Research ArticleExperimental Studies
Open Access

17-AAG Induces Endoplasmic Reticulum Stress-mediated Apoptosis in Breast Cancer Cells, Possibly Through PERK/eIF2α Up-regulation

PRASIT SUWANNALERT, PIMCHANOK PANPINYAPORN, PITCHAPA WANTANACHAISAENG, TEERAPAT TEEPPAIBUL, THANAPHAT WORAWICHITCHAIKUN, THIDARAT KOOMSANG, CHONNAPAT NAKTUBTIM and WITCHUDA PAYUHAKRIT
In Vivo September 2024, 38 (5) 2228-2238; DOI: https://doi.org/10.21873/invivo.13687

Abstract

Background/Aim: Breast cancer is the most predominant type of cancer affecting women worldwide and the current therapeutic treatment for breast cancer patients is not adequately effective. This study aimed to investigate the mechanism of 17-AAG, a heat shock protein (HSP90) inhibitor, as a treatment for inducing breast cancer cell apoptosis. Materials and Methods: The pharmacology network was employed to examine the correlation of 17-AAG with the gene expression profiles of breast cancer, obtained by Gene Expression Profiling Interactive Analysis (GEPIA). MTT and flow cytometry were utilized to investigate cell proliferation and cell apoptosis, respectively. Dichloro-dihydro-fluorescein diacetate (DCFH-DA) assay and western blot analysis were employed to examine the correlation between cellular oxidant levels and protein expression. Immunofluorescence staining was utilized to confirm the protein localization and assess DNA damage. Results: The pharmacological network analysis revealed that HSP90 serves as the common target connecting 17-AAG and breast cancer genes. Treatment with 17-AAG significantly increased cell apoptosis. Moreover, the treatment resulted in up-regulation of cellular oxidant levels and PERK/eIF2α expression. In line with these, protein localization after treatment revealed an increase in DNA damage, correlating with higher ER stress levels. Furthermore, GEPIA demonstrated that PERK and eIF2α expression were significantly higher in breast invasive carcinoma compared to other tumor types. Conclusion: HSP90 emerges as a potential target for inducing apoptosis in breast cancer cells by disrupting protein homeostasis in the endoplasmic reticulum, possibly through PERK/eIF2α up-regulation. 17-AAG, an HSP90 inhibitor, may therefore potentially hold an alternative therapeutic strategy for breast cancer treatment.

Key Words:

Breast cancer is the most commonly diagnosed type of cancer and the leading cause of cancer mortality in women worldwide (1). The rates of breast cancer incidence have steadily increased through the past decades, and the data from recent years illustrate an annual rise of cases (2). Currently, the most common treatments of breast cancer include surgery, radiotherapy, chemotherapy, hormone therapy, and targeted therapy (3). However, certain instances of breast cancer, varying in type and characteristics, exhibit aggressiveness and develop resistance to drugs, resulting in treatment failure (4). Hence, the challenges of breast cancer therapy remain, and the continuing exploration of novel therapeutic regimens is essential.

It is well-acknowledged that internal and external stresses play an important role in the progression of breast cancer. Among them, endoplasmic reticulum (ER) stress is a contributing factor in the development of cancer as it causes changes in the protein folding ability and gives rise to the accumulation of misfolded and unfolded proteins. This subsequently leads to the activation of the unfolded protein response (UPR), which can assist in cancer development (5). Conversely, the presence of ER stress at certain levels and the aberrant induction of the UPR that causes the overaccumulation of misfolded proteins can trigger immunogenic cell death (ICD) and protective antitumor immunity. This suggests that the use of ER stress inducers could be of benefit for the inhibition of cancer development and progression (6).

There are several FDA-approved compounds used as ER stress inducers, such as methotrexate, folinic acid, and fludarabine phosphate, whose mechanisms are correlated with the treatment of certain cancers (7). Among the preexisting ER stress inducers is 17-AAG, which acts as heat shock protein 90 (HSP90) inhibitor. 17-AAG binds to HSP90 and disrupts the folding of proteins. This consequently results in the accumulation of unfolded proteins and contributes to the triggering of ER stress and the UPR (8). Currently, 17-AAG has been approved by the FDA for use in a phase II clinical study of breast cancer patients (9). This implies the potential of 17-AAG as a promising agent against breast cancer.

The primary mechanism of 17-AAG is binding to HSP90 at its N-terminal ATP binding site, as mentioned in a phase II clinical study of breast cancer patients (9). Additionally, another study has shown the development of various novel novobiocin analogues as C-terminal HSP90 inhibitors with a correlation to targeting cancer stem cells, preventing sphere formation, migration, invasion, and EMT transition (10). However, the mechanism of how 17-AAG induces apoptosis in breast cancer has not yet been investigated. Therefore, the present study aimed to elucidate the mechanism by which 17-AAG induces apoptosis in MCF-7 and MDA-MB-231 cell lines, with the aim of enhancing understanding and mitigating chemoresistance in breast cancer cells.

