Introduction

Bearing in mind that breast cancer is the most common cancer in women and the leading cause of cancer death, its burden is increasing worldwide.1,2 Although an early diagnosis and a comprehensive treatment strategy have shown an improvement in the prognosis of breast cancer patients, the overall survival rate of 5 years is less than 20% once metastasis occurs.3,4 It is therefore imminent to find biomarkers related to the prognosis of breast cancer.

Ferroptosis describes a type of programmed cell death different from apoptosis that is iron-dependent and characterized by lipid peroxidation and the production of reactive oxygen species.5 Studies have shown that some ferroptosis-related genes have been identified as suppressor genes in the process of BRCA.6,7

CISD1 is a protein containing the CDGSH iron-sulfur domain, which is located in the outer membrane of mitochondria and is known to negatively regulate ferroptosis.8 While studies have found that CISD1 plays an important role in promoting tumorigenesis and tumor progression in many cancer types,9 recent studies also suggest that CISD1 can be used as a biomarker and target for breast cancer.10 At present, although there is not much research on CISD1 in breast cancer, we were able to determine the role of CISD1 in breast cancer through TCGA-BRCA database analysis.

With the maturity of high-throughput sequencing technology, the generation of large-scale omics data has become possible,11,12 and the characteristics of these TCGA-BRCA genes can explain the etiology of cancer and have significant diagnostic and prognostic value. In this study, the transcriptional level and prognostic significance of CISD1 were analysed by reviewing the data obtained by TCGA-BRCA, and we explored its biological mechanism through GO and KEGG analysis and further evaluated the association between CISD1 and immune infiltration levels, with q-PCR, immunohistochemistry, and cell proliferation experiments also confirming our conclusion.

Materials and Methods Data Processing

Gene expression data of 1109 BRCA tissues and 113 adjacent tissues were downloaded from the TCGA database, which we then screened for clinicopathological features and prognostic data. Unified processing of RNA-seq data in TPM format of TCGA. The expression of CISD1 was analysed by the TCGA database, and we extracted CISD1 from UCSC Xena to assess CISD1 expression levels in pancancer (https://xenabrowser.net/datapages/).

Patients and Tissues

Bearing in mind, all participants were informed by written consent. A total of 12 breast cancer samples and matched nontumor tissues were obtained from Liaoning Cancer Hospital. The study was also approved by the Ethics Committee of Liaoning Cancer Hospital. Breast cancer tissue was frozen rapidly in liquid nitrogen and stored at −80℃ after the operation for q-PCR detection.

Gene Enrichment Analysis

In this study, gene expression data were divided into high expression and low expression CISD1 groups (R package cluster profile). According to the transcriptional sequence of TCGA, we used GO and KEGG to identify the genome and pathway related to CISD1.

Methylation Analysis of the CISD1 Gene

We used the MethSurv (https://biit.cs.ut.ee/methsurv/, accessed on October 17, 2022) database to conduct multivariate survival analysis on the DNA methylation of breast cancer patients,13 which was used to analyse the impact of CISD1 methylation on the survival and prognosis of breast cancer patients.

Immune Cell Infiltration

While we performed ssGSEA (single sample gene set enrichment analysis) to assess the relative abundance of infiltrating immune cells in tumor tissues, we also analysed the infiltration level of immune cells in BRCA expression profile data by using “GSVA” (R package) and the immune data set, which included 24 immune cells.

We also used TIMER (http://timer.cistrome.org/, accessed on October 18, 2022) to examine the relationships among immune cells and breast cancer.14

Connections Between Small Molecules and Genes via a Connectivity Map

The differentially expressed genes between the CMap (connectivity map) database and CISD1 in breast cancer were used to reveal the interactions among drugs, compounds and diseases.15

Survival and Prognosis Analysis

We used the R package “survival” to obtain the overall survival (OS) survival map of CISD1, of which a critical value of 50% was selected as the division threshold, and the cohort was divided into high expression and low expression groups. Additionally, we used the R package “ROC” to analyse and visualize it using “ggplot2” to evaluate the value of CISD1 in predicting the prognosis of breast cancer patients.

Cell Culture and Transfection

The MCF7 cell line belonged to the Chinese Academy of Sciences and was cultured in MEM supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% penicillin‒streptomycin in an incubator humidified with 5% CO2 at 37 °C. Moreover, MCF7 cells were seeded in 50–60% confluent six-well plates 24 hours before transfection, and siRNA was then transfected with Lipofectamine 2000 according to the manufacturer’s instructions.

