CCDC170 affects breast cancer apoptosis through IRE1 pathway

The influence of CCDC170 on the gene expression profile of MCF7 cells

To identify genes that could be induced by CCDC170, we transfected MCF7 breast cancer cells with either a pCMV-N-Flag-CCDC170 vector or a pCMV-N-Flag control vector. We extracted mRNA from the cells 24 or 48 hours post-transfection, and used microarrays to assess their gene expression profiles. Then, we performed an enrichment analysis of the differentially expressed genes (DEGs) between the CCDC170-overexpressing group and the control group. When we compared the top 20 significantly enriched pathways between the 24-hour group (Figure 1A) and the 48-hour group (Figure 1B), we identified eight common pathways (Figure 1C). We also determined the top 20 genes with consistent expression-change tendencies in the 24- and 48-hour groups (Figure 1D). Among these genes, only IRE1 and AKT1 were involved in the eight pathways mentioned above. The change in IRE1 expression (24h: log[fold ratio] = 0.69; 48h: log[fold ratio] = 0.33) was the most obvious in the apoptotic pathway (Figure 1E).

Next, we determined the correlation between CCDC170 and IRE1 expression in breast cancer tissues from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). There was a significant positive correlation between CCDC170 and IRE1 levels in TCGA (r = 0.273, P = 5.89×10^-14; (Figure 1F), as well as in GEO (r = 0.262, P = 9.19×10^-55; (Supplementary Figure 1A). When cases in TCGA were stratified by subtype, CCDC170 mRNA levels were higher in the luminal A and luminal B subtypes, but were lower in the human epidermal growth factor receptor 2 (Her2)-positive and Basal-like breast cancer subtypes (Figure 1G). A similar tendency was found in the GEO database (Supplementary Figure 2F).

The expression of CCDC170, IRE1α and XBP1s in breast cancer tissues

To investigate the prognostic value of CCDC170, IRE1α and XBP1s in breast cancer, we used immunohistochemistry to assess the expression of these proteins in 100 patients with invasive ductal carcinoma. General information about the included breast cancer patients can be found in Supplementary Table 1. IRE1α levels were found to correlate with CCDC170 levels (r = 0.233, P = 0.020; Figure 2B) and XBP1s levels (r = 0.212, P = 0.034; Table 1). Additionally, CCDC170 levels correlated positively with XBP1s levels (r = 0.339, P = 0.001; Table 1). In terms of clinicopathological characteristics, CCDC170 levels correlated positively with ERα levels (r = 0.389, P = 9.90×10^-5). IRE1α levels correlated positively with Her2 levels (r = 0.293, P = 0.003) and Ki67 levels (r = 0.208, P = 0.038). XBP1s levels exhibited a notable positive correlation with ERα levels (r = 0.286, P = 0.004). Moreover, CCDC170 levels (r = 0.333, P = 0.006) and IRE1α levels (r = 0.353, P = 0.003) correlated with the molecular subtypes. However, CCDC170, IRE1α and XBP1s levels were not significantly associated with the tumor size, lymph node status, Tumor-Node-Metastasis (TNM) stage or histologic grade (Table 1). Based on these findings, we explored the correlation of CCDC170 levels with XBP1 and ESR1 levels in TCGA and the GEO database. In TCGA, CCDC170 levels were positively associated with XBP1 levels (r = 0.636, P = 2.42×10^-84; (Supplementary Figure 3A) and ESR1 levels (r = 0.805, P = 1.34×10^-167; (Supplementary Figure 3B). Similarly, CCDC170 levels were positively associated with XBP1 levels (r = 0.601, P = 0.000; (Supplementary Figure 1B) and ESR1 levels (r = 0.747, P = 0.000; (Supplementary Figure 1C) in the GEO database. In addition, a positive correlation between IRE1 expression and XBP1 expression was found in both TCGA (r = 0.291, P = 1.06×10^-15; (Supplementary Figure 3C) and GEO (r = 0.244, P = 1.48×10^-47; (Supplementary Figure 1D) database. We also analyzed the associations of CCDC170, IRE1 and XBP1 mRNA levels with clinicopathologic characteristics such as ERα, progesterone receptor (PR), Her2 and Ki67 levels, the PAM50 subtype, tumor size, lymph node status and TNM stage in TCGA (Supplementary Figures 3D, 3E, 4A–4R) and GEO (Supplementary Figure 2A–2R). The details of these correlations are presented in Supplementary Figures 2, 4.

