A Novel Ferroptosis-Related Gene Signature for Chemotherapy Resistance Prediction in Triple-negative Breast Cancer

A Novel Ferroptosis-Related Gene Signature for Chemotherapy Resistance Prediction in Triple-negative Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Novel Ferroptosis-Related Gene Signature for Chemotherapy Resistance Prediction in Triple-negative Breast Cancer Huan You, Hongyan Qian, Shichen Miao, Xuan Li, Bingyi Liu, Dan Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3176896/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 Taxanes are first-line chemotherapeutic agents for patients with triple-negative breast cancer (TNBC). However, resistance, the main cause of clinical treatment failure and poor prognosis, reduces their effectiveness and has become an increasingly important problem. Recently, a form of iron-dependent programmed cell death called ferroptosis was reported to play an important role in regulating tumor biological behavior. In this study, we revealed the prognostic significance of the ferroptosis‑related gene (FERG) model and clarified that ferroptosis-related genes may be promising candidate biomarkers in cancer therapy. First, resistance-related FERGs were screened, and univariate Cox regression analysis was used to construct a prognostic model, including GRIK3, IDO1, and CLGN. Then, the patients with TNBC in the TCGA database were classified into high-risk and low-risk groups. The identification of TNBC in the TCGA database revealed that patients with high scores had a higher probability of dying earlier than those with low scores. Moreover, these three genes were associated with immune infiltrates and checkpoints in TNBC patients. In conclusion, this study suggested that FERGs are significantly associated with chemotherapy resistance in patients with TNBC and that these genes can be used as prognostic predictors in these patients and possibly for targeted therapy in the future. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction According to the latest global cancer data published by the International Agency for Research on Cancer, breast cancer in women is the most common cancer worldwide, exceeding lung cancer for the first time 1 . In addition, breast cancer is the most common malignancy and the leading cause of cancer-related death in women, and triple-negative breast cancer (TNBC) has one of the worst prognoses of all breast cancer variants 2 . TNBC is a subtype of breast tumor that lacks hormone receptor expression and human epidermal growth factor receptor 2 (HER2) gene amplification and exhibits high relapse risk, high metastasis, and low overall survival 3 . The current treatment options are very limited owing to the lack of effective therapeutic targets in TNBC 4 . At present, there are no clinical guidelines for TNBC treatment, and it is usually treated according to the conventional standard of care for breast cancer 5 . The conventional treatment modalities of breast cancer include surgery, radiotherapy and chemotherapy, endocrine therapy, and targeted therapy 6 . Among these options, the remission rate of TNBC after chemotherapy is higher than that after other types of treatment, making it the current preferred treatment for TNBC 7 . The efficacy of currently approved several chemotherapeutics, including cisplatin, anthracycline, paclitaxel, and tamoxifen, is limited owing to heterogeneity with oncogenic drivers and resistance development 8 , 9 . Accordingly, in recent years, efforts have been made to investigate the chemoresistance of TNBC to improve the chemotherapy effect and survival rate of patients with TNBC. At present, taxane-based regimens represent the mainstay in TNBC therapy. Although paclitaxel is effective for some patients with TNBC, approximately 30–50% develop resistance, resulting in a lower overall survival rate 10 . The genomic and molecular basis of chemotherapy resistance in patients with TNBC remains poorly understood because the development of TNBC chemotherapy resistance is multifaceted and based on complex interactions among the tumor microenvironment (TME), drug effector, cancer stem cells, and many tumor cells 11 . Because drug resistance is a complex process driven by disrupted gene expression, it is critical to screen differentially expressed genes (DEGs) associated with drug resistance. Furthermore, it is of great significance to analyze the mechanism of DEGs on Taxol sensitivity for identifying new targets to reverse drug resistance and improve prognosis. Ferroptosis, a new type of cell death discovered recently, is usually characterized by abundant iron accumulation and lipid peroxidation during cell death 12 . Different from the necrosis modes of apoptosis and autophagy, ferroptosis has unique manifestations 13 . In terms of morphology, mitochondrial atrophy, mitochondrial membrane shrinkage, and rupture, a normal nucleus with a lack of chromatin condensation can be observed during ferroptosis 14 . Cellular metabolism in cells undergoing ferroptosis is characterized by the accumulation of intracellular ferrous ions and Reactive Oxygen Species (ROS), significant phospholipid peroxidation, and impaired lipid peroxide repair function 15 . Studies have shown that regulating programmed cell death has a significant potential in the treatment of cancer drug resistance and that inducing ferroptosis can inhibit tumor cells resistant to conventional therapy and enhance the effect of immunotherapy 16 , 17 . Ferroptosis can induce or inhibit tumor cells with specific drugs and regulate the expression of related genes. Studies have revealed that traditional anti-breast cancer drugs can induce ferroptosis, which can subsequently induce apoptosis in breast cancer cells 18 , 19 . The combination of the lysosomal promoter siramesine and the kinase inhibitor lapatinib was found to induce ferroptosis. This binding leads to an increase in iron levels by decreasing ferritin expression and increasing transferrin expression, leading to ferroptosis 20 . Another study found that sulfasalazine caused iron ptosis in breast cancer cells, particularly in those with low Estrogen Receptor (ER) expression, suggesting that it could be used as a potential therapeutic agent for breast cancer 21 . NR5A2 synergized with NCOA3 was used to prevent BET-induced ferroptosis in breast cancer cells by inducing NRF2 expression and enhancing the anticancer effect of BET inhibitors in breast cancer 22 . Another study confirmed that ferroptosis was significantly inhibited in gastric cancer and that cisplatin and paclitaxel promoted miR-522 secretion by Cancer-Associated Fibroblasts, reduced lipid-ROS accumulation in cancer cells, and ultimately resulted in reduced chemotherapy sensitivity 23 . However, to our knowledge, no study has evaluated the relationship between ferroptosis-related genes and paclitaxel resistance in breast cancer. It was hypothesized that ferroptosis is closely related to the occurrence and development of paclitaxel resistance and that exploring its potential mechanism is of great significance to identify therapeutic targets to overcome paclitaxel resistance in patients with breast cancer. Thus, the present study aimed to use bioinformatic methods to find new targets and biomarkers. In this study, DEGs associated with Taxol resistance were screened by analyzing the gene chip expression data in the GSE dataset of BC cells with Taxol resistance. Moreover, cluster analysis and functional enrichment analysis were performed. The Gene Expression Omnibus (GEO) dataset related to paclitaxel resistance was used to screen drug-resistant genes and construct a prognostic model. Moreover, an association between genes associated with paclitaxel resistance and immune infiltration and immune checkpoints in patients with TNBC was demonstrated in this study. Thus, our study may be provides potentially useful prognostic biomarker and predictor for individualized treatment in TNBC. Materials and Methods Data collection The processed gene expression datasets of clinical samples collected from patients with breast cancer who did (n = 8) and did not (n = 20) achieve clinicopathologic response after paclitaxel treatment were retrieved from the comprehensive gene expression profile database GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) (ID: GSE22513). The RNA-sequencing raw data of 116 TNBC samples obtained from The Cancer Genome Atlas ( https://portal.gdc.cancer.gov/ ) included therapeutic information, somatic mutation data, and CNV data files. These raw data were first standardized to fragments per kilobase million expression levels prior to evaluating the expression of FERGs using the limma program. Identification of genes associated with paclitaxel resistance in patients with breast cancer The paclitaxel resistance dataset of 20 patients with breast cancer who were resistant and 8 who were sensitive to paclitaxel therapy. The “Limma” R package was used to obtain DEGs between the paclitaxel-resistant and -nonresistant patients. The conditions for DEG screening were difference multiple (FC) ≥ 2 and P < 0.05. The “survival” analysis package was used for the univariate Cox regression analysis of 116 TNBC samples from TCGA database (P < 0.05) to obtain DEGs associated with prognosis. The data from TCGA database are freely available to the public, and this study strictly followed the access policies of the database and publication guidelines. Thus, this study did not require ethical review and approval from an ethics committee. Weighted Gene Co-expression Network Analysis (WGCNA) A total of 711 DEGs were evaluated to test their availability, and the R package “WGCNA” was used to construct a gene coexpression network. First, Pearson’s correlation matrices and average linkage method were both performed for all pairwise genes. Then, a weighted adjacency matrix was constructed using a power function A_mn = |C_mn| ^ β (C_mn = Pearson’s correlation between gene_m and gene_n; A_mn = adjacency between gene m and gene n). β was a soft-thresholding parameter that emphasized strong correlations between genes and penalized weak correlations. After choosing a power of 9, the adjacency was transformed into a topological overlap matrix (TOM), which could measure the network connectivity of a gene defined as the sum of its adjacency with all other genes for network generation. The corresponding dissimilarity (1-TOM) was then calculated. To classify genes with similar expression profiles into gene modules, average linkage hierarchical clustering was performed according to the TOM-based dissimilarity measure with a minimum size (gene group) of 50 for the gene dendrogram. The sensitivity was set to 4. To further analyze the module, the dissimilarity of module eigengenes was calculated and a cut line for a module dendrogram was selected by merging certain modules. In addition, modules with distance < 0.25 were merged to finally produce five coexpression