RNA Isolation and qPCR Analysis

RNA extraction from tissues was performed using TRIzol reagent. RNA was reverse-transcribed into cDNA using the QuantiTect Reverse Transcription Kit. qPCR analyses were quantified by SYBR-Green, and levels were normalized to GAPDH levels. The primers used were as follows: CISD1 forwards primer, 5′-GCTCTCGGTTACCTGGCTTA-3′; reverse, 5′-TTGTCTCCAGTCTCCTCATTGT-3′.

Immunohistochemistry

BRCA samples were fixed in 10% formalin, embedded in paraffin and processed into 5-µm sequential sections. Samples were dewaxed with ethanol and blocked to inhibit endogenous peroxidase activity. Samples were incubated overnight at 4 °C with rabbit anti-CISD1 (Thermo Fisher Scientific PA5-106281), followed by incubation with horseradish peroxidase-coupled goat anti-rabbit secondary antibody at 37 °C for 30 min and then stained using 3,3′-diaminobenzidine. Cell nuclei were stained blue with hematoxylin. The sections were then dehydrated, cleared with xylene, and mounted. CISD1 expression was determined with IHC (immunohistochemistry) using a streptavidin peroxidase method, with adjacent tissues as controls. The experimental procedure was performed according to the manufacturers’ instructions. Image-Pro Plus 6.0 Software (MediaCybernetics, USA) was used to analyse protein expression and perform statistics on the results obtained from immunohistochemistry.

Statistical Analysis

The statistical analysis of CISD1 expression in normal and BRCA groups was calculated using the Wilcoxon rank-sum test, and the patients were divided into two categories according to the CISD1’s “median” expression. The clinicopathological features of CISD1 were analysed by the Wilcoxon rank sum test or Kruskal‒Wallis test and logistic regression, whereas Kaplan‒Meier survival analysis and Cox univariate and multivariate analysis were used for the prognostic analysis. The receiver operating characteristic (ROC) curve was generated using the “pROC” package to evaluate the diagnostic significance of differentially expressed genes.

Results Analysis of CISD1 Expression Across Cancers and Breast Cancers

While data downloaded from UCSC Xena were used to analyse the expression of CISD1 in 33 cancers, we evaluated the expression of CISD1 in breast cancer in TCGA database and were then able to confirm that CISD1 was overexpressed in breast cancer. (Figure 1A and B). The results showed that CISD1 was overexpressed in most cancers, including BLCA, BRCA, CESC, CHOL, COAD, DLBC, HNSC, KIRC, LIHC, LUAD, LUSC, OV, PAAD, PCPG, SKCM, STAD, THCA, THYM, UCEC, and UCS, but the expression of CISD1 was low in LAML, LGG, PRAD, PEAD, and TGCT (Figure 1C).

Figure 1 (A) Comparison of CISD1 expression levels between BRCA and normal tissues. (B) CISD1 expression levels in matched BRCA tissues and adjacent normal tissues. (C) Expression level of CISD1 in different cancer types. P<0.05 indicates that the data are statistically significant; NS P>0.05, *P < 0.05, **P < 0.01, ***P < 0.001.

Abbreviations: BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; ESCA, esophageal carcinoma; GBM, glioblastoma; HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; PRAD, prostate adenocarcinoma; READ, rectal adenocarcinoma; SARC, sarcoma; STAD, stomach adenocarcinoma; THCA, thyroid carcinoma; UCEC, uterine corpus endometrial carcinoma.

In addition, we found that N stage (P = 0.02), M stage (P = 0.048), race (P = 0.01), age (P = 0.01), PR status (P < 0.001), and ER status (P < 0.001) were also significantly correlated with CISD1 mRNA expression (Figure 2A–F).

Figure 2 The mRNA expression level of CISD1 was analysed using TCGA-BRCA data sets. (A) N stage (B) M stage (C) Race (D) Age (E) PR status (F) ER status.

Clinical Relevance of CISD1 Expression in Breast Cancer Patients

The patients were divided into the CISD1 high expression (n = 542) and CISD1 low expression groups (n = 541), and the clinical characteristics and gene expression data of 1083 patients with primary breast cancer were downloaded from the TCGA database to investigate the correlation between CISD1 expression level and the patients’ clinicopathological features. We found that CISD1 expression was correlated with N stage (P = 0.012), M stage (P = 0.047), age (P = 0.022), race (P < 0.001), PR status (P < 0.001), and ER status (P < 0.001) by using the chi-square test or Fisher’s exact test, whereas the Wilcoxon rank-sum test showed that CISD1 expression was associated with age (P = 0.013) (Table 1).