Survival analysis based on CCDC170, IRE1α and XBP1s expression

Next, we performed Kaplan-Meier analyses in our 100 patients according to their expression of CCDC170, IRE1α and XBP1s. The CCDC170 high-expression group exhibited better disease-free survival (DFS) than the low-expression group (hazard ratio [HR] [95% confidence interval (CI)] = 0.37 [0.14 - 0.98], P = 0.037; Figure 2D). However, there was no significant difference in overall survival (OS) between the CCDC170 low- and high-expression groups (HR [95% CI] = 0.42 [0.11 - 1.57], P = 0.183; Figure 2C). We did not find differences in DFS or OS between the high and low-expression groups for IRE1α (Figure 2E, 2F) or XBP1s (Figure 2G, 2H). Considering that our patients had received different post-operative treatments, we also analyzed the association between CCDC170 expression and prognosis within cohorts that had received the same treatment. The results indicated that higher CCDC170 expression predicted better DFS in the chemotherapy cohort (Supplementary Figure 5D) and the radiation cohort (Supplementary Figure 5H), and predicted better OS in the radiation cohort (Supplementary Figure 5G).

We also performed Kaplan-Meier analyses using mRNA expression data from TCGA and GEO. In the GEO database, the high-expression groups for CCDC170 (HR [95% CI] = 0.59 [0.47 - 0.73], P = 8.81×10^-7; (Supplementary Figure 1E), IRE1 (HR [95% CI] = 0.74 [0.60 - 0.91], P = 4.52×10^-3; Supplementary Figure 1F) and XBP1 (HR [95% CI] = 0.59 [0.48 - 0.73], P = 1.00×10^-6;(Supplementary Figure 1G) exhibited better OS than their respective low-expression groups (based on the median). However, in TCGA, the expression of these genes was not significantly associated with the OS or DFS (Supplementary Figure 4S–4X).

CCDC170 activated IRE1α/XBP1s signaling

To further explore the relationship of CCDC170 with IRE1α, XBP1s and ERα, we conducted a series of in vitro assays in MCF7 breast cancer cells in which CCDC170 was overexpressed or silenced. Western blot analyses indicated that CCDC170 overexpression for 24 hours (Figure 3A, 3F) or 48 hours (Figure 3B, 3G) induced the expression of IRE1α (24h: P = 0.001; 48h: P = 0.025). CCDC170 overexpression also obviously increased the expression of ERα (24h: P = 0.009; 48h: P = 9.08×10^-4) and XBP1s (24h: P = 0.016; 48h: P = 3.96×10^-5). On the other hand, when CCDC170 was knocked down for 24 hours (Figure 3C, 3H) or 48 hours (Figure 3D, 3I), the expression of IRE1α decreased significantly (24h: P = 1.08×10^-4; 48h: P = 1.69×10^-4). However, the expression of XBP1s (24h: P = 5.05×10^-4; 48h: P = 0.255) and ERα (24h: P = 0.010; 48h: P = 0.202) only decreased significantly at the 24-hour time point.

We also examined the effects of stably overexpressing CCDC170 in MCF7 breast cancer cells. When CCDC170 was continuously upregulated, the levels of IRE1α (P = 0.002), XBP1s (P = 0.026) and ERα (P = 0.012) all increased significantly (Figure 3E, 3J). Consistent with our microarray and Western blotting results, immunofluorescence analyses demonstrated that the fluorescence signals of IRE1α (P = 0.001; Figure 3K, 3N), XBP1s (P = 2.42×10^-4; Figure 3L, 3O) and ERα (P = 0.041; Figure 3M, 3P) became stronger in MCF7 cells stably overexpressing CCDC170.

