The ternary complex of progesterone receptor membrane component 1 (PGRMC1)–sigma-2 receptor/transmembrane protein 97 (σ2R/TMEM97)–low-density lipoprotein receptor (LDLR) has recently been discovered and plays a role in cholesterol transport. This study investigated whether individual components of that complex are prognostic breast cancer biomarkers and have defined expression in established molecular subtypes. A total of 4,463 invasive breast cancers were analyzed as a function of molecular and phenotypic markers, estimates of cellular proliferation, and recurrence-free survival. A gene expression signature–based assay was utilized to estimate cellular proliferation. Cox proportional hazards regression estimated relapse-free survival and multivariate Cox analysis adjusted for the association of proliferation with early relapse. PGRMC1–σ2R/TMEM97–LDLR expression was stratified by immunohistochemical (IHC) and molecular subtype, tumor grade, and size. TMEM97 exhibited the strongest correlation with proliferation, highest in estrogen receptor (ER)–positive disease (r = 0.59, P = 8.1−114). TMEM97 and PGRMC1 were associated with a risk of early recurrence, dependent upon their association with proliferation. The risk of early recurrence was highest with TMEM97 and only seen in ER+/HER2− disease [HR = 1.5; 95% confidence interval (CI) = 1.35–1.67; P = 5.4−14] and ER+ malignancies (HR = 1.49; 95% CI = 1.31–1.68; P = 3.1−10). There was no increased risk of recurrence with TMEM97 expression in ER−/HER2− (HR = 1.05; 95% CI = 0.88–1.25; P = 0.63) or ER− disease (HR = 1.02; 95% CI = 0.89–1.17; P = 0.75). Components of a ternary complex associated with rapid internalization of low-density lipoprotein are biomarkers associated with cellular proliferation and early recurrence, which should help guide studies exploring them in the context of additional markers of aggressive disease. Elucidating the role of PGRMC1, TMEM97, and LDLR in breast cancer will facilitate a mechanistic understanding of how proliferation interplays with cholesterol metabolism in malignant transformation or propagation.

Significance:

This first large-scale analysis of the putative ternary complex responsible for rapid low-density lipoprotein internalization in breast cancer reveals a link between component expression and recurrence, with prognostic implications for identifying patients needing supplemental posttreatment surveillance and/or additional therapeutic approaches.

Cholesterol is an essential component of cell membranes, and its metabolism is often altered in cancer (1). Although some studies have not found a significant link between lipoproteins and breast cancer, others have identified a correlation between low-density lipoprotein (LDL) cholesterol levels and breast cancer risk (2). A recent large analysis indicates that elevated levels of both high-density lipoprotein (HDL) and LDL cholesterol are associated with an increased risk of breast cancer (3), and additional investigations are ongoing.

Statins, which are used to lower cholesterol levels, have been investigated for their potential role in cancer treatment and recurrence. However, the results have been inconsistent, and no definitive benefits have been established (47). The biochemical pathways of cholesterol are complicated, including biosynthesis and uptake through the LDL receptor (LDLR) pathway. Statins downregulate cholesterol production by the liver (preventing biosynthesis). Still, if cancer cells have other ways to get cholesterol, then the cell may be able to circumvent the lower production levels.

Selective estrogen receptor modulators (SERM), such as tamoxifen, are estrogen receptor (ER) antagonists that have long been used for the treatment of patients with ER+ breast cancer. However, tamoxifen and other SERM can inhibit angiogenesis, independent of their inhibitory effect on ERs (8). One molecular mechanism that allows SERM to inhibit angiogenesis is inhibiting cholesterol trafficking in endothelial cells (9). In endothelial cells, VEGFR2 and mTOR are major signaling proteins that are regulated by cholesterol levels (10, 11). The inhibitory effects of SERM on VEGFR2 and mTOR signaling, as well as angiogenesis, were rescued by replenishing endothelial cells with cholesterol, suggesting that inhibition of cholesterol trafficking is a primary effect of SERM for + antiangiogenic activity (12). Although the cholesterol trafficking inhibition is ER independent, the exact molecular target is still unknown.

