, while comparisons involving three groups or more utilized one-way ANOVA with the Scheffe' and Tukey tests for post hoc analysis, all data meet the assumption of homogeneity of variance. All experiments were replicated independently a minimum of three times. Significance threshold set at P < 0.05. 3. Result 3.1 The expression of disulfidptosis-related genes across 32 different tumor tissues The entire experimental procedure is illustrated in Fig. 1 . In the initial investigation into disulfidptosis, seven genes were identified as major players in this process: SLC7A11, SLC3A2, RPN1, NUBPL, LRPPRC, NCKAP1, GYS1. Figures 2 (A-G) depict the varying expression levels of these seven key genes across 32 tumor tissues from the CCLE database. From the results, it is apparent that the expression levels of the majority of key genes in breast cancer are similar to those observed in other types of tumors. Particularly noteworthy is the significantly higher expression of RPN1 in breast cancer (BRCA), ovarian cancer (OV), and stomach adenocarcinoma (STAD) compared to the average in other tumors. Similarly, NCKAP1 exhibits advantageous expression levels in breast cancer (BRCA)and lung adenocarcinoma (LUAD), suggesting potential roles of these two genes in breast cancer diagnosis. However, SLC7A11 and SLC3A2 in breast cancer exhibit few advantages compared to other tumors, possibly due to their generally high expression levels across tumors. Furthermore, a gene set from GSE42568 containing 121 samples (24421 genes) was used for clustering based on Pearson correlation coefficients. A heatmap (Fig. 2 .H) was generated based on the correlation with gene sets related to the process of disulfidptosis, it can be observed that amino acid and carbohydrate metabolism play significant roles in this association. 3.2 83 disulfidptosis-related genes were selected through WGCNA By employing a soft threshold of 8 to establish a scale-free network (R^2 = 0.9), an adjacency matrix was generated, followed by the construction of a topological overlap matrix (Fig. 3 .A), Subsequently, within the nine modules showing positive correlations with tumor samples, the top 200 genes most correlated with the seven disulfidptosis-associated gene sets were earmarked as functionally significant (Fig. 3 .B). Within the 9 modules positively correlated with tumor samples, hub genes (top 5% of intra-module connectivity) were chosen as tumor-related target genes( 13 ) (Fig. 3 .C) The convergence of these two sets of genes with the previously identified DEGs resulted in a final selection of 83 genes related to disulfidptosis. 3.3 Integrative establishment of prognostic signature in breast cancer 58 prognostic genes were identified through univariate Cox analysis within the TCGA-BRCA database, as per the expression profiles of 83 disulfidptosis-related genes. Machine learning algorithms were subsequently employed to construct the signature, with the most robust model being a combination of stepwise Cox regression with bidirectional elimination and SuperPC, achieving a C-index of 0.612 (Fig. 3 .D). The stepwise Cox model was utilized to build a regression model, incorporating predictor variables that demonstrated statistically significant associations with prognosis. Principal component analysis further refined these variables to a panel of 13 genes (PTBP1, TIMM50, COMMD5, TUBA1C, CDCA3, KIF20A, NUSAP1, PSMC3, CLK1, LRRN4CL, RNF5, YKT6, TMEM37), which constituted the final prognostic signature based on their survival analysis outcomes. 3.4 Evaluation of the prognostic signature The efficacy of the signature was scrutinized through four BRCA datasets (TCGA-BRCA, GSE42568, GSE21653, GSE20685), classifying patients into high/low-risk groups in reliance on the expression profiles of 13 genes. Kaplan-Meier survival analysis manifested considerable distinctions in survival time between different risk groups beyond 2000 days, with longer survival observed among those categorized as low-risk (Fig. 4 .A-D). Additionally, time-dependent ROC curves (Fig. 4 .E-F) demonstrated that the AUC for 1/3/5-years survival predictions were all above 0.5 (Fig. 4 .G), indicating the signature's predictive accuracy. Tumor mutational burden (TMB) analysis was performed utilizing somatic mutation data obtained from 991 breast cancer patients within the TCGA-BRCA cohort (Fig. 4 .H). It was observed that among the top twenty mutated genes, no significant disparities were noted between the two groups. Subsequently, the mutation status of genes included in the signature was visualized in breast cancer samples from the ICGC database (Fig. 4 .I), revealing varying degrees of mutations in all thirteen genes, with NUBPL, LRPPRC, and NCKAP1 exhibiting the highest mutation rates. 3.5 CDCA3 is overexpressed in breast cancer In prior bioinformatics analyses, CDCA3 was discerned as one of the 13 pivotal genes within the TCGA-BRCA database, exhibiting marked overexpression in breast cancer compared to counterparts. Data from the online platform GEPIA( 14 ),indicated that in breast cancer cells, CDCA3 expression is elevated compared to that in normal breast tissue (Fig. 5 .A). Further validation through RT-qPCR on SKBR3 and MCF7, revealed a substantial increase in CDCA3 mRNA levels when contrasted with the normal breast cell line MCF10A (Fig. 5 B). Western blot analyses reinforced the RT-qPCR results, confirming the overexpression of CDCA3 in breast cancer cells (Figs. 5 E-F). 3.6 CDCA3 shRNA hampers breast cancer cell growth and mobility in vitro. Lentiviral vectors harboring CDCA3-targeting shRNA constructs ("CDCA3-shS1/S2/S3") (Genechem-sourced, GFP-free) were introduced to human breast cancer cell lines (SKBR3 and MCF7). Following puromycin selection, stable cell lines were established( 15 , 16 ). Simultaneously, lentiviral particles containing scrambled control shRNA ("C-sh") was used to form stable transduced control group cell. The most effective cell line, in terms of knockdown, were identified via RT-qPCR (Fig. 5 .C-D), and confirmed by Western blot analyses (Fig. 5 .G-H). Figures 6 A,E illustrate the results of "Transwell" and "Matrigel Transwell" experiments conducted in vitro using immortalized MCF7 cells( 17 – 19 ). Within 36 hours, there was a reduction in the number of migrated and invaded cells in the "CDCA3-sh" group in contrast to the “C-sh” and WT groups. Similar patterns were noted in immortalized SKBR3 cells (Fig. 6 .C,G), indicating that CDCA3 shRNA triggers a decline in the migratory and invasive capabilities of breast cancer cells. In the EdU experiments( 20 ) conducted with SKBR3 cells(Fig. 6 .I), within a 2-hour treatment period, the "CDCA3-sh" group exhibited fewer proliferating cells and a lower nuclear positivity rate compared to the "C-sh" and WT groups, consistent with the results observed in MCF7 cells(Fig. 6 .K). In addition, plate cloning experiments were conducted using the aforementioned two different cell lines (Fig. 6 .M,O). The results showed that after 15 days, the number of cell colonies formed by the CDCA3-sh cell lines was fewer compared to the WT and control groups. This supports the inhibitory effect of CDCA3 shRNA on the proliferative capacity of breast cancer cells. 3.7 CDCA3 overexpression promotes carcinogenesis in vitro breast cancer cells As described earlier, the lentiviral vectors encoding CDCA3 cDNA were introduced into two human breast cancer cell lines (SKBR3 and MCF7)( 21 ), followed by selection to establish stable overexpression cell lines, with empty vector-transduced cells ("Vec") serving as the control group. Results from "Transwell" and "Matrigel Transwell" assays (Fig. 6 .B,D,F,H) indicated that within the same time frame, the number of migrating cells in the CDCA3-OE cell group significantly surpassed those in the "Vec" and WT groups. Similarly, as observed in Figs. 6 (J,L,N,P), CDCA3 overexpression markedly increased the EdU incorporation rate and the formation of cell colonies. To sum up, breast cancer cells notably experience boosted proliferation and mobility due to the specific overexpression of CDCA3. 3.8 CDCA3 inhibits the contraction of the cellular cytoskeleton during cell glucose starvation CDCA3 appears to mitigate the cytoskeletal contraction associated with cellular response to glucose deprivation—a key characteristic of cells undergoing disulfidptosis. To elucidate CDCA3's influence during glucose starvation, we stained F-actin with phalloidin at four intervals post-deprivation: 0, 1, 4, and 7 hours. Initially, the cell morphology was elongated, and the distribution of F-actin was organized and loose. In WT MCF7 cells and sh-CDCA3 cells, cell contraction occurred after 4 hours of starvation, with the cytoskeleton gathering towards the cell center, becoming dense. After 7 hours of starvation, the cells significantly shrank, and the nuclear-cytoplasmic ratio increased (Fig. 6 .A-B). Conversely, CDCA3-overexpressing (OE-CDCA3) cells retained their morphology longer, with contraction initiating only after 7 hours of starvation (Fig. 6 .C). Similarly, In WT SKBR3 cells and sh-CDCA3 cells, cell contraction and F-actin wrinkling and aggregation started after 4 hours of starvation, and sh-CDCA3 cells almost completely contracted after 7 hours of starvation (Fig. 6 .D-E). In OE-CDCA3 cells, the cells maintained their initial morphology even after 7 hours of starvation (Fig. 6 .F). Collectively, these findings indicate that CDCA3 overexpression can impede the morphological and cytoskeletal contraction in breast cancer cells under glucose starvation, whereas its downregulation accelerates this process. 