CCDC170 promoted apoptosis and enhanced the sensitivity of MCF7 cells to paclitaxel

We then investigated the effects of CCDC170 on breast cancer cell apoptosis and viability. In cells that were transiently transfected with a eukaryotic CCDC170 expression vector for 24 hours, the percentage of apoptotic cells was 1.31 times higher than in the control group (P = 0.044; Figure 4A, 4B). On the other hand, when CCDC170 was transiently silenced using small interfering RNA (siRNA), flow cytometry revealed that the percentage of apoptotic cells decreased by 28.30% (P = 0.047; Figure 4C, 4D). We also found that the number of apoptotic nuclei increased in cells that transiently overexpressed CCDC170 (P = 7.00 ×10^-3; Figure 4E, 4F). The expression of Caspase-12 increased when CCDC170 was upregulated transiently (P = 0.005; Figure 4G, 4H) or stably (P = 0.002; Figure 4I, 4J). Furthermore, whether CCDC170 was overexpressed transiently (P = 0.001; Figure 5J) or stably (P = 0.003; Figure 5K) in MCF7 breast cancer cells, the cell viability was significantly lower than that of the control group in a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay.

Next, we used immunohistochemistry to assess the localization of CCDC170 in paraffin-embedded breast cancer tissues, and found that it was expressed in the cytoplasm. Immunofluorescence staining further indicated that CCDC170 partially overlapped with calnexin, a marker of the endoplasmic reticulum (Figure 5A). Nevertheless, no binding between CCDC170 and IRE1α was observed in a co-immunoprecipitation assay (data not presented).

Endoplasmic reticulum stress is ubiquitous in the tumor microenvironment; thus, we simulated endoplasmic reticulum stress in vitro by treating MCF7 cells with 300 nm thapsigargin for 24 hours. Western blot analyses demonstrated that MCF7 breast cancer cells that transiently overexpressed CCDC170 expressed higher levels of IRE1α (P = 0.018), cleaved poly [ADP-ribose] polymerase (PARP, an apoptotic regulatory protein; P = 0.001) and Caspase-7 (P = 0.001) and lower levels of Bcl-2 (an apoptosis-inhibiting protein; P = 0.013) than control cells under endoplasmic reticulum stress (Figure 5B, 5C). Likewise, in MCF7 cells that stably overexpressed CCDC170, the expression of IRE1α (P = 0.009), cleaved PARP (P = 9.27×10^-4) and Caspase-7 (P = 4.36×10^-4) increased while the expression of Bcl-2 decreased (P = 0.025) compared with control cells under endoplasmic reticulum stress for 24 hours (Figure 5D, 5E). Flow cytometry analysis demonstrated that the proportion of apoptotic cells was 1.37 times higher in the CCDC170-overexpressing group than in the control group after 24 hours of endoplasmic reticulum stress (P = 0.001; Figure 5F, 5G). In addition, we found that the percentage of apoptotic cells decreased when IRE1 was knocked down without endoplasmic reticulum stress in MCF7 cells that transiently overexpressed CCDC170 (P = 0.046; Figure 5H, 5I).

To compare the paclitaxel chemosensitivity between breast cancer cells with different CCDC170 levels, we treated CCDC170-stably-overexpressing MCF7 cells and control MCF7 cells with 100 nM paclitaxel. An MTT assay indicated that paclitaxel treatment inhibited growth more effectively in CCDC170-stably-overexpressing cells than in control cells (Figure 5L).

Clinical samples

We obtained 100 formalin-fixed, paraffin-embedded tumor tissues from patients with breast invasive ductal carcinoma who had undergone surgical resection at Tianjin Medical University Cancer Institute and Hospital from December 2008 to May 2009. All the patients were followed up effectively through telephone calls or outpatient electronic medical records until September 2017. Detailed demographic data and clinicopathological information were collected retrospectively. The use of the patients’ specimens and information was approved by the ethics committee of Tianjin Medical University Cancer Institute and Hospital.

Breast cancer data from public databases

We collected the gene expression profiles of 742 patients with stage I, II or III invasive ductal carcinoma from TCGA (http://www.cbioportal.org/index.do) and 3409 samples from GSE96058 in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) All gene expression data were uniformly normalized, and pathological information was downloaded. Details on the patients involved in our study are shown in Supplementary Tables 2, 3.