modules. Of note, the grey module was considered a gene set that could not be assigned to any module. Identification of clinically significant modules and screening of hub genes The coexpression module is a collection of genes with high topological overlap similarity. Genes in the same module often have a higher degree of coexpression. In the current study, two methods were used to identify the important modules relevant to clinical traits. The module eigengene (ME) represents the first principal component of the module that is used to describe the expression pattern of the module in each sample. Module membership (MM) refers to the correlation coefficient between genes and module eigengenes that is used to describe the reliability of a gene belonging to a module. The correlation between the modules and clinical data was evaluated to identify significant clinical modules. Based the cutoff criteria (|MM| > 0.7), 158 genes with high connectivity in the clinical significant module were identified as hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses For gene set functional enrichment analysis, the GO annotation of genes in the R package (version 3.1.0) was used as the background, followed by mapping the genes into the background set and using the R package cluster Profiler (version 3.14.3) for enrichment analysis. At the same time, the KEGG rest API ( https://www.kegg.jp/kegg/rest/keggapi.html ) was used to obtain the latest KEGG pathway gene annotation as the background. Development of prognostic models Based on all TNBC patients, the risk model was established using the lasso Cox regression analysis of drug resistance-related DEGs. A risk score was calculated for each patient according to the formula, and patients were classified into high or low risk subtypes based on the median risk score. Kaplan–Meier analysis was used to compare survival differences between the high- and low-risk subtypes. A receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) values over time were used to assess the prognostic value of the associated risk models. Analysis of drug susceptibility The pRRophetic package was used for drug susceptibility prediction. The input of the R package algorithm was mainly the large cell line expression profiles and the corresponding IC 50 information. Ridge regression was used to build a model, which was then used to predict the chemotherapeutic response in clinical samples. Real-time quantitative polymerase chain reaction (RT-qPCR) Total RNA was extracted from samples using the Trizol reagent (R1100, Solarbio) and purified using chloroform and ethanol. A cDNA kit (RR036A, TaKaRa) with reverse transcriptase was used to synthesize cDNA as the template for qPCR analysis according to the manufacturer’s protocol. The qPCR reaction mixture included 1.6 µL of a mixture of forward and reverse primers, 10 µL of TB Green mix (RR430A, TaKaRa), 2 µL of cDNA sample, and 6.4 µL of RNase-free water. According to the manufacturer’s protocol, reactions were performed on Light Cycler 96 Real-Time System (Roche, Switzerland). Each sample was run in triplicate. The Ct values and the relative expression levels were determined using the 2 −ΔΔCt method. The β-actin expression levels were used for normalization. Statistical analysis All statistical analyses were performed using the R (R 4.1.0) software. Statistical analyses of RT-qPCR results was were performed using GraphPad Prism (version 8, GraphPad Software Inc., San Diego, CA, USA). All statistical p-values were two-sided, and p < 0.05 represented statistical significance (*p < 0.05; **p < 0.01; and ***p < 0.001). Results Acquisition of DEGs associated with drug resistance Figure 1 shows the flowchart of the construction and validation of data collection and analysis. Breast cancer paclitaxel resistance-related datasets (GSE22513) were retrieved from the GEO database. Limma differential analysis was performed on the dataset to identify DEGs between the drug resistance and the non-drug resistance groups. Limma differential expression analysis revealed 711 DEGs between the GSE22513 paclitaxel-resistant group and paclitaxel-sensitive group (257 upregulated and 454 downregulated genes; P < 0.05) (Fig. 2 A). Figure 2 B is the heat map of the expression of relevant DEGs in different samples (Fig. 2 B). To further elucidate the potential mechanisms of DEGs related to paclitaxel resistance, a functional enrichment analysis of these genes was performed. GO enrichment analysis showed that the DEGs related to drug resistance were mainly enriched in chemical carcinogenesis, cell differentiation, tissue differentiation, cell adhesion, metal ion homeostasis, and immune response (Fig. 2 C). Moreover, KEGG enrichment analysis showed that the DEGs were closely related to cytokine receptor interaction, chemical carcinogenesis, primary immunodeficiency, platinum resistance, and ABC transporters (Fig. 2 D). Hub genes screened via WGCNA analysis To screen genes associated with Taxol resistance, a WGCNA analysis of the differential gene expression matrices obtained in the previous step was performed. Five coexpressed modules were identified in GSE22513 (blue, brown, gray, turquoise, and yellow), of which the gray modules were considered as the collections of genes that could not be assigned to any module (Fig. 3 A). As shown in the figure, the blue module had the highest correlation with the turquoise module (Fig. 3 B). The clinical information of each sample was used to calculate the correlation between gene modules and phenotypes to identify modules with resistant phenotypes. Figure 3 C shows that there was a significant positive correlation between the turquoise module and the phenotype of drug resistance (Fig. 3 C). Therefore, the turquoise module was selected for further analysis. To screen the hub genes, the expression correlation between the module feature vector and genes was calculated to obtain the MM value. A total of 154 genes with MM values > 0.7 for the yellow–green module were obtained as hub genes. DEGs related to drug resistance associated with ferroptosis The present study aimed to explore the interaction between ferroptosis and Taxol-resistant phenotype in breast cancer. Therefore, FERG datasets retrieved from the GeneCards website were intercrossed with hub genes identified in the turquoise module to obtain 48 genes (Fig. 4 A). To investigate the impact of these genes on the prognosis of patients with TNBC, a batch survival analysis of these 48 genes was performed. According to the median value of gene expression as a grouping method, the prognostic differences of different groups were analyzed, identifying three genes with a significant effect on the prognosis of patients with TNBC patients (Fig. 4 B). Among the three genes, patients with a high expression of GRIK3 exhibited a poor prognosis, suggesting that GRIK3 is a risk factor of tumor development (Fig. 4 D). Patients with a low expression of IDO1 (Fig. 4 E) and CLGN (Fig. 4 C) exhibited poor prognosis, indicating that these two genes are protective against tumor development. GRIK3, IDO1, and CLGN expression were quantified in 10 pairs of TNBC and adjacent normal breast samples. Compared with the neighboring normal breast samples, TNBC samples had a significantly higher expression of GRIK3 and GRIK3 and significantly lower expression of IDO1 and CLGN, consistent with a previous prognostic model construction (Fig. 4 F–H). These results indicated that a novel ferroptosis-related gene model can be used for prognostic prediction in TNBC. Prognostic model construction To investigate the impact of these three genes on the prognosis of patients with breast cancer, the lasso regression method was used to reduce dimension and construct a prognostic model (− 0.282) * IDO1 + (− 0.1804) * CLGN + (0.2349) * GRIK3 (Fig. 5 A–B). In Fig. 5 C, the scatter plots of the risk scores from low to high are shown from left to right and the different colors represent the heatmap of expression of the genes included in this label in different risk groups. Information of 116 patients with TNBC obtained from TCGA database was used to calculate a risk score for each TNBC sample based on the prognostic model. The average risk score of all samples was calculated, and all patients with TNBC were categorized into high- and low-risk groups according to the average value. The KM survival curve distribution of the risk model in the TNBC dataset showed that the overall survival time of the high-risk group was significantly lower than that of the low-risk group, indicating that the risk model strongly predicts the prognosis of patients (Fig. 5 D). The ROC curves and AUCs plotted with regard to different time risk models revealed that higher AUC values indicated a better predictive power of the model. The 1 -, 3 -, and 5-year AUC values of the model were 0.84, 0.84, and 0.78, respectively, suggesting that the model has a strong predictive ability (Fig. 5 E). To evaluate the clinical usefulness of the prognostic model, decision curves were computed to calculate the net benefit. Decision curve analysis, a novel method to assess diagnostic tests and prediction models, showed that the prognostic model had a higher overall net benefit, indicating that the model was clinically useful (Fig. 5 F–H). DEGs screened for enrichment analysis according to the risk score Patients with TNBC were grouped according to the risk model score, and the R package limma was used for differential analysis to identify DEGs between the high- and low-risk groups (Fig. 6 A). All 1.5-fold DEGs were selected, and GO and KEGG enrichment analyses were performed for these genes. In the KEGG enrichment analysis, the top enriched entries were mainly immune-related pathways, including antigen processing and presentation, cell adhesion molecules, Th1/Th2 cell differentiation, and PD-L1 expression and PD-1 checkpoint pathway in cancer (Fig. 6 B). GO enrichment analysis suggested that these DEGs were related to certain factors of the immune system, such as the immune response, defensive responses, leukocyte activation, and lymphocyte activation (Fig. 6 C). Immunoinfiltration TME is a complex network composed of various cell types and factors that play important roles in the occurrence and development of tumors. Tumor-infiltrating lymphocytes (TILs) and other tumor-infiltrating immune cells (TIICs) are key to the understanding of tumor immune surveillance based on tumor immunogenicity, which refers to the density and location of TILs and TIICs in TME. Their functional programs, including the “immune score,” play important roles in the prognosis and prediction of several cancers. First, an in-depth study was conducted on the relationship between the risk score and degree of immune cell infiltration. Based on the TIMER database, the risk score was negatively correlated with the infiltration levels of B cells, CD4 + T cells, CD8 + T cells, neutrophils, and myeloid dendritic cells (Fig. 7 A). All patients with TNBC were classified into two groups according to the risk model score, and different algorithms were used to compare the degree of immune cell infiltration between the two groups. Immune infiltration analysis performed on the corrected TCGA-TNBC dataset using mcpcounter showed that there were significant differences in the expression of B cells, CD8 + T cells, monocytes, myeloid dendritic cells, natural killer (NK) cells, and T cells between the two groups. Moreover, their expression was significantly lower in the high-risk group than in the low-risk group, whereas no significant difference was found between the groups with respect to endothelial cells, fibroblasts, and neutrophils (Fig. 7 B). This suggests, to some extent, that there is a significant correlation between the risk model and the degree of immune cell infiltration. The epic algorithm was used to verify the above results, which revealed that the differential expression of certain immune cells, such as B cells, CD4 + T cells, CD8 + T cells, and macrophages, was statistically significant and negatively correlated with the risk score (Fig. 7 C). Finally, the infiltration abundance of TIICs, fibroblasts, and epithelial cells in the two groups was assessed via the xcell algorithm, and the results were generally consistent with those of the other two algorithms (Fig. 7 D). These results indicate that this risk model can be used to predict the degree of immune cell infiltration in tumors. A higher level of immune cell infiltration represents a better prognosis. Immune checkpoints The relationship between the risk score and expression level of the common immune checkpoints was assessed. Figure 10A shows the distribution of common immune checkpoints in different risk patients. As the risk score increased (left to right on the X-axis), the survival rate of patients significantly decreased (see the middle panel) and the expression levels of common immune checkpoints showed a downward trend (Fig. 8 A). Patients with TNBC were grouped according to the risk score, and differences in immune checkpoint expression between the two groups were compared. The results revealed that the expression of some common immune checkpoints, such as PDCD1, ICOS, and CTLA4, in the high-risk group was significantly lower than that in the low-risk group (Fig. 8 B). Figure 8 C demonstrates the relatively low expression of immune checkpoints in the high-risk group (Fig. 8 C). The tumor inflammation signature (TIS) is a set of 18 genes, including the IFN-γ signaling pathway as well as T cell and NK cell abundance, that are highly associated with clinical response to immune checkpoint inhibitors. Thus, TIS can better reflect the degree of immune cell infiltration in TME. At present, TIS has been used in a number of clinical trials on tumor immunotherapy 24 . In addition to early melanoma, head and neck cancer, and gastric cancer, TIS has begun to be used in difficulties recognized by clinical frontline workers in other cancers, such as TNBC, offering strong clinical benefits 25 – 27 . The TIS score has been significantly associated with the survival benefit of patients treated with ICIS combined with chemotherapy. Figure 8 D shows that the TIS score was significantly lower in the high-risk group than in the low-risk group (Fig. 8 D). These results suggest that the risk model is significant in predicting the efficacy of immunotherapy in patients with TNBC. For patients with low score, in addition to conventional Taxol chemotherapy drugs that are prone to resistance, the effect of immunotherapy was not optimistic. The maftools package was used to analyze any difference in the distribution of somatic mutations in low and high clinical risk group (CRG) scores in the TCGA-TNBC cohort. As shown in Figs. 8 E and F, TMB was more extensive in the low score group. In theory, the higher the TMB, the more neoantigens are recognized by T cells and the better the response to immune checkpoint inhibitor treatment. The most obvious somatic mutations in the low score group were TP53 (91%) and TTN (29%), whereas those in the high score group were TP53 (85%) and PTEN (18%) (Fig. 8 E–F). Evidence suggests that patients with L-TNBC may benefit from immunotherapy. Further survival analysis demonstrated that the H-TMB subgroup had significant survival benefits. Sensitivity analysis was performed between the two groups with regard to a few medications that are currently used to treat breast cancer. The results showed that the paclitaxel IC 50 was significantly higher in the high score group than in the low score group, indicating the importance of the risk model in predicting paclitaxel sensitivity in patients with TNBC. In patients with high CRG scores, the IC 50 values of doxorubicin, tamoxifen, vinblastine, bleomycin, and AUY922, among others, were considerably higher (Fig. 8 G). This may provide new options for the treatment of patients with paclitaxel-resistant triple-negative breast cancer. Discussion The most common chemotherapy drug for TNBC is paclitaxel. In recent years, several studies have focused on the efficacy of paclitaxel to enhance the final therapeutic effect 28 , 29 . However, its therapeutic effect is limited, and the most important disadvantage of paclitaxel is the acquired drug resistance. The mechanism of paclitaxel action includes not only mitosis but also angiogenesis, apoptosis, inflammation, and ROS generation 30 . A prognostic model was established by analyzing the intersection of DEGs related to paclitaxel resistance and ferroptosis. To improve treatment outcomes in breast cancer, it is critical to first identify patients who are more susceptible to drug resistance and then find measures to reduce this risk. The discovery of regulatory cell death has led to great progress in the field of cancer treatment. In recent years, scientists have identified ferroptosis as a new form of iron-dependent regulatory cell death caused by excessive lipid peroxidation 31 , 32 . Ferroptosis is associated with the occurrence and treatment response of various cancer types, attracting widespread attention 33 , 34 . Previous studies have shown that resistance to ferroptosis can affect cell proliferation, migration, and drug resistance in different cancer types 35 , 36 . In addition, ferroptosis has been shown to play a crucial role in tumor progression and cancer therapy 37 . Chemotherapy resistance is a complex process involving multiple genes and signaling pathways. Tumor cells can develop drug resistance through various mechanisms, such as reducing drug intake, increasing drug pump, inhibiting apoptosis, increasing DNA repair capacity, and inhibiting apoptosis 38 , 39 . Studies have shown that when tumor cells are exposed to chemotherapeutic drugs, these drugs can induce significant ROS production, in turn leading to tumor cell death 40 . However, once tumor cells initiate a mechanism to change their metabolic microenvironment, inhibit ROS formation, and enhance oxidative stress defense or tolerance ability, drug resistance is induced 41 . From DEGs identified in two clusters, three significant genes (IDO1, CLGN, and GRIK3) were selected, which were used to construct a prognostic model through univariate and lasso Cox analysis. IDO1 is an intracellular heme-dependent oxidase that causes local tryptophan depletion and ferroptosis inhibition, and the regulation of immune cells via tryptophan metabolism regulation is associated with chemotherapy tolerance promotion and poor cancer prognosis 42 – 44 . IDO1 is strongly expressed in tamoxifen-resistant breast cancer cells and mediates the proliferation, metastasis, and T resistance of tamoxifen-resistant breast cancer cells in vitro and in vivo via STAT1 and IL-6/STAT3 activation 45 . GRIK3, an important excitatory neurotransmitter receptor, has been positively correlated with the prognosis of patients with breast cancer as it affects various signaling pathways and key signal transduction pathways, including two epithelial–mesenchymal transition regulators, SPDEF and CDH1 46 . The low expression of CLGN was confirmed to be associated with tamoxifen sensitivity in premenopausal patients with lumina A subtype breast cancer 47 . However, the biological function of GRIK3 and CLGN in malignant tumors is largely unknown. Several prognostic models based on FERGs have been reported to explore prognostic biomarkers and predict the prognosis of various cancer types 48 . In one study, a 13-gene prognostic model of acute myeloid leukemia was developed. According to this model, patients were classified into high- and low-risk groups, with higher risk scores indicating shorter survival and association with tumor-associated immune abnormalities, mutational patterns and pathway dysregulation, and clinical outcomes 49 . Another study used the GEO and TCGA databases to collect thyroid cancer gene expression data and clinical outcomes to evaluate the prognostic value of 75 FERG expression. Five FERG prognostic models and nomograms were developed to provide unique insights for predicting the prognosis of thyroid cancer 50 . In a head and neck squamous cell carcinoma study, three stable molecular subtypes with different prognostic, mutational, and immunologic profiles were identified using consensus clustering with ferroptosis marker genes. WGCNA was then used to identify the gene modules related to molecular subtypes. After screening and lasso regression analysis, eight genes were determined to be related to prognosis. A prognostic model with score related to ferroptosis was finally constructed, which reflected the risk and positive prognostic factors of patients with head and neck squamous cell carcinoma 51 . The role of ferroptosis related to drug resistance in TNBC, however, has not been fully elucidated 52 , 53 . It is well known that TME is composed of tumor cells and their surrounding cells, such as lymphocytes, TIICs, and tumor vasculature 54 . There is strong evidence to support the hypothesis that TME is essential for tumor formation, progression, and treatment resistance 55 , 56 . In the present study results, B cells, CD4 + T cells, CD8 + T cells, neutrophils, and myeloid dendritic cells were all significantly negatively correlated with the risk score. In the TNBC low-risk group, the expression of these TIICs was significantly higher than that in the high-risk group. In view of the in-depth research that has been conducted on breast cancer immunotherapy, the study of TME and immune cell infiltration may be helpful to discover new directions and mechanisms of breast cancer immunotherapy. First, DEGs were identified from the GEO database to establish a FERG-based breast cancer prediction model. Because ferroptosis is different from the other accepted modes of cell death, it may offer new therapeutic possibilities for treating cancer. The model in the present study was validated by comparing the included data derived from