Research ArticleExperimental Studies
Open Access

High Expression of CD58 and ALDH1A3 Predicts a Poor Prognosis in Basal-like Breast Cancer

YUYUN XIONG, HITOMI MOTOMURA, SHOMA TAMORI, AYAKA OZAKI, CHOTARO ONAGA, YASUSHI HARA, KEIKO SATO, KOUJI TAHATA, YOHSUKE HARADA, KAZUNORI SASAKI, YUN-WEN ZHENG, SHIGEO OHNO and KAZUNORI AKIMOTO
Anticancer Research November 2022, 42 (11) 5223-5232; DOI: https://doi.org/10.21873/anticanres.16029

Abstract

Background/Aim: CD58 is an immune adhesion molecule on the cellular surface. It was previously found that a high expression of CD58 predicted a poor prognosis of patients with lower-grade gliomas. Therefore, the aim of this paper was to investigate the association between CD58 and breast cancer. Materials and Methods: CD58 gene expression data downloaded from cBioPortal was compared between the different subtypes of breast cancer. Clinical prognosis was examined using Kaplan-Meier analysis and multivariable Cox regression analysis. The association between CD58 expression and immune cell infiltration was estimated using the TIMER 2.0 web platform. Finally, the tumour sphere formation of aldehyde dehydrogenase 1 (ALDH1)high basal-like breast cancer stem cells in which CD58 was knocked down using siRNA was measured. Results: CD58 mRNA was mainly enriched in claudin-low and basal-like subtypes. The high expression of CD58 predicted a good prognosis in patients with luminal A and luminal B breast cancer. This prediction may be due to the association of immune cell infiltration with CD58. Notably, patients with luminal A breast cancer with a high expression of CD58 in association with ALDH1A3 exhibited a good prognosis; however, this did not apply to patients with basal-like breast cancer. The in vitro experiments revealed that knockdown of CD58 inhibited the tumour sphere formation ability of ALDH1high basal-like cancer cells. Conclusion: CD58 may function as a potential prognostic biomarker and therapeutic target in ALDH-positive basal-like cancer stem cells.

Key Words:

According to the enumeration published in 2022, over 287,000 new cases of breast cancer have been estimated to be diagnosed in the United States each year, with breast cancer accounting for 31% of all cancers among females. In addition, over 43,000 women are estimated to succumb to the disease each year (1). Breast cancer is a heterogeneous disease which can be subtyped by predictive analysis of the microarrays 50 (PAM50) gene panel. Breast cancer PAM50 molecular subtypes include luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, normal-like and claudin-low. Luminal A and B subtypes are mainly associated with oestrogen receptor (ER)+ cancers (2, 3), while the majority of triple-negative breast cancers (TNBCs) are also classified as claudin-low and basal-like subtypes (4).

Based on the subtyping, treatment of breast cancer mainly entails surgery, radiotherapy, and drug therapy, including chemotherapy, endocrine therapy and molecular targeted therapy. In general, patients with luminal A breast cancer have a favourable prognosis owing to the effectiveness of anti-hormone receptor therapy (5). By contrast, patients with the basal-like subtype have an unfavourable prognosis due to resistance to conventional therapy (6). Therefore, there is an urgent need for the development of effective biomarkers for prognosis and treatment targets for diverse individual tumours of different subtypes. We have previously published a series of articles in recent years and screened a number of genes playing a key role in the diagnosis, prognosis, and therapy for each subtype of breast cancer (7-15). Hence, the present study continued the search for characteristic genes and the identification of explicit gene functions among different subtypes in breast cancer.

The unfavourable prognosis of patients with basal-like breast cancer may be partially due to its stem-like properties (11, 16). Breast cancer stem cells (BCSCs) are a small subpopulation of tumour cells that are associated with tumour recurrence, metastasis, and drug resistance (17). Aldehyde dehydrogenase 1 (ALDH1) is a potential CSC marker which exhibits endogenous enzyme activity in various types of cancer, including breast cancer (18, 19). ALDH1A3 is particularly associated with metastasis, tumour grade, and cancer type in breast cancer (9, 11-13, 15, 20, 21). Thus, it is crucial to understand the biological nature of basal-like BCSCs in order to provide optimal therapy.

A previous study by the authors screened out candidate genes in lower-grade gliomas (LGGs) using information-theoretic approaches and found that a high expression of CD58 predicted poor prognosis of patients with LGGs (22). CD58 is an immune adhesion molecule which is located on the cell surface and participates in cell-cell adhesion and T-cell or natural killer cell activation during the antigen presenting process (23). CD58 is overexpressed in basal-like cell lines (24) and functions as a representative basal gene in breast cancer cells. The increased expression of CD58 has been shown to remodel luminal breast cancer cells into basal breast cancer cells (25). However, its role in breast cancer remains obscure.

Novel biomarker combinations are the basis for increasingly complex diagnostic algorithms and are useful for a more comprehensive diagnosis and prognosis of diseases than any isolated biomarker (26). The present study analysed the expression of CD58 and its association with patient prognosis, as well as the combination of CD58 and ALDH1A3 in different subtypes of breast cancer. The present study identified diverse functions of CD58 in luminal A and basal-like breast cancer. The findings presented herein may aid in the identification of novel potential biomarkers for the prognosis and pharmacological targeting of breast cancer.

