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High Expression of PKCζ And CTNNBIP1 Is Associated With Poor Prognosis in Luminal B Breast Cancer

YUKA NAGASHIMA, KAZUNORI SASAKI, RYOSUKE CHIWAKI, HAYATO ISHII, KANA NOHATA, YUKI MAEMURA, TAKAHIRO KASAI, AYAKA OZAKI, SHOMA TAMORI, SHIGEO OHNO and KAZUNORI AKIMOTO
Cancer Genomics & Proteomics July 2025, 22 (4) 538-556; DOI: https://doi.org/10.21873/cgp.20520

Abstract

Background/Aim: The relationship between protein kinase C zeta (PKCζ) expression and medical treatment resistance in breast cancer subtypes is unclear. Therefore, the present study aimed to analyze this relationship using disease-specific survival.

Materials and Methods: Open-source datasets with clinical and gene expression information (METABRIC, n=2509; and TCGA Pan-Cancer Atlas, n=1084) were downloaded and Kaplan-Meier survival and Cox proportional hazard analyses were performed.

Results: High expression of PKCζ indicated a poor prognosis in patients with luminal B type treated with endocrine therapy and aromatase inhibitor as endocrine therapy. Furthermore, catenin beta interacting protein 1 (CTNNBIP1) was identified as a differentially expressed gene between the PKCζhigh and PKCζlow luminal B breast cancer cohorts treated with endocrine therapy and aromatase inhibitors. PKCζhigh CTNNBIP1high luminal B breast cancer treated with endocrine therapy and aromatase inhibitor indicated a poor prognosis. These results suggest that PKCζ and CTNNBIP1 are involved in breast cancer progression and contribute to reduced susceptibility to endocrine therapy in the luminal B breast cancer subtype.

Conclusion: PKCζ and CTNNBIP1 may serve as a prognostic biomarker for predicting the efficacy of endocrine therapy in the luminal B breast cancer.

Keywords:

Introduction

Breast cancer is the most common cancer among women worldwide and accounts for the highest number of deaths (1). The standard treatment for breast cancer typically includes surgery, radiation therapy and drug therapies such as endocrine therapy, chemotherapy and molecular-targeted therapy (2, 3). However, there are still few treatment options available for the various forms of breast cancer. Therefore, it is important to clarify the characteristics of breast cancer in terms of onset, progression and recurrence, and develop effective treatments.

Breast cancer is categorized into several subtypes such as via immunohistochemical classification [including luminal A, luminal B, human epidermal growth factor receptor type 2 (her2) and triple negative breast cancer (TNBC)] based on receptor expression and via classification based on gene expression patterns (including PAM50+claudin-low: normal-like, luminal A, luminal B, her2-enriched, claudin-low and basal-like) (4-10). Both the luminal A and luminal B subtypes are ER-positive and account for 70-80% of breast cancer cases (11). In addition, a number of luminal B tumors highly express her2 and proliferation markers such as Ki-67 (MKI67) (2, 12, 13). Among the standard treatments currently applied, endocrine therapy is administered for luminal A and luminal B breast cancer, molecular-targeted agents are administered for luminal B. However, resistance to these clinical treatments is a major issue. Luminal B breast cancer is associated with a poorer prognosis, unlike luminal A (2, 12-18). Therefore, it is important to identify biomarkers that further stratify the luminal B breast cancer subtype and to predict the effects of certain treatments.

Atypical protein kinase C (aPKC) is a Ser/Thr kinase belonging to the PKC subfamily and is insensitive to diacylglycerol, Ca2+ and phorbol esters (19-21). aPKC has two isoforms: PKCζ and PKCλ/℩ (19-21). PKCζ activation depends on lipids such as PI(3,4,5)P3 or ceramide (22-25) and regulates diverse biological functions such as cell polarity (20), inflammation (26) and anti-apoptosis (27-29). PKCζ also has important roles in cancer cell proliferation and invasion (30-32). Studies using HeLa cells have reported that PKCζ is involved in chemoresistance in cervical cancer (33). Additionally, a series of experimental studies using cell lines and animal models have reported that PKCζ is involved in chemoresistance and radioresistance in the 4T1 mouse TNBC cell line (34, 35) and endocrine resistance in the T47D human luminal A cell line (36). Furthermore, down-regulation of PKCζ is involved in chemoresistance in renal carcinoma cells (37) and the inhibition of nuclear PKCζ restores the effectiveness of chemotherapy in chemoresistant HeLa cells (33). However, the relationship between PKCζ expression and the efficacy of endocrine therapy in luminal A and luminal B breast cancer subtypes remains unclear.