There are four proteins related to progesterone receptor membrane component (PGRMC) that have a cytochrome b5–like heme/sterol-binding domain, but of these, only PGRMC1 is known to bind progesterone (P4) in the low nanomolar range (13). PGRMC1 may be related to both breast cancer proliferation and cholesterol transport. PGRMC1 facilitates triple-negative breast cancer tumor growth in vivo (14), and in an ER+ human breast cancer cell line that overexpresses PGRMC1, medroxyprogesterone acetate and norethisterone treatment significantly increased proliferation (15, 16). PGRMC1 may also be associated with breast cancer chemotherapeutic resistance in vitro. Doxorubicin-mediated apoptosis was decreased by 50% when a PGRMC1 triple-negative breast cancer cell line was pretreated with progesterone. PGRMC1-depleted cells lost the progesterone-mediated survival advantage (14). Thus, PGRMC1 could be an important breast cancer biomarker.

Sigma-2 receptor/transmembrane protein 97 (σ2R/TMEM97) is a protein involved in cholesterol homeostasis and regulation of cell growth found in cellular membranes (17), lipid rafts (18), endoplasmic reticulum, lysosomes, and plasma membranes (19). σ2R density is high in multiple cancers (2022), and σ2R levels are elevated in aldehyde dehydrogenase (ALDH)–high compared with ALDH-low MDA-MB-435 cells. The ALDH phenotype has been reported as a surrogate marker for tumor-initiating cells (cancer stem cells; ref. 23). Elevated σ2R levels are found in lung tumors and plasma from patients with lung cancer (24), and preclinical evidence suggests that σ2R may be a therapeutic target as σ2R ligands potentiate the efficacy of chemotherapeutic agents in mouse models of pancreatic cancer and improve survival (2527). Studies have shown that σ2R/TMEM97 ligands may also be useful in the treatment of a number of neurologic disorders, including Huntington disease (28), neuropathic pain (29), and Alzheimer disease (30). As a result, a diverse set of σ2R/TMEM97 radiotracers and ligands has been developed for use in strategies targeting cancer diagnosis and treatment (31). One such radiotracer is the σ2R-selective radioligand imaging agent N-(4-(6,7-dimethoxy-3,4-dihydroisoquinolin-2(1H)-yl)butyl)-2-(2-18F-fluoroethoxy)-5-methylbenzamide (18F-ISO-1; ref. 32). The ability of this imaging agent to measure both σ2R density and cellular proliferation has been validated in preclinical models (33) and in early results in a variety of solid tumors (34). In vitro work has demonstrated that sigma-2 agents can be used as alternative tracers for proliferation in breast cancer cell lines (35).

Our lab has previously linked these two important proteins by showing that PGRMC1 and σ2R/TMEM97 form a complex with the LDLR, and the intact complex is required for efficient uptake of lipoproteins such as LDL (36). This complex represents a common biological mechanism for cholesterol uptake in a variety of cells including neurons (37) and breast cancer cells (manuscript in preparation). Supporting this, siRNA studies knocking down TMEM97 demonstrated a reduction in the rate of internalization of LDL by the LDLR (38). We also demonstrated in a subsequent clinical trial that in vivo quantification of a radioligand targeting TMEM97 correlates with proliferation in ER+ breast cancer (39). Although PGRMC1 is a membrane-associated progesterone receptor, its role in cell biology is historically poorly understood. It is likely a molecular chaperone that is involved in the translocation of lipophilic molecules such as cholesterol and other steroids from the plasma membrane and the endoplasmic reticulum, mitochondria, and other organelles. Before its identification as TMEM97, the σ2R had also been implicated in cholesterol biosynthesis. Although we have demonstrated that PGRMC1 and σ2R/TMEM97 are involved in the same biochemical pathways within the cell, little is known about the impact of the individual components on breast cancer clinical outcomes. The high association of PGRMC1 and σ2R/TMEM97 and the suspected role of both proteins in proliferation spurred this investigation.