Materials and Methods

Analysis of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. The METABRIC dataset was downloaded from cBioPortal (https://www.cbioportal.org/), with the last entry made on April 7, 2020 (27, 28). The clinicopathological data were summarized and have been reported by Tamori et al. (13).

Analysis of immune cell infiltration. The association between CD58 expression and immune cell infiltration was estimated using TIMER 2.0 (https://cistrome.shinyapps.io/timer/), which revealed the association between gene expression and the infiltration of six types of immune cells, including CD4+ T-cells, CD8+ T-cells, B-cells, neutrophils, macrophages and dendritic cells (29).

Cells lines and cell culture. Human basal-like cancer cell lines (MDA-MB-468 and BT-20) were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). Dulbecco’s modified Eagle’s medium (DMEM) and Eagle’s minimal essential medium (EMEM) were obtained from Nissui Pharmaceutical Co., Ltd (Tokyo, Japan). Foetal bovine serum (FBS) was purchased from Capricorn Scientific GmbH (Ebsdorfergrund, Germany). The MDA-MB-468 cells were cultured in DMEM, while the BT-20 cells were cultured in EMEM. All media were supplemented with 10% FBS. The cells were cultured in a humidified atmosphere of 95% air and 5% CO2 at 37°C.

CD58 siRNA transfection. OPTI-MEM was obtained from Gibco (Thermo Fisher Scientific, Inc., Waltham, MA, USA) and Lipofectamine™ RNAiMAX transfection reagent was obtained from Invitrogen (Thermo Fisher Scientific, Inc.). Prior to transfection, 3×105 cells were seeded in 6-well plates and cultured for 24 h. A total of 10 nM CD58 siRNA-1 or CD58 siRNA-2 was mixed with OPTI-MEM and Lipofectamine™ RNAiMAX transfection reagent. Of note, two types of CD58 siRNA (CD58 siRNA-1: 5′-CAA CUU AAC AUC AUC AGA U-3′ and CD58 siRNA-2: 5′-CUG AUA CCA UGA AGU UCU U-3′) and scrambled negative control were purchased from MilliporeSigma (St. Louis, MO, USA). Transfection was performed two times for 24 h, respectively.

Western blot analysis. Western blot analysis was performed as previously described (9-13, 15). Anti-CD58 (LFA-3) antibody (330902) was obtained from BioLegend, Inc. (San Diego, CA, USA) and was diluted to 1:1,000. The mouse anti-β-actin monoclonal antibody (60008-I-Ig) was purchased from ProteinTech Group, Inc. (Rosemont, IL, USA) and was diluted to 1:5,000. The goat anti-mouse IgG horseradish peroxidase (HRP)-conjugated antibody (7076S) was purchased from Cell Signaling Technology, Inc. (Danvers, MA, USA). The secondary antibody was diluted to 1:2,000. The signals were detected using Chem Doc MP (Bio-Rad Laboratories, Inc., Hercules, CA, USA) according to the manufacturer’s instructions.

ALDEFLUOR assay. The ALDEFLUOR™ assay kit was obtained from Stemcell Technologies, Inc. (Vancouver, BC, Canada). Following transfection, ALDH1high cells were sorted from MDA-MB-468 and BT-20 cells using the ALDEFLUOR™ assay kit as previously described (8, 9, 11-13, 15). Cells were sorted using a FACS Aria III cell sorter (BD Biosciences, San Jose, CA, USA).

Tumour sphere formation assay. Tumour sphere formation assay was performed using methyl cellulose from FUJIFILM Wako Pure Chemical Corporation (Osaka, Japan) and 2-mercaptoethanol (MilliporeSigma) as previously described (8-13, 15). Tumour spheres were visualized under an inverted microscope (DMIL LED, Leica Microsystems GmbH, Wetzlar, Germany) and areas with tumour spheres of >4 cells (314 μm2) were determined using ImageJ software version 1.53e (National Institutes of Health). The average area is presented as the mean±SD from three independent experiments. All experiments were conducted three times.

Statistical analysis. In the analysis of gene expression, the mRNA levels (n=1,904) were compared using the Kruskal-Wallis test followed by Steel-Dwass’s multiple comparison test for post hoc analysis. The optimal cut-off values were then determined on the receiver operating characteristic (ROC) curve using the Youden index and the optimal cut-off values were used to divide the patients into the high and low expression groups, as previously described (12). Survival curves were plotted using Kaplan-Meier analysis. p-Values were calculated using the log-rank (Cochran-Mantel-Haenszel) test. Multiplicity was adjusted using the Holm’s method for post hoc analysis. Multivariate Cox regression analysis was used to adjust hazard ratios calculated with age at diagnosis as a confounding factor. For tumour sphere formation analysis, differences between data were examined using Dunnett’s test. All statistical analyses were performed using Bell-Curve for Excel, version 3.10 (Social Survey Research Information Co., Ltd., Tokyo, Japan). A p-value <0.05 was considered to indicate a statistically significant difference.

Results

High expression of CD58 predicts a good prognosis of patients with luminal A and luminal B breast cancer. To determine the role of CD58 in breast cancer, the METABRIC dataset was downloaded from cBioportal and gene expression was examined in the subtypes according to PAM50. As shown in Figure 1A, CD58 mRNA was found to be significantly enriched in basal-like and claudin-low subtypes, in comparison with the other PAM 50 molecular subtypes.

Figure 1.
Figure 1.