In the present study, we aimed to examine the effect of PKCζ expression on endocrine therapy in luminal A and luminal B breast cancer subtypes. The results suggest that high expression of PKCζ contributed to the reduced effectiveness of endocrine therapy in the luminal B subtype. Furthermore, catenin beta interacting protein 1 (CTNNBIP1) was identified as a differentially expressed gene between the PKCζhigh and PKCζlow luminal B breast cancer cohorts treated with endocrine therapy, and PKCζhigh CTNNBIP1high luminal B breast cancer indicated a poor prognosis. These results suggest that PKCζ and CTNNBIP1 are involved in breast cancer progression and contribute to sensitivity to endocrine therapy in the luminal B breast cancer subtype.

Materials and Methods

Molecular taxonomy of breast cancer international consortium (METABRIC) dataset. The METABRIC dataset (n=2509) (38, 39) was downloaded from cBioPortal (https://www.cbioportal.org/) (40, 41) on March 29, 2022. The METABRIC dataset contains disease-specific survival (DSS) with mRNA expression level data (n=1423) (Figure 1). The dataset includes data on endocrine therapy (luminal A: without endocrine therapy, n=218, and with endocrine therapy, n=461; luminal B: without endocrine therapy, n=92, and with endocrine therapy, n=369) (Figure 1).

Figure 1.
Figure 1.

Overall workflow and design of this study.

The Cancer Genome Atlas (TCGA) dataset. TCGA Pan-Cancer Atlas dataset (n=1,084) (42) was downloaded from cBioPortal (https://www.cbioportal.org/) (40, 41) on July 15, 2024. TCGA Pan-Cancer Atlas dataset contains DSS with mRNA expression level data (n=1,061) (Figure 1). The dataset includes data on two different methods of endocrine therapy, including the anti-estrogen drug, tamoxifen, and aromatase inhibitors (anastrazole, letrozole and exemestane; luminal A: without tamoxifen, n=351, with tamoxifen, n=140, without aromatase inhibitors, n=311, and with aromatase inhibitors, n=180; luminal B: without tamoxifen, n=144, with tamoxifen, n=48, without aromatase inhibitors, n=124, and with aromatase inhibitors, n=68) (Figure 1).

Prognostic analyses. The Kaplan-Meier method, log-rank (Cochran-Mantel-Haenszel) test and multivariate Cox regression analysis of the DSS were performed as previously described (43-47). Briefly, patients were divided into high and low PKCζ expression groups (Figure 1), receiver operating characteristic curves were plotted using the DSS data and the Youden index was utilized as the optimal cut-off (Table I). The multivariate Cox regression analysis used age at diagnosis as a confounding factor in Table II, Table III, Table IV, and Table V; in Table III, chemotherapy and radiation therapy were also included as additional confounding factors. Two-sided p<0.05 was considered to indicate a statistically significant difference. These statistical analyses were performed using BellCurve for Excel (version 4.05; Social Survey Research Information Co., Ltd.; Tokyo, Japan).

View this table:
Table I.

Youden’s index calculated by receiver operating characteristic (ROC) analysis in each group from METABRIC and TCGA datasets.

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Table II.

Multivariate Cox regression analyses of DSS between the PKCζhigh and PKCζlow groups of patients with luminal A and luminal B breast cancer subtypes.

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Table III.

Multivariate Cox regression analyses of DSS between the PKCζhigh and PKCζlow groups of patients with luminal A and luminal B breast cancer treated without and with endocrine therapy.

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Table IV.

Multivariate Cox regression analyses of DSS between the PKCζhigh and PKCζlow groups of patients with luminal A and luminal B breast cancer treated without and with endocrine therapy, tamoxifen and aromatase inhibitor.

View this table:
Table V.