Materials and Methods

Cell culture. The breast cancer cell lines MCF-7 and MDA-MB-231 were purchased from the American Type Culture Collection (ATCC, Biomedia Thailand Co., Ltd, Nontaburi, Thailand). The MDA-MB-231 cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM)-high glucose (Life Technologies Corporation, Grand Island, NY, USA), and the MCF-7 cells were cultured in Eagle’s Minimum Essential Medium (EMEM; Life Technologies Corporation) supplemented with 10% of heat-inactivated fetal bovine serum (FBS), 1% of L-glutamine, 1% of penicillin-streptomycin, and 1% of non-essential amino acids. The cells were cultured at 37°C with 5% CO2 in a humidified incubator.

Target gene prediction. The structural information of 17-AAG was obtained from the PubChem platform (https://pubchem.ncbi.nlm.nih.gov/). 17-AAG targets were predicted from three databases: Swiss Target Prediction Database (www.swisstargetprediction.ch/), SuperPred (https://prediction.charite.de/), and Sea Search Server (https://sea.bkslab.org/). In all searches, the attributes were set to “Homo sapiens”. The breast cancer-related genes were accessed from three databases: GeneCards (www.genecards.org/), DisGeNET (www.disgenet.org/), and OMIM (www.omim.org). Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) was used to determine the targets of 17-AAG on breast cancer.

Protein interaction network. The common genes shared by 17-AAG-related targets and the breast cancer-related targets were obtained by a Venn diagram. The STRING11.0 database (https://cn.string-db.org/) was updated with the integrated target data to construct the protein-protein interaction (PPI) network and the species was restricted to “Homo sapiens”. The PPI network was visualized by Cytoscape 3.10.1 (11), and topology analysis was performed using the CytoHubba plug-in (12) to identify the hub genes in terms of degree.

KEGG Enrichment Analysis. KEGG is an online database used for the pathway analysis of a large amount of genetic information. The KEGG pathway enrichment analysis was performed using the web interface on https://www.bioinformatics.com.cn/, where we entered the target gene name for enrichment analysis. The significant enrichment threshold was set at a p-Value of <0.05, and 10 pathways with the lowest p-Values were selected as the top 10 pathways.

MTT assay. The MCF-7 and MDA-MB-231 cells were seeded in 96-well plates at a density of 3×104 cells per well and incubated at 37°C with 5% CO2 in a humidified incubator for 24 h. Then, cells were treated with various concentrations of 17-AAG for 24 h. After 24 h, the cells were incubated with 100 μl of MTT solution for 3 h, followed by DMSO. The results were measured with a microplate reader (MR-850 BioPlate Reader, Biometrics Technologies, Santa Clara, CA, USA) at the optical density of 570 nm.

Analysis of apoptosis. The MCF-7 and MDA-MB-231 cells were seeded in 6-well plates at a density of 3×105 cells per well and incubated at 37°C with 5% CO2 inside a humidified incubator for 24 h. Then, the cells were treated with various concentrations of 17-AAG for 24 h. After that, the cells were washed with PBS and resuspended in 100 μl of Annexin V binding buffer (BioLegend, San Diego, CA, USA) prior to having 0.5 μL FITC-Annexin V (BioLegend) added and then being incubated in the dark at 4°C for 15 min. PBS was then added to wash the cells. Subsequently, the cells were resuspended in 200 μl 1% paraformaldehyde. Propidium iodide solution (BioLegend) was added and the cells were analyzed by flow cytometry (FACScan; Becton, Dickinson and Company, Franklin Lakes, NJ, USA).

Dichloro-dihydro-fluorescein diacetate (DCFH-DA) assay. The MCF-7 and MDA-MB-231 cells were seeded in 96-well plates at a density of 3×104 cells per well and incubated at 37°C with 5% CO2 in a humidified incubator for 24 h. The cells were then treated with various concentrations of 17-AAG for 24 h. Subsequently, the medium was removed prior to the addition of 10 μM 2′,7′-Dichlorofluorescin diacetate (Sigma-Aldrich, Darmstadt, Germany) and then they were incubated at 37°C with 5% CO2 for 1 h. After that, the fluorescence intensity was measured by a fluorescence microplate reader (The Spark® multimode microplate reader, TECAN, Switzerland) at the wavelengths of 485/535 nm.