A possible link between cholesterol metabolism and ER+ breast cancer has been considered for decades, but the mechanism has been elusive, limiting possible therapeutic interventions for risk modification. Building on the studies above, our clinical question was whether components of the PGRMC1–TMEM97–LDLR protein complex affect clinical outcomes in breast cancer. To accomplish this on a large scale, we linked 17 publicly available databases. We also validated a new proliferation signature to allow adjustment for the clinically suspected link between this complex and proliferation in breast cancer because standard measures of proliferation like Ki-67 expression were not available in these datasets. Although the PGRMC1–TMEM97–LDLR protein complex could be a potential diagnostic or therapeutic target, little is known about the in vivo expression of these proteins in subtypes of human breast cancer or their association with clinical outcomes. We tested the hypothesis that these proteins correlate with proliferation in human breast cancer in order to examine the relationship between proteins affecting cholesterol transport and breast cancer subtypes, cellular proliferation, and markers of proliferation. We also evaluated the association among PGRMC1, TMEM97, LDLR, and breast cancer recurrence to determine whether they are prognostic biomarkers for aggressive disease.

Human breast cancer microarray datasets

A multiple-platform data integration method was utilized to normalize and simultaneously analyze microarray data from 17 publicly available primary breast cancer microarray datasets (“Integrated Dataset,” Supplementary Table S1). Microarray data and corresponding clinical annotations were downloaded from NCBI Gene Expression Omnibus (RRID: SCR_005012) or the original authors’ websites. Microarray data were converted to a log2 scale where necessary. Affymetrix microarray data were renormalized using robust multiarray average when .CEL files were available. Five breast cancer subtypes were used according to the PAM50 classification (40). PAM50 is a gene expression assay that can be used to categorize breast tumors into intrinsic subtypes that indicate distinct tumor behaviors. In total, data were available from 4,463 invasive breast cancers: 1,164 luminal A, 921 luminal B, 645 HER2-enriched, 860 basal, and 543 normal-like. In four datasets, patients received no systemic treatment; in two datasets, patients received neoadjuvant treatment; and the remaining datasets represented a mixture of adjuvant and no treatment (Supplementary Table S2).

Gene expression and prognostic variables/subtypes

The association between mRNA expression and categorical prognostic variables in human breast cancers, including ER status, progesterone receptor (PR) status, HER2 status, lymph node involvement, tumor size, tumor grade, and intrinsic molecular subtype, was assessed by ANOVA in pooled microarray datasets. For each categorical prognostic variable, gene expression was normalized against the mean expression of the same baseline group in each dataset and pooled across all datasets for which the prognostic variable was available. Baseline normalization was performed by subtracting mean gene expression (log2 scale) in the baseline group from gene expression in each sample.

The Cancer Genome Atlas data analysis

The Cancer Genome Atlas (TCGA; RRID: SCR_003193) was queried for invasive breast cancers with available RNA sequencing data (RNA Seq V2 RSEM) and differentiated according to IHC subtype. In total, data were available from 1,019 invasive breast cancers (Supplementary Table S3): 738 ER+, 215 ER−, 643 PR+, 307 PR, 149 HER2+, 508 HER2−, and 102 triple-negative breast cancer (TNBC; ER−/PR/HER2−). PGRMC1 expression was first compared between all groups and plotted according to subtype. PGRMC1 was then tested for correlation with 20,531 genes, which resulted in the identification of 461 genes in which expression was correlated with PGRMC1 expression (Pearson r > 0.25) within tumor samples. A heatmap of expression levels for the top positively and negatively correlated genes was generated. These were analyzed for enrichment of pathways, functions, networks, and upstream regulators using Ingenuity Pathway Analysis (RRID: SCR_008653; QIAGEN Redwood City, www.qiagen.com/ingenuity; Supplementary Table S4). The analysis was done using functions from MATLAB R2012b (RRID: SCR_001622).