the TCGA database with those from different database sources, which improved the effectiveness of the model. A prognostic model with score related to ferroptosis developed in this study can appropriately reflect the risk and positive prognostic factors of patients with TNBC. Thus, the model can be used to guide individualized adjuvant therapy and chemotherapy for patients with TNBC. The current study has a few limitations. Only data from public sources were used in this study, necessitating further validation using more accurate clinical data. Because the prognostic signature was developed and validated using publicly sourced data, experimental studies and extensive prospective studies are needed to confirm these results. In conclusion, a novel ferroptosis-related gene model can be used for prognostic prediction in TNBC. New ferroptosisrelated genes might be used for TNBC targeting therapy in the future. Declarations Data availability statement The datasets supporting the results and conclusions of this study were downloaded from the TCGA (https://portal.gdc.cancer.gov/) and GEO (accession no. GSE22513, http://www.ncbi.nlm.nih.gov/geo/). The data used for the prediction of potential drugs were obtained from the CellMiner and DrugBank databases. Sources of funding This work was supported by grants from The National Natural Science Foundation of China (No. 81672596 to Qichao Ni). Author contributions H.Y. designed the study, analyzed data and writed manuscript. HY.Q. and SC.M. collected and analyzed data. X.L., BY.L., D.Z., and YP.C. collected samples and performed experiment. CY.S. and QC.N. concepted and supervised the study, provided funding, and edited manuscript. Declaration of competing interest The authors have nothing to disclose. References Burstein, H. J. et al. 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Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer. Cancer Cell 39 , 1578-1593 e1578 (2021). https://doi.org:10.1016/j.ccell.2021.09.010 Zhu, L. & Chen, L. Progress in research on paclitaxel and tumor immunotherapy. Cell Mol Biol Lett 24 , 40 (2019). https://doi.org:10.1186/s11658-019-0164-y Li, D. & Li, Y. The interaction between ferroptosis and lipid metabolism in cancer. Signal Transduction and Targeted Therapy 5 (2020). https://doi.org:10.1038/s41392-020-00216-5 Gao, W., Wang, X., Zhou, Y., Wang, X. & Yu, Y. Autophagy, ferroptosis, pyroptosis, and necroptosis in tumor immunotherapy. Signal Transduction and Targeted Therapy 7 (2022). https://doi.org:10.1038/s41392-022-01046-3 Tong, X. et al. Targeting cell death pathways for cancer therapy: recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research. 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GCH1 induces immunosuppression through metabolic reprogramming and IDO1 upregulation in triple-negative breast cancer. J Immunother Cancer 9 (2021). https://doi.org:10.1136/jitc-2021-002383 Zeitler, L. & Murray, P. J. IL4i1 and IDO1: oxidases that control a tryptophan metabolic nexus in cancer. J Biol Chem , 104827 (2023). https://doi.org:10.1016/j.jbc.2023.104827 Zhao, X. et al. Indoleamine 2,3-dioxygenase 1 regulates breast cancer tamoxifen resistance through interleukin-6/signal transducer and activator of transcription. Toxicol Appl Pharmacol 440 , 115921 (2022). https://doi.org:10.1016/j.taap.2022.115921 Xiao, B. et al. Glutamate Ionotropic Receptor Kainate Type Subunit 3 (GRIK3) promotes epithelial-mesenchymal transition in breast cancer cells by regulating SPDEF/CDH1 signaling. Mol Carcinog 58 , 1314-1323 (2019). https://doi.org:10.1002/mc.23014 Deng, K. et al. Soy Foods Might Weaken the Sensitivity of Tamoxifen in Premenopausal Patients With Lumina A Subtype of Breast Cancer. Clin Breast Cancer 19 , e337-e342 (2019). https://doi.org:10.1016/j.clbc.2018.12.003 Zhao, L. et al. Ferroptosis in cancer and cancer immunotherapy. Cancer Commun (Lond) 42 , 88-116 (2022). https://doi.org:10.1002/cac2.12250 Cui, Z. et al. Comprehensive Analysis of a Ferroptosis Pattern and Associated Prognostic Signature in Acute Myeloid Leukemia. Front Pharmacol 13 , 866325 (2022). https://doi.org:10.3389/fphar.2022.866325 Wang, Y., Yang, J., Chen, S., Wang, W. & Teng, L. Identification and Validation of a Prognostic Signature for Thyroid Cancer Based on Ferroptosis-Related Genes. Genes (Basel) 13 (2022). https://doi.org:10.3390/genes13060997 Wei, M., Tian, Y., Lv, Y., Liu, G. & Cai, G. Identification and validation of a prognostic model based on ferroptosis-associated genes in head and neck squamous cancer. Frontiers in Genetics 13 (2022). https://doi.org:10.3389/fgene.2022.1065546 Zhang, Y. et al. A novel ferroptosis‑related gene signature for overall survival prediction and immune infiltration in patients with breast cancer. Int J Oncol 61 (2022). https://doi.org:10.3892/ijo.2022.5438 Zhu, L. et al. A Novel Ferroptosis-Related Gene Signature for Overall Survival Prediction in Patients With Breast Cancer. Front Cell Dev Biol 9 , 670184 (2021). https://doi.org:10.3389/fcell.2021.670184 Emens, L. A. Breast Cancer Immunotherapy: Facts and Hopes. Clin Cancer Res 24 , 511-520 (2018). https://doi.org:10.1158/1078-0432.CCR-16-3001 Liang, Y., Zhang, H., Song, X. & Yang, Q. Metastatic heterogeneity of breast cancer: Molecular mechanism and potential therapeutic targets. Semin Cancer Biol 60 , 14-27 (2020). https://doi.org:10.1016/j.semcancer.2019.08.012 Hanker, A. B., Sudhan, D. R. & Arteaga, C. L. Overcoming Endocrine Resistance in Breast Cancer. Cancer Cell 37 , 496-513 (2020). https://doi.org:10.1016/j.ccell.2020.03.009 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3176896","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":220477188,"identity":"a02b548c-ad6f-4880-a7f0-aa24a7c6ebb4","order_by":0,"name":"Huan You","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIie3RPQrCMBiA4UigU6xrumiP8EEhU27ikhLJpiiuHQoOLoJXEXqBlg+6uhZ0iDfo6OBgurklo0PeLZAnv4TEYv8Yp51VwJcLStEGkkSD3csiOycGAgkTmR1NCXeW8yABj0sBClAVyAiQSq795Nkf3F1wJ3DeWtKbbe0lg26mXY4CUwWzGkOIEtyRsjkx4IFkMxFT3mgoyQaj3cFkwdE9sgq5Szro7vX+uK+8Itqxkn6St78j5Zs+tfIuGovFYrEvUpJEeSU7t84AAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Nantong University, Nantong University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"You","suffix":""},{"id":220477189,"identity":"775f8414-c6e5-449c-8c24-353ad2bf97b8","order_by":1,"name":"Hongyan Qian","email":"","orcid":"","institution":"Affiliated Tumor Hospital of Nantong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Qian","suffix":""},{"id":220477190,"identity":"0753bbfe-742e-4cae-85fe-e4a438c2feaf","order_by":2,"name":"Shichen Miao","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University, Nantong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shichen","middleName":"","lastName":"Miao","suffix":""},{"id":220477191,"identity":"f52007b6-b8ba-4b01-b5fb-a025d1f26c4e","order_by":3,"name":"Xuan Li","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University, Nantong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Li","suffix":""},{"id":220477192,"identity":"d21a278e-e46e-45e6-8ee5-d5e8d9f93bed","order_by":4,"name":"Bingyi Liu","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University, Nantong 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University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qichao","middleName":"","lastName":"Ni","suffix":""},{"id":220477196,"identity":"c50df4e0-2435-4309-b113-2ca97196f3d2","order_by":8,"name":"Chenyi Sheng","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University, Nantong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chenyi","middleName":"","lastName":"Sheng","suffix":""}],"badges":[],"createdAt":"2023-07-17 07:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3176896/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3176896/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":40549669,"identity":"a5d620b7-600e-4818-ba10-f22d233c7a4e","added_by":"auto","created_at":"2023-07-25 15:20:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":158964,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of construction and validation of data collection and analysis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3176896/v1/dc76be320ab4e2a048b20dc4.png"},{"id":40549673,"identity":"7d560f5d-dbc0-49be-86c4-9a59911daa02","added_by":"auto","created_at":"2023-07-25 15:20:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2847740,"visible":true,"origin":"","legend":"\u003cp\u003eAcquisition and enrichment analysis of differentially expressed genes related to drug resistance. (A) Volcano plot shows related differentially expressed genes. (B) Heat map of differential gene expression. (C) GO enrichment analysis of differential genes related to paclitaxel resistance. (D) KEGG enrichment analysis of differential genes related to paclitaxel resistance.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3176896/v1/8b2ec8870c00650e8e826fb0.png"},{"id":40551265,"identity":"91d0f1f0-fba0-404e-b11b-f8e43c290311","added_by":"auto","created_at":"2023-07-25 15:36:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1020467,"visible":true,"origin":"","legend":"\u003cp\u003eThe co-expression modules analysis. (A) Clustering dendrogram of genes, various colors represent different modules. (B) Correlation between modules. (C) Relationship between the five modules and clinical features.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3176896/v1/1a93537a1ad875f34093260e.png"},{"id":40549670,"identity":"d1f14d49-0a45-4b4e-be11-9a63613d39ea","added_by":"auto","created_at":"2023-07-25 15:20:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1553361,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate regression was used to screen prognostic genes. (A) Venn diagram showing the intersection of differential genes related to paclitaxel resistance and ferroptosis-related genes. (B) Single factor regression analysis. (C) CLGN Kaplan-Meier survival analysis. (D) GRIK3 Kaplan-Meier survival analysis. (E) IDO1 Kaplan-Meier survival analysis. (G) RT-qPCR to analyze the expression of GRIK3, IDO1, and CLGN in TNBC and adjacent normal breast samples (n=10). Expression levels were normalized against the geometric mean of β-actin.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3176896/v1/a78dbcfd915e254dc53640a0.png"},{"id":40550581,"identity":"9c93f4ae-3e1d-4a9c-8515-f649387ba47d","added_by":"auto","created_at":"2023-07-25 15:28:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2381969,"visible":true,"origin":"","legend":"\u003cp\u003eA risk model was constructed in the TNBC cohort. (A) Cross-validation of LASSO regression parameter selection. (B) LASSO was used for regression analysis of differentially expressed genes. (C) Risk plot distribution, survival status of patients, and heat map of expression of included genes in the whole TCGA-TNBC dataset. (D) Kaplan–Meier survival curves for the risk model based on the TCGA-TNBC dataset. (E) Receiver operating characteristic (ROC) curves for the risk model in the TNBC. (F-H) 1 year, 3 years and 5 years Decision curve analysis (DCA).