Role of CD58 in breast cancer. (A) CD58 mRNA expression levels in all subtypes and in each of the six predictive analysis of microarrays 50 (PAM50) subtypes of breast cancer. Values are shown as box-and-whisker plots (Kruskal-Wallis test followed by the Steel-Dwass multiple comparison test for post hoc analysis, ****p<0.0001). (B) Kaplan-Meier analysis of CD58 based on the overall survival status according to the PAM50 classification. HER2, Human epidermal growth factor receptor 2.

Subsequently, the patients were divided into the CD58high and CD58low expression groups based on the ROC curves and prognosis was assessed based on the overall survival status using Kaplan-Meier analysis. As illustrated in Figure 1B, patients with luminal A and luminal B breast cancer in the CD58high group had a significantly better prognosis than those with in the CD58low group, based on the overall survival status. However, no significant differences were observed in the overall survival of patients with the normal-like, HER2-enriched, claudin-low, and basal-like subtypes. These results thus indicated that high expression of CD58 in patients with luminal A and luminal B breast cancer may be beneficial for breast cancer therapy.

CD58 is associated with immune cell infiltration in all patients and patients with the luminal subtype. CD58 functions as an immune adhesion molecule and plays a key role in recruiting T-cells or natural killer cells during the antigen presenting process (23). Thus, the present study examined whether CD58 is associated with immune cell infiltration in breast cancer using the TIMER 2.0 web platform. As demonstrated in Figure 2A and D, CD58 expression was significantly and positively associated with the infiltration of B-cells, CD8+ T-cells, CD4+ T-cells, macrophages, neutrophils and dendritic cells, in the total patients (all subtypes) and in those with the luminal subtype. As shown in Figure 2B, CD58 expression was plotted in a scattered mode and exhibited no association with infiltration of immune cells, apart from macrophages. It was also found that CD58 expression in HER2-enriched breast cancer was only significantly and positively associated with the infiltration of CD8+ T-cells and neutrophils (Figure 2C). These results suggest that the cell surface molecule CD58 may function as an immune modulator to facilitate the therapy for luminal breast cancer.

Figure 2.
Figure 2.

Correlation between CD58 and immune cell infiltration in (A) all patients, and patients with (B) the basal-like, (C) HER2-enriched and (D) luminal subtypes. cor, Correlation; TPM, transcript per million.

CD58highALDH1A3high predicts a poor prognosis in patients with basal-like breast cancer. The poor prognosis of patients with breast cancer may partially be due to its stem-like properties (11, 16); thus, the present study combined CD58 and the CSC marker, ALDH1A3, to predict the prognosis of patients with breast cancer. As shown in Figure 3, it was found that in patients with luminal A breast cancer, those in the ALDH1A3high group exhibited a significantly better prognosis than those in the ALDH1A3low group, based on the overall survival status. No significant difference was observed between patients with the luminal B, HER2-enriched, claudin-low, and basal-like subtypes in the ALDH1A3high and ALDH1A3low groups. Moreover, patients with the luminal A subtype in the CD58highALDH1A3high group also had better overall survival than those in the CD58lowALDH1A3low group. On the contrary, patients with the basal-like subtype in the CD58highALDH1A3high group had a poorer prognosis than those in the CD58lowALDH1A3low group, based on the overall survival status. There were no significant differences between CD58highALDH1A3high and CD58lowALDH1A3low in the other subtypes of breast cancer patients.

Figure 3.
Figure 3.

Overall survival analysis in patients with different breast cancer subtypes, according to aldehyde dehydrogenase 1 family member A3 (ALDH1A3) and CD58, and ALDH1A3 expressions. Patients with the basal-like subtype in the CD58highALDH1A3high group have a poor prognosis. p-Values were calculated using the log-rank (Cochran-Mantel-Haenszel) test. Multiplicity was adjusted using the Holm’s method for post hoc analysis. Statistically significant p-values are shown in bold. HER2, Human epidermal growth factor receptor 2.

Multivariate Cox regression analysis (Table I), which was calculated with age at diagnosis as a confounding factor, also confirmed the significantly poorer prognosis of patients in the CD58highALDH1A3high group with the basal-like subtype of breast cancer compared those in the CD58highALDH1A3low and CD58lowALDH1A3low groups. There were no significant differences in the other PAM50 subtypes of breast cancer.

View this table:
Table I.

Multivariable Cox regression analysis for overall survival in PAM50 molecular subtypes of breast cancer.

CD58 siRNA knockdown inhibits the tumour sphere formation of basal-like cancer cells. To clarify the role of CD58 in ALDH1-positive BCSC, CD58 was knocked down using siRNA in basal-like cancer cell lines (MDA-MB-468 and BT-20) and ALDH1high cells were then sorted using ALDEFLUOR assay. The formation of tumour spheres in the ALDH1high cells was detected after culturing for 6 days. As shown in Figure 4A and B, CD58 siRNA knockdown successfully inhibited CD58 protein expression in both MDA-MB-468 and BT-20 cells. Moreover, CD58 knockdown significantly inhibited the tumour sphere formation of ALDH1high cells sorted from MDA-MB-468 and BT-20 cells (Figure 4C-F). These results suggest that CD58 may promote the stem cell properties of ALDH1-positive basal-like cancer stem cells.

Figure 4.
Figure 4.