4. Discussion Breast cancer's emergence as the predominant malignancy among women worldwide has transformed it into a pressing public health concern. In 2020, it surpassed lung cancer to claim the top spot as the most frequently diagnosed malignancy (22) .Despite the availability of generally utilized breast cancer risk assessment tools like the Gail/Breast Cancer Risk Assessment Tool (BCRAT) and the Breast Cancer Surveillance Consortium (BCSC), there is significant molecular heterogeneity among various subtypes of breast cancer. Thus, a singular predictive approach may not accurately guide diagnosis and treatment ( 23 , 24 ). In response to this complexity, the last decade has seen a paradigm shift in breast cancer detection and treatment strategies, moving towards personalized medicine. Searching for prognostic markers and therapeutic targets at the genetic level based on the molecular basis of cancer has become a new mainstream ( 25 ).Programmed cell death (PCD), including copper death, iron death, and cell pyroptosis, among others, has become a hot research topic in tumor gene diagnosis and treatment in recent years( 5 , 26 ). As a recently identified type of programmed cell demise, disulfidptosis occurs when intracellular glucose deficiency leads to an elevated ratio of cystine uptake to cysteine in tumor cells, depleting the NADPH pool and resulting in the formation of disulfide bonds in the intracellular actin cytoskeleton, ultimately leading to cell death( 6 ). Since this process is based on the dysregulation of the redox state triggered by high intracellular glucose consumption, which is similar to the physiological characteristics of breast cancer, exploring the connection between disulfidptosis and breast cancer at the genetic level may lead to more accurate prognosis models and targeted treatment options. To develop an innovative and precise prognostic signature, we introduced a metabolic gene set containing seven key genes associated with disulfidptosis. Through WGCNA and the machine learning we constructed a signature consisting of 13 genes. Stepwise Cox employs stepwise regression to select the best predictive genes, with bidirectional selection combining forward selection and backward elimination, adding variables with predictive capability and removing those with minimal impact on the model. SuperPC utilizes the "Super Learner" concept, based on Principal Component Analysis (PCA), to combine and weigh multiple learning algorithms to select the most accurate one. Subsequently, the superiority of this signature was validated through Kaplan-Meier survival analysis, time-dependent ROC curves, and tumor mutation burden. Based on this prognostic signature, we attempted to assess the impact of individual prognostic genes on the process of disulfidptosis in breast cancer by evaluating mRNA expression levels and gene mutation status. We found that CDCA3 displayed heightened expression and significant mutations in breast cancer samples. This suggests that CDCA3 holds significant importance in guiding breast cancer prognosis and may directly participate in the process of disulfidptosis in breast cancer. It could potentially serve as a target for improving breast cancer prognosis by regulating the process of disulfidptosis. While the involvement of CDCA3 in the advancement of pancreatic cancer has been established, and oral squamous cell carcinoma ( 27 , 28 ),However, there is still limited research on its role in breast cancer. CDCA3 belongs to the cell cycle protein family, alongside CDCA1 through CDCA8, and its primary function is to regulate cell cycle transitions. It induces cell arrest in the G1 phase by targeting wee1, a mitotic inhibitor kinase, as part of the S-phase kinase-associated protein 1 (SKP1)/Cullin 1/F-box (SCF) E3 ubiquitin ligase complex. Additionally, CDCA3 itself is regulated by transcription and degradation at the G1 checkpoint( 29 , 30 ).It's important to mention that alterations during cell cycle are governed and facilitated by the cytoskeleton. Especially during the G1 phase, the cytoskeleton regulates the progression from G1 to S phase by modulating intracellular signaling pathways. Additionally, during the G1/S phase transition, the cytoskeleton undergoes the following changes: remodeling of actin filaments, alterations in the ratio of cytoplasm to nucleus, and adjustments in cell adhesion. These changes aim to facilitate smooth cell growth and migration( 31 – 33 ).To validate the impact of CDCA3 on breast cancer cells, functional experiments were conducted in vitro using primary breast cancer cells and cells with CDCA3 knocked down or overexpressed, generated through lentiviral particles. As mentioned earlier, the findings indicated that the suppression of CDCA3 markedly impeded the proliferation, invasion and migration abilities of breast cancer cells, while upregulation of CDCA3 enhanced these abilities. This indicates that the expression of CDCA3 is pivotal in the initiation and advancement of breast cancer. Interestingly, during disulfidptosis, clear morphological changes in the cytoskeleton can be observed, leading us to speculate whether the morphological changes in the cytoskeleton during disulfidptosis are regulated by CDCA3. Observation of the actin cytoskeleton in breast cancer cells under simulated conditions of disulfidptosis revealed that WT breast cancer cells and sh-CDCA3 cells exhibited cytoskeleton contraction after 4 hours of glucose starvation. Moreover, in sh-CDCA3 cells, the decrease in the cytoplasm-to-nucleus ratio was more pronounced. Conversely, OE-CDCA3 cells required 7 hours or more to exhibit cytoskeleton contraction, with a slower decline in the cytoplasm-to-nucleus ratio. This suggests that knocking down CDCA3 may synergize with the impact on the cytoskeleton during disulfidptosis, while overexpressing CDCA3 may counteract this change. The underlying mechanism remains unclear, but it may be related to the influence of CDCA3 on cytoskeletal protein remodeling. While our gene-based prognostic signature offers a novel approach to guide breast cancer patient prognosis, it is important to acknowledge the limitation of our sample set encompassing all breast cancer types without subclassification, which may affect the predictive accuracy for different patient subgroups. CDCA3's identification as a prognostic gene linked to disulfidptosis opens new avenues for understanding its role in cell death mechanisms and its potential as an oncological biomarker and therapeutic target. However, additional research is required to elucidate the precise mechanisms by which CDCA3 influences breast cancer progression and to validate its clinical utility in personalized medicine. In conclusion, our study presents a promising direction for breast cancer prognosis and therapy, highlighting CDCA3 as a potential oncogenic biomarker and therapeutic target. Our findings underscore the need for tailored approaches to breast cancer treatment that consider the unique molecular profiles of individual tumors. Conclusion In brief, the development of a novel risk prediction model significantly enhances the accuracy of prognostic signatures for breast cancer, facilitating the customization of treatment approaches. The identification of CDCA3 as a key regulator of disulfidptosis positions it as a critical protein implicated in breast cancer progression. This insight may open avenues for innovative therapeutic interventions in breast cancer management. Abbreviations AUC Areas under the ROC curve BRCA Breast cancer CCLE Cancer Cell Line Encyclopedia EDU 5-Ethynyl-2'-deoxyuridine Enet Elastic network GBM Generalized Boosted Regression Modeling GSVA Gene set variation analysis GEO Gene Expression Omnibus GEPIA Gene expression profiling interactive analysis LUAD lung adenocarcinoma OV Ovarian cancer PCD Programmed cell death plsRcox Partial Least Squares Regression for Cox ROC Receiver operating characteristic curve RSF Random Survival Forest ssGSEA Single-sample gene set enrichment analysis STAD Stomach adenocarcinoma SuperPC Supervised Principal Components Survival-SVM Survival Support Vector Machine TCGA The Cancer Genome Atlas TNM Tumor node metastasis classification TMB Tumor mutation burden WGCNA Weighted correlation network analysis Declarations Ethics approval and consent to participate: Not applicable Consent for publication: Not applicable Availability of data and codes: The datasets and codes required for this study would be provided from the corresponding author to scientists with reasonable needs for non-commercial purposes. Competing interests: The authors declare that they have no competing interests Funding: This work was supported by the National Natural Science Foundation (grant no. 82072712); Health Commission Medical Research Program of Jiangsu Province (Z2023096); Suzhou Clinical Key Disease Diagnosis and Treatment Technology Program(LCZX202339); Suzhou Science and Technology Development Program(SLT2023020、SKY2023093); Key Healthcare Talent in Gusu District(054). Acknowledgements: We express our gratitude to all co-authors for their collaborative efforts and contributions to this research. Contributions: A.R.W and X.R.Z proposed the research concept and basic framework, N.L and Y.J.W collected data and performed data analysis, A.R.W, M.E.X and Y.Y.L contributed to the fundamental experimental research, A.R.W and X.R.Z drafted the manuscript, N.L, Y.J.W and Y.Y.L discussed and analyzed the research results and contributed to the improvement of the manuscript, and M.B.C supervised the study and provided funding. All authors had full access to the data and approved the manuscript for publication References Houghton SC, Hankinson SE. Cancer Progress and Priorities: Breast Cancer. Cancer Epidemiol Biomarkers Prev. 2021;30(5):822–44. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48. Ginsburg O, Bray F, Coleman MP, Vanderpuye V, Eniu A, Kotha SR, et al. The global burden of women's cancers: a grand challenge in global health. Lancet. 2017;389(10071):847–60. Tsimberidou AM, Fountzilas E, Nikanjam M, Kurzrock R. 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Goel MK, Khanna P, Kishore J. Understanding survival analysis: Kaplan-Meier estimate. Int J Ayurveda Res. 2010;1(4):274–8. Jardim DL, Goodman A, de Melo Gagliato D, Kurzrock R. The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker. Cancer Cell. 2021;39(2):154–73. Kumari A, Kesarwani S, Javoor MG, Vinothkumar KR, Sirajuddin M. Structural insights into actin filament recognition by commonly used cellular actin markers. Embo j. 2020;39(14):e104006. Feng S, Xu Y, Dai Z, Yin H, Zhang K, Shen Y. Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer. Front Immunol. 2022;13:951582. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98-w102. Rao DD, Vorhies JS, Senzer N, Nemunaitis J. siRNA vs. shRNA: similarities and differences. Adv Drug Deliv Rev. 2009;61(9):746–59. Pushparaj PN, Aarthi JJ, Manikandan J, Kumar SD. siRNA, miRNA, and shRNA: in vivo applications. J Dent Res. 2008;87(11):992–1003. Marshall J. Transwell(®) invasion assays. Methods Mol Biol. 2011;769:97–110. Justus CR, Marie MA, Sanderlin EJ, Yang LV. Transwell In Vitro Cell Migration and Invasion Assays. Methods Mol Biol. 2023;2644:349–59. Chen B, Sang Y, Song X, Zhang D, Wang L, Zhao W, et al. Exosomal miR-500a-5p derived from cancer-associated fibroblasts promotes breast cancer cell proliferation and metastasis through targeting USP28. Theranostics. 2021;11(8):3932–47. Salic A, Mitchison TJ. A chemical method for fast and sensitive detection of DNA synthesis in vivo. Proc Natl Acad Sci U S A. 2008;105(7):2415–20. Joung J, Konermann S, Gootenberg JS, Abudayyeh OO, Platt RJ, Brigham MD, et al. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat Protoc. 2017;12(4):828–63. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. Malhotra GK, Zhao X, Band H, Band V. Histological, molecular and functional subtypes of breast cancers. Cancer Biol Ther. 2010;10(10):955–60. Gail MH. Performance of BCRAT in high-risk patients with breast cancer. Lancet Oncol. 2019;20(6):e285. Dastjerd NT, Valibeik A, Rahimi Monfared S, Goodarzi G, Moradi Sarabi M, Hajabdollahi F, et al. Gene therapy: A promising approach for breast cancer treatment. Cell Biochem Funct. 2022;40(1):28–48. Bertheloot D, Latz E, Franklin BS. Necroptosis, pyroptosis and apoptosis: an intricate game of cell death. Cell Mol Immunol. 2021;18(5):1106–21. Zou RC, Guo ZT, Wei D, Shi ZT, Ye ZC, Zhai G, et al. Downregulation of CDCA3 expression inhibits tumor formation in pancreatic cancer. Neoplasma. 2020;67(6):1223–32. Uchida F, Uzawa K, Kasamatsu A, Takatori H, Sakamoto Y, Ogawara K, et al. Overexpression of cell cycle regulator CDCA3 promotes oral cancer progression by enhancing cell proliferation with prevention of G1 phase arrest. BMC Cancer. 2012;12:321. Yoshida K. Cell-cycle-dependent regulation of the human and mouse Tome-1 promoters. FEBS Lett. 2005;579(6):1488–92. Lim HH, Surana U. Tome-1, wee1, and the onset of mitosis: coupled destruction for timely entry. Mol Cell. 2003;11(4):845–6. Matthews HK, Bertoli C, de Bruin RAM. Cell cycle control in cancer. Nat Rev Mol Cell Biol. 2022;23(1):74–88. Heng YW, Koh CG. Actin cytoskeleton dynamics and the cell division cycle. Int J Biochem Cell Biol. 2010;42(10):1622–33. Ridley AJ. Rho-related proteins: actin cytoskeleton and cell cycle. Curr Opin Genet Dev. 1995;5(1):24–30. Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations (Not answered) Supplementary Files TABLE1.xlsx Table 1 TABLE2.xlsx Table2 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-4154876","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":297495751,"identity":"1b2178ba-ad6e-4bc9-98f9-f6aef1136026","order_by":0,"name":"minbin chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYFACHoYDHyok5NjY2w8QrYXx4IwzFsZ8PGcSiNbCfJi3rSJxnoSDAXEa+KedPXCY54xEepsEQwLDj4pthLVI3M5LODinQiK3TbrxAGPPmduEtRhI5xgceHMGqEXmQAIzYxuxWnjbJNLZJBIMiNdyEKglgXgtYL/MOCNh2AYM5INE+YV/du7hDx8q6uTl29sPPvhRQYQWFHCARPWjYBSMglEwCnABAJKbPs3xi7jdAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Radiotherapy and Oncology, Gusu School, Nanjing Medical University, The First People's Hospital of Kunshan, Suzhou 215300, Jiangsu Province, China.","correspondingAuthor":true,"prefix":"","firstName":"minbin","middleName":"","lastName":"chen","suffix":""},{"id":297495752,"identity":"8424e1fc-a513-493a-b7bd-f665325707fb","order_by":1,"name":"anran wang","email":"","orcid":"","institution":"Department of Radiotherapy and Oncology, Gusu School, Nanjing Medical University, The First People's Hospital of Kunshan, Suzhou 215300, Jiangsu Province, China.","correspondingAuthor":false,"prefix":"","firstName":"anran","middleName":"","lastName":"wang","suffix":""},{"id":297495753,"identity":"cc45dc9a-eae5-4965-93cd-d20bb3f8b5bd","order_by":2,"name":"Xiaoren Zhu","email":"","orcid":"","institution":"Affiliated Kunshan Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoren","middleName":"","lastName":"Zhu","suffix":""},{"id":297495754,"identity":"db0b0836-694f-4ee6-a0b3-88e0bc5af447","order_by":3,"name":"xaioren zhu","email":"","orcid":"","institution":"Affiliated Kunshan Hospital of Jiangsu University,","correspondingAuthor":false,"prefix":"","firstName":"xaioren","middleName":"","lastName":"zhu","suffix":""},{"id":297495755,"identity":"fd42c55f-b426-44ef-9403-4dc3842b4cb5","order_by":4,"name":"Yuanyuan Liu","email":"","orcid":"","institution":"Affiliated Kunshan Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Liu","suffix":""},{"id":297495756,"identity":"e44395f1-e1ef-4183-b998-4e9a66f9de43","order_by":5,"name":"YuJiao Wu","email":"","orcid":"","institution":"Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"YuJiao","middleName":"","lastName":"Wu","suffix":""},{"id":297495757,"identity":"12a24410-52ef-479d-8ba3-fa815d08ffa8","order_by":6,"name":"MengEn Xue","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"MengEn","middleName":"","lastName":"Xue","suffix":""}],"badges":[],"createdAt":"2024-03-23 14:45:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4154876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4154876/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55988296,"identity":"c52a1766-efb8-4e3a-bb1b-0c1b1708df3e","added_by":"auto","created_at":"2024-05-07 08:28:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1259019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of constructing prognose signature.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4154876/v1/3191847654d5f0a1ea104bc7.png"},{"id":55988293,"identity":"ffb663ae-d1bf-420b-be09-9d565af92d1e","added_by":"auto","created_at":"2024-05-07 08:28:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":567165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of key genes related to disulfidptosis in breast cancer, and the relation of process gene sets. \u003c/strong\u003eThe relative mRNA transcript levels of key genes involved in disulfidptosis, including SLC7A11 (A), SLC3A2 (B), RPN1 (C), NUBPL (D), NCKAP1 (E), LRPPRC (F), and GYS1 (G), in 32 different tumor tissues from the CCLE database; and a heatmap (H) depicting the correlation between 121 samples clustered from the GEO dataset (GSE42568) and 7 gene sets related to the disulfidptosis process.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4154876/v1/4b9b5879a45bd757c09461a3.png"},{"id":55988763,"identity":"aca8f17f-6eea-41f4-b4df-b08878e7d1a4","added_by":"auto","created_at":"2024-05-07 08:36:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1107097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe prognostic signature related to disulfidptosis constructed based on WGCNA and machine learning.\u003c/strong\u003e(A）An examination of the scale-free index and average connectivity across different soft-threshold powers (β). (B) Heatmap depicting the relationship between module trait genes and gene sets associated with disulfidptosis process.(C)Heatmap illustrating the correspondence between module trait genes and samples.(D) The GEO dataset GSE20685 serves as the training set, while GSE21653 and GSE42568 serve as the test sets. 117 machine learning algorithms are ranked originating from the average C-index across three datasets.