Cell maintenance

MCF7 breast cancer cells were obtained from the American Type Culture Collection. The cells were maintained in RPMI-1640 medium (Invitrogen, USA) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific, USA) at 37° C in a humidified atmosphere containing 5% CO₂.

Cell transformation

DH5α E. coli were transformed with plasmid DNA (either the pCMV-N-Flag-CCDC170 vector or the pCMV-N-Flag control vector) using the heat shock method. Specifically, 10 ng of the plasmid was mixed with 5 uL of DH5α E. coli and placed on ice for 30 min. Then, the mixture of chemically competent bacteria and DNA was incubated at 42° C for 90 s (heat shock) and placed back on ice for 10 min. Subsequently, 500 uL of lysogeny broth medium was added, and the mixture was incubated at 37° C for 0.5-1 hour. Then, the mixture was centrifuged for 5 min at 8000 x g, and the supernatant was removed. Freshly prepared agar plates with ampicillin were inoculated with 150 uL of this mixture overnight at 37° C.

Subsequently, 4-6 mL of lysogeny broth medium was inoculated with single colonies picked from the freshly prepared agar plates and incubated on a shaker at 37° C for 12-16 hours. The plasmid was extracted and purified using the EZgene™ EndoFree Plasmid ezFlow Miniprep Kit II (PD1222, BIOMIGA) in strict adherence to the manufacturer’s instructions. The plasmid concentration was measured on a NanoDrop 2000c Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).

Transfection

MCF7 cells (3 × 10⁵) were seeded on six-well plates for 12-16 hours. For the transient CCDC170 overexpression experiments, the cells were transfected with the pCMV-N-Flag-CCDC170 or pCMV-N-Flag control vector using Lipofectamine^TM 3000 Reagent (Invitrogen, USA). For the CCDC170 and IRE1 knockdown experiments, the cells were transfected with CCDC170 siRNA, IRE1 siRNA or the corresponding negative control (NC) siRNA (GenePharma, China) using Lipofectamine^TM RNAiMAX transfection reagent (Invitrogen, USA) according to the manufacturer’s instructions.

The CCDC170 siRNAs targeted the following sequences:

siRNA-1: sense 5′-GCCCACAAUUUGCAGAGAATT-3′

antisense 5′-UUCUCUGCAAAUUGUGGGCTT-3′

siRNA-2: sense 5′-GCAGCAACUUUGGUCAAAUTT-3′

antisense 5′-AUUUGACCAAAGUUGCUGCTT-3′

NC: sense 5′-UUCUCCGAACGUGUCACGUTT-3′

antisense 5′-ACGUGACACGUUCGGAGAATT-3′

The IRE1 siRNAs targeted the following sequences:

siRNA-1: sense 5′-GCAGAUAGUCUCUGCCCAUTT-3′

antisense 5′-AUGGGCAGAGACUAUCUGCTT-3′

siRNA-2: sense 5′-GCAAGAACAAGCUCAACUATT-3′

antisense 5′-UAGUUGAGCUUGCUUGCTT-3′

NC: sense 5′-UUCUCCGAACGUGUCACGUTT-3′

antisense 5′-ACGUGACACGUUCGGAGAATT-3′

Lentiviral infection

CCDC170 was stably overexpressed using the recombinant lentiviral vector LV8(EF-1a/RFP+Puro) (Shanghai GeneChem Co., Ltd., Cina), and the empty vector was used as a negative control. MCF7 cells were plated on six-well dishes at 30-40% confluence and infected with the retroviruses. Polybrene was added at a concentration of 5 μg/mL to enhance the infection efficiency. Seventy-two hours after the infection, 2 μg/mL puromycin was applied for 10 days to select the stable pooled cell population.

RNA extraction, amplification and labeling

Total RNA was extracted using an RNeasy Mini Kit (Qiagen, Valencia, CA, USA) in accordance with the manufacturer’s instructions. The overall RNA concentration and purity were measured on a NanoDrop 2000 Spectrophotometer. Total RNA was amplified and labeled using a Low Input Quick Amp Labeling Kit (Agilent Technologies, USA).