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3176896/v1/e51e908e6d0238c25b366871.png"},{"id":40549674,"identity":"e9307381-1fa7-4faf-a08b-27cf99c7d0f5","added_by":"auto","created_at":"2023-07-25 15:20:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":9025289,"visible":true,"origin":"","legend":"\u003cp\u003ePotential biological pathways affected by Prognostic models. (A) The different expression genes (DEGs) between the high-risk and low-risk groups. (B) The KEGG enrichment of high and low risk groups. (C) The Gene Ontology (GO) enrichment of DEGs.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3176896/v1/9cd17871155f5ab2267275e6.png"},{"id":40549677,"identity":"6319fd19-e4ef-41af-815f-20ab9e77e43c","added_by":"auto","created_at":"2023-07-25 15:20:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4283138,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between the risk model and infiltration abundances of immune cells. (A) Correlations between the risk score and six types of tumor-infiltrating immune cells. (B-D) Comparison of compositional fractions of immune cells between the high-risk and low-risk groups evaluated using the mcpcounter formula (B), epic formula (C) and xcell formula (D).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3176896/v1/4d20610ecffde2b11af6e282.png"},{"id":40550583,"identity":"3866e77c-5aca-4673-841e-d43e7af5ab42","added_by":"auto","created_at":"2023-07-25 15:28:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2870383,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of risk models with immunotherapy. (A)The relationship between different risk scores and patient follow-up time and changes in the expression of each immune checkpoint gene. (B) Immune checkpoint expression differences between high and low risk groups. (C) Correlation of risk score with Immune checkpoint expression. (D)The TIS score was higher in the low-risk score group. (E-F) The waterfall plot of tumor somatic mutation created by groups with high-risk score(E) and low risk score(F). (G) Differential chemotherapeutic drug responses responses in high- and low-risk patients.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3176896/v1/78d3cd62f112d0316d011e56.png"},{"id":42803755,"identity":"5fefc3ff-dd4e-4f23-9fbf-f8938a6bf3db","added_by":"auto","created_at":"2023-09-08 04:22:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3144702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3176896/v1/34daf931-4935-4d4e-bd87-fa8245bf0779.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Ferroptosis-Related Gene Signature for Chemotherapy Resistance Prediction in Triple-negative Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the latest global cancer data published by the International Agency for Research on Cancer, breast cancer in women is the most common cancer worldwide, exceeding lung cancer for the first time\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In addition, breast cancer is the most common malignancy and the leading cause of cancer-related death in women, and triple-negative breast cancer (TNBC) has one of the worst prognoses of all breast cancer variants\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. TNBC is a subtype of breast tumor that lacks hormone receptor expression and human epidermal growth factor receptor 2 (HER2) gene amplification and exhibits high relapse risk, high metastasis, and low overall survival\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The current treatment options are very limited owing to the lack of effective therapeutic targets in TNBC\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. At present, there are no clinical guidelines for TNBC treatment, and it is usually treated according to the conventional standard of care for breast cancer\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The conventional treatment modalities of breast cancer include surgery, radiotherapy and chemotherapy, endocrine therapy, and targeted therapy\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Among these options, the remission rate of TNBC after chemotherapy is higher than that after other types of treatment, making it the current preferred treatment for TNBC\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The efficacy of currently approved several chemotherapeutics, including cisplatin, anthracycline, paclitaxel, and tamoxifen, is limited owing to heterogeneity with oncogenic drivers and resistance development\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Accordingly, in recent years, efforts have been made to investigate the chemoresistance of TNBC to improve the chemotherapy effect and survival rate of patients with TNBC.\u003c/p\u003e \u003cp\u003eAt present, taxane-based regimens represent the mainstay in TNBC therapy. Although paclitaxel is effective for some patients with TNBC, approximately 30\u0026ndash;50% develop resistance, resulting in a lower overall survival rate\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The genomic and molecular basis of chemotherapy resistance in patients with TNBC remains poorly understood because the development of TNBC chemotherapy resistance is multifaceted and based on complex interactions among the tumor microenvironment (TME), drug effector, cancer stem cells, and many tumor cells\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Because drug resistance is a complex process driven by disrupted gene expression, it is critical to screen differentially expressed genes (DEGs) associated with drug resistance. Furthermore, it is of great significance to analyze the mechanism of DEGs on Taxol sensitivity for identifying new targets to reverse drug resistance and improve prognosis.\u003c/p\u003e \u003cp\u003eFerroptosis, a new type of cell death discovered recently, is usually characterized by abundant iron accumulation and lipid peroxidation during cell death\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Different from the necrosis modes of apoptosis and autophagy, ferroptosis has unique manifestations\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In terms of morphology, mitochondrial atrophy, mitochondrial membrane shrinkage, and rupture, a normal nucleus with a lack of chromatin condensation can be observed during ferroptosis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Cellular metabolism in cells undergoing ferroptosis is characterized by the accumulation of intracellular ferrous ions and Reactive Oxygen Species (ROS), significant phospholipid peroxidation, and impaired lipid peroxide repair function\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Studies have shown that regulating programmed cell death has a significant potential in the treatment of cancer drug resistance and that inducing ferroptosis can inhibit tumor cells resistant to conventional therapy and enhance the effect of immunotherapy\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFerroptosis can induce or inhibit tumor cells with specific drugs and regulate the expression of related genes. Studies have revealed that traditional anti-breast cancer drugs can induce ferroptosis, which can subsequently induce apoptosis in breast cancer cells\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The combination of the lysosomal promoter siramesine and the kinase inhibitor lapatinib was found to induce ferroptosis. This binding leads to an increase in iron levels by decreasing ferritin expression and increasing transferrin expression, leading to ferroptosis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Another study found that sulfasalazine caused iron ptosis in breast cancer cells, particularly in those with low Estrogen Receptor (ER) expression, suggesting that it could be used as a potential therapeutic agent for breast cancer\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. NR5A2 synergized with NCOA3 was used to prevent BET-induced ferroptosis in breast cancer cells by inducing NRF2 expression and enhancing the anticancer effect of BET inhibitors in breast cancer\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Another study confirmed that ferroptosis was significantly inhibited in gastric cancer and that cisplatin and paclitaxel promoted miR-522 secretion by Cancer-Associated Fibroblasts, reduced lipid-ROS accumulation in cancer cells, and ultimately resulted in reduced chemotherapy sensitivity\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, to our knowledge, no study has evaluated the relationship between ferroptosis-related genes and paclitaxel resistance in breast cancer. It was hypothesized that ferroptosis is closely related to the occurrence and development of paclitaxel resistance and that exploring its potential mechanism is of great significance to identify therapeutic targets to overcome paclitaxel resistance in patients with breast cancer. Thus, the present study aimed to use bioinformatic methods to find new targets and biomarkers.\u003c/p\u003e \u003cp\u003eIn this study, DEGs associated with Taxol resistance were screened by analyzing the gene chip expression data in the GSE dataset of BC cells with Taxol resistance. Moreover, cluster analysis and functional enrichment analysis were performed. The Gene Expression Omnibus (GEO) dataset related to paclitaxel resistance was used to screen drug-resistant genes and construct a prognostic model. Moreover, an association between genes associated with paclitaxel resistance and immune infiltration and immune checkpoints in patients with TNBC was demonstrated in this study. Thus, our study may be provides potentially useful prognostic biomarker and predictor for individualized treatment in TNBC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe processed gene expression datasets of clinical samples collected from patients with breast cancer who did (n\u0026thinsp;=\u0026thinsp;8) and did not (n\u0026thinsp;=\u0026thinsp;20) achieve clinicopathologic response after paclitaxel treatment were retrieved from the comprehensive gene expression profile database GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (ID: GSE22513). The RNA-sequencing raw data of 116 TNBC samples obtained from The Cancer Genome Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) included therapeutic information, somatic mutation data, and CNV data files. These raw data were first standardized to fragments per kilobase million expression levels prior to evaluating the expression of FERGs using the limma program.