Table 1 CISD1 Expression in BRCA Patients with Different Clinical Parameters

Association Between CISD1 Expression and Survival Prognosis of Cancer Patients

In addition, we studied the relationship between CISD1 expression, overall survival (OS), and disease-related survival (DSS) in breast cancer patients. According to the KM diagram, patients with higher CISD1 had a worse prognosis for OS (HR = 1.51, 95% CI: 1.09–2.09, P = 0.013) (Figure 3A), whereas for DSS, patients with higher CISD1 still had a worse prognosis (HR = 1.66, 95% CI: 1.07–2.57, P = 0.024) (Figure 3B). Moreover, we conducted a receiver operating characteristic (ROC) curve to evaluate the applicability of CISD1 expression in differentiating breast cancer from normal breast tissue, with an area under the ROC curve (AUC) of 0.718 (Figure 3C). We also conducted a time-dependent receiver operating characteristic (ROC) curve, in which the areas under the ROC curve (AUCs) of 1, 3, and 5 years were 0.575, 0.601, and 0.601, respectively (Figure 3D). Therefore, CISD1 may be a promising biomarker for prognosis in breast cancer patients.

Figure 3 CISD1 is an independent predictor of prognosis in BRCA. (A and B) Patients with low CISD1 have significantly higher survival than patients with high CISD1 (OS and DSS). (C) ROC analysis illustrated that CISD1 expression accurately discriminated BRCA tumor tissues from normal tissues with an AUC of 0.718 (95% CI = 0.680–0.756) from TCGA-BRCA data sets. (D) ROC curves were used to assess the efficiency of CISD1 for predicting 1-year, 3-year and 5-year survival rates in the TCGA-BRCA data sets.

Abbreviations: OS, overall survival; DSS, disease-specific survival; ROC, receiver operating characteristic; AUC, area under the curve.

Furthermore, we performed a subgroup analysis to evaluate the effect of CISD1 expression on OS according to T stage, N stage, M stage, pathological stage, age, PR status, ER status and Histological type and found that high expression of CISD1 continued to lead to poor survival in each subgroup by T stage, N stage, M stage, pathological stage, age, PR status, ER status and Histological type (Figure 4A–H).

Figure 4 Subgroup analysis in BRCA. (A) Kaplan–Meier curves for the T2 stage subgroup. (B) Kaplan–Meier curves for the N1 stage subgroup. (C) Kaplan–Meier curves for the M0 stage subgroup. (D) Kaplan–Meier curves for the pathologic stage II subgroup. (E) Kaplan–Meier curves for the age <=60 years subgroup. (F) Kaplan–Meier curves for the negative PR status subgroup. (G) Kaplan–Meier curves for the negative ER status subgroup. (H) Kaplan–Meier curves for the Histological type subgroup.

Correlation Between Methylation and CISD1 Expression

We also used online tools to study the correlation between CISD1 expression level and methylation status in breast cancer. First, we observed that most of the methylation sites in the CISD1 DNA sequence are hypomethylated in breast cancer (Figure 5A). Moreover, the degree of methylation is related to the prognosis of patients. The total survival time of patients with low CISD1 methylation levels was lower than that of patients with high CISD1 methylation levels (Figure 5B).

Figure 5 DNA methylation level of CISD1 and its impact on the prognosis of breast cancer patients. (A) Correlation between CISD1 mRNA expression level and methylation level. (B) Kaplan–Meier curves of CISD1 methylation level.

Univariate and Multivariate Analysis of Survival

We conducted univariate and multivariate analyses to further explore the risk factors for BRCA patients, noting that univariate analysis using the Cox regression model showed that stages T3 and T4 of T stage, N-stage, M-stage, age, stages 3 and 4 of the pathological stage and CISD1 were related to OS (Table 2). Then, we conducted a multivariate analysis to reveal independent risk factors, and we found that major M1 stage, age > 60 years and CISD1 were independent prognostic factors for OS in hospitalized patients with BRCA.