CD58 reduces the tumour sphere formation ability of aldehyde dehydrogenase 1 (ALDH1)high cells. CD58 protein expression in (A) MDA-MB-468 and (B) BT-20 cells in which CD58 was knocked down using siRNA. Knockdown of CD58 resulted in the suppression of tumour sphere formation. Representative images illustrate tumour sphere formation in ALDH1high basal-like cancer cells (C and D), within a single visual field at magnification 100× (upper images,) or at magnification 300× (lower images). Scale bar, 50 μm. Quantification of average tumour sphere area is presented in bar charts (E and F) in ALDH1high basal-like cancer cells. Data represent the mean±SD from three independent experiments. Statistical significance was determined using Dunnett’s test. *p<0.05, **p<0.01 vs. cells transfected with control siRNA. Ctrl, Control.

Discussion

CD58 has been reported to be associated with haematological malignancies and solid tumours (23); however, its expression and its role in the prognosis of breast cancer patients remain obscure. The present study performed bioinformatics analysis according to the online cancer genomics database and found that CD58 was more enriched in the basal-like and claudin-low breast cancer subtypes than in other PAM50 subtypes. Notably, patients with the luminal A subtype in the CD58highALDH1A3high group exhibited a good prognosis; however, those with the basal-like breast cancer subtype in the same group exhibited a poor prognosis. The good prognosis of patients with the luminal A subtype in the CD58high group may be due to the modulation of infiltrated immune cells. The poor prognosis of patients with CD58high may be related to the progression of BCSCs. The data indicate that CD58 may play a differential role in the luminal A and basal-like subtypes and may function as a novel prognostic biomarker and potential therapeutic target for breast cancer.

In the present study, CD58 was highly expressed in the claudin-low and basal-like subtypes of breast cancer. Patients with luminal A and luminal B subtypes with a high expression of CD58 exhibited a better prognosis than patients with a low expression. Patients with luminal subtype breast cancer with an active immune system have a longer survival time than those with an inactive immune system (30). An active immune system in the tumour environment could increase the antitumour immunity, which will then eliminate the tumour (31). As an immune adhesion molecule, CD58 can mediate the cytotoxicity of lymphokine-activated killing and natural killer cells (32) in neuroblastoma, and may stimulate T-cell proliferation and augment cytotoxic T-lymphocyte (CTL) cytotoxicity in colorectal cancer (33). Similarly, through TIMER analysis, it was found that in all breast cancers (analysed as a total) and in luminal A breast cancer, CD58 enhanced immunity via its association with immune cell infiltration, thus possibly contributing to tumour eradication. These results suggest that CD58 may enhance the anti-tumour immunity in luminal breast cancer by recruiting immune cells.

Breast cancer subtypes can be altered under certain conditions. Need et al. (34) found that treatment with oestrogen and progesterone transformed luminal A breast cancer cells into a basal-like type. Although the present study found that a high expression of CD58 may be beneficial to the therapy of luminal A breast cancer, other researchers have reported that CD58 is overexpressed in basal-like cells (24) and functions as a representative basal gene in breast cancer cells. The expression of CD58 has been shown to increase during the process of the transition of luminal breast cancer cells into basal breast cancer cells (25). This suggests that CD58 may play a differential role in basal-like breast cancer. Among the six PAM50 subtypes of breast cancer, the basal-like subtype is associated with the poorest clinical outcomes and its high rates of recurrence and metastasis may be related to its stem-like properties (11, 16). Therefore, the present study combined CD58 and the representative gene of BCSC, ALDH1A3, and analysed their association with the prognosis of patients with breast cancer. It was found that patients with luminal A breast cancer in the ALDH1A3high group had a significantly better prognosis than those in the ALDH1A3low group, based on the overall survival status. No significant difference was found between patients in the ALDH1A3high and ALDH1A3low groups with the luminal B, HER2-enriched, claudin-low, and basal-like subtypes. Moreover, patients with the luminal A subtype in the CD58highALDH1A3high group had a better prognosis than those in the CD58lowALDH1A3low group, based on the overall survival status. It is noteworthy that patients with the basal-like subtype in the CD58highALDH1A3high group had a poorer prognosis than those in the CD58lowALDH1A3low group, based on the overall survival status. Since the majority of TNBCs are of the claudin-low and basal-like subtypes (4), the role of CD58 in TNBC based on classification using immunohistochemistry warrants further investigation in the future.

Both MDA-MB-468 and BT-20 cells belong to the basal-like subtype of breast cancer cells (35). By using these two types of basal-like cancer cell lines, the present study found that CD58 siRNA knockdown inhibited the formation of tumour spheres in ALDH1high BCSCs. Consistent with these results, Xu et al. (36) found that the knockdown of CD58 significantly impaired sphere formation and tumour growth in colorectal cancer. CD58 was found to upregulate the Wnt/β-catenin pathway via the degradation of Dickkopf 3 and thus promoted the self-renewal of CSCs. These results indicate that CD58 may be a novel biomarker involved in the regulation of BCSCs in basal-like breast cancer leading to a potential anti-CD58 therapy.

In conclusion, CD58 may play a differential role in the luminal A and basal-like subtypes. Patients with the luminal A subtype in the CD58highALDH1A3high group had a better prognosis than those in the CD58lowALDH1A3low group. On the contrary, patients with the basal-like subtype in the CD58highALDH1A3high group had a poorer prognosis than those in the CD58lowALDH1A3low group. The findings presented herein may help to guide more precise therapeutic strategies in clinical practice; however, the role of CD58 in the luminal A and basal-like subtypes warrants further investigation.