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4154876/v1/bb969527cfff1a56de87961c.png"},{"id":55988294,"identity":"c94e907d-8391-40fb-bcc2-ca4d759eb2a4","added_by":"auto","created_at":"2024-05-07 08:28:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":396410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe prognostic signature demonstrates superior predictive performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour breast cancer datasets GSE20685 (A), GSE21653 (B), GSE42568 (C), and TCGA-BRCA (D) were segmented into low-risk (blue) and high-risk (red) groups rooted in the signature, and then subjected to Kaplan-Meier survival analysis. Presented are the time-dependent ROC curves for GSE42568(E)and TCGA-BRCA(F), with the AUC values computed for 1-year, 3-years, and 5-years survival across all four datasets(G). Waterfall plot generated based on mutation information from high-risk (blue) and low-risk (red) samples in TCGA-BRCA(H). waterfall plot depicting the mutation information of genes (mutation frequency) comprising the signature in the ICGC database(I).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4154876/v1/8e14455477df586b9cdbe1d2.png"},{"id":55988299,"identity":"bea1b727-039a-44bb-85d7-59204e56a672","added_by":"auto","created_at":"2024-05-07 08:28:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":704081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of expression patterns.\u003c/strong\u003e(A) Box plots depicting the relative mRNA expression levels of genes constructed signature in breast cancer tissue (red) with a normal tissue (gray) from the GEPIA database (B) Relative mRNA expression quantified through RT-qPCR in 2 breast cancer cell lines (MCF7 and SKBR3) and 1 normal breast cell line (MCF10A); Relative mRNA expression levels of the “C-sh” and3 KD sequences(S1/S2/S3) of CDCA3 in MCF7 (C) and SKBR3 (D) cells. Protein expression of CDCA3 in 2 breast cancer cell and a normal breast cell(E-F). Validation of CDCA3 protein expression levels in KD/OE group cells(G-J). The error bars represent the mean ± standard deviation. \"*\" indicates P \u0026lt; 0.05 in parallel with the blank control group cells, while \"NS\" represents P \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4154876/v1/09fef6da514726e171d157c4.png"},{"id":55988297,"identity":"6739eb78-42cb-4d8f-af5b-b79b63188fc9","added_by":"auto","created_at":"2024-05-07 08:28:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2384104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCDCA3 has a specific oncogenic effect in breast cancer cells\u003c/strong\u003e. Breast cancer cell lines (SKBR3 and MCF7) and their respective stable cell lines with CDCA3 knocked down were selected, migration(A,C), invasion(E,G), EdU incorporation(I,K), and colony formation experiments(M,O) were conducted. experiments for cell migration (B, D) and invasion (E, F), EdU incorporation (J, L), and colony formation (N, P) were conducted using the same methods as described earlier in cells overexpressing CDCA3. The error bars represent the mean ± standard deviation (SD). \"*\" indicates p \u0026lt; 0.05 relative to the blank control group cells. \"NS\" implies p \u0026gt; 0.05. Scale bar = 100 μm\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4154876/v1/7ac695c6f9043ebf95f9ee3b.png"},{"id":55988765,"identity":"84ee6868-1b4d-481d-a951-89924255b646","added_by":"auto","created_at":"2024-05-07 08:36:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":664581,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCDCA3 influences F-actin contraction during disulfidptosis.\u003c/strong\u003e (A-C) F-actin in WT, CDCA3-KD, and CDCA3-OE MCF7 cells stained with phalloidin, and imaged by confocal microscopy after being treated with glucose deprivation for 0h, 1h, 4h, and 7h. (left: cell cytoskeleton, middle: cell nuclei, right: merged image). (D-F) SKBR3 cells subjected to the same starvation treatment were stained with phalloidin (left: cell cytoskeleton, middle: cell nuclei, right: merged image). Scale bar = 20 μm\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4154876/v1/52ededf757b091ad871cb3ec.png"},{"id":60337396,"identity":"d64578be-b09a-4cac-85b9-7569439563f2","added_by":"auto","created_at":"2024-07-15 17:38:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8007370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4154876/v1/517f9d56-b252-41f5-9718-22b7a5c74a85.pdf"},{"id":55988292,"identity":"9d2f96c1-f1bb-497c-9410-deee1ca27815","added_by":"auto","created_at":"2024-05-07 08:28:21","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9124,"visible":true,"origin":"","legend":"Table 1","description":"","filename":"TABLE1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4154876/v1/83c23458520c0b67b39a05b5.xlsx"},{"id":55988291,"identity":"000948e6-91c0-4d3c-853b-604d59912163","added_by":"auto","created_at":"2024-05-07 08:28:21","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10317,"visible":true,"origin":"","legend":"Table2","description":"","filename":"TABLE2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4154876/v1/bea3eac3e0d3ffc3673fa192.xlsx"}],"financialInterests":"(Not answered)","formattedTitle":"The Role of CDCA3 in Disulfidptosis: Implications for Breast Cancer Cell Viability and Treatment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer stands as the most widespread form of malignancy affecting women worldwide, drawing extensive focus due to its high incidence rates(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). According to statistics from the American Cancer Society, the incidence of female breast cancer exhibited a yearly increase from 2015 to 2019. In 2023, breast cancer is projected to account for 31% of new cancer diagnoses in women(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This substantial burden of disease exerts significant strain on individuals, communities, and healthcare systems(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).in the era of precision medicine, exploring new prognostic biomarkers and treatment targets is crucial for accurately assessing patients' conditions, seeking improved therapeutic approaches, and enhancing overall prognosis for breast cancer patients(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In recent years, the regulated cell death pathway has become increasingly important as a target for cancer medication development(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDisulfidptosis, an emerging mode of cell death, is illustrated by the involvement of Solute Carrier Family 7 Member 11 (xCT), a crucial amino acid transporter embedded in the cell membrane (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Unlike ferroptosis or cuproptosis, disulfidptosis is specifically mediated by SLC7A11, facilitating extracellular cystine uptake crucial for cancer cell proliferation via the Xc-transport system (consisting of SLC7A11 and SLC3A2 subunits). However, under glucose-deprived conditions, cells with elevated SLC7A11 expression rapidly deplete their NADPH reservoirs, resulting in an excessive accumulation of disulfide bonds, notably cystine. This buildup induces disulfide stress, leading to aberrant cross-linking of disulfide bonds within cytoskeletal proteins like actin, triggering cytoskeletal contraction and collapse, ultimately culminating in cell demise. Notably, this unique form of cell demise, disulfidptosis, remains resistant to traditional cell death inhibitors and remains unaffected by the knockout of critical genes associated with ferroptosis or apoptosis. Thiols oxidants, such as diamide and dimethyl malonate, significantly exacerbate disulfidptosis, underscoring its unique nomenclature.\u003c/p\u003e \u003cp\u003eSo far, there has been limited research on the relationship between breast cancer and disulfidptosis. This study aims to unearth innovative therapeutic targets for breast cancer by examining the involvement of disulfidptosis. We scrutinized the gene expression variability of key disulfidptosis-related genes (SLC7A11, SLC3A2, RPN1, NCKAP1) across 32 cancer types. Specifically, we pinpointed genes that not only are associated with disulfidptosis in breast cancer but also participate collaboratively in this mechanism. Using machine learning integration, we constructed a novel risk prognostic signature. Subsequent validation demonstrated the signature's robustness in forecasting clinical outcomes and alignment with tumor mutation patterns.\u003c/p\u003e \u003cp\u003eCell division cycle-associated protein-3 (CDCA3) is one of the key genes regulating the cell cycle, and its mechanism of action involves controlling the process of mitosis(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). While prior research has highlighted the promoting influence of CDCA3 in diverse tumors, investigations into its specific role in breast cancer are still relatively sparse.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Regents\u003c/h2\u003e\n \u003cp\u003eThe culture medium, antibiotics, fetal bovine serum, Polybrene, puromycin, and fluorescence probes put to use in this experiment were procured from Sigma-Aldrich (St. Louis, Missouri). The fluorescence probe, Actin Stain 555 Phalloidin, and DAPI (4\u0026apos;,6-diamidino-2-phenylindole), as well as EdU (5-ethynyl-2\u0026apos;-deoxyuridine), were provided by Thermo-Fisher Invitrogen (Shanghai, China). Primary antibodies used were as follows employed were as listed below: anti-\u0026beta;-tubulin (1:3000; EPR16778, Abcam); anti-CDCA3 (1:800; BM4788, BOSTER, Wuhan, China).