Immunofluorescence. The MCF-7 and MDA-MB-231 cells were seeded in 6-well plates on a coverslip at a density of 3×104 cells and incubated at 37°C with 5% CO2 in a humidified incubator for 24 h. The cells were then treated with various concentrations of 17-AAG for 24 h. Then, the cells were washed with PBS and fixed with absolute methanol at 4°C for 45 min. Following fixation, the cells were permeabilized with 0.25% Triton X-100 and incubated in a blocking buffer (1% BSA in PBS) for 1 h at room temperature. Subsequently, they were incubated with primary antibodies [anti-PERK; 1:1,000 (ab65142, rabbit polyclonal, Abcam, Waltham, MA, USA) and anti-γH2AX; 1:1,000 (ab26350, mouse monoclonal, Abcam)] for 90 min at room temperature, and then with secondary antibodies (Alexa Fluor® 488, ab150077, Abcam and Alexa Fluor® 594, ab150116, Abcam) for 30 min at room temperature. Next, DAPI was used to counterstain the nucleus for 15 min. Finally, coverslips were mounted with mounting media on a glass slide before being observed under a fluorescence microscope (Eclipse Ni-U, Nikon, Tokyo, Japan).

Western blot analysis. The MCF-7 and MDA-MB-231 cells were seeded into 6-well plates at a density of 3×105 and incubated at 37°C with 5% CO2 in a humidified incubator for 24 h. Subsequently, following treatment with various concentrations of 17-AAG for 24 h, RIPA lysis buffer (Merck, Darmstadt, Germany) and a protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA) were added to the cells to obtain the cell lysates. Then, the lysates were homogenized by a homogenizer (Vibra-cell, Sonics, Newtown, CT, USA) and centrifuged at 15,294 g at 4°C for 20 min to collect the supernatant. The total protein was then evaluated by the commercial Bradford assay (HiMedia, Maharashtra, India). The protein was stained with a Laemmli loading buffer (Merck) and electrophoresed in SDS-PAGE before being transferred to nitrocellulose membranes (Merck). Subsequently, the membranes were blocked with skimmed milk (Merck) for 1h at room temperature. The membranes were then incubated with primary antibodies (anti-PERK; ab65142, rabbit polyclonal, Abcam), anti-eIF2α (ab227593, phosphor S52 rabbit polyclonal, Abcam) at 4°C overnight and then with secondary antibodies (goat anti-rabbit IgG-HRP; ab205718, goat, Abcam) for 2 h. Prior to the analysis, the membranes were incubated with Luminata Forte Western HRP substrate (Merck) for 5 min before protein expression was detected by the ChemiDoc Imaging System (Bio-Rad, Hercules, CA, USA).

Gene expression profiling interactive analysis. The PERK gene expression across different tumors compared to those of TCGA normal and GTEx data were investigated using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database (13). Pearson correlation analysis and the individual expression of PERK and eIF2α were conducted in the breast invasive carcinoma (BRCA) dataset.

Statistical analysis. All results were analyzed using one-way ANOVA followed by Tukey’s Honestly Significant Difference (HSD) test in GraphPad Prism version 9 (GraphPad, San Diego, CA, USA). The results are presented as mean±standard deviation (SD) of three independent experiments (n=3). A p-Value of <0.05 was considered significantly different.

Results

Prediction of 17-AAG targets in breast cancer through pharmacological network analysis. The 17-AAG-related targets and breast cancer-related targets were intersected, providing 7 shared drug-disease targets (Figure 1A). The STRING database was used to generate a PPI network, which illustrates the interaction of seven therapeutic targets during the progression of breast cancer. The PPI network is shown in Figure 1B, where each edge represents a protein interaction, and each node represents a target. The node color and score are correlated, with darker and more red nodes signifying higher scores. HSP90 showed the highest degree (score 49.0), suggesting they are strongly linked to each other and could be the hub targets.

Figure 1.
Figure 1.

Pharmacological network of 17-AAG on breast cancer. (A) Target intersection of 17-AAG-related gene and breast cancer-related genes. (B) The protein–protein interaction (PPI) network. The red color indicates the nodes with high MCC scores, and the yellow indicates the nodes with a low MCC score. (C) Bubble chart of the top ten KEGG pathway enrichment findings. The color scales indicate the different thresholds for p-values, and the size of the dots represent the number of genes corresponding to each term. (D) Pathview map of the PI3K-AKT signaling pathway. Red boxes represent the targets related to the core component-target-pathway network.

To explore the underlying mechanisms of 17-AAG, the KEGG pathway analysis was performed to determine the significant signaling pathways associated with 17-AAG anti-breast cancer activity. The KEGG pathway enrichment of 183 potential targets yielded 90 signaling pathways with p<0.01. The 10 pathways with the most significant gene ratio in the enrichment results were selected (Figure 1C). It is noteworthy that the majority of the genes were involved in the PI3K-AKT signaling pathway. The target genes of 17-AAG in the PI3K-AKT signaling pathway are depicted in Figure 1D. These results suggest that HSP90 is the targeted gene of 17-AAG, which functions by mediating the AKT pathway, which regulates cellular processes by phosphorylating the substrates involved in apoptosis, protein synthesis, metabolism, and the cell cycle.