Acknowledgements

This work was supported by Grant-in-Aid for Scientific Research (C) of JSPS (20K07207) (K.A.), JST Moonshot R&D (JPMJPS2022) (S.O.), Tokyo University of Science Grant for President’s Research Promotion (K.A.), Grant-in-Aid for JSPS Research Fellows (21J13718) (H.M.), Grant-in-Aid for Research Activity Start-up (21K20732) (S.T.), JST SPRING (JPMJSP2151) (A.O.), and Nagai Memorial Research Scholarship from the Pharmaceutical Society of Japan (A.O. and H.M.). Postdoctoral Fellow (PD) via a budget allocated by Tokyo University of Science.

Footnotes

  • Authors’ Contributions

    Y. X., H. M., S. T., A. O., C. O. and Ya. H. performed the experiments; Y. X., H. M., S. T., Ke. S. and K. T. performed bioinformatics; Y. X., H. M., S. T., A. O. and C. O. performed analysed the data; Yo. H., Ka. S. and S. O. supplied experimental materials and resources; Y. X., H.M. and K.A. conceived the study; Y. X. and K.A. drafted the manuscript; Y. X., H. M., S. T., Ka S., Y. Z. and K. A. contributed to discussion and review of the final manuscript; all the Authors approved the final manuscript.

  • Conflicts of Interest

    The Authors declare that they have no competing interests in relation to this study.

  • Received August 7, 2022.
  • Revision received September 7, 2022.
  • Accepted September 12, 2022.