Proliferation gene expression signature

Measures of proliferation, such as Ki-67 expression or mitotic index, were not available for the majority of breast cancer samples. Therefore, to estimate relative proliferation levels in human breast cancer samples, we generated a gene expression signature containing 224 genes (Prolif224) from the overlap of two gene sets: (i) 651 cell cycle–regulated genes identified in HeLa cells (41) and (ii) 1,882 serum-responsive genes identified in human fibroblasts (42). Serum-responsive genes were identified by differential expression analysis between the 0.1% and 10% serum groups using Cyber-T (43) at an FDR of 10%. In each human breast cancer dataset, levels of proliferation were estimated using these 224 genes and a previously described scoring method (44), in which each gene was weighted using its log fold change between the 0.1% and 10% serum groups (42).

Correlation between the expression of individual genes and estimated relative proliferation level was assessed in human breast cancer datasets using the Pearson correlation coefficient and summarized across datasets by meta-analysis. Additional meta-analyses of Pearson correlation were performed in subsets of samples stratified by ER status, HER2 status, lymph node status, or molecular subtype. Correlation between two different genes was assessed in a similar fashion.

Gene expression and relapse-free survival

Within each dataset, the effect size of the association between mRNA expression and 5-year relapse-free survival was estimated using the HR from Cox proportional hazards regression in which gene expression was modeled as a continuous variable. Effect size estimates were combined across datasets by meta-analysis using the inverse variance weighting method (45). Between-study homogeneity of survival association was tested using the χ2 test on Cochran’s Q statistic (46), for which a P value of less than 0.05 was interpreted as evidence of significant heterogeneity. In the presence of significant heterogeneity, the random-effects model (47) was used for meta-analysis. In the absence of significant heterogeneity, the fixed-effects model (48) was used. Cox proportional hazards regression and meta-analysis were performed using the “coxph” function in the “survival” package and the “metagen” function in the “meta” packages in R 2.15.0. For datasets in which relapse-free survival information was not available, distant metastasis-free survival or disease-specific survival information, when available, was used for survival analysis.

Additional meta-analyses were performed in subsets of samples stratified by ER status, HER2 status, lymph node status, or intrinsic molecular subtype, as well as in the subset of patients who, according to available treatment information, did not receive any adjuvant systemic treatment. As HER2 IHC status was not available for several datasets, HER2 status was approximated by ERBB2 mRNA expression as measured by microarray in a similar fashion as the Cancer Outlier Profile Analysis (49). In each dataset, HER2+ and HER2− samples were defined as being above and below a cutoff of 1.5 absolute deviations above the median, respectively, which resulted in an average specificity of 98% and sensitivity of 78% in five validation datasets (5054). Due to the nonrandom association between ER and HER2 status, an approximation of HER2 status was not attempted in datasets consisting entirely of hormone receptor–positive or hormone receptor–negative cancers. Assignments of intrinsic subtype were done using the PAM50 classifier (40) after expression data were median-centered for each gene.

Data availability

The data generated in this study are available upon request from the corresponding authors.

PGRMC1, TMEM97, and LDLR are overexpressed in ER− breast cancer

Publicly available microarray data for 4,463 patients contained within 17 human primary breast cancer datasets (5052, 5567), along with the corresponding clinical annotations, were downloaded and converted to a log2 scale where necessary. Affymetrix microarray data for which .CEL files were available were renormalized using robust multiarray average (68). PGRMC1 is overexpressed in human breast cancers of the basal subtype using PAM50 (P = 4 × 10−30). TMEM97 has the highest expression in luminal B tumors (Fig. 1A). In an analogous manner, PGRMC1 was expressed at higher levels in ER− tumors, PR tumors, and ER−/HER2− tumors (Fig. 1B). PGRMC1 was also expressed at higher levels in tumors of higher grade (P = 2.5 × 10−14) and smaller size (P = 4.7 × 10−7; Fig. 1C). In the integrated dataset, LDLR and TMEM97 also had the highest expression in ER− disease (P = 4.7e−14 and P = 0.041, respectively; Fig. 1B); LDLR and TMEM97 were also each expressed at higher levels in tumors of higher grade (P = 1.1 × 10−08 and 6.1 × 10−64, respectively; Fig. 1C).