Multivariate Cox regression analyses of DSS among the CTNNBIP1high, CTNNBIP1low, PKCζhigh CTNNBIP1high and the PKCζhigh CTNNBIP1low, PKCζlow CTNNBIP1high, PKCζlow CTNNBIP1low groups of patients with luminal B breast cancer treated with endocrine therapy, tamoxifen and aromatase inhibitors.

Screening for differentially expressed genes (DEGs) and analysis of biological functions and pathways. The potential DEGs between the PKCζhigh and PKCζlow breast cancer subtypes were screened using the limma package for the METABRIC dataset (38, 39) and the DESeq2 package for TCGA Pan-Cancer Atlas dataset (42). The average observation period in TCGA Pan-Cancer Atlas dataset (DSS, 40.5 months) was shorter than that in the METABRIC dataset (DSS, 123.6 months). Therefore, to adjust observation period between the two cohorts, the DEGs of the METABRIC dataset were analyzed based on the observation period of TCGA Pan-Cancer Atlas dataset. p<0.05 and Fold Change (FC; FC >1.25 or FC <−1.25) of gene expression were used as the DEG screening cut-offs. These DEGs were displayed using a volcano plot, which was constructed using the ggplot2 R package. In the correlation analysis of gene expression, the Pearson’s correlation coefficient (r) and p-value are indicated. p-Values were calculated using a test for non-correlation. These bioinformatic analyses were performed using R (version 4.4.1, R Foundation, Vienna, Austria). For the functional and pathway enrichment analyses, DEGs were analyzed using Metascape (http://metascape.org/gp/index.html#/main/step1) (48).

Results

PKCζhigh indicates a poor DSS prognosis in patients with the luminal B breast cancer subtypes in the METABRIC dataset. We previously reported that PKCζ gene expression was higher in breast cancer than normal tissues (49), and that the PKCζ gene alteration frequency in breast cancer was lower than lung and ovarian cancer (49). Additionally, there was no significant difference in the expression of PKCζ among the breast cancer subtypes (49). However, the relationship between PKCζ gene expression and prognosis in the luminal A and luminal B breast cancer subtypes remained unclear. In the present study, to evaluate the prognosis of patients with higher PKCζ expression in the luminal A and luminal B breast cancer subtypes, Kaplan-Meier analysis and multivariate analysis of DSS were performed. The overall workflow of this study is presented in Figure 1. The results indicated that luminal B patients with PKCζhigh had a poorer prognosis (luminal B, p=0.047; log-rank test) (Figure 2A-C). Multivariate analysis of DSS also demonstrated that luminal B patients with PKCζhigh had a poorer prognosis [luminal B, hazard ratio (HR)=1.45, 95% confidence interval (CI)=1.06-1.98, p=0.019] (Table II). These results suggest that PKCζ is involved in breast cancer progression and contributes to poor prognosis in luminal B breast cancer.

Figure 2.
Figure 2.

DSS Kaplan-Meier analyses according to PKCζ expression, breast cancer subtype and endocrine therapy in METABRIC. (A-C) METABRIC data were downloaded from cBioPortal. (A) All patients with breast cancer. (B) Patients with luminal A breast cancer. (C) Patients with luminal B breast cancer. (D) All patients with breast cancer, (E) patients with luminal A breast cancer and (F) patients with luminal B breast cancer treated without endocrine therapy. (G) All patients with breast cancer, (H) patients with luminal A breast cancer and (I) patients with luminal B breast cancer treated with endocrine therapy. p-Values were calculated by the Cochran-Mantel-Haenszel generalized log-rank test. METABRIC, Molecular Taxonomy of Breast Cancer International Consortium; DSS, disease-specific survival; PKCζ, protein kinase C zeta.