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of genes associated with paclitaxel resistance in patients with breast cancer\u003c/h2\u003e \u003cp\u003eThe paclitaxel resistance dataset of 20 patients with breast cancer who were resistant and 8 who were sensitive to paclitaxel therapy. The \u0026ldquo;Limma\u0026rdquo; R package was used to obtain DEGs between the paclitaxel-resistant and -nonresistant patients. The conditions for DEG screening were difference multiple (FC)\u0026thinsp;\u0026ge;\u0026thinsp;2 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The \u0026ldquo;survival\u0026rdquo; analysis package was used for the univariate Cox regression analysis of 116 TNBC samples from TCGA database (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to obtain DEGs associated with prognosis. The data from TCGA database are freely available to the public, and this study strictly followed the access policies of the database and publication guidelines. Thus, this study did not require ethical review and approval from an ethics committee.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eWeighted Gene Co-expression Network Analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eA total of 711 DEGs were evaluated to test their availability, and the R package \u0026ldquo;WGCNA\u0026rdquo; was used to construct a gene coexpression network. First, Pearson\u0026rsquo;s correlation matrices and average linkage method were both performed for all pairwise genes. Then, a weighted adjacency matrix was constructed using a power function A_mn = |C_mn| ^ β (C_mn\u0026thinsp;=\u0026thinsp;Pearson\u0026rsquo;s correlation between gene_m and gene_n; A_mn\u0026thinsp;=\u0026thinsp;adjacency between gene m and gene n). β was a soft-thresholding parameter that emphasized strong correlations between genes and penalized weak correlations. After choosing a power of 9, the adjacency was transformed into a topological overlap matrix (TOM), which could measure the network connectivity of a gene defined as the sum of its adjacency with all other genes for network generation. The corresponding dissimilarity (1-TOM) was then calculated. To classify genes with similar expression profiles into gene modules, average linkage hierarchical clustering was performed according to the TOM-based dissimilarity measure with a minimum size (gene group) of 50 for the gene dendrogram. The sensitivity was set to 4. To further analyze the module, the dissimilarity of module eigengenes was calculated and a cut line for a module dendrogram was selected by merging certain modules. In addition, modules with distance\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were merged to finally produce five coexpression modules. Of note, the grey module was considered a gene set that could not be assigned to any module.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of clinically significant modules and screening of hub genes\u003c/h2\u003e \u003cp\u003eThe coexpression module is a collection of genes with high topological overlap similarity. Genes in the same module often have a higher degree of coexpression. In the current study, two methods were used to identify the important modules relevant to clinical traits. The module eigengene (ME) represents the first principal component of the module that is used to describe the expression pattern of the module in each sample. Module membership (MM) refers to the correlation coefficient between genes and module eigengenes that is used to describe the reliability of a gene belonging to a module. The correlation between the modules and clinical data was evaluated to identify significant clinical modules. Based the cutoff criteria (|MM| \u0026gt; 0.7), 158 genes with high connectivity in the clinical significant module were identified as hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses\u003c/h2\u003e \u003cp\u003eFor gene set functional enrichment analysis, the GO annotation of genes in the R package (version 3.1.0) was used as the background, followed by mapping the genes into the background set and using the R package cluster Profiler (version 3.14.3) for enrichment analysis. At the same time, the KEGG rest API (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/kegg/rest/keggapi.html\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/kegg/rest/keggapi.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to obtain the latest KEGG pathway gene annotation as the background.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of prognostic models\u003c/h2\u003e \u003cp\u003eBased on all TNBC patients, the risk model was established using the lasso Cox regression analysis of drug resistance-related DEGs. A risk score was calculated for each patient according to the formula, and patients were classified into high or low risk subtypes based on the median risk score. Kaplan\u0026ndash;Meier analysis was used to compare survival differences between the high- and low-risk subtypes. A receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) values over time were used to assess the prognostic value of the associated risk models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of drug susceptibility\u003c/h2\u003e \u003cp\u003eThe pRRophetic package was used for drug susceptibility prediction. The input of the R package algorithm was mainly the large cell line expression profiles and the corresponding IC\u003csub\u003e50\u003c/sub\u003e information. Ridge regression was used to build a model, which was then used to predict the chemotherapeutic response in clinical samples.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReal-time quantitative polymerase chain reaction (RT-qPCR)\u003c/b\u003eTotal RNA was extracted from samples using the Trizol reagent (R1100, Solarbio) and purified using chloroform and ethanol. A cDNA kit (RR036A, TaKaRa) with reverse transcriptase was used to synthesize cDNA as the template for qPCR analysis according to the manufacturer\u0026rsquo;s protocol. The qPCR reaction mixture included 1.6 \u0026micro;L of a mixture of forward and reverse primers, 10 \u0026micro;L of TB Green mix (RR430A, TaKaRa), 2 \u0026micro;L of cDNA sample, and 6.4 \u0026micro;L of RNase-free water. According to the manufacturer\u0026rsquo;s protocol, reactions were performed on Light Cycler 96 Real-Time System (Roche, Switzerland). Each sample was run in triplicate. The Ct values and the relative expression levels were determined using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. The β-actin expression levels were used for normalization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using the R (R 4.1.0) software. Statistical analyses of RT-qPCR results was were performed using GraphPad Prism (version 8, GraphPad Software Inc., San Diego, CA, USA). All statistical p-values were two-sided, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 represented statistical significance (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; and ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of DEGs associated with drug resistance\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the flowchart of the construction and validation of data collection and analysis. Breast cancer paclitaxel resistance-related datasets (GSE22513) were retrieved from the GEO database. Limma differential analysis was performed on the dataset to identify DEGs between the drug resistance and the non-drug resistance groups. Limma differential expression analysis revealed 711 DEGs between the GSE22513 paclitaxel-resistant group and paclitaxel-sensitive group (257 upregulated and 454 downregulated genes; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB is the heat map of the expression of relevant DEGs in different samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). To further elucidate the potential mechanisms of DEGs related to paclitaxel resistance, a functional enrichment analysis of these genes was performed. GO enrichment analysis showed that the DEGs related to drug resistance were mainly enriched in chemical carcinogenesis, cell differentiation, tissue differentiation, cell adhesion, metal ion homeostasis, and immune response (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Moreover, KEGG enrichment analysis showed that the DEGs were closely related to cytokine receptor interaction, chemical carcinogenesis, primary immunodeficiency, platinum resistance, and ABC transporters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHub genes screened via WGCNA analysis\u003c/h2\u003e \u003cp\u003eTo screen genes associated with Taxol resistance, a WGCNA analysis of the differential gene expression matrices obtained in the previous step was performed. Five coexpressed modules were identified in GSE22513 (blue, brown, gray, turquoise, and yellow), of which the gray modules were considered as the collections of genes that could not be assigned to any module (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). As shown in the figure, the blue module had the highest correlation with the turquoise module (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The clinical information of each sample was used to calculate the correlation between gene modules and phenotypes to identify modules with resistant phenotypes. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC shows that there was a significant positive correlation between the turquoise module and the phenotype of drug resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Therefore, the turquoise module was selected for further analysis. To screen the hub genes, the expression correlation between the module feature vector and genes was calculated to obtain the MM value. A total of 154 genes with MM values\u0026thinsp;\u0026gt;\u0026thinsp;0.7 for the yellow\u0026ndash;green module were obtained as hub genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDEGs related to drug resistance associated with ferroptosis\u003c/h2\u003e \u003cp\u003eThe present study aimed to explore the interaction between ferroptosis and Taxol-resistant phenotype in breast cancer. Therefore, FERG datasets retrieved from the GeneCards website were intercrossed with hub genes identified in the turquoise module to obtain 48 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). To investigate the impact of these genes on the prognosis of patients with TNBC, a batch survival analysis of these 48 genes was performed. According to the median value of gene expression as a grouping method, the prognostic differences of different groups were analyzed, identifying three genes with a significant effect on the prognosis of patients with TNBC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Among the three genes, patients with a high expression of GRIK3 exhibited a poor prognosis, suggesting that GRIK3 is a risk factor of tumor development (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Patients with a low expression of IDO1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) and CLGN (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) exhibited poor prognosis, indicating that these two genes are protective against tumor development. GRIK3, IDO1, and CLGN expression were quantified in 10 pairs of TNBC and adjacent normal breast samples. Compared with the neighboring normal breast samples, TNBC samples had