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Cultured cell lines\u003c/h2\u003e\n \u003cp\u003eThe MCF7 and SKBR3 cell lines, along with MCF10A, were sourced from the Cell Bank of the Shanghai Institute of Biological Science (Shanghai, China). The MCF7 and SKBR3 cell lines, along with MCF10A, are representative cell lines commonly used in breast cancer research. The MCF7 cell line represents the luminal A subtype of breast cancer cells, which is one of the most common subtypes of breast cancer. It is typically used to study hormone-sensitive breast cancer. The SKBR3 cell line represents a subtype of breast cancer cells that overexpress the HER2 protein, leading to a more aggressive subtype with a poorer prognosis. The MCF10A cell line serves as a control or comparison group in breast cancer research. It is derived from human mammary epithelial cells and represents normal breast epithelial cells. It is often used as a model for studying normal breast cell biology and for comparison with breast cancer cell lines.. Prior to acquisition, the vendor confirmed that all cell lines were devoid of mycoplasma contamination. All cells were cultured in the same condition (5% CO2 and 37\u0026deg;C). For experiments involving glucose deprivation, the medium is devoid of glucose, but the serum concentration is consistent with that of normal culture medium (10% FBS)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Public Database and processing\u003c/h2\u003e\n \u003cp\u003eDownloaded gene expression data of tumor cells from the CCLE database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portals.broadinstitute.org/ccle\u003c/span\u003e\u003c/span\u003e) for pan-cancer analysis(\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e). The Cancer Genome Atlas (TCGA) database was the source of complete RNA-seq raw read counts, mutational profiles, and associated clinical data related to breast cancer. Expression profiles from microarray data and clinical information were gathered from three distinct breast cancer datasets (GSE42568, GSE21653, GSE20685) which from the Gene Expression Omnibus (GEO) database, using the Affymetrix GPL570 platform.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Appropriate screening and processing.\u003c/h2\u003e\n \u003cp\u003eThe study examined the expression patterns of essential genes (SLC7A11, SLC3A2, RPN1, NCKAP1) associated with the disulfidptosis, across 32 cancer types utilizing data from the CCLE database. On the horizontal axis are represented distinct sample groups, while on the vertical axis is indicated the distribution of mRNA expression.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 The enrichment scores of samples in the disulfidptosis-related gene sets\u003c/h2\u003e\n \u003cp\u003eWe utilized the single-sample gene set enrichment analysis (ssGSEA) algorithm, implemented via the R package GSVA, to categorize samples in a breast cancer dataset (GSE42568), which consisted of 17 normal and 104 breast cancer specimens. A significance threshold with an adjusted P-value of beneath 0.05 was set to identify statistical validity within the clusters. For each sample-gene set pairing, enrichment scores were computed to quantify the coordinated expression changes of gene sets within individual samples. Specifically, the ssGSEA enrichment score for each sample indicates the collective upregulation or downregulation of genes within a specific gene set. Seven gene sets related to disulfidptosis and containing key genes involved in the process were selected from the Molecular Signatures Database (MSigDB). The analysis results were visualized using the \u0026quot;pheatmap\u0026quot; package to generate a heatmap. Gene sets showed in table 1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Candidate genes identification\u003c/h2\u003e\n \u003cp\u003eThe process of identifying candidate gene using WGCNA and DEGs. WGCNA on the expression profile of GSE42568 (104 tumor and 17 normal samples) is outlined as follows(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003eTo build a co-expression similarity matrix (sij), the Pearson\u0026rsquo;s correlation coefficient between any two genes (xi and xj) was calculated.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSij= |cor(xi,xj) |\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eCompute a weighted adjacency matrix (aij) by applying a soft thresholding power (\u0026beta;\u0026thinsp;=\u0026thinsp;8) to the co-expression similarity matrix (sij).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eaij\u0026thinsp;=\u0026thinsp;sij\u003c/em\u003e \u003csup\u003e\u0026nbsp;\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eConvert the adjacency matrix into both a topological overlap matrix (TOM) and a dissimilarity matrix based on correlation (1-TOM). This process clusters highly correlated genes into diverse modules (minModuleSize\u0026thinsp;=\u0026thinsp;30). We identify hub genes for the co-expression network by selecting the top 5% of genes with the highest intra-module connectivity. The limma package was utilized to detect the Differentially Expressed Genes (DEGs) using samples from the TCGA-BRCA dataset within the R software. The following criterions were needed to satisfied: log2 | fold change (FC) | \u0026ge; 0.5 and P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Integrative establishment of prognostic signature in breast cancer\u003c/h2\u003e\n \u003cp\u003eIn this study, gene expression profiles linked to disulfidptosis were utilized to identify prognostic genes through univariate Cox regression analysis within the TCGA-BRCA dataset. This study introduced a more sophisticated prediction approach, which involved constructing prediction signature using 10-fold cross-validation to assess 117 combinations of 10 different machine learning algorithms. These algorithms comprised Elastic Net (Enet), Lasso, Ridge, Random Survival Forest (RSF), Partial Least Squares Regression for Cox (plsRcox), Survival Support Vector Machine (Survival-SVM), CoxBoost, Stepwise Cox, Generalized Boosted Regression Modeling (GBM), Supervised Principal Components (SuperPC). GSE20685 dataset served as the training cohort, with GSE21653 and GSE42568 employed as validation sets. The concordance index (C-index) of each model was tallied for validating the accuracy of signature prediction. Kaplan-Meier survival analysis was performed, followed by the plotting of time-dependent ROC curves. The area under the curve (AUC) was then calculated at different time points (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Tumor mutational burden (TMB), clinical correlation analysis, and target gene mutations\u003c/h2\u003e\n \u003cp\u003eFor TMB calculation(\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e), somatic mutation information from 991 breast cancer patients from TCGA-BRCA was downloaded using R, and the \u0026quot;maftools\u0026quot; package was utilized to map the mutation profiles. Subsequently, clinical information was correlated with mutation data, and patients were stratified according to the expression levels of genes within the prognostic signature. The differences in mutation profiles were then compared. Moreover, we conducted an analysis of mutations in target genes within breast cancer samples and visualized the results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9 Validation of the expression patterns of prognostic signature\u003c/h2\u003e\n \u003cp\u003eThe mRNA expression profiles of prognostic-associated genes were validated via the Gene Expression Profiling Interactive Analysis (GEPIA) platform(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003c/span\u003e), and further quantified by real-time quantitative PCR (RT-qPCR). The RNA-Quick purification kit (YiShan Biotechnology, Shanghai, China) was used to extracted and purified mRNA from two breast cancer cell lines (MCF7 and SKBR3) and one normal breast cell line (MCF10A). Subsequently, cDNA was synthesized via reverse transcription of the mRNA using EasyQuick RT MasterMix (CWBIO, China). RT-qPCR was carried out by the UltraSYBR One Step RT-qPCR Kit, with GAPDH serving as the reference gene for normalization. Each cDNA amplification was repeated at least three times during the RT-qPCR analysis. The primer sequences utilized for detection are provided in Table\u0026nbsp;2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.10 CDCA3 shRNA or overexpressing\u003c/h2\u003e\n \u003cp\u003eRead from the instructions supplied by the manufacturer, constructs containing shRNA (\u0026quot;CDCA3-sh-S1/S2/S3\u0026quot;, with 3 different verified sequences, Genechem) or CDCA3 cDNA were transfected into 293T packaging cells using GV248 vectors. CDCA3 shRNA lentiviral particles or CDCA3 overexpression lentiviral particles were obtained 72 hours later. These viral particles were then used to transfect cells in six-well plates. After 24 hours of transfection, puromycin was applied to select for stable transfectants. Control groups were treated with scramble shRNA lentiviral particles (\u0026quot;c-sh\u0026quot;) or empty vector lentiviral particles (\u0026quot;Vec\u0026quot;). The efficacy of transfection was confirmed by PCR and Western blot analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.11 Immunofluorescence Staining\u003c/h2\u003e\n \u003cp\u003eImmunofluorescence staining was performed after subjecting cells to glucose deprivation in a specialized culture dish (150680; Thermo Fisher Scientific). Subsequently, the cells were first fixed with 3.7% paraformaldehyde in PBS at room temperature for 15 minutes. Next, they were permeabilized using 0.5% Triton X-100 in PBS for 5 minutes. After another 3 washes with PBS, the cells were subjected to immunofluorescence staining with 100 nM Actin Stain 555 Phalloidin (A30106; Thermo Fisher Scientific) for cell cytoskeleton labeling(\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e). The staining procedure was undertaken in a light-protected environment at ambient temperature for a duration of 30 minutes. Following two washes with PBS, the specimens were fixed using an Antifade solution containing DAPI (beyotime, P0131, Shanghai, China). All fluorescence images were captured using a confocal microscope.