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. ↵
    1. Siegel RL,
    2. Miller KD,
    3. Fuchs HE and
    4. Jemal A
    : Cancer statistics, 2022. CA Cancer J Clin 72(1): 7-33, 2022. PMID: 35020204. DOI: 10.3322/caac.21708
    OpenUrlCrossRefPubMed
  2. ↵
    1. Gao JJ and
    2. Swain SM
    : Luminal A breast cancer and molecular assays: A review. Oncologist 23(5): 556-565, 2018. PMID: 29472313. DOI: 10.1634/theoncologist.2017-0535
    OpenUrlAbstract/FREE Full Text
  3. ↵
    1. Ades F,
    2. Zardavas D,
    3. Bozovic-Spasojevic I,
    4. Pugliano L,
    5. Fumagalli D,
    6. de Azambuja E,
    7. Viale G,
    8. Sotiriou C and
    9. Piccart M
    : Luminal B breast cancer: molecular characterization, clinical management, and future perspectives. J Clin Oncol 32(25): 2794-2803, 2014. PMID: 25049332. DOI: 10.1200/JCO.2013.54.1870
    OpenUrlAbstract/FREE Full Text
  4. ↵
    1. Badve S,
    2. Dabbs DJ,
    3. Schnitt SJ,
    4. Baehner FL,
    5. Decker T,
    6. Eusebi V,
    7. Fox SB,
    8. Ichihara S,
    9. Jacquemier J,
    10. Lakhani SR,
    11. Palacios J,
    12. Rakha EA,
    13. Richardson AL,
    14. Schmitt FC,
    15. Tan PH,
    16. Tse GM,
    17. Weigelt B,
    18. Ellis IO and
    19. Reis-Filho JS
    : Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists. Mod Pathol 24(2): 157-167, 2011. PMID: 21076464. DOI: 10.1038/modpathol.2010.200
    OpenUrlCrossRefPubMed
  5. ↵
    1. Early Breast Cancer Trialists’ Collaborative Group (EBCTCG)
    : Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 365(9472): 1687-1717, 2005. PMID: 15894097. DOI: 10.1016/S0140-6736(05)66544-0
    OpenUrlCrossRefPubMed
  6. ↵
    1. Rakha EA,
    2. Reis-Filho JS and
    3. Ellis IO
    : Basal-like breast cancer: a critical review. J Clin Oncol 26(15): 2568-2581, 2008. PMID: 18487574. DOI: 10.1200/JCO.2007.13.1748
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Onaga C,
    2. Tamori S,
    3. Matsuoka I,
    4. Ozaki A,
    5. Motomura H,
    6. Nagashima Y,
    7. Sato T,
    8. Sato K,
    9. Xiong Y,
    10. Sasaki K,
    11. Ohno S and
    12. Akimoto K
    : High expression of SLC20A1 is less effective for endocrine therapy and predicts late recurrence in ER-positive breast cancer. PLoS One 17(5): e0268799, 2022. PMID: 35605014. DOI: 10.1371/journal.pone.0268799
    OpenUrlCrossRefPubMed
  8. ↵
    1. Onaga C,
    2. Tamori S,
    3. Motomura H,
    4. Ozaki A,
    5. Matsuda C,
    6. Matsuoka I,
    7. Fujita T,
    8. Nozaki Y,
    9. Hara Y,
    10. Kawano Y,
    11. Harada Y,
    12. Sato T,
    13. Mano Y,
    14. Sato K and
    15. Akimoto K
    : High SLC20A1 expression is associated with poor prognoses in claudin-low and basal-like breast cancers. Anticancer Res 41(1): 43-54, 2021. PMID: 33419798. DOI: 10.21873/anticanres.14750
    OpenUrlAbstract/FREE Full Text
  9. ↵
    1. Motomura H,
    2. Tamori S,
    3. Yatani MA,
    4. Namiki A,
    5. Onaga C,
    6. Ozaki A,
    7. Takasawa R,
    8. Mano Y,
    9. Sato T,
    10. Hara Y,
    11. Sato K,
    12. Xiong Y,
    13. Harada Y,
    14. Hanawa T,
    15. Tanuma SI,
    16. Sasaki K,
    17. Ohno S and
    18. Akimoto K
    : GLO 1 and PKCλ regulate ALDH1-positive breast cancer stem cell survival. Anticancer Res 41(12): 5959-5971, 2021. PMID: 34848450. DOI: 10.21873/anticanres.15415
    OpenUrlAbstract/FREE Full Text
    1. Motomura H,
    2. Ozaki A,
    3. Tamori S,
    4. Onaga C,
    5. Nozaki Y,
    6. Waki Y,
    7. Takasawa R,
    8. Yoshizawa K,
    9. Mano Y,
    10. Sato T,
    11. Sasaki K,
    12. Ishiguro H,
    13. Miyagi Y,
    14. Nagashima Y,
    15. Yamamoto K,
    16. Sato K,
    17. Hanawa T,
    18. Tanuma SI,
    19. Ohno S and
    20. Akimoto K
    : Glyoxalase 1 and protein kinase Cλ as potential therapeutic targets for late-stage breast cancer. Oncol Lett 22(1): 547, 2021. PMID: 34093768. DOI: 10.3892/ol.2021.12808
    OpenUrlCrossRefPubMed
  11. ↵
    1. Nozaki Y,
    2. Motomura H,
    3. Tamori S,
    4. Kimura Y,
    5. Onaga C,
    6. Kanai S,
    7. Ishihara Y,
    8. Ozaki A,
    9. Hara Y,
    10. Harada Y,
    11. Mano Y,
    12. Sato T,
    13. Sato K,
    14. Sasaki K,
    15. Ishiguro H,
    16. Ohno S and
    17. Akimoto K
    : High PKCλ expression is required for ALDH1-positive cancer stem cell function and indicates a poor clinical outcome in late-stage breast cancer patients. PLoS One 15(7): e0235747, 2020. PMID: 32658903. DOI: 10.1371/journal.pone.0235747
    OpenUrlCrossRefPubMed
  12. ↵
    1. Motomura H,
    2. Nozaki Y,
    3. Onaga C,
    4. Ozaki A,
    5. Tamori S,
    6. Shiina TA,
    7. Kanai S,
    8. Ohira C,
    9. Hara Y,
    10. Harada Y,
    11. Takasawa R,
    12. Hanawa T,
    13. Tanuma SI,
    14. Mano Y,
    15. Sato T,
    16. Sato K and
    17. Akimoto K
    : High expression of c-Met, PKCλ and ALDH1A3 predicts a poor prognosis in late-stage breast cancer. Anticancer Res 40(1): 35-52, 2020. PMID: 31892551. DOI: 10.21873/anticanres.13924
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Tamori S,
    2. Nozaki Y,