Figure 1

A, PGRMC1 is overexpressed in human breast cancers of the basal subtype using PAM50. TMEM97 has the highest expression in luminal B tumors. B, PGRMC1 is overexpressed in hormone receptor–negative cancers. LDLR expression is highest in ER−/HER2+. TMEM97 has the highest expression in ER+/HER2+ tumors. C, PGRMC1 is overexpressed in smaller higher-grade tumors. LDLR and TMEM97 expression is highest in higher-grade tumors.

Figure 1

A, PGRMC1 is overexpressed in human breast cancers of the basal subtype using PAM50. TMEM97 has the highest expression in luminal B tumors. B, PGRMC1 is overexpressed in hormone receptor–negative cancers. LDLR expression is highest in ER−/HER2+. TMEM97 has the highest expression in ER+/HER2+ tumors. C, PGRMC1 is overexpressed in smaller higher-grade tumors. LDLR and TMEM97 expression is highest in higher-grade tumors.

Close modal

To examine PGRMC1 expression in cancers compared with normal breast tissue, TCGA data were analyzed based on tumor IHC classification for 738 ER+, 215 ER−, 643 PR+, 307 PR, 149 HER2+, 508 HER2−, and 102 TNBC tumors and 108 normal controls. Normal tissues in TCGA database are matched samples (normal tissue from patients who also have a primary tumor). Consistent with its elevated expression in the basal subtype, PGRMC1 was overexpressed in ER−, PR, and TNBC compared with normal breast tissue (1.33-fold, P = 2 × 10−06; 1.23-fold, P = 3 × 10−04; and 1.30-fold, P = 6 × 10−6, respectively; Supplementary Fig. S1).

PGRMC1 and TMEM97 expression are associated with cellular proliferation in breast cancer

The association between components of the ternary complex and cellular proliferation in human breast cancer was assessed, in comparison with TK1 as a validated marker for cellular proliferation (69). Because the vast majority of clinical samples for which PGRMC1 expression was available did not have documented measures of cellular proliferation, such as Ki-67 or mitotic index, we pursued a computational approach utilizing a gene expression signature for proliferation to estimate cellular proliferation rates. First, we generated a gene expression signature containing 224 genes from the overlap of gene sets representing 651 cell cycle–regulated genes in HeLa cells (41) and 1,882 serum-responsive genes in human fibroblasts (see “Materials and Methods”; ref. 42). Next, in each human breast cancer dataset, levels of proliferation were estimated for each sample using this 224-gene set in combination with a previously described scoring method (44) in which each gene was weighted using its log fold change between the 0.1% and 10% serum groups (42).

PGRMC1 exhibited a robust positive association with signature-derived proliferation scores across all breast cancers (r = 0.268; P = 6.5 × 10−17; Table 1). When PAM50 molecular subtypes were considered, PGRMC1 displayed a significant correlation with proliferation scores within each of the five subtypes, with the strongest association observed for the basal subtype (r = 0.415; P = 2.4 × 10−37). PGRMC1 expression was also correlated with proliferation within each receptor subtype, except for ER+/HER2+ tumors (Table 1). As such, PGRMC1 was a consistent marker for proliferation across subtypes, significantly correlating with proliferation in HER2+, ER−/HER2+, ERBB2-enriched, and luminal B tumors. PGRMC1 expression was significantly associated with proliferation scores within lymph node–positive and lymph node–negative tumors (Table 1). In the PGRMC1–σ2R/TMEM97–LDLR complex, TMEM97 exhibited the strongest correlation with proliferation and the highest in ER+ disease (all: r = 0.509; P = 6.1e−67 and ER+: r = 0.588; P = 8.1−114), and LDLR only had a weak correlation with proliferation, regardless of subtype or IHC status (all: r = 0.16; P = 6.6−11; Table 1).