17-AAG inhibits cell proliferation and induces apoptosis. The cytotoxicity test of 17-AAG on breast cancer cell lines MCF-7 and MDA-MB-231 was performed using the MTT assay. The control was set as 100% cell viability. The MCF-7 cells treated with 10 μM of 17-AAG showed a slight drop to approximately 66.79±5.48% of cell viability. The cells treated with 100 and 1,000 μM of 17-AAG showed more significant drops to 2.85±1.16% and 7.15±0.92%, respectively, as shown in Figure 2A. The MDA-MB-231 cells treated with 10 and 100 μM of 17-AAG showed gradual but significant drops in their cell viability percentages to approximately 81.81±1.64% and 71.90±1.73%, respectively. The cells treated with 1,000 μM of 17-AAG showed a dramatic drop to 2.06±0.27%, as shown in Figure 2A.

Figure 2.
Figure 2.

The effect of 17-AAG on breast cancer cell proliferation and apoptosis. (A) Cytotoxicity (MTT) assay results showing the percentage of cell viability of MCF-7 and MDA-MB-231 cells treated with various concentrations of 17-AAG (0.1, 1, 10, 100, and 1,000 μM) for 24 h. (B, C) The flow cytometric analysis showing the percentage of apoptotic cells in MCF-7 and MDA-MB-231 cells treated with various concentrations of 17-AAG (10, 15 and 20 μM). Data are shown as mean±SD, n=3. **p<0.01, ***p<0.001 and ****p<0.0001 using one-way ANOVA followed by Tukey’s HSD test. 17-AAG, 17-N-allylamino-17-demethoxygeldanamycin.

Flow cytometric analysis was used to confirm the cell apoptosis, as shown in Figure 2B. The apoptotic MCF-7 cells treated with various concentrations of 17-AAG, namely 10, 15, and 20 μM, showed significant increases in the percentages of apoptotic cells at 24.41±1.95, 27.31±1.70, and 40.90±2.86%, respectively (Figure 2C), while the MDA-MB-231 cells treated with 10 μM showed an increase in the percentage of apoptotic cells of 12.49±1.11%. The cells treated with 15 and 20 μM showed much higher percentages of apoptotic cells when compared to the control at 32.09±0.97 and 39.46±1.96%, respectively (Figure 2C). The results suggest that 17-AAG inhibits cell proliferation and induces apoptosis in breast cancer cells.

17-AAG enhances ER stress by up-regulating PERK and phosphorylated eIF2α. To investigate the effect of 17-AAG on ER stress induction in breast cancer cells, the breast cancer cell lines MCF-7 and MDA-MB-231 were treated with 5, 10, 15, and 20 μM of 17-AAG, and then analyzed using the DCFH-DA assay and western blot. The DCFH-DA assay demonstrated a significant increase in cellular oxidant levels in a dose-dependent manner in the MCF-7 (Figure 3A) and MDA-MB-231 cells (Figure 3B) cells compared to the control. However, the oxidant levels were reduced after treatment with 20 μM of 17-AAG in both the MCF-7 and MDA-MB-231 cells. These results indicate that 17-AAG increased the cellular oxidative levels at low concentrations. Moreover, the western blot analysis demonstrated a significant increase in PERK/eIF2α expression in MCF-7 and MDA-MB-231 cell lines (Figure 3C). While the PERK expression increased in a dose-dependent manner in both MCF-7 and MDA-MB-231 cells, and reduced as observed in the cellular oxidant levels, the eIF2α expression showed slight fluctuations across these concentrations. However, the eIF2α expression was significantly increased with 20 μM of 17-AAG in both the MCF-7 and MDA-MB-231 cell lines compared to the control. These results suggest that 17-AAG increases the ER stress associated with the up-regulation of PERK and the expression of phosphorylated eIF2α.

Figure 3.
Figure 3.
Figure 3.

The effect of 17-AAG on intracellular oxidant levels and ER stress. DCFH-DA assay demonstrating the cellular oxidant levels in (A) MCF-7 and (B) MDA-MB-231 cells. (C) Western blot analysis and bar graph showing the expression of PERK and EIF2a normalized by β-actin in MCF-7 and MDA-MB-231 cells. Data are shown as mean±SD, n=3. **p<0.01, ***p<0.001 and ****p<0.0001 using one-way ANOVA followed by Tukey’s HSD test. 17-AAG, 17-N-allylamino-17-demethoxygeldanamycin.

17-AAG increases the ER stress and causes DNA damage. Immunofluorescent assay was employed to investigate PERK and γH2AX, a marker for double-strand breaks (DSBs) in DNA. The results indicate that 17-AAG increased the expression of PERK, which was highly expressed around the nucleus, as depicted in Figure 4A and B. The intensity of the PERK level was significantly increased in a dose dependent manner (Figure 4C, D). The expression of γH2AX, a marker of DSBs, was also increased, as shown in Figure 4A, B. Nevertheless, the expression of PERK declined in response to high doses of 17-AAG. These results indicate that 17-AAG induces ER stress, leading to DNA damage in breast cancer cell lines.