Table 2 Univariate Analysis and Multivariate Analysis of the Correlation Between Clinicopathological Characteristics and OS in BRCA

Enrichment Analysis of CISD1-Related Genes

We downloaded data from the TCGA database to further study the function of CISD1 and searched CISD1 expression-related genes for related pathway analysis. Having obtained 20 top genes positively and negatively correlated with CISD1 through the “cluster profile” R package for GO and KEGG enrichment analysis (Figure 6A and C), positive correlation gene analysis data showed that most genes were related to the rRNA metabolic process, rRNA processing and mitochondrial RNA metabolic process (Figure 6B). In turn, the data of the negative correlation gene analysis showed that most genes were related to the cell cortex part, cortical cytoskeleton, and cortical actin cytoskeleton (Figure 6D).

Figure 6 Gene enrichment analysis of CISD1 in TCGA-BRCA data sets. (A) Heatmaps showing genes positively correlated with CISD1 in BRCA (top 20). (B) Enriched GO terms and KEGG pathways of positively correlated CISD1 genes. (C) Heatmaps showing genes negatively correlated with CISD1 in BRCA (top 20). (D) Enriched GO terms and KEGG pathways of CISD1 negatively correlated genes. P<0.05 indicates that the data are statistically significant; NS P>0.05, *P < 0.05, ***P < 0.001.

We uploaded the upregulated and downregulated genes to the CMap (connectivity map) database to predict potential drugs for breast cancer treatment. The top 10 drugs/molecules with positive correlations and the top 10 drugs/molecules with negative correlations were obtained from CMap, and these drugs/molecules were ranked by score (Supplementary Tables 1 and 2). After searching these 20 drugs, we found that BMS-345541, PIK-75, etoposide, triptolide, taurocholic acid, lonidamine, 17 beta estradiol, hydrastine, and MK-1775 can be used as potential drugs to treat breast cancer patients. These data confirm the validity of the CMap database.

Relationship Between CISD1 Expression and Immune Cell Infiltration

We further evaluated whether CISD1 expression levels were associated with immune cell infiltration. We used ssgsea and Spearman’s R from the R package to study the potential association between CISD1 expression levels and 24 immune cell types (Figure 7). The results showed that CISD1 expression was significantly correlated with B cells, eosinophils, macrophages, neutrophils, NK cells, T helper cells, Tregs, Th1 cells, Th17 cells, and Th2 cells. Further studies showed that the expression of CISD1 was positively correlated with the infiltration levels of B cells, macrophages, neutrophils, T helper cells, Tregs, Th1 cells, and Th2 cells. In contrast, CISD1 expression was negatively correlated with eosinophils, NK cells, and Th17 cells (Figure 8A–J).

Figure 7 The association between CISD1 expression and 24 tumor-infiltrating lymphocytes.

Figure 8 The correlation of CISD1 expression with the immune infiltration level of tumor-infiltrating lymphocytes. (A) B cells. (B) Eosinophils. (C) Macrophages. (D) Neutrophils. (E) NK cells. (F) T helper cells. (G) Tregs. (H) Th1 cells. (I) Th17 cells. (J) Th2 cells.

We also used the TIMER database to explore the immune microenvironment and determined the correlation between the level of immune invasion in breast cancer and the expression of the CISD1 gene (Figure 9A–L). The results showed that the expression of CISD1 was positively correlated with CD8 T cells, CD4 T cells, neutrophils, macrophages, plasmoid dendritic cells, and MDSCs and negatively correlated with mast cells and NK cells.

Figure 9 (A–L) Scatter plot of the correlation between the expression of the CISD1 gene and the level of immune invasion in breast cancer.

CISD1 is Highly Expressed in Breast Cancer Tissues

We used qPCR and immunohistochemistry to evaluate the potential utility of CISD1 as a BRCA biomarker and to further verify the expression of CISD1 in breast cancer tissues. While both qPCR and immunohistochemical results suggested that CISD1 is highly expressed in breast cancer (Figure 10A and B), we then transfected CISD1-targeted siRNA into the MCF7 cell line, with CCK8 results showing a decrease in the proliferation ability of si-MCF7 cells (Figure 10C).

Figure 10 (A and B) The expression level of CISD1 in BRCA breast cancer tissues and matched nontumor tissues. (C) Expression of the CISD1 gene in MCF7 cells was silenced using RNA interference technology. Proliferation was significantly reduced in the si-CISD1. P<0.05 indicates that the data are statistically significant; *P < 0.05.

Discussion

Breast cancer is the most frequently diagnosed cancer among women, ranking second among the causes of cancer-related deaths in women.16,17 It becomes necessary to find accurate biomarkers to detect and monitor disease progression early. In turn, according to previous studies, CISD1 is overexpressed in a variety of cancers and has been identified as a prognostic factor,18–20 but no relationship between the expression of CISD1 and the prognosis of breast cancer has been studied.