PKCζhigh indicates a poor DSS prognosis in patients with luminal B breast cancer treated with endocrine therapy in the METABRIC dataset. We next, examined the prognoses of patients with PKCζhigh luminal A or luminal B breast cancer treated with endocrine therapy (Figure 2D-I). The results indicated that patients with PKCζhigh luminal A breast cancer treated without endocrine therapy showed a good prognosis, but there was no significant difference in patients with PKCζhigh luminal A breast cancer treated with endocrine therapy (Figure 2E, H). Kaplan-Meier analysis indicated that patients with PKCζhigh luminal B breast cancer treated without endocrine therapy did not have a poorer clinical prognosis (p=0.094; log-rank test); however, patients with PKCζhigh luminal B breast cancer treated with endocrine therapy did have a poorer clinical prognosis (p=0.022; log-rank test) (Figure 2F, I). Multivariate analysis also indicated that patients with PKCζhigh luminal B breast cancer treated with endocrine therapy had a poorer clinical prognosis (HR=1,55, 95% CI=1.12-2.15, p=0.0085), but patients with PKCζhigh luminal B treated without endocrine therapy did not (HR=0.69, 95% CI=0.34-1.39, p=0.30) (Table III). These results suggest that PKCζ contributes to reducing the effectiveness of endocrine therapy in luminal B breast cancer.

PKCζhigh indicates a poor DSS prognosis in patients with the luminal B subtype treated with aromatase inhibitor as endocrine therapy in TCGA dataset. To validate the above results from the analyses of the METABRIC dataset, another breast cancer cohort, TCGA Pan-Cancer Atlas (42), was analyzed, which includes DSS information on 1084 patients; however, the average observation period in this dataset (DSS, 40.5 months) was shorter than that in the METABRIC dataset (DSS, 123.6 months). Thus, TCGA Pan-Cancer Atlas dataset was used to examine the effects of endocrine therapy on DSS in luminal A and luminal B breast cancer subtypes with PKCζhigh and PKCζlow using Kaplan-Meier and multivariate Cox regression analyses. As shown in Figure 3, unlike the METABRIC dataset, PKCζhigh was not associated with a poor prognosis in any of the subtypes (Figure 3A-C and Table II). The difference between the results from the two patient cohorts may be due to the smaller number of patients, more censoring and a shorter observation period in TCGA Pan-Cancer Atlas dataset compared with the METABRIC dataset. For endocrine therapy in the luminal A and luminal B breast cancer subtypes, TCGA dataset contained data on drugs with two different methods of action, including the anti-estrogen drug, tamoxifen, and aromatase inhibitors administered to postmenopausal women such as anastrazole, letrozole and exemestane. As shown in Figure 2D-I and Figure 3A-F, unlike in the METABRIC dataset, patients with PKCζhigh luminal B breast cancer treated with tamoxifen did not demonstrate a poor prognosis. However, as in the METABRIC dataset, patients with PKCζhigh luminal B breast cancer treated with aromatase inhibitors did demonstrate a poor clinical outcome, although the number of PKCζhigh luminal B breast cancer treated with aromatase inhibitors is small (Figure 4G-L and Table IV). These results suggest that the reduced effectiveness of endocrine therapy in PKCζhigh luminal B breast cancer in the METABRIC dataset is due to the reduced effect of aromatase inhibitors, as indicated by TCGA dataset. Thus, the results indicated that patients with PKCζhigh luminal B breast cancer treated with aromatase inhibitors as endocrine therapy demonstrated a poor clinical outcome.

Figure 3.
Figure 3.

DSS Kaplan-Meier analyses according to PKCζ expression, luminal subtypes and endocrine therapy in TCGA Pan-Cancer. (A) All patients with breast cancer. (B) Patients with luminal A breast cancer. (C) Patients with luminal B breast cancer. (D) All patients with breast cancer, (E) patients with luminal A breast cancer and (F) patients with luminal B breast cancer treated without endocrine therapy. (G) All patients with breast cancer, (H) patients with luminal A breast cancer and (I) patients with luminal B breast cancer treated with endocrine therapy. p-Values were calculated by the Cochran-Mantel-Haenszel generalized log-rank test. TCGA, The Cancer Genome Atlas; DSS, disease-specific survival; PKCζ, protein kinase C zeta.

Figure 4.
Figure 4.
Figure 4.