Figure 4.
Figure 4.

Effect of 17-AAG on DNA damage and ER stress. Immunofluorescent images of (A) MCF-7 and (B) MDA-MB-231 cells treated with 17-AAG (10, 15 and 20 μM) for 24h. PERK is represented as green, γH2AX as red, and DAPI as blue. (C, D) The relative intensity of PERK was quantified. Data are shown as mean±SD, n=3. **p<0.01, ***p<0.001 and ****p<0.0001 using one-way ANOVA followed by Tukey’s HSD test. 17-AAG, 17-N-allylamino-17-demethoxygeldanamycin.

The expression of PERK across different cancer cell types and normal tissue analyzed by GEPIA. The expression analysis of PERK was further investigated from the TCGA database. GEPIA was used to analyze and compare the expression of PERK between normal and tumor tissues across different types of cancer. The highest expression of PERK was observed in breast invasive carcinoma (BRCA) when compared to other types of cancer (Figure 5A). The expression of PERK and eIF2α, which is the downstream signaling of PERK, are increased in breast cancer when compared to normal tissue (Figure 5B). The correlation analysis between PERK and eIF2α utilizing the Pearson correlation revealed a weak positive correlation (Figure 5C). These results suggest that PERK/eIF2α is a crucial protein that is up-regulated in the breast cancer patients.

Figure 5.
Figure 5.

GEPIA analysis of PERK and eIF2α in breast cancer patients. (A) The expression of PERK across different types of cancer and normal tissue was analyzed by GEPIA. (B) The expression of PERK and eIF2α in breast cancer and normal tissues was analyzed by GEPIA (Nnormal=291, Ntumor=1,085), p<0.01. (C) Pearson correlation analysis between PERK and eIF2α by GEPIA, p<0.0001. ACC, Adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, diffuse large B-cell lymphoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma, SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma.

Discussion

17-AAG is a synthetic derivative of the antibiotic geldanamycin, belonging to a class of compounds known as HSP90 inhibitors. HSP90 is a molecular chaperone protein that plays a pivotal role in the folding, stabilization, and function of numerous target proteins involved in cell signaling, growth, and survival. However, the role of 17-AAG in mediating apoptosis remains unclear. Upon conducting protein-protein interaction network analysis, the findings confirm that HSP90 serves as a pivotal node within the network, potentially playing crucial roles in mediating the effects of 17-AAG on breast cancer. Furthermore, KEGG enrichment analysis underscores the enrichment of the PI3K-AKT signaling pathway, which is activated by HSP90. Therefore 17-AAG may induce apoptosis by targeting the HSP90 mediated AKT pathway. AKT is a substrate of HSP90, which is required for AKT function by generating chaperone-substrate protein complexes. The HSP90/Akt pathway has been demonstrated to play a vital role in various types of cell survival and anti-apoptosis (14, 15). AKT isoforms have been shown to be elevated in human cancer, acting as oncogenes and promoting tumor growth at various levels (16). In previous studies, AKT1 knockdown has been shown to inhibit tumor growth by either preventing cell cycle progression or increasing apoptosis in breast cancer cells (17).

The treatments of breast cancer cell lines MCF-7 and MDA-MB-231 with 17-AAG resulted in decreasing percentages of cell viability as illustrated with a cytotoxicity test determined by MTT assay. The findings show that the use of 17-AAG has a detrimental effect on the survival of breast cancer cells. The percentages of apoptotic cells were assessed by apoptosis assay and analyzed by flow cytometry. There was no significant difference in the characteristics of apoptosis between the two cell lines, illustrating that 17-AAG provides similar effects of inducing apoptotic cell death.

Regarding the elevated ER stress resulting from 17-AAG treatment, the results of this study demonstrated an increase in cellular oxidant levels, PERK, and elF2α protein expression in both the MCF-7 and MDA-MB-231 cells, subsequent to their treatment with various concentrations of 17-AAG. Overaccumulation of unfolded protein leads to excessive cellular oxidative stress, which plays a significant role in contributing to ER stress (18, 19). The UPR, involved in the activation of inositol requiring enzyme 1α (IRE1), protein kinase RNA-activated-like ER kinase (PERK), and activating transcription factor 6 (ATF6), has been reported to alleviate ER stress (5, 20, 21), yet it has also been reported to promote cancer adaptation, proliferation, and metastasis (20). However, prolonged ER stress that exceeds the threshold can induce apoptosis in cancer cells (20, 22, 23). In addition, prior studies have revealed that ER stress can inhibit androgen receptor expression through the PERK/eIF2α/ATF4 pathway, contributing to cancer cell growth inhibition (22). Moreover, ER stress overexpression has been reported to trigger cancer cell death in chemoresistant breast cancer cells (23). ER stress has emerged as a target for cancer treatment and 17-AAG, as an ER stress inducer, shows promising potential in becoming an alternative therapeutic treatment for cancer.