In this study, we explored the potential mechanism of CISD1 in promoting breast cancer and its feasibility as a molecular biomarker. In the pancancer analysis, while the higher expression of CISD1 was associated with a decrease in the overall survival (OS) of breast cancer patients, we also found that CISD1 was upregulated in most cancer types. Similarly, analysis of different clinical stages found that CISD1 was significantly correlated with clinical stages, and univariate and multivariate Cox analyses showed that CISD1 was an independent factor predicting the prognosis of patients. All these results, including the ROC analysis, suggest that CISD1 may be a prognostic biomarker for breast cancer patients.

Ferroptosis is a kind of cell death that plays a vital role in inhibiting tumorigenesis by removing cells lacking or excessive key nutrients or damaged by environmental pressure.21 Unlike autophagy and apoptosis, ferroptosis is a form of cell death that depends on iron (FE) and reactive oxygen species (ROS). It regulates cell death through excessive production of phospholipid hydroperoxide, and its mechanism is different from autophagy and apoptosis.22 Recent studies have shown that ferroptosis can affect cell metabolism, redox status, degenerative diseases, and ischemic reperfusion injury.23 Ferroptosis plays an important regulatory role in the occurrence and development of tumors, providing a promising treatment strategy for BRCA.24,25 CDGSH iron sulfur domain 1 (CISD1) is a mitochondrial protein located in the outer membrane that plays an important role in mediating the crosstalk between mitochondrial iron uptake and oxidative stress in normal and cancer cells. Upregulation of CISD1 protein expression in cancer cells limits autophagy activity.26

In turn, the tumor microenvironment (TME) plays an important role in tumor progression, metastasis, and therapeutic drug resistance.27 Tumor-infiltrating immune cells affect the tumor microenvironment and tumor proliferation, invasion, and migration. Our gene enrichment analysis shows that the biological function of CISD1 involves an immune response, which likewise confirms that CISD1 expression was associated with immune cell infiltration.

While studies have shown that eosinophils can produce cytotoxicity by releasing particles or regulate the immune response, especially by attracting CD8+ T cells,28 and eosinophils can also inhibit the growth of colorectal cancer through IL-33,29 there is a correlation in breast cancer between high baseline eosinophil counts and better response to treatment or survival rate.30 For peripheral NK cells, the increase in NK cytotoxic activity is positively correlated with the decrease in cancer risk,31 whereas in BRCA, the abundance of NK cells can also reflect a good survival rate.32

Recent studies have shown that Th17 cells are usually associated with a variety of cancers, including lung cancer, breast cancer, prostate cancer, colon cancer, and melanoma.33 In BRCA, the increase in Th17-cell number enhances the antitumour immune response in BC tissue,34 with the abundance of Th17 cells increasing and BRCA having a better prognosis.35 Our study found that the expression of CISD1 in breast cancer is negatively correlated with eosinophils, NK cells, and Th17 cells, suggesting that CISD1 may play an important role in regulating the breast cancer immune microenvironment. Additionally, we also conducted PCR and immunohistochemical studies on the clinical samples of breast cancer, and the results suggested that CISD1 is highly expressed in breast cancer and that the proliferation of MCF7 cells with CISD1 silencing also decreased.

In conclusion, we demonstrated that CISD1 expression was upregulated in breast cancer and was significantly associated with poor survival, while CISD1 may also participate in the development of breast cancer by affecting the level of immune cell infiltration. The present study thus reveals the role of CISD1 in breast cancer and identifies a promising biomarker for prognosis. The mechanism by which CISD1 affects the tumor immune microenvironment and tumor progression in BRCA remains unclear and needs further basic and clinical trials to fully elucidate its biological effects.

Conclusion

Overall, the high expression of CISD1 was associated with prognostic significance. CISD1 was negatively correlated with eosinophils, NK cells, and Th17 cells, which may be related to immune infiltration and can therefore be used as a prognostic factor in patients with BRCA.

Data Sharing Statement

The data supporting the findings of this study are available through OPEN ACCESS, as well as from the corresponding author upon request.

Ethics Approval and Informed Consent

The study was conducted in accordance with the declaration of Helsinki. The study was also approved by the Ethics Committee of Liaoning Cancer Hospital, number: 2021-239.

Author Contributions

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that there are no conflicts of interest.

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