Microarray construction

The effects of CCDC170 on mRNA expression in MCF7 cells were analyzed using microarrays. After transcription, purification and fragmentation, samples were hybridized onto Agilent Whole Human Genome 4×44K 60 mer oligonucleotide arrays at 60° C for 16 hours. The arrays were then washed and scanned so that gene expression could be quantified. The fluorescence scanning image signals from the chip were converted to digital signals using Feature Extraction software v.10.7 (Agilent Technologies, USA).

DEG identification

Raw microarray data were normalized using the Quantile algorithm after background correction. Each gene expression value was calculated as the weighted average of all the forward or reverse probe sets. The ratio of the signal intensity in the experimental group (with 24- or 48-hour CCDC170 overexpression) to that in the control group was used to analyze the chip data for each gene (fold ratio = experimental group/control group). The DEGs between the CCDC170-overexpressing and control MCF7 cells were selected according to the cut-off criteria of log|(fold ratio)| > 0.1761 and P < 0.05.

Pathway enrichment analysis

The Kyoto Encyclopedia of Genes and Genomes database was used for the pathway enrichment analysis of the DEGs, including Annotation, Visualization and Integrated Discovery. The data were analyzed using the “clusterProfiler” package in R, and P < 0.05 was considered statistically significant.

Immunohistochemistry

Formalin-fixed, paraffin-embedded tissue sections were stained at 4° C overnight using the following antibodies: 1:100 anti-CCDC170 (PA5-34723, Thermo Fisher Scientific, USA), 1:400 anti-XBP1 (PA5-27650, Thermo Fisher Scientific, USA) and 1:50 anti-IRE1α (H-190, Santa Cruz Biotechnology, USA). For antigen retrieval, the tissue sections were boiled in a sodium citrate solution (pH 6.0) in a pressure cooker for 2.5 min. For antibody visualization, an Ultra View DAB Detection kit (Dako, 20015510) was applied. Negative control samples were treated in the same manner without the addition of primary antibodies.

Comprehensive staining scores were calculated based on the staining intensity and percentage of positive cells in five randomly chosen visual fields. The staining intensity was scored as follows: 0 (no staining), 1 (weak), 2 (moderate) and 3 (strong). The mean percentage of positive cells was scored as follows: 0 (<5%), 1 (5-25%), 2 (26-50%), 3 (51-75%) and 4 (76-100%). The final scores were calculated as the staining proportion score multiplied by the staining intensity score. Based on their final scores, patients were divided into the low (immunohistochemistry scores ≤ 2) and high (immunohistochemistry scores > 2) expression groups.

Western blotting

Total proteins were electrophoretically separated on sodium dodecyl sulfate polyacrylamide gels (10-12%) according to the molecular size of the target protein, and were subsequently transferred onto polyvinylidene difluoride membranes. After being blocked with 5% skim milk, the membranes were incubated at 4° C overnight with the following primary antibodies: anti-CCDC170 (1:500, PA5-34723, Thermo Fisher Scientific, USA), anti-XBP1 (1:1000, PA5-27650, Thermo Fisher Scientific, USA), anti-IRE1α (1:1000, 14C10, Cell Signaling Technology, USA), anti-cleaved PARP (1:1000, 9542S, Cell Signaling Technology), anti-Caspase-12 (1:500, sc-21747, Santa Cruz Biotechnology, USA), anti-Caspase-7 (1:1000, D2Q3L, Cell Signaling Technology, USA), anti-Bcl2 (1:500, ab692, Abcam, USA) and anti-β-actin (1:2000, Santa Cruz Biotechnology, USA). Then, the membranes were washed thoroughly and incubated with secondary antibodies (1:3000 anti-mouse or 1:5000 anti-rabbit) at room temperature for 1 hour. The signals were visualized using the enhanced chemiluminescence method (Immobilon Western Chemiluminescent HRP Substrate, Millipore, USA). The samples were analyzed in duplicate, and the experiment was performed three times.