ER stress can control cancer growth and apoptosis via the ER stress homeostasis which is regulated by the UPR (24). In the UPR, the PERK pathway has the property of controlling cancer growth and cancer apoptosis (25, 26). The finding of elevated intracellular oxidative stress from 17-AAG treatment might be a result of the accumulation of unfolded protein. Elevated levels of oxidative stress induce DNA damage and initiate the activation of the DNA damage response (DDR), a cellular mechanism responsible for detecting and repairing DNA damage. Upon encountering significant DNA damage, the DDR pathway can be activated, leading to apoptosis.

While endoplasmic reticulum (ER) stress has emerged as a target for cancer therapy (27, 28), its induction leads to the activation of the UPR, recognized as a pivotal cellular stress response, particularly implicated in cancer proliferation and chemoresistance (5). Accumulating evidence suggests that persistent stress can facilitate tumor initiation, progression, and drug resistance by triggering the UPR (29). Therefore, elucidating the molecular mechanisms of the UPR holds promise for addressing the challenge of preventing chemoresistance in cancer therapy. Our findings represent the cellular mechanism by which 17-AAG induces apoptosis in breast cancer cells via the modulation of ER stress. These insights may provide valuable information for mitigating chemoresistance in breast cancer treatment.

Conclusion

In conclusion, HSP90 was identified as a pivotal target linking 17-AAG and breast cancer genes. Elevated PERK and eIF2α expression in breast invasive carcinoma, as determined by GEPIA analysis, highlighted their potential importance. It is suggested that disrupting HSP90 with 17-AAG, an HSP90 inhibitor, triggers apoptosis by disturbing endoplasmic reticulum protein homeostasis, potentially involving the up-regulation of PERK and eIF2α in breast cancer cells.

Acknowledgements

The authors thank the Department of Pathobiology, Faculty of Science, Mahidol University, Bangkok, Thailand for providing the supporting facilities for this project.

Footnotes

  • Authors’ Contributions

    Conceptualization and supervision: PS and WP; data curation, formal analysis and investigation: PP, PW, TT, TW, TK and CN; writing - original draft: PS; writing - review & editing: WP. The Authors have read and agreed to the published version of the manuscript.

  • Funding

    This research project is supported by Mahidol University [Fundamental Fund: fiscal year 2023 by National Science Research and Innovation Fund (NSRF)].

  • Conflicts of Interest

    The Authors declare no conflicts of interest in relation to this study.

  • Received April 20, 2024.
  • Revision received May 22, 2024.
  • Accepted May 27, 2024.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