    3. Motomura H,
    4. Nakane H,
    5. Katayama R,
    6. Onaga C,
    7. Kikuchi E,
    8. Shimada N,
    9. Suzuki Y,
    10. Noike M,
    11. Hara Y,
    12. Sato K,
    13. Sato T,
    14. Yamamoto K,
    15. Hanawa T,
    16. Imai M,
    17. Abe R,
    18. Yoshimori A,
    19. Takasawa R,
    20. Tanuma SI and
    21. Akimoto K
    : Glyoxalase 1 gene is highly expressed in basal-like human breast cancers and contributes to survival of ALDH1-positive breast cancer stem cells. Oncotarget 9(92): 36515-36529, 2018. PMID: 30559934. DOI: 10.18632/oncotarget.26369
    OpenUrlCrossRefPubMed
    1. Sato K and
    2. Akimoto K
    : Expression levels of KMT2C and SLC20A1 identified by information-theoretical analysis are powerful prognostic biomarkers in estrogen receptor-positive breast cancer. Clin Breast Cancer 17(3): e135-e142, 2017. PMID: 27986439. DOI: 10.1016/j.clbc.2016.11.005
    OpenUrlCrossRefPubMed
  15. ↵
    1. Nozaki Y,
    2. Tamori S,
    3. Inada M,
    4. Katayama R,
    5. Nakane H,
    6. Minamishima O,
    7. Onodera Y,
    8. Abe M,
    9. Shiina S,
    10. Tamura K,
    11. Kodama D,
    12. Sato K,
    13. Hara Y,
    14. Abe R,
    15. Takasawa R,
    16. Yoshimori A,
    17. Shinomiya N,
    18. Tanuma SI and
    19. Akimoto K
    : Correlation between c-Met and ALDH1 contributes to the survival and tumor-sphere formation of ALDH1 positive breast cancer stem cells and predicts poor clinical outcome in breast cancer. Genes Cancer 8(7-8): 628-639, 2017. PMID: 28966724. DOI: 10.18632/genesandcancer.148
    OpenUrlCrossRefPubMed
  16. ↵
    1. Sørlie T,
    2. Perou CM,
    3. Tibshirani R,
    4. Aas T,
    5. Geisler S,
    6. Johnsen H,
    7. Hastie T,
    8. Eisen MB,
    9. van de Rijn M,
    10. Jeffrey SS,
    11. Thorsen T,
    12. Quist H,
    13. Matese JC,
    14. Brown PO,
    15. Botstein D,
    16. Lønning PE and
    17. Børresen-Dale AL
    : Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U.S.A. 98(19): 10869-10874, 2001. PMID: 11553815. DOI: 10.1073/pnas.191367098
    OpenUrlAbstract/FREE Full Text
  17. ↵
    1. Mannello F
    : Understanding breast cancer stem cell heterogeneity: time to move on to a new research paradigm. BMC Med 11: 169, 2013. PMID: 23879988. DOI: 10.1186/1741-7015-11-169
    OpenUrlCrossRefPubMed
  18. ↵
    1. Liu S,
    2. Ginestier C,
    3. Ou SJ,
    4. Clouthier SG,
    5. Patel SH,
    6. Monville F,
    7. Korkaya H,
    8. Heath A,
    9. Dutcher J,
    10. Kleer CG,
    11. Jung Y,
    12. Dontu G,
    13. Taichman R and
    14. Wicha MS
    : Breast cancer stem cells are regulated by mesenchymal stem cells through cytokine networks. Cancer Res 71(2): 614-624, 2011. PMID: 21224357. DOI: 10.1158/0008-5472.CAN-10-0538
    OpenUrlAbstract/FREE Full Text
  19. ↵
    1. Ricardo S,
    2. Vieira AF,
    3. Gerhard R,
    4. Leitão D,
    5. Pinto R,
    6. Cameselle-Teijeiro JF,
    7. Milanezi F,
    8. Schmitt F and
    9. Paredes J
    : Breast cancer stem cell markers CD44, CD24 and ALDH1: expression distribution within intrinsic molecular subtype. J Clin Pathol 64(11): 937-946, 2011. PMID: 21680574. DOI: 10.1136/jcp.2011.090456
    OpenUrlAbstract/FREE Full Text
  20. ↵
    1. Yamashita D,
    2. Minata M,
    3. Ibrahim AN,
    4. Yamaguchi S,
    5. Coviello V,
    6. Bernstock JD,
    7. Harada S,
    8. Cerione RA,
    9. Tannous BA,
    10. La Motta C and
    11. Nakano I
    : Identification of ALDH1A3 as a viable therapeutic target in breast cancer metastasis-initiating cells. Mol Cancer Ther 19(5): 1134-1147, 2020. PMID: 32127468. DOI: 10.1158/1535-7163.MCT-19-0461
    OpenUrlAbstract/FREE Full Text
  21. ↵
    1. Marcato P,
    2. Dean CA,
    3. Liu RZ,
    4. Coyle KM,
    5. Bydoun M,
    6. Wallace M,
    7. Clements D,
    8. Turner C,
    9. Mathenge EG,
    10. Gujar SA,
    11. Giacomantonio CA,
    12. Mackey JR,
    13. Godbout R and
    14. Lee PW
    : Aldehyde dehydrogenase 1A3 influences breast cancer progression via differential retinoic acid signaling. Mol Oncol 9(1): 17-31, 2015. PMID: 25106087. DOI: 10.1016/j.molonc.2014.07.010
    OpenUrlCrossRefPubMed
  22. ↵
    1. Sato K,
    2. Tahata K and
    3. Akimoto K
    : Five Genes associated with survival in patients with lower-grade gliomas were identified by information-theoretical analysis. Anticancer Res 40(5): 2777-2785, 2020. PMID: 32366424. DOI: 10.21873/anticanres.14250
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Zhang Y,
    2. Liu Q,
    3. Yang S and
    4. Liao Q
    : CD58 immunobiology at a glance. Front Immunol 12: 705260, 2021. PMID: 34168659. DOI: 10.3389/fimmu.2021.705260
    OpenUrlCrossRefPubMed
  24. ↵
    1. Charafe-Jauffret E,
    2. Ginestier C,
    3. Monville F,
    4. Finetti P,
    5. Adélaïde J,
    6. Cervera N,
    7. Fekairi S,
    8. Xerri L,
    9. Jacquemier J,
    10. Birnbaum D and
    11. Bertucci F
    : Gene expression profiling of breast cell lines identifies potential new basal markers. Oncogene 25(15): 2273-2284, 2006. PMID: 16288205. DOI: 10.1038/sj.onc.1209254