DSS Kaplan-Meier analyses according to PKCζ expression, luminal subtypes and endocrine therapy type in TCGA Pan-Cancer. (A) All patients with breast cancer, (B) patients with luminal A breast cancer and (C) patients with luminal B breast cancer treated without tamoxifen. (D) All patients with breast cancer, (E) patients with luminal A breast cancer and (F) patients with luminal B breast cancer treated with tamoxifen. (G) All patients with breast cancer, (H) patients with luminal A breast cancer and (I) patients with luminal B breast cancer treated without aromatase inhibitor. (J) All patients with breast cancer, (K) patients with luminal A breast cancer and (L) patients with luminal B breast cancer treated with aromatase inhibitor. p-Values were calculated by the Cochran-Mantel-Haenszel generalized log-rank test. TCGA, The Cancer Genome Atlas; DSS, disease-specific survival; PKCζ, protein kinase C zeta.

Biological properties of the patients with PKCζhigh luminal B breast cancer treated with endocrine therapy. In order to reveal the biological properties of patients with PKCζhigh luminal B breast cancer treated with endocrine therapy, we next analysed the DEGs between PKCζhigh and PKCζlow luminal B breast cancer (Figure 5A-F). The volcano plots show 197 up-regulated genes and 380 down-regulated genes in the METABRIC dataset (Figure 5A), and 20 up-regulated genes and 21 down-regulated genes in TCGA Pan-Cancer Atlas dataset (Figure 5D). Next, functional enrichment analysis was performed using Metascape (Figure 5B, C, E and F). The DEGs between PKCζhigh and PKCζlow luminal B breast cancer from both datasets were not significantly enriched in the same molecular function Gene Ontology terms. However, we identified CTNNBIP1 as a gene commonly up-regulated in PKCζhigh luminal B breast cancer in both the METABRIC and TCGA datasets. CTNNBIP1 expression was correlated with PKCζ expression (Figure 5G-I). Furthermore, patients with PKCζhigh CTNNBIP1high luminal B breast cancer treated with endocrine therapy in the METABRIC dataset (Figure 6A-C and Table V) and with aromatase inhibitor in TCGA Pan-Cancer Atlas dataset (Figure 6D-L and Table V) demonstrated a poor prognosis.

Figure 5.
Figure 5.
Figure 5.

DEGs between PKCζhigh and PKCζlow luminal B treated with endocrine therapy in METABRIC and TCGA datasets. (A-C) The DEGs between PKCζhigh and PKCζlow luminal B breast cancer treated with endocrine therapy in the METABRIC dataset. (A) Volcano plot of the DEGs. The red dots represent the genes that are up-regulated and the blue dots represent the genes that are down-regulated. GO enrichment analysis by Metascape of (B) the up-regulated genes and (C) the down-regulated genes. (D-F) The DEGs between PKCζhigh and PKCζlow luminal B breast cancer treated with aromatase inhibitors in TCGA Pan-Cancer Atlas dataset. (D) Volcano plot of the DEGs. The red dots represent the genes that are up-regulated and the blue dots represent the genes that are down-regulated. GO enrichment analysis by Metascape of (E) the up-regulated genes and (F) the down-regulated genes. Scatter plot analysis between CTNNBIP1 and PKCζ luminal B breast cancer treated (G) with endocrine therapy in the METABRIC dataset, (H) with endocrine therapy in TCGA Pan-Cancer Atlas dataset and (I) with aromatase inhibitors in TCGA Pan-Cancer Atlas dataset. METABRIC, Molecular Taxonomy of Breast Cancer International Consortium; TCGA, The Cancer Genome Atlas; DEG, differentially expressed genes; GO, Gene Ontology; PKCζ, protein kinase C zeta.

Figure 6.
Figure 6.
Figure 6.