a significantly higher expression of GRIK3 and GRIK3 and significantly lower expression of IDO1 and CLGN, consistent with a previous prognostic model construction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF\u0026ndash;H). These results indicated that a novel ferroptosis-related gene model can be used for prognostic prediction in TNBC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic model construction\u003c/h2\u003e \u003cp\u003eTo investigate the impact of these three genes on the prognosis of patients with breast cancer, the lasso regression method was used to reduce dimension and construct a prognostic model (\u0026minus;\u0026thinsp;0.282) * IDO1 + (\u0026minus;\u0026thinsp;0.1804) * CLGN + (0.2349) * GRIK3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;B). In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, the scatter plots of the risk scores from low to high are shown from left to right and the different colors represent the heatmap of expression of the genes included in this label in different risk groups. Information of 116 patients with TNBC obtained from TCGA database was used to calculate a risk score for each TNBC sample based on the prognostic model. The average risk score of all samples was calculated, and all patients with TNBC were categorized into high- and low-risk groups according to the average value. The KM survival curve distribution of the risk model in the TNBC dataset showed that the overall survival time of the high-risk group was significantly lower than that of the low-risk group, indicating that the risk model strongly predicts the prognosis of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The ROC curves and AUCs plotted with regard to different time risk models revealed that higher AUC values indicated a better predictive power of the model. The 1 -, 3 -, and 5-year AUC values of the model were 0.84, 0.84, and 0.78, respectively, suggesting that the model has a strong predictive ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). To evaluate the clinical usefulness of the prognostic model, decision curves were computed to calculate the net benefit. Decision curve analysis, a novel method to assess diagnostic tests and prediction models, showed that the prognostic model had a higher overall net benefit, indicating that the model was clinically useful (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF\u0026ndash;H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDEGs screened for enrichment analysis according to the risk score\u003c/h2\u003e \u003cp\u003ePatients with TNBC were grouped according to the risk model score, and the R package limma was used for differential analysis to identify DEGs between the high- and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). All 1.5-fold DEGs were selected, and GO and KEGG enrichment analyses were performed for these genes. In the KEGG enrichment analysis, the top enriched entries were mainly immune-related pathways, including antigen processing and presentation, cell adhesion molecules, Th1/Th2 cell differentiation, and PD-L1 expression and PD-1 checkpoint pathway in cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). GO enrichment analysis suggested that these DEGs were related to certain factors of the immune system, such as the immune response, defensive responses, leukocyte activation, and lymphocyte activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImmunoinfiltration\u003c/h2\u003e \u003cp\u003eTME is a complex network composed of various cell types and factors that play important roles in the occurrence and development of tumors. Tumor-infiltrating lymphocytes (TILs) and other tumor-infiltrating immune cells (TIICs) are key to the understanding of tumor immune surveillance based on tumor immunogenicity, which refers to the density and location of TILs and TIICs in TME. Their functional programs, including the \u0026ldquo;immune score,\u0026rdquo; play important roles in the prognosis and prediction of several cancers. First, an in-depth study was conducted on the relationship between the risk score and degree of immune cell infiltration. Based on the TIMER database, the risk score was negatively correlated with the infiltration levels of B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, neutrophils, and myeloid dendritic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). All patients with TNBC were classified into two groups according to the risk model score, and different algorithms were used to compare the degree of immune cell infiltration between the two groups. Immune infiltration analysis performed on the corrected TCGA-TNBC dataset using mcpcounter showed that there were significant differences in the expression of B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, monocytes, myeloid dendritic cells, natural killer (NK) cells, and T cells between the two groups. Moreover, their expression was significantly lower in the high-risk group than in the low-risk group, whereas no significant difference was found between the groups with respect to endothelial cells, fibroblasts, and neutrophils (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). This suggests, to some extent, that there is a significant correlation between the risk model and the degree of immune cell infiltration. The epic algorithm was used to verify the above results, which revealed that the differential expression of certain immune cells, such as B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and macrophages, was statistically significant and negatively correlated with the risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Finally, the infiltration abundance of TIICs, fibroblasts, and epithelial cells in the two groups was assessed via the xcell algorithm, and the results were generally consistent with those of the other two algorithms (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). These results indicate that this risk model can be used to predict the degree of immune cell infiltration in tumors. A higher level of immune cell infiltration represents a better prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImmune checkpoints\u003c/h2\u003e \u003cp\u003eThe relationship between the risk score and expression level of the common immune checkpoints was assessed. Figure\u0026nbsp;10A shows the distribution of common immune checkpoints in different risk patients. As the risk score increased (left to right on the X-axis), the survival rate of patients significantly decreased (see the middle panel) and the expression levels of common immune checkpoints showed a downward trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Patients with TNBC were grouped according to the risk score, and differences in immune checkpoint expression between the two groups were compared. The results revealed that the expression of some common immune checkpoints, such as PDCD1, ICOS, and CTLA4, in the high-risk group was significantly lower than that in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC demonstrates the relatively low expression of immune checkpoints in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). The tumor inflammation signature (TIS) is a set of 18 genes, including the IFN-γ signaling pathway as well as T cell and NK cell abundance, that are highly associated with clinical response to immune checkpoint inhibitors. Thus, TIS can better reflect the degree of immune cell infiltration in TME. At present, TIS has been used in a number of clinical trials on tumor immunotherapy\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In addition to early melanoma, head and neck cancer, and gastric cancer, TIS has begun to be used in difficulties recognized by clinical frontline workers in other cancers, such as TNBC, offering strong clinical benefits\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The TIS score has been significantly associated with the survival benefit of patients treated with ICIS combined with chemotherapy. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD shows that the TIS score was significantly lower in the high-risk group than in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). These results suggest that the risk model is significant in predicting the efficacy of immunotherapy in patients with TNBC. For patients with low score, in addition to conventional Taxol chemotherapy drugs that are prone to resistance, the effect of immunotherapy was not optimistic.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe maftools package was used to analyze any difference in the distribution of somatic mutations in low and high clinical risk group (CRG) scores in the TCGA-TNBC cohort. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE and F, TMB was more extensive in the low score group. In theory, the higher the TMB, the more neoantigens are recognized by T cells and the better the response to immune checkpoint inhibitor treatment. The most obvious somatic mutations in the low score group were TP53 (91%) and TTN (29%), whereas those in the high score group were TP53 (85%) and PTEN (18%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE\u0026ndash;F). Evidence suggests that patients with L-TNBC may benefit from immunotherapy. Further survival analysis demonstrated that the H-TMB subgroup had significant survival benefits.\u003c/p\u003e \u003cp\u003eSensitivity analysis was performed between the two groups with regard to a few medications that are currently used to treat breast cancer. The results showed that the paclitaxel IC\u003csub\u003e50\u003c/sub\u003e was significantly higher in the high score group than in the low score group, indicating the importance of the risk model in predicting paclitaxel sensitivity in patients with TNBC. In patients with high CRG scores, the IC\u003csub\u003e50\u003c/sub\u003e values of doxorubicin, tamoxifen, vinblastine, bleomycin, and AUY922, among others, were considerably higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). This may provide new options for the treatment of patients with paclitaxel-resistant triple-negative breast cancer.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe most common chemotherapy drug for TNBC is paclitaxel. In recent years, several studies have focused on the efficacy of paclitaxel to enhance the final therapeutic effect\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, its therapeutic effect is limited, and the most important disadvantage of paclitaxel is the acquired drug resistance. The mechanism of paclitaxel action includes not only mitosis but also angiogenesis, apoptosis, inflammation, and ROS generation\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. A prognostic model was established by analyzing the intersection of DEGs related to paclitaxel resistance and ferroptosis. To improve treatment outcomes in breast cancer, it is critical to first identify patients who are more susceptible to drug resistance and then find measures to reduce this risk.