Table 1

Association of PGRMC1, TK1, LDLR, and TMEM97 with proliferation. Meta-analysis was performed to examine the association among a) PGRMC1, b) TMEM97, c) TK1, and d) LDLR and estimated proliferation rates of tumors

StrataCorrelation coefficientP valueStrataCorrelation coefficientP value
a) PGRMC1 vs. proliferation signature b) TMEM97 vs. proliferation signature 
 All 0.268 6.50e−17  All 0.509 6.1e−67 
 ER+ 0.15 0.00017  ER+ 0.588 8.1e−114 
 ER− 0.289 1.00e−10  ER− 0.38 8.2e−38 
 HER2+ 0.204 0.0021  HER2+ 0.392 1.5e−22 
 HER2− 0.271 1.40e−14  HER2− 0.524 1.6e−101 
 ER+/HER2+ 0.098 0.11  ER+/HER2+ 0.422 5.6e−13 
 ER+/HER2− 0.115 0.0019  ER+/HER2− 0.587 1.1e−110 
 ER−/HER2+ 0.279 0.0086  ER−/HER2+ 0.399 1.4e−10 
 ER−/HER2− 0.279 5.50e−15  ER−/HER2− 0.493 3.1e−47 
 Node+ 0.302 1.10e−09  Node+ 0.486 6.7e−30 
 Node 0.221 7.20e−13  Node 0.472 1.5e−38 
 Basal 0.415 2.40e−37  Basal 0.43 1.2e−16 
 ERBB2-enriched 0.254 1.70e−05  ERBB2-enriched 0.385 1.7e−10 
 Luminal A 0.07 0.018  Luminal A 0.394 2.7e−44 
 Luminal B 0.102 0.0021  Luminal B 0.486 2.8e−56 
 Normal-like 0.091 0.038  Normal-like 0.501 1.9e−35 
c) TK1 vs. proliferation signature d) LDLR vs. proliferation signature 
 All 0.688 2.50e−145  All 0.16 6.6e−11 
 ER+ 0.712 6.50e−200  ER+ 0.176 2.4e−10 
 ER− 0.531 4.00e−27  ER− 0.043 0.16 
 HER2+ 0.445 4.80e−31  HER2+ 0.137 0.00082 
 HER2− 0.725 6.60e−177  HER2− 0.154 2.3e−07 
 ER+/HER2+ 0.528 2.10e−21  ER+/HER2+ 0.159 0.0098 
 ER+/HER2− 0.727 1.40e−202  ER+/HER2− 0.155 6.7e−06 
 ER−/HER2+ 0.338 4.20e−08  ER−/HER2+ 0.143 0.025 
 ER−/HER2− 0.616 2.00e−27  ER−/HER2− 0.086 0.019 
 Node+ 0.688 1.30e−44  Node+ 0.137 2.9e−08 
 Node 0.692 1.00e−150  Node 0.176 5.8e−19 
 Basal 0.574 4.70e−30  Basal 0.1 0.0039 
 ERBB2-enriched 0.365 1.80e−21  ERBB2-enriched 0.136 0.00069 
 Luminal A 0.551 5.00e−32  Luminal A 0.143 0.0017 