References

  1. ↵
    Global cancer burden growing, amidst mounting need for services. Saudi Med J 45(3): 326-327, 2024.
    OpenUrlFREE Full Text
  2. ↵
    1. Giaquinto AN,
    2. Sung H,
    3. Miller KD,
    4. Kramer JL,
    5. Newman LA,
    6. Minihan A,
    7. Jemal A,
    8. Siegel RL
    : Breast Cancer Statistics, 2022. CA Cancer J Clin 72(6): 524-541, 2022. DOI: 10.3322/caac.21754
    OpenUrlCrossRefPubMed
  3. ↵
    1. Wang J,
    2. Wu SG
    : Breast Cancer: An overview of current therapeutic strategies, challenge, and perspectives. Breast Cancer (Dove Med Press) 15: 721-730, 2023. DOI: 10.2147/BCTT.S432526
    OpenUrlCrossRef
  4. ↵
    1. Burguin A,
    2. Diorio C,
    3. Durocher F
    : Breast cancer treatments: updates and new challenges. J Pers Med 11(8): 808, 2021. DOI: 10.3390/jpm11080808
    OpenUrlCrossRef
  5. ↵
    1. Xu D,
    2. Liu Z,
    3. Liang MX,
    4. Fei YJ,
    5. Zhang W,
    6. Wu Y,
    7. Tang JH
    : Endoplasmic reticulum stress targeted therapy for breast cancer. Cell Commun Signal 20(1): 174, 2022. DOI: 10.1186/s12964-022-00964-7
    OpenUrlCrossRef
  6. ↵
    1. Chen X,
    2. Cubillos-Ruiz JR
    : Endoplasmic reticulum stress signals in the tumour and its microenvironment. Nat Rev Cancer 21(2): 71-88, 2021. DOI: 10.1038/s41568-020-00312-2
    OpenUrlCrossRefPubMed
  7. ↵
    1. Doultsinos D,
    2. Carlesso A,
    3. Chintha C,
    4. Paton JC,
    5. Paton AW,
    6. Samali A,
    7. Chevet E,
    8. Eriksson LA
    : Peptidomimetic-based identification of FDA-approved compounds inhibiting IRE1 activity. Febs J 288(3): 945-960, 2021. DOI: 10.1111/febs.15372
    OpenUrlCrossRef
  8. ↵
    1. Ho SW,
    2. Tsui YT,
    3. Wong TT,
    4. Cheung SK,
    5. Goggins WB,
    6. Yi LM,
    7. Cheng KK,
    8. Baum L
    : Effects of 17-allylamino-17-demethoxygel-danamycin (17-AAG) in transgenic mouse models of frontotemporal lobar degeneration and Alzheimer’s disease. Transl Neurodegener 2(1): 24, 2013. DOI: 10.1186/2047-9158-2-24
    OpenUrlCrossRefPubMed
  9. ↵
    1. Gartner EM,
    2. Silverman P,
    3. Simon M,
    4. Flaherty L,
    5. Abrams J,
    6. Ivy P,
    7. Lorusso PM
    : A phase II study of 17-allylamino-17-demethoxygeldanamycin in metastatic or locally advanced, unresectable breast cancer. Breast Cancer Res Treat 131(3): 933-937, 2012. DOI: 10.1007/s10549-011-1866-7
    OpenUrlCrossRefPubMed
  10. ↵
    1. Subramanian C,
    2. Grogan PT,
    3. Wang T,
    4. Bazzill J,
    5. Zuo A,
    6. White PT,
    7. Kalidindi A,
    8. Kuszynski D,
    9. Wang G,
    10. Blagg BSJ,
    11. Cohen MS
    : Novel C-terminal heat shock protein 90 inhibitors target breast cancer stem cells and block migration, self-renewal, and epithelial-mesenchymal transition. Mol Oncol 14(9): 2058-2068, 2020. DOI: 10.1002/1878-0261.12686
    OpenUrlCrossRef
  11. ↵
    1. Szklarczyk D,
    2. Kirsch R,
    3. Koutrouli M,
    4. Nastou K,
    5. Mehryary F,
    6. Hachilif R,
    7. Gable AL,
    8. Fang T,
    9. Doncheva NT,
    10. Pyysalo S,
    11. Bork P,
    12. Jensen LJ,
    13. von Mering C
    : The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51(D1): D638-D646, 2023. DOI: 10.1093/nar/gkac1000
    OpenUrlCrossRefPubMed
  12. ↵
    1. Chin CH,
    2. Chen SH,
    3. Wu HH,
    4. Ho CW,
    5. Ko MT,
    6. Lin CY
    : cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8 Suppl 4(Suppl 4): S11, 2014. DOI: 10.1186/1752-0509-8-S4-S11
    OpenUrlCrossRefPubMed
  13. ↵
    1. Tang Z,
    2. Kang B,
    3. Li C,
    4. Chen T,
    5. Zhang Z
    : GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 47(W1): W556-W560, 2019. DOI: 10.1093/nar/gkz430
    OpenUrlCrossRefPubMed
  14. ↵
    1. Wu J,
    2. Li L,
    3. Wang Y,
    4. Ren X,
    5. Lin K,
    6. He Y
    : The HSP90/Akt pathway may mediate artemether-induced apoptosis of Cal27 cells. FEBS Open Bio 9(10): 1726-1733, 2019. DOI: 10.1002/2211-5463.12711
    OpenUrlCrossRef
  15. ↵
    1. Dimas DT,
    2. Perlepe CD,
    3. Sergentanis TN,
    4. Misitzis I,
    5. Kontzoglou K,
    6. Patsouris E,
    7. Kouraklis G,
    8. Psaltopoulou T,
    9. Nonni A
    : The prognostic significance of HSP70/HSP90 expression in breast cancer: A systematic review and meta-analysis. Anticancer Res 38(3): 1551-1562, 2018. DOI: 10.21873/anticanres.12384
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Hinz N,
    2. Jücker M