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.12 Statistic analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analysis was performed on experimental data that were confirmed to follow a normal distribution and are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Image data were analyzed with GraphPad Prism software. Two-tailed Student\u0026apos;s t-tests were employed for comparisons between two groups, while comparisons involving three groups or more utilized one-way ANOVA with the Scheffe\u0026apos; and Tukey tests for post hoc analysis, all data meet the assumption of homogeneity of variance. All experiments were replicated independently a minimum of three times. Significance threshold set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The expression of disulfidptosis-related genes across 32 different tumor tissues\u003c/h2\u003e \u003cp\u003eThe entire experimental procedure is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the initial investigation into disulfidptosis, seven genes were identified as major players in this process: SLC7A11, SLC3A2, RPN1, NUBPL, LRPPRC, NCKAP1, GYS1. Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (A-G) depict the varying expression levels of these seven key genes across 32 tumor tissues from the CCLE database. From the results, it is apparent that the expression levels of the majority of key genes in breast cancer are similar to those observed in other types of tumors. Particularly noteworthy is the significantly higher expression of RPN1 in breast cancer (BRCA), ovarian cancer (OV), and stomach adenocarcinoma (STAD) compared to the average in other tumors. Similarly, NCKAP1 exhibits advantageous expression levels in breast cancer (BRCA)and lung adenocarcinoma (LUAD), suggesting potential roles of these two genes in breast cancer diagnosis. However, SLC7A11 and SLC3A2 in breast cancer exhibit few advantages compared to other tumors, possibly due to their generally high expression levels across tumors. Furthermore, a gene set from GSE42568 containing 121 samples (24421 genes) was used for clustering based on Pearson correlation coefficients. A heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.H) was generated based on the correlation with gene sets related to the process of disulfidptosis, it can be observed that amino acid and carbohydrate metabolism play significant roles in this association.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 83 disulfidptosis-related genes were selected through WGCNA\u003c/h2\u003e \u003cp\u003eBy employing a soft threshold of 8 to establish a scale-free network (R^2\u0026thinsp;=\u0026thinsp;0.9), an adjacency matrix was generated, followed by the construction of a topological overlap matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.A), Subsequently, within the nine modules showing positive correlations with tumor samples, the top 200 genes most correlated with the seven disulfidptosis-associated gene sets were earmarked as functionally significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.B). Within the 9 modules positively correlated with tumor samples, hub genes (top 5% of intra-module connectivity) were chosen as tumor-related target genes(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.C) The convergence of these two sets of genes with the previously identified DEGs resulted in a final selection of 83 genes related to disulfidptosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Integrative establishment of prognostic signature in breast cancer\u003c/h2\u003e \u003cp\u003e58 prognostic genes were identified through univariate Cox analysis within the TCGA-BRCA database, as per the expression profiles of 83 disulfidptosis-related genes. Machine learning algorithms were subsequently employed to construct the signature, with the most robust model being a combination of stepwise Cox regression with bidirectional elimination and SuperPC, achieving a C-index of 0.612 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.D). The stepwise Cox model was utilized to build a regression model, incorporating predictor variables that demonstrated statistically significant associations with prognosis. Principal component analysis further refined these variables to a panel of 13 genes (PTBP1, TIMM50, COMMD5, TUBA1C, CDCA3, KIF20A, NUSAP1, PSMC3, CLK1, LRRN4CL, RNF5, YKT6, TMEM37), which constituted the final prognostic signature based on their survival analysis outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Evaluation of the prognostic signature\u003c/h2\u003e \u003cp\u003eThe efficacy of the signature was scrutinized through four BRCA datasets (TCGA-BRCA, GSE42568, GSE21653, GSE20685), classifying patients into high/low-risk groups in reliance on the expression profiles of 13 genes. Kaplan-Meier survival analysis manifested considerable distinctions in survival time between different risk groups beyond 2000 days, with longer survival observed among those categorized as low-risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.A-D). Additionally, time-dependent ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.E-F) demonstrated that the AUC for 1/3/5-years survival predictions were all above 0.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.G), indicating the signature's predictive accuracy. Tumor mutational burden (TMB) analysis was performed utilizing somatic mutation data obtained from 991 breast cancer patients within the TCGA-BRCA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.H). It was observed that among the top twenty mutated genes, no significant disparities were noted between the two groups. Subsequently, the mutation status of genes included in the signature was visualized in breast cancer samples from the ICGC database (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.I), revealing varying degrees of mutations in all thirteen genes, with NUBPL, LRPPRC, and NCKAP1 exhibiting the highest mutation rates.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 CDCA3 is overexpressed in breast cancer\u003c/h2\u003e \u003cp\u003eIn prior bioinformatics analyses, CDCA3 was discerned as one of the 13 pivotal genes within the TCGA-BRCA database, exhibiting marked overexpression in breast cancer compared to counterparts. Data from the online platform GEPIA(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e),indicated that in breast cancer cells, CDCA3 expression is elevated compared to that in normal breast tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.A). Further validation through RT-qPCR on SKBR3 and MCF7, revealed a substantial increase in CDCA3 mRNA levels when contrasted with the normal breast cell line MCF10A (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Western blot analyses reinforced the RT-qPCR results, confirming the overexpression of CDCA3 in breast cancer cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 CDCA3 shRNA hampers breast cancer cell growth and mobility in vitro.