    OpenUrlCrossRefPubMed
  25. ↵
    1. Bernardo GM,
    2. Bebek G,
    3. Ginther CL,
    4. Sizemore ST,
    5. Lozada KL,
    6. Miedler JD,
    7. Anderson LA,
    8. Godwin AK,
    9. Abdul-Karim FW,
    10. Slamon DJ and
    11. Keri RA
    : FOXA1 represses the molecular phenotype of basal breast cancer cells. Oncogene 32(5): 554-563, 2013. PMID: 22391567. DOI: 10.1038/onc.2012.62
    OpenUrlCrossRefPubMed
  26. ↵
    1. Loibl S,
    2. Poortmans P,
    3. Morrow M,
    4. Denkert C and
    5. Curigliano G
    : Breast cancer. Lancet 397(10286): 1750-1769, 2021. PMID: 33812473. DOI: 10.1016/S0140-6736(20)32381-3
    OpenUrlCrossRefPubMed
  27. ↵
    1. Cerami E,
    2. Gao J,
    3. Dogrusoz U,
    4. Gross BE,
    5. Sumer SO,
    6. Aksoy BA,
    7. Jacobsen A,
    8. Byrne CJ,
    9. Heuer ML,
    10. Larsson E,
    11. Antipin Y,
    12. Reva B,
    13. Goldberg AP,
    14. Sander C and
    15. Schultz N
    : The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2(5): 401-404, 2012. PMID: 22588877. DOI: 10.1158/2159-8290.CD-12-0095
    OpenUrlAbstract/FREE Full Text
  28. ↵
    1. Gao J,
    2. Aksoy BA,
    3. Dogrusoz U,
    4. Dresdner G,
    5. Gross B,
    6. Sumer SO,
    7. Sun Y,
    8. Jacobsen A,
    9. Sinha R,
    10. Larsson E,
    11. Cerami E,
    12. Sander C and
    13. Schultz N
    : Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6(269): pl1, 2013. PMID: 23550210. DOI: 10.1126/scisignal.2004088
    OpenUrlAbstract/FREE Full Text
  29. ↵
    1. Li T,
    2. Fan J,
    3. Wang B,
    4. Traugh N,
    5. Chen Q,
    6. Liu JS,
    7. Li B and
    8. Liu XS
    : TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 77(21): e108-e110, 2017. PMID: 29092952. DOI: 10.1158/0008-5472.CAN-17-0307
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Wang Y,
    2. Zhu M,
    3. Guo F,
    4. Song Y,
    5. Fan X and
    6. Qin G
    : Identification of tumor microenvironment-related prognostic biomarkers in luminal breast cancer. Front Genet 11: 555865, 2020. PMID: 33329695. DOI: 10.3389/fgene.2020.555865
    OpenUrlCrossRefPubMed
  31. ↵
    1. Soongsathitanon J,
    2. Jamjuntra P,
    3. Sumransub N,
    4. Yangngam S,
    5. De la Fuente M,
    6. Landskron G,
    7. Thuwajit P,
    8. Hermoso MA and
    9. Thuwajit C
    : Crosstalk between tumor-infiltrating immune cells and cancer-associated fibroblasts in tumor growth and immunosuppression of breast cancer. J Immunol Res 2021: 8840066, 2021. PMID: 34337083. DOI: 10.1155/2021/8840066
    OpenUrlCrossRefPubMed
  32. ↵
    1. Foreman NK,
    2. Rill DR,
    3. Coustan-Smith E,
    4. Douglass EC and
    5. Brenner MK
    : Mechanisms of selective killing of neuroblastoma cells by natural killer cells and lymphokine activated killer cells. Potential for residual disease eradication. Br J Cancer 67(5): 933-938, 1993. PMID: 8494726. DOI: 10.1038/bjc.1993.173
    OpenUrlCrossRefPubMed
  33. ↵
    1. Lorenz MG,
    2. Kantor JA,
    3. Schlom J and
    4. Hodge JW
    : Induction of anti-tumor immunity elicited by tumor cells expressing a murine LFA-3 analog via a recombinant vaccinia virus. Hum Gene Ther 10(4): 623-631, 1999. PMID: 10094205. DOI: 10.1089/10430349950018698
    OpenUrlCrossRefPubMed
  34. ↵
    1. Need EF,
    2. Selth LA,
    3. Trotta AP,
    4. Leach DA,
    5. Giorgio L,
    6. O’Loughlin MA,
    7. Smith E,
    8. Gill PG,
    9. Ingman WV,
    10. Graham JD and
    11. Buchanan G
    : The unique transcriptional response produced by concurrent estrogen and progesterone treatment in breast cancer cells results in upregulation of growth factor pathways and switching from a Luminal A to a Basal-like subtype. BMC Cancer 15: 791, 2015. PMID: 26498662. DOI: 10.1186/s12885-015-1819-3
    OpenUrlCrossRefPubMed
  35. ↵
    1. Subik K,
    2. Lee JF,
    3. Baxter L,
    4. Strzepek T,
    5. Costello D,
    6. Crowley P,
    7. Xing L,
    8. Hung MC,
    9. Bonfiglio T,
    10. Hicks DG and
    11. Tang P
    : The Expression Patterns of ER, PR, HER2, CK5/6, EGFR, Ki-67 and AR by Immunohistochemical Analysis in Breast Cancer Cell Lines. Breast Cancer (Auckl) 4: 35-41, 2010. PMID: 20697531.
    OpenUrlCrossRefPubMed
  36. ↵
    1. Xu S,
    2. Wen Z,
    3. Jiang Q,
    4. Zhu L,
    5. Feng S,
    6. Zhao Y,
    7. Wu J,
    8. Dong Q,
    9. Mao J and
    10. Zhu Y
    : CD58, a novel surface marker, promotes self-renewal of tumor-initiating cells in colorectal cancer. Oncogene 34(12): 1520-1531, 2015. PMID: 24727892. DOI: 10.1038/onc.2014.95
    OpenUrlCrossRefPubMed
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Reprints and Permissions
High Expression of CD58 and ALDH1A3 Predicts a Poor Prognosis in Basal-like Breast Cancer
YUYUN XIONG, HITOMI MOTOMURA, SHOMA TAMORI, AYAKA OZAKI, CHOTARO ONAGA, YASUSHI HARA, KEIKO SATO, KOUJI TAHATA, YOHSUKE HARADA, KAZUNORI SASAKI, YUN-WEN ZHENG, SHIGEO OHNO, KAZUNORI AKIMOTO
Anticancer Research Nov 2022, 42 (11) 5223-5232; DOI: 10.21873/anticanres.16029
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo

Jump to section

Keywords