 Luminal B 0.524 3.00e−68  Luminal B 0.115 0.00051 
 Normal-like 0.636 2.00e−65  Normal-like 0.137 0.0017 
StrataCorrelation coefficientP valueStrataCorrelation coefficientP value
a) PGRMC1 vs. proliferation signature b) TMEM97 vs. proliferation signature 
 All 0.268 6.50e−17  All 0.509 6.1e−67 
 ER+ 0.15 0.00017  ER+ 0.588 8.1e−114 
 ER− 0.289 1.00e−10  ER− 0.38 8.2e−38 
 HER2+ 0.204 0.0021  HER2+ 0.392 1.5e−22 
 HER2− 0.271 1.40e−14  HER2− 0.524 1.6e−101 
 ER+/HER2+ 0.098 0.11  ER+/HER2+ 0.422 5.6e−13 
 ER+/HER2− 0.115 0.0019  ER+/HER2− 0.587 1.1e−110 
 ER−/HER2+ 0.279 0.0086  ER−/HER2+ 0.399 1.4e−10 
 ER−/HER2− 0.279 5.50e−15  ER−/HER2− 0.493 3.1e−47 
 Node+ 0.302 1.10e−09  Node+ 0.486 6.7e−30 
 Node 0.221 7.20e−13  Node 0.472 1.5e−38 
 Basal 0.415 2.40e−37  Basal 0.43 1.2e−16 
 ERBB2-enriched 0.254 1.70e−05  ERBB2-enriched 0.385 1.7e−10 
 Luminal A 0.07 0.018  Luminal A 0.394 2.7e−44 
 Luminal B 0.102 0.0021  Luminal B 0.486 2.8e−56 
 Normal-like 0.091 0.038  Normal-like 0.501 1.9e−35 
c) TK1 vs. proliferation signature d) LDLR vs. proliferation signature 
 All 0.688 2.50e−145  All 0.16 6.6e−11 
 ER+ 0.712 6.50e−200  ER+ 0.176 2.4e−10 
 ER− 0.531 4.00e−27  ER− 0.043 0.16 
 HER2+ 0.445 4.80e−31  HER2+ 0.137 0.00082 
 HER2− 0.725 6.60e−177  HER2− 0.154 2.3e−07 
 ER+/HER2+ 0.528 2.10e−21  ER+/HER2+ 0.159 0.0098 
 ER+/HER2− 0.727 1.40e−202  ER+/HER2− 0.155 6.7e−06 
 ER−/HER2+ 0.338 4.20e−08  ER−/HER2+ 0.143 0.025 
 ER−/HER2− 0.616 2.00e−27  ER−/HER2− 0.086 0.019 
 Node+ 0.688 1.30e−44  Node+ 0.137 2.9e−08 
 Node 0.692 1.00e−150  Node 0.176 5.8e−19 
 Basal 0.574 4.70e−30  Basal 0.1 0.0039 
 ERBB2-enriched 0.365 1.80e−21  ERBB2-enriched 0.136 0.00069 
 Luminal A 0.551 5.00e−32  Luminal A 0.143 0.0017 
 Luminal B 0.524 3.00e−68  Luminal B 0.115 0.00051 
 Normal-like 0.636 2.00e−65  Normal-like 0.137 0.0017 