\u003c/h2\u003e \u003cp\u003eLentiviral vectors harboring CDCA3-targeting shRNA constructs (\"CDCA3-shS1/S2/S3\") (Genechem-sourced, GFP-free) were introduced to human breast cancer cell lines (SKBR3 and MCF7). Following puromycin selection, stable cell lines were established(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Simultaneously, lentiviral particles containing scrambled control shRNA (\"C-sh\") was used to form stable transduced control group cell. The most effective cell line, in terms of knockdown, were identified via RT-qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.C-D), and confirmed by Western blot analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.G-H). Figures\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA,E illustrate the results of \"Transwell\" and \"Matrigel Transwell\" experiments conducted in vitro using immortalized MCF7 cells(\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Within 36 hours, there was a reduction in the number of migrated and invaded cells in the \"CDCA3-sh\" group in contrast to the \u0026ldquo;C-sh\u0026rdquo; and WT groups. Similar patterns were noted in immortalized SKBR3 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.C,G), indicating that CDCA3 shRNA triggers a decline in the migratory and invasive capabilities of breast cancer cells. In the EdU experiments(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) conducted with SKBR3 cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.I), within a 2-hour treatment period, the \"CDCA3-sh\" group exhibited fewer proliferating cells and a lower nuclear positivity rate compared to the \"C-sh\" and WT groups, consistent with the results observed in MCF7 cells(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.K). In addition, plate cloning experiments were conducted using the aforementioned two different cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.M,O). The results showed that after 15 days, the number of cell colonies formed by the CDCA3-sh cell lines was fewer compared to the WT and control groups. This supports the inhibitory effect of CDCA3 shRNA on the proliferative capacity of breast cancer cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7 CDCA3 overexpression promotes carcinogenesis in vitro breast cancer cells\u003c/h2\u003e \u003cp\u003eAs described earlier, the lentiviral vectors encoding CDCA3 cDNA were introduced into two human breast cancer cell lines (SKBR3 and MCF7)(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), followed by selection to establish stable overexpression cell lines, with empty vector-transduced cells (\"Vec\") serving as the control group. Results from \"Transwell\" and \"Matrigel Transwell\" assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.B,D,F,H) indicated that within the same time frame, the number of migrating cells in the CDCA3-OE cell group significantly surpassed those in the \"Vec\" and WT groups. Similarly, as observed in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(J,L,N,P), CDCA3 overexpression markedly increased the EdU incorporation rate and the formation of cell colonies. To sum up, breast cancer cells notably experience boosted proliferation and mobility due to the specific overexpression of CDCA3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.8 CDCA3 inhibits the contraction of the cellular cytoskeleton during cell glucose starvation\u003c/h2\u003e \u003cp\u003eCDCA3 appears to mitigate the cytoskeletal contraction associated with cellular response to glucose deprivation\u0026mdash;a key characteristic of cells undergoing disulfidptosis. To elucidate CDCA3's influence during glucose starvation, we stained F-actin with phalloidin at four intervals post-deprivation: 0, 1, 4, and 7 hours. Initially, the cell morphology was elongated, and the distribution of F-actin was organized and loose. In WT MCF7 cells and sh-CDCA3 cells, cell contraction occurred after 4 hours of starvation, with the cytoskeleton gathering towards the cell center, becoming dense. After 7 hours of starvation, the cells significantly shrank, and the nuclear-cytoplasmic ratio increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.A-B). Conversely, CDCA3-overexpressing (OE-CDCA3) cells retained their morphology longer, with contraction initiating only after 7 hours of starvation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.C). Similarly, In WT SKBR3 cells and sh-CDCA3 cells, cell contraction and F-actin wrinkling and aggregation started after 4 hours of starvation, and sh-CDCA3 cells almost completely contracted after 7 hours of starvation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.D-E). In OE-CDCA3 cells, the cells maintained their initial morphology even after 7 hours of starvation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.F). Collectively, these findings indicate that CDCA3 overexpression can impede the morphological and cytoskeletal contraction in breast cancer cells under glucose starvation, whereas its downregulation accelerates this process.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBreast cancer's emergence as the predominant malignancy among women worldwide has transformed it into a pressing public health concern. In 2020, it surpassed lung cancer to claim the top spot as the most frequently diagnosed malignancy \u003csup\u003e(22)\u003c/sup\u003e.Despite the availability of generally utilized breast cancer risk assessment tools like the Gail/Breast Cancer Risk Assessment Tool (BCRAT) and the Breast Cancer Surveillance Consortium (BCSC), there is significant molecular heterogeneity among various subtypes of breast cancer. Thus, a singular predictive approach may not accurately guide diagnosis and treatment (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In response to this complexity, the last decade has seen a paradigm shift in breast cancer detection and treatment strategies, moving towards personalized medicine. Searching for prognostic markers and therapeutic targets at the genetic level based on the molecular basis of cancer has become a new mainstream (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).Programmed cell death (PCD), including copper death, iron death, and cell pyroptosis, among others, has become a hot research topic in tumor gene diagnosis and treatment in recent years(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). As a recently identified type of programmed cell demise, disulfidptosis occurs when intracellular glucose deficiency leads to an elevated ratio of cystine uptake to cysteine in tumor cells, depleting the NADPH pool and resulting in the formation of disulfide bonds in the intracellular actin cytoskeleton, ultimately leading to cell death(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Since this process is based on the dysregulation of the redox state triggered by high intracellular glucose consumption, which is similar to the physiological characteristics of breast cancer, exploring the connection between disulfidptosis and breast cancer at the genetic level may lead to more accurate prognosis models and targeted treatment options.\u003c/p\u003e \u003cp\u003eTo develop an innovative and precise prognostic signature, we introduced a metabolic gene set containing seven key genes associated with disulfidptosis. Through WGCNA and the machine learning we constructed a signature consisting of 13 genes. Stepwise Cox employs stepwise regression to select the best predictive genes, with bidirectional selection combining forward selection and backward elimination, adding variables with predictive capability and removing those with minimal impact on the model. SuperPC utilizes the \"Super Learner\" concept, based on Principal Component Analysis (PCA), to combine and weigh multiple learning algorithms to select the most accurate one. Subsequently, the superiority of this signature was validated through Kaplan-Meier survival analysis, time-dependent ROC curves, and tumor mutation burden.\u003c/p\u003e \u003cp\u003eBased on this prognostic signature, we attempted to assess the impact of individual prognostic genes on the process of disulfidptosis in breast cancer by evaluating mRNA expression levels and gene mutation status. We found that CDCA3 displayed heightened expression and significant mutations in breast cancer samples. This suggests that CDCA3 holds significant importance in guiding breast cancer prognosis and may directly participate in the process of disulfidptosis in breast cancer. It could potentially serve as a target for improving breast cancer prognosis by regulating the process of disulfidptosis. While the involvement of CDCA3 in the advancement of pancreatic cancer has been established, and oral squamous cell carcinoma (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e),However, there is still limited research on its role in breast cancer. CDCA3 belongs to the cell cycle protein family, alongside CDCA1 through CDCA8, and its primary function is to regulate cell cycle transitions. It induces cell arrest in the G1 phase by targeting wee1, a mitotic inhibitor kinase, as part of the S-phase kinase-associated protein 1 (SKP1)/Cullin 1/F-box (SCF) E3 ubiquitin ligase complex. Additionally, CDCA3 itself is regulated by transcription and degradation at the G1 checkpoint(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).It's important to mention that alterations during cell cycle are governed and facilitated by the cytoskeleton. Especially during the G1 phase, the cytoskeleton regulates the progression from G1 to S phase by modulating intracellular signaling pathways. Additionally, during the G1/S phase transition, the cytoskeleton undergoes the following changes: remodeling of actin filaments, alterations in the ratio of cytoplasm to nucleus, and adjustments in cell adhesion. These changes aim to facilitate smooth cell growth and migration(\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).To validate the impact of CDCA3 on breast cancer cells, functional experiments were conducted in vitro using primary breast cancer cells and cells with CDCA3 knocked down or overexpressed, generated through lentiviral particles. As mentioned earlier, the findings indicated that the suppression of CDCA3 markedly impeded the proliferation, invasion and migration abilities of breast cancer cells, while upregulation of CDCA3 enhanced these abilities. This indicates that the expression of CDCA3 is pivotal in the initiation and advancement of breast cancer.