Immunofluorescence

Cells were grown on a microscope cover glass (PA 15275, Thermo Fisher Scientific, USA) laid on the bottom of the well of a 24-well plate (1 × 10⁴ cells per well). The cells were fixed with 4% paraformaldehyde for 15 min, permeabilized with 0.2% Triton X-100 for 20 min and blocked with 10% goat serum for 30 min at room temperature. Then, the slides were incubated with anti-CCDC170 (1:200, PA5-34723, Thermo Fisher Scientific, USA) or anti-calnexin (1:500, MA3-027, Invitrogen, USA) primary antibodies overnight at 4° C. The slides were subsequently incubated for 1 hour at room temperature with fluorescence-conjugated secondary antibodies (A21206/Alexa Fluor®488 donkey anti-rabbit IgG or A21203/Alexa Fluor®594 donkey anti-mouse IgG) diluted 1:1000. The nuclei were counterstained with 4’,6-diamidino-2-phenylindole (DAPI) after the slides had been washed thoroughly. Images were captured using phase-contrast fluorescence microscopy.

Flow cytometric apoptosis detection

The percentage of apoptotic cells was ascertained using flow cytometry, based on the binding of Annexin V-PE and 7-ADD. Samples of 2x10⁴ to 2x10⁵ cells were prepared in 10% bovine serum albumin, according to the manufacturer’s instructions. Then, 100 uL of Guava Nexin® Reagent (4500-0450, Millipore, USA) was added to each sample, and the mixture was incubated for 20 min at room temperature in the dark. Finally, the samples were analyzed on a Guava system. The samples were analyzed in duplicate, and the experiment was performed three times.

Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay

Late-stage apoptosis was detected with a TUNEL BrightGreen Apoptosis Detection Kit (Vazyme Biotech, A112-03). Adherent MCF7 cells were cultured on Chamber Slides (PA 15275, Thermo Fisher Scientific, USA) in 24-well plates at a density of 1 × 10⁴ cells per well. The cells were then fixed, washed, labeled and detected in accordance with the kit manual. After the TUNEL labeling, the nuclei were labeled with DAPI. Fluorescein isothiocyanate-12-dUTP-labeled DNA was observed directly under a fluorescence microscope. For the positive control slide, cells were permeabilized and treated with DNase I. For the negative control, no terminal deoxynucleotidyl transferase enzyme was added.

MTT assay

The MTT assay involves the conversion of the water-soluble yellow dye MTT to insoluble purple formazan by the action of mitochondrial reductase. MCF7 cells were reseeded in a sterile 96-well plate at a density of 3000 cells per well and grown for three days. Then, 50 μL of diluted MTT was added to each well and incubated with the cells for 4 hours in an incubator. The medium was removed, and 100 μL of dimethyl sulfoxide was added to each well to dissolve the formazan. Finally, the optical density of each well was measured on a spectrophotometer at a wavelength of 595 nm. The samples were analyzed in triplicate, and the experiment was repeated three times.

Statistical analyses

Statistical analyses were performed with SPSS 19.0 (SPSS Inc., Chicago, IL, USA). Categorical variables were compared using the Chi-square test, while continuous variables were analyzed using nonparametric tests (Kruskal-Wallis test and Mann-Whitney test), one-way analysis of variance (ANOVA) and Student’s t-test. Spearman correlations were calculated because the data were not normally distributed, even after log-transformation.

The survival time was measured in months from the date of breast cancer diagnosis until recurrence, metastasis or death. OS was evaluated from the date of diagnosis to the date of death or last follow-up. DFS was calculated as the time from diagnosis to the observation of disease progression or death for any reason. Kaplan-Meier analyses and log-rank tests were performed to estimate the probability of OS and DFS. Cox logistic regression models with 95% CIs were used to evaluate the independent prognostic factors.

Data from cell experiments were analyzed using unpaired Student’s t-tests, and images based on the statistical analyses were made in GraphPad Prism 5.0 (GraphPad Software Inc, La Jolla, CA, USA). All hypothetical tests were two-sided, and P-values less than 0.05 were considered statistically significant in all tests.

Ethics approval and consent to participate

Permission to use the paraffin-embedded tissues from this study for research purposes was provided by the Department of Breast Pathology, Tianjin Medical University Cancer Institute and Hospital. The ethics committee of Tianjin Medical University approved the study.

Availability of data and materials

The gene expression profiles used in the current study are available on the websites of TCGA (www.cbioportal.org/index.do) and GEO (www.ncbi.nlm.nih.gov/geo; accession number GSE96058).