    : Distinct functions of AKT isoforms in breast cancer: a comprehensive review. Cell Commun Signal 17(1): 154, 2019. DOI: 10.1186/s12964-019-0450-3
    OpenUrlCrossRefPubMed
  17. ↵
    1. George B,
    2. Gui B,
    3. Raguraman R,
    4. Paul AM,
    5. Nakshatri H,
    6. Pillai MR,
    7. Kumar R
    : AKT1 transcriptomic landscape in breast cancer cells. Cells 11(15): 2290, 2022. DOI: 10.3390/cells11152290
    OpenUrlCrossRef
  18. ↵
    1. Cao SS,
    2. Kaufman RJ
    : Endoplasmic reticulum stress and oxidative stress in cell fate decision and human disease. Antioxid Redox Signal 21(3): 396-413, 2014. DOI: 10.1089/ars.2014.5851
    OpenUrlCrossRefPubMed
  19. ↵
    1. Victor P,
    2. Umapathy D,
    3. George L,
    4. Juttada U,
    5. Ganesh GV,
    6. Amin KN,
    7. Viswanathan V,
    8. Ramkumar KM
    : Crosstalk between endoplasmic reticulum stress and oxidative stress in the progression of diabetic nephropathy. Cell Stress Chaperones 26(2): 311-321, 2021. DOI: 10.1007/s12192-020-01176-z
    OpenUrlCrossRef
  20. ↵
    1. Madden E,
    2. Logue SE,
    3. Healy SJ,
    4. Manie S,
    5. Samali A
    : The role of the unfolded protein response in cancer progression: From oncogenesis to chemoresistance. Biol Cell 111(1): 1-17, 2019. DOI: 10.1111/boc.201800050
    OpenUrlCrossRefPubMed
  21. ↵
    1. Liang D,
    2. Khoonkari M,
    3. Avril T,
    4. Chevet E,
    5. Kruyt FA
    : The unfolded protein response as regulator of cancer stemness and differentiation: Mechanisms and implications for cancer therapy. Biochem Pharmacol 192: 114737, 2021. DOI: 10.1016/j.bcp.2021.114737
    OpenUrlCrossRef
  22. ↵
    1. Li X,
    2. Zhou D,
    3. Cai Y,
    4. Yu X,
    5. Zheng X,
    6. Chen B,
    7. Li W,
    8. Zeng H,
    9. Hassan M,
    10. Zhao Y,
    11. Zhou W
    : Endoplasmic reticulum stress inhibits AR expression via the PERK/eIF2α/ATF4 pathway in luminal androgen receptor triple-negative breast cancer and prostate cancer. NPJ Breast Cancer 8(1): 2, 2022. DOI: 10.1038/s41523-021-00370-1
    OpenUrlCrossRef
  23. ↵
    1. Zhu Y,
    2. Xie M,
    3. Meng Z,
    4. Leung LK,
    5. Chan FL,
    6. Hu X,
    7. Chi K,
    8. Liu C,
    9. Yao X
    : Knockdown of TM9SF4 boosts ER stress to trigger cell death of chemoresistant breast cancer cells. Oncogene 38(29): 5778-5791, 2019. DOI: 10.1038/s41388-019-0846-y
    OpenUrlCrossRef
  24. ↵
    1. Bonsignore G,
    2. Martinotti S,
    3. Ranzato E
    : Endoplasmic reticulum stress and cancer: could unfolded protein response be a druggable target for cancer therapy? Int J Mol Sci 24(2): 1566, 2023. DOI: 10.3390/ijms24021566
    OpenUrlCrossRef
  25. ↵
    1. Bobrovnikova-Marjon E,
    2. Grigoriadou C,
    3. Pytel D,
    4. Zhang F,
    5. Ye J,
    6. Koumenis C,
    7. Cavener D,
    8. Diehl JA
    : PERK promotes cancer cell proliferation and tumor growth by limiting oxidative DNA damage. Oncogene 29(27): 3881-3895, 2010. DOI: 10.1038/onc.2010.153
    OpenUrlCrossRefPubMed
  26. ↵
    1. Wu MS,
    2. Chien CC,
    3. Jargalsaikhan G,
    4. Ilsan NA,
    5. Chen YC
    : Activation of PERK contributes to apoptosis and G(2)/M arrest by microtubule disruptors in human colorectal carcinoma cells (‡). Cancers (Basel) 12(1): 97, 2019. DOI: 10.3390/cancers12010097
    OpenUrlCrossRef
  27. ↵
    1. Yadav RK,
    2. Chae SW,
    3. Kim HR,
    4. Chae HJ
    : Endoplasmic reticulum stress and cancer. J Cancer Prev 19(2): 75-88, 2014. DOI: 10.15430/JCP.2014.19.2.75
    OpenUrlCrossRefPubMed
  28. ↵
    1. Oakes SA
    : Endoplasmic reticulum stress signaling in cancer cells. Am J Pathol 190(5): 934-946, 2020. DOI: 10.1016/j.ajpath.2020.01.010
    OpenUrlCrossRefPubMed
  29. ↵
    1. Qing B,
    2. Wang S,
    3. Du Y,
    4. Liu C,
    5. Li W
    : Crosstalk between endoplasmic reticulum stress and multidrug-resistant cancers: hope or frustration. Front Pharmacol 14: 1273987, 2023. DOI: 10.3389/fphar.2023.1273987
    OpenUrlCrossRef
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Reprints and Permissions
17-AAG Induces Endoplasmic Reticulum Stress-mediated Apoptosis in Breast Cancer Cells, Possibly Through PERK/eIF2α Up-regulation
PRASIT SUWANNALERT, PIMCHANOK PANPINYAPORN, PITCHAPA WANTANACHAISAENG, TEERAPAT TEEPPAIBUL, THANAPHAT WORAWICHITCHAIKUN, THIDARAT KOOMSANG, CHONNAPAT NAKTUBTIM, WITCHUDA PAYUHAKRIT
In Vivo Sep 2024, 38 (5) 2228-2238; DOI: 10.21873/invivo.13687
Twitter logo Facebook logo Mendeley logo

Jump to section

Keywords