\u003c/p\u003e \u003cp\u003eInterestingly, during disulfidptosis, clear morphological changes in the cytoskeleton can be observed, leading us to speculate whether the morphological changes in the cytoskeleton during disulfidptosis are regulated by CDCA3. Observation of the actin cytoskeleton in breast cancer cells under simulated conditions of disulfidptosis revealed that WT breast cancer cells and sh-CDCA3 cells exhibited cytoskeleton contraction after 4 hours of glucose starvation. Moreover, in sh-CDCA3 cells, the decrease in the cytoplasm-to-nucleus ratio was more pronounced. Conversely, OE-CDCA3 cells required 7 hours or more to exhibit cytoskeleton contraction, with a slower decline in the cytoplasm-to-nucleus ratio. This suggests that knocking down CDCA3 may synergize with the impact on the cytoskeleton during disulfidptosis, while overexpressing CDCA3 may counteract this change. The underlying mechanism remains unclear, but it may be related to the influence of CDCA3 on cytoskeletal protein remodeling.\u003c/p\u003e \u003cp\u003eWhile our gene-based prognostic signature offers a novel approach to guide breast cancer patient prognosis, it is important to acknowledge the limitation of our sample set encompassing all breast cancer types without subclassification, which may affect the predictive accuracy for different patient subgroups. CDCA3's identification as a prognostic gene linked to disulfidptosis opens new avenues for understanding its role in cell death mechanisms and its potential as an oncological biomarker and therapeutic target. However, additional research is required to elucidate the precise mechanisms by which CDCA3 influences breast cancer progression and to validate its clinical utility in personalized medicine.\u003c/p\u003e \u003cp\u003eIn conclusion, our study presents a promising direction for breast cancer prognosis and therapy, highlighting CDCA3 as a potential oncogenic biomarker and therapeutic target. Our findings underscore the need for tailored approaches to breast cancer treatment that consider the unique molecular profiles of individual tumors.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eIn brief, the development of a novel risk prediction model significantly enhances the accuracy of prognostic signatures for breast cancer, facilitating the customization of treatment approaches. The identification of CDCA3 as a key regulator of disulfidptosis positions it as a critical protein implicated in breast cancer progression. This insight may open avenues for innovative therapeutic interventions in breast cancer management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cb\u003eAUC\u003c/b\u003e Areas under the ROC curve\u003c/p\u003e\u003cp\u003e\u003cb\u003eBRCA\u003c/b\u003e Breast cancer\u003c/p\u003e\u003cp\u003e\u003cb\u003eCCLE\u003c/b\u003e Cancer Cell Line Encyclopedia\u003c/p\u003e\u003cp\u003e\u003cb\u003eEDU\u003c/b\u003e 5-Ethynyl-2'-deoxyuridine\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnet\u003c/b\u003e Elastic network\u003c/p\u003e\u003cp\u003e\u003cb\u003eGBM\u003c/b\u003e Generalized Boosted Regression Modeling\u003c/p\u003e\u003cp\u003e\u003cb\u003eGSVA\u003c/b\u003e Gene set variation analysis\u003c/p\u003e\u003cp\u003e\u003cb\u003eGEO\u003c/b\u003e Gene Expression Omnibus\u003c/p\u003e\u003cp\u003e\u003cb\u003eGEPIA\u003c/b\u003e Gene expression profiling interactive analysis\u003c/p\u003e\u003cp\u003e\u003cb\u003eLUAD\u003c/b\u003e lung adenocarcinoma\u003c/p\u003e\u003cp\u003e\u003cb\u003eOV\u003c/b\u003e Ovarian cancer\u003c/p\u003e\u003cp\u003e\u003cb\u003ePCD\u003c/b\u003e Programmed cell death\u003c/p\u003e\u003cp\u003e\u003cb\u003eplsRcox\u003c/b\u003e Partial Least Squares Regression for Cox\u003c/p\u003e\u003cp\u003e\u003cb\u003eROC\u003c/b\u003e Receiver operating characteristic curve\u003c/p\u003e\u003cp\u003e\u003cb\u003eRSF\u003c/b\u003e Random Survival Forest\u003c/p\u003e\u003cp\u003e\u003cb\u003essGSEA\u003c/b\u003e Single-sample gene set enrichment analysis\u003c/p\u003e\u003cp\u003e\u003cb\u003eSTAD\u003c/b\u003e Stomach adenocarcinoma\u003c/p\u003e\u003cp\u003e\u003cb\u003eSuperPC\u003c/b\u003e Supervised Principal Components\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurvival-SVM\u003c/b\u003e Survival Support Vector Machine\u003c/p\u003e\u003cp\u003e\u003cb\u003eTCGA\u003c/b\u003e The Cancer Genome Atlas\u003c/p\u003e\u003cp\u003e\u003cb\u003eTNM\u003c/b\u003e Tumor node metastasis classification\u003c/p\u003e\u003cp\u003e\u003cb\u003eTMB\u003c/b\u003e Tumor mutation burden\u003c/p\u003e\u003cp\u003e\u003cb\u003eWGCNA\u003c/b\u003e Weighted correlation network analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and codes:\u0026nbsp;\u003c/strong\u003eThe datasets and codes required for this study would be provided from the corresponding author to scientists with reasonable needs for non-commercial purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Natural Science Foundation (grant no. 82072712); Health Commission Medical Research Program of Jiangsu Province (Z2023096); Suzhou Clinical Key Disease Diagnosis and Treatment Technology Program(LCZX202339); Suzhou Science and Technology Development Program(SLT2023020、SKY2023093); Key Healthcare Talent in Gusu District(054).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe express our gratitude to all co-authors for their collaborative efforts and contributions to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions:\u0026nbsp;\u003c/strong\u003eA.R.W and X.R.Z proposed the research concept and basic framework, N.L and Y.J.W collected data and performed data analysis, A.R.W, M.E.X and Y.Y.L contributed to the fundamental experimental research, A.R.W and X.R.Z drafted the manuscript, N.L, Y.J.W and Y.Y.L discussed and analyzed the research results and contributed to the improvement of the manuscript, and M.B.C supervised the study and provided funding. All authors had full access to the data and approved the manuscript for publication\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoughton SC, Hankinson SE. Cancer Progress and Priorities: Breast Cancer. Cancer Epidemiol Biomarkers Prev. 2021;30(5):822\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGinsburg O, Bray F, Coleman MP, Vanderpuye V, Eniu A, Kotha SR, et al. The global burden of women's cancers: a grand challenge in global health. Lancet. 2017;389(10071):847\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsimberidou AM, Fountzilas E, Nikanjam M, Kurzrock R. Review of precision cancer medicine: Evolution of the treatment paradigm. 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Int J Biochem Cell Biol. 2010;42(10):1622\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRidley AJ. Rho-related proteins: actin cytoskeleton and cell cycle. Curr Opin Genet Dev. 1995;5(1):24\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are 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":"Disulfidptosis, CDCA3, Predictive Signature, Breast Cancer, Glucose Deprivation, Metabolic Stress","lastPublishedDoi":"10.21203/rs.3.rs-4154876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4154876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\"Disulfidptosis\" is a novel concept, primarily characterized by the depletion of intracellular NADPH during glucose deprivation, leading to the accumulation of disulfides. This accumulation triggers disulfide stress, breakdown of the actin cytoskeleton, and ultimately, cell death. Breast cancer, being a highly prevalent cancer type, requires a substantial amount of glucose for its rapid growth and division. Glucose deprivation induces rapid cell death, possibly associated with the process of disulfidptosis. In this study, we developed a novel signature based on 10 machine learning algorithms integration. Compared to the traditional TNM staging, this new predictive model demonstrates superior predictive capabilities. Among the 13 genes included in the signature, CDCA3, a cell cycle-regulating protein, is expressed at higher levels in breast cancer. Our experiments indicate that breast cancer cells (SKBR-3 and MCF-7 lines) with downregulated expression of CDCA3 through shRNA exhibit reduced proliferation, migration, and invasion capabilities compared to the WT cells. Conversely, breast cancer cells with forced overexpression of CDCA3 show enhanced growth and mobility. Moreover, observations of primary and immortalized breast cancer cells in a glucose-deprived environment reveal that overexpressing CDCA3 can slow down the process of cell cytoskeleton contraction under starvation conditions, while knockdown CDCA3 accelerates this change. In conclusion, CDCA3, associated with the process of disulfidptosis, may represent a new target for breast cancer treatment.\u003c/p\u003e","manuscriptTitle":"The Role of CDCA3 in Disulfidptosis: Implications for Breast Cancer Cell Viability and Treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 08:28:16","doi":"10.21203/rs.3.rs-4154876/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":"f289e03d-6a9f-46a8-9c81-ecf7813e3591","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31382718,"name":"Biological sciences/Cancer"},{"id":31382719,"name":"Biological sciences/Cell biology"}],"tags":[],"updatedAt":"2024-07-15T17:30:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 08:28:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4154876","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4154876","identity":"rs-4154876","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}