DSS Kaplan-Meier analyses of luminal B according to CTNNBIP1 expression, PKCζ expression and endocrine therapy. (A-C) Patients treated with endocrine therapy in the METABRIC dataset. (A) Comparison of CTNNBIP1high and CTNNBIP1low, (B) comparison of PKCζhigh CTNNBIP1high and other groups of patients and (C) comparison of PKCζhigh CTNNBIP1high vs. PKCζhigh CTNNBIP1low vs. PKCζlow CTNNBIP1high vs. PKCζlow CTNNBIP1low groups of patients. (D-F) Patients treated with endocrine therapy in TCGA Pan-Cancer Atlas dataset. (D) Comparison of CTNNBIP1high and CTNNBIP1low, (E) comparison of PKCζhigh CTNNBIP1high and other groups of patients, (F) comparison of PKCζhigh CTNNBIP1high vs. PKCζhigh CTNNBIP1low vs. PKCζlow CTNNBIP1high vs. PKCζlow CTNNBIP1low groups of patients. (G-I) Patients treated with tamoxifen in TCGA Pan-Cancer Atlas dataset. (G) Comparison of CTNNBIP1high and CTNNBIP1low, (H) comparison of PKCζhigh CTNNBIP1high and other groups of patients, (I) comparison of PKCζhigh CTNNBIP1high vs. PKCζhigh CTNNBIP1low vs. PKCζlow CTNNBIP1high vs. PKCζlow CTNNBIP1low groups of patients. (J-L) Patients treated with aromatase inhibitors in TCGA Pan-Cancer Atlas dataset. (J) Comparison of CTNNBIP1high and CTNNBIP1low, (K) comparison of PKCζhigh CTNNBIP1high and other groups of patients, (L) comparison of CTNNBIP1high PKCζhigh vs. CTNNBIP1high PKCζlow vs. CTNNBIP1low PKCζhigh vs. CTNNBIP1low PKCζlow groups of patients. p-Values were calculated by the Cochran-Mantel-Haenszel generalized log-rank test. In C, F and I, the adjusted p-values were determined for PKCζhigh CTNNBIP1high vs. PKCζhigh CTNNBIP1low, PKCζlow CTNNBIP1high, PKCζlow CTNNBIP1low groups using the Holm method. METABRIC, Molecular Taxonomy of Breast Cancer International Consortium; TCGA, The Cancer Genome Atlas; DSS, disease-specific survival; PKCζ, protein kinase C zeta; CTNNBIP1, catenin beta interacting protein 1.

Discussion

In the present study, we showed that high expression of PKCζ contributed to the reduced effectiveness of endocrine therapy (including aromatase inhibitor as endocrine therapy) in patients with the luminal B breast cancer subtype. These results suggest that PKCζ is involved in breast cancer progression and contributes to the sensitivity of endocrine therapy in the luminal B subtype.

The present study revealed that PKCζ contributed to the reduced effectiveness of endocrine therapy in patients with the luminal B breast cancer subtype in the METABRIC dataset (Figure 2 and Table II). Consistently, PKCζ contributed to the reduced effectiveness of aromatase inhibitor therapy in patients with the luminal B breast cancer subtype in TCGA Pan-Cancer Atlas dataset (Figure 4). The up-regulation of CTNNBIP1 as a DEG was identified in both the METABRIC and TCGA Pan-Cancer Atlas datasets. CTNNBIP1 has an inhibitory interaction with the Wnt signaling pathway and mutations of this gene are found in breast cancer (50). PKCζ phosphorylates and enhances GSK-3β activity, which activates the Wnt/β-Catenin signaling pathway in cancer cells (51). PKCζ activity also regulates the nuclear localization of β-catenin (52, 53) and PKCζ interacts with and down-regulates the protein level of β-catenin by phosphorylating Ser45 (54). Thus, PKCζ serves an important role in the positive regulation of the Wnt/β-Catenin signaling pathway. Conversely, PKCζ suppresses Wnt/β-Catenin signaling in cardiomyocytes (55). How PKCζ and CTNNBIP1 are involved in the poor efficacy of aromatase inhibitors in luminal B breast cancer remains an important future question. A previous report using cell lines and a mouse model demonstrated that PKCζ is degraded by miR-200c, which is up-regulated by TET2, resulting in increased sensitivity to tamoxifen (36). Notably, the multidomain protein, p62, interacts with PKCζ (32, 56, 57). We recently reported that patients with luminal B breast cancer with high p62 expression demonstrated a poor prognosis (44) and p62 deficiency in luminal B cell lines suppresses tumor-sphere formation (44). Thus, although the site of action of tamoxifen and aromatase inhibitors is different, the down-regulation of PKCζ protein by miR-200c and the interaction with p62 may be involved in the PKCζ-mediated reduced effectiveness of endocrine therapy and aromatase inhibitors in luminal B breast cancer.