\u003c/p\u003e \u003cp\u003eThe discovery of regulatory cell death has led to great progress in the field of cancer treatment. In recent years, scientists have identified ferroptosis as a new form of iron-dependent regulatory cell death caused by excessive lipid peroxidation\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Ferroptosis is associated with the occurrence and treatment response of various cancer types, attracting widespread attention\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown that resistance to ferroptosis can affect cell proliferation, migration, and drug resistance in different cancer types\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In addition, ferroptosis has been shown to play a crucial role in tumor progression and cancer therapy\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChemotherapy resistance is a complex process involving multiple genes and signaling pathways. Tumor cells can develop drug resistance through various mechanisms, such as reducing drug intake, increasing drug pump, inhibiting apoptosis, increasing DNA repair capacity, and inhibiting apoptosis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Studies have shown that when tumor cells are exposed to chemotherapeutic drugs, these drugs can induce significant ROS production, in turn leading to tumor cell death\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, once tumor cells initiate a mechanism to change their metabolic microenvironment, inhibit ROS formation, and enhance oxidative stress defense or tolerance ability, drug resistance is induced\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFrom DEGs identified in two clusters, three significant genes (IDO1, CLGN, and GRIK3) were selected, which were used to construct a prognostic model through univariate and lasso Cox analysis. IDO1 is an intracellular heme-dependent oxidase that causes local tryptophan depletion and ferroptosis inhibition, and the regulation of immune cells via tryptophan metabolism regulation is associated with chemotherapy tolerance promotion and poor cancer prognosis\u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. IDO1 is strongly expressed in tamoxifen-resistant breast cancer cells and mediates the proliferation, metastasis, and T resistance of tamoxifen-resistant breast cancer cells \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e via STAT1 and IL-6/STAT3 activation\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. GRIK3, an important excitatory neurotransmitter receptor, has been positively correlated with the prognosis of patients with breast cancer as it affects various signaling pathways and key signal transduction pathways, including two epithelial\u0026ndash;mesenchymal transition regulators, SPDEF and CDH1\u003csup\u003e46\u003c/sup\u003e. The low expression of CLGN was confirmed to be associated with tamoxifen sensitivity in premenopausal patients with lumina A subtype breast cancer\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. However, the biological function of GRIK3 and CLGN in malignant tumors is largely unknown.\u003c/p\u003e \u003cp\u003eSeveral prognostic models based on FERGs have been reported to explore prognostic biomarkers and predict the prognosis of various cancer types\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In one study, a 13-gene prognostic model of acute myeloid leukemia was developed. According to this model, patients were classified into high- and low-risk groups, with higher risk scores indicating shorter survival and association with tumor-associated immune abnormalities, mutational patterns and pathway dysregulation, and clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Another study used the GEO and TCGA databases to collect thyroid cancer gene expression data and clinical outcomes to evaluate the prognostic value of 75 FERG expression. Five FERG prognostic models and nomograms were developed to provide unique insights for predicting the prognosis of thyroid cancer\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In a head and neck squamous cell carcinoma study, three stable molecular subtypes with different prognostic, mutational, and immunologic profiles were identified using consensus clustering with ferroptosis marker genes. WGCNA was then used to identify the gene modules related to molecular subtypes. After screening and lasso regression analysis, eight genes were determined to be related to prognosis. A prognostic model with score related to ferroptosis was finally constructed, which reflected the risk and positive prognostic factors of patients with head and neck squamous cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The role of ferroptosis related to drug resistance in TNBC, however, has not been fully elucidated\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is well known that TME is composed of tumor cells and their surrounding cells, such as lymphocytes, TIICs, and tumor vasculature\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. There is strong evidence to support the hypothesis that TME is essential for tumor formation, progression, and treatment resistance\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. In the present study results, B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, neutrophils, and myeloid dendritic cells were all significantly negatively correlated with the risk score. In the TNBC low-risk group, the expression of these TIICs was significantly higher than that in the high-risk group. In view of the in-depth research that has been conducted on breast cancer immunotherapy, the study of TME and immune cell infiltration may be helpful to discover new directions and mechanisms of breast cancer immunotherapy.\u003c/p\u003e \u003cp\u003eFirst, DEGs were identified from the GEO database to establish a FERG-based breast cancer prediction model. Because ferroptosis is different from the other accepted modes of cell death, it may offer new therapeutic possibilities for treating cancer. The model in the present study was validated by comparing the included data derived from the TCGA database with those from different database sources, which improved the effectiveness of the model. A prognostic model with score related to ferroptosis developed in this study can appropriately reflect the risk and positive prognostic factors of patients with TNBC. Thus, the model can be used to guide individualized adjuvant therapy and chemotherapy for patients with TNBC.\u003c/p\u003e \u003cp\u003eThe current study has a few limitations. Only data from public sources were used in this study, necessitating further validation using more accurate clinical data. Because the prognostic signature was developed and validated using publicly sourced data, experimental studies and extensive prospective studies are needed to confirm these results.\u003c/p\u003e \u003cp\u003eIn conclusion, a novel ferroptosis-related gene model can be used for prognostic prediction in TNBC. New ferroptosisrelated genes might be used for TNBC targeting therapy in the future.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the results and conclusions of this study were downloaded from the TCGA (https://portal.gdc.cancer.gov/) and GEO (accession no. GSE22513, http://www.ncbi.nlm.nih.gov/geo/). The data used for the prediction of potential drugs were obtained from the CellMiner and DrugBank databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSources of funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from The\u0026nbsp;National Natural Science\u0026nbsp;Foundation of China (No. 81672596 to Qichao Ni).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.Y. designed the study, analyzed data and writed manuscript. HY.Q. and SC.M. collected and analyzed data. X.L., BY.L., D.Z., and YP.C. collected samples and performed experiment. CY.S. and QC.N. concepted and supervised the study, provided funding, and edited manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBurstein, H. J.\u003cem\u003e et al.\u003c/em\u003e Customizing local and systemic therapies for women with early breast cancer: the St. Gallen International Consensus Guidelines for treatment of early breast cancer 2021. \u003cem\u003eAnn Oncol\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 1216-1235 (2021). https://doi.org:10.1016/j.annonc.2021.06.023\u003c/li\u003e\n\u003cli\u003eGarrido-Castro, A. C., Lin, N. U. \u0026amp; Polyak, K. 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Overcoming Endocrine Resistance in Breast Cancer. \u003cem\u003eCancer Cell\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 496-513 (2020). https://doi.org:10.1016/j.ccell.2020.03.009\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3176896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3176896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTaxanes are first-line chemotherapeutic agents for patients with triple-negative breast cancer (TNBC). However, resistance, the main cause of clinical treatment failure and poor prognosis, reduces their effectiveness and has become an increasingly important problem. Recently, a form of iron-dependent programmed cell death called ferroptosis was reported to play an important role in regulating tumor biological behavior. In this study, we revealed the prognostic significance of the ferroptosis‑related gene (FERG) model and clarified that ferroptosis-related genes may be promising candidate biomarkers in cancer therapy. First, resistance-related FERGs were screened, and univariate Cox regression analysis was used to construct a prognostic model, including GRIK3, IDO1, and CLGN. Then, the patients with TNBC in the TCGA database were classified into high-risk and low-risk groups. The identification of TNBC in the TCGA database revealed that patients with high scores had a higher probability of dying earlier than those with low scores. Moreover, these three genes were associated with immune infiltrates and checkpoints in TNBC patients. In conclusion, this study suggested that FERGs are significantly associated with chemotherapy resistance in patients with TNBC and that these genes can be used as prognostic predictors in these patients and possibly for targeted therapy in the future.\u003c/p\u003e","manuscriptTitle":"A Novel Ferroptosis-Related Gene Signature for Chemotherapy Resistance Prediction in Triple-negative Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-25 15:20:34","doi":"10.21203/rs.3.rs-3176896/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":"b588e784-4bdd-40c5-bc6a-b9b7abbba412","owner":[],"postedDate":"July 25th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-09-08T04:14:20+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-25 15:20:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3176896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3176896","identity":"rs-3176896","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}