The characteristics of the luminal B subtype, in addition to high ER expression, often include her2 positivity and high expression of proliferation-related genes such as MKI67, as well as a higher histological grade and a worse prognosis (2, 9, 12, 13, 16-18, 58). TCGA Pan-Cancer Atlas dataset contains information on her2-targeted therapy. Therefore, we attempted to examine the effect of PKCζhigh on her2-targeted therapy in the luminal B subtype, but the prognosis analysis was impossible as no deaths occurred among the patients who received this therapy in this dataset. Thus, the effect of her2-targeted therapy in PKCζhigh breast tumors remains to be determined.

PKCλ, another subtype that belongs to the same aPKC family as PKCζ, has not been reported to be involved in treatment resistance of breast cancer. On the other hand, the resistance to chemotherapy such as cisplatin and gemcitabine has been reported in laryngeal squamous cell carcinoma and gallbladder cancer in cell experiments and human sample analysis (59, 60). Further analysis is needed to determine the similarities and differences in the functions of PKCζ and PKCλ for the treatment resistance in breast cancer.

Cancer stem cells (CSCs) or tumor-initiating cells are resistant to standard medical treatments, such as chemotherapy and radiotherapy, and cause cancer relapse (44, 46, 61). PKCζ is involved in the tumor-sphere formation of cervical cancer stem cells (53). In addition, the PKCζ binding protein, p62, is also involved in the tumor formation and radiotherapy insensitivity of aldehyde dehydrogenase 1 positive CSCs (44, 45). Thus, CSCs should also be considered when examining the insensitivity to medical treatments inferred by PKCζ in breast cancer subtypes.

Conclusion

In the present study, we showed that PKCζ is involved in breast cancer progression and contributes to the sensitivity to endocrine therapy in the luminal B subtype. Furthermore, CTNNBIP1 was identified as a differentially up-regulated gene between patients with PKCζhigh and PKCζlow luminal B breast cancer treated with endocrine therapy and aromatase inhibitors. Patients with PKCζhigh CTNNBIP1high luminal B breast cancer treated with endocrine therapy and aromatase inhibitors demonstrated a poor prognosis. Therefore, we concluded that PKCζ and CTNNBIP1 may serve as a prognostic biomarker for predicting the efficacy of endocrine therapy in the luminal B breast cancer.

Footnotes

  • Authors’ Contributions

    Conceptualization: YN, KS, and KA; formal analysis: YN, KS, RC, HI, and KN; Funding Acquisition: YN, KS, ST, SO, and KA; Investigation: YN, KS, RC, KN, and KA; Methodology: KS, KN, YM, AO, and KA; Project Administration: KA; Supervision: KA; Validation: KS, RC, HI, YM, TK, and ST; Visualization: YN, HI, KN, and YM; Writing – Original Draft Preparation: YN, HI, and KA; Writing – Review & Editing: YN, KS, HI, KN, YM, TK, AO, ST, SO, and KA.

  • Conflicts of Interest

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

  • Funding

    The present study was supported by a Tokyo University of Science Grant for President’s Research Promotion, JST Moonshot R&D (grant no. JPMJPS2024), Grant-in-Aid for Scientific Research (C) (grant no. 20K08936, 24K12517), Grant-in-Aid for Early-Career Scientists (grant no. 23K14352), JST SPRING (grant no. JPMJSP2151), Grant-in-Aid for Special Research in Subsidies for ordinary expenses of private schools from The Promotion and Mutual Aid Corporation for Private Schools of Japan, Grant from Institute for Environmental & Gender-specific Medicine, Juntendo University.

  • Received March 31, 2025.
  • Revision received April 30, 2025.
  • Accepted May 1, 2025.

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).

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High Expression of PKCζ And CTNNBIP1 Is Associated With Poor Prognosis in Luminal B Breast Cancer
YUKA NAGASHIMA, KAZUNORI SASAKI, RYOSUKE CHIWAKI, HAYATO ISHII, KANA NOHATA, YUKI MAEMURA, TAKAHIRO KASAI, AYAKA OZAKI, SHOMA TAMORI, SHIGEO OHNO, KAZUNORI AKIMOTO
Cancer Genomics & Proteomics Jul 2025, 22 (4) 538-556; DOI: 10.21873/cgp.20520
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