P value < 0.05.

The association of proliferation scores with TK1 expression was also analyzed, given its known positive correlation with proliferation. TK1 is a cell cycle–regulated target of E2F in which expression and function are associated with cell-cycle status. TK1 expression also correlates with the uptake of 3′-deoxy-3′-[18F]fluorothymidine (18F-FLT; refs. 70, 71). 18F-FLT is trapped in cells after undergoing phosphorylation by TK1, which is catalytically active during S-phase and represents the first metabolic step in the salvage pathway for incorporating exogenous thymidine into DNA (7274). 18F-FLT is currently the most widely used radiotracer for imaging tumor proliferation rates (7577) with uptake reflecting ex vivo S-phase–specific bromodeoxyuridine incorporation and TK expression. 18F-FLT was demonstrated to be a useful biomarker for breast cancer treatment response in a large multicenter trial (78). In our study, TK1 exhibited a strong positive association with proliferation scores (r = 0.688; P = 2.5 × 10−145), particularly within ER+/HER2− tumors (r = 0.727; P = 1.4 × 10−202; Table 1).

PGRMC1 and TMEM97 expression are associated with early breast cancer relapse

To address whether components of the ternary complex were associated with the risk of breast cancer relapse, effect size estimates from Cox proportional hazards regression using gene expression as a continuous variable were aggregated across datasets by meta-analysis. The results demonstrated that PGRMC1 expression is associated with a higher risk of early relapse (within 5 years) across all patients with breast cancer [HR = 1.25; 95% confidence interval (CI) = 1.12–1.39; P = 6.4 × 10−5; Fig. 2A]. Within the basal subtype, PGRMC1 expression was also associated with relapse (HR = 1.29; 95% CI = 1.04–1.60; P = 0.018; Fig. 2B). The risk of early recurrence with TMEM97 was present only in ER+/HER2− disease (HR = 1.5; 95% CI = 1.35–1.67; P = 5.4−14) and ER+ malignancies (HR = 1.49; 95% CI = 1.31–1.68; P = 3.1−10) and was not present in ER−/HER2− (HR = 1.05; 95% CI = 0.88–1.25; P = 0.63) or ER− disease (HR = 1.02; 95% CI = 0.89–1.17; P = 0.75; Fig. 3A and B). LDLR was not associated with a risk of early recurrence in ER+ disease (HR = 0.99; 95% CI = 0.87–1.13; P = 0.93) or ER+/HER2− tumors (HR = 1.01; 95% CI = 0.87; 1.17; P = 0.9).

Figure 2

PGRMC1 is associated with early breast cancer relapse in a proliferation-dependent manner. Effect size estimates were aggregated across datasets by meta-analysis to determine the risk of relapse within 5 years from all cancers. A, Association of PGRMC1 with early breast cancer relapse. B, Association of PGRMC1 with early breast cancer relapse within the basal subtype. C, Association of PGRMC1 with early relapse adjusted for estimated proliferation. JRH, John Radcliffe Hospital.

Figure 2

PGRMC1 is associated with early breast cancer relapse in a proliferation-dependent manner. Effect size estimates were aggregated across datasets by meta-analysis to determine the risk of relapse within 5 years from all cancers. A, Association of PGRMC1 with early breast cancer relapse. B, Association of PGRMC1 with early breast cancer relapse within the basal subtype. C, Association of PGRMC1 with early relapse adjusted for estimated proliferation. JRH, John Radcliffe Hospital.

Close modal
Figure 3

TMEM97 is associated with early breast cancer relapse in a proliferation-dependent manner. Effect size estimates were aggregated across datasets by meta-analysis to determine the risk of relapse within 5 years. A, TMEM97 is associated with early breast cancer relapse only in ER+ tumors. B, TMEM97 is associated with early breast cancer relapse only in ER+/HER2− tumors. C, Association of TMEM97 with early breast cancer relapse in ER+/HER2− tumors adjusted for proliferation. JRH, John Radcliffe Hospital; RFS, recurrence-free survival.

Figure 3

TMEM97 is associated with early breast cancer relapse in a proliferation-dependent manner. Effect size estimates were aggregated across datasets by meta-analysis to determine the risk of relapse within 5 years. A, TMEM97 is associated with early breast cancer relapse only in ER+ tumors. B, TMEM97 is associated with early breast cancer relapse only in ER+/HER2− tumors. C, Association of TMEM97 with early breast cancer relapse in ER+/HER2− tumors adjusted for proliferation. JRH, John Radcliffe Hospital; RFS, recurrence-free survival.

Close modal

The association of TK1 expression with recurrence-free survival was tested to evaluate whether the association of PGRMC1 and TMEM97 expression with recurrence-free survival might be linked to their association with proliferation. Expression of TK1 was associated with decreased relapse-free survival overall (HR = 1.45; 95% CI = 1.32–1.60; P = 3.4 × 10−14), particularly within the luminal A subtype (HR = 1.81; 95% CI = 1.29–2.54; P = 5.4 × 10−4) but not in the basal subtype (HR = 1.14; 95% CI = 0.97–1.34; P = 0.13; Supplementary Fig. S2). In contrast, PGRMC1 was associated with decreased relapse-free survival overall (Fig. 2A), with no effect in the luminal A subtype (HR = 1.03; 95% CI = 0.72–1.49; P = 0.86). TK1 expression was also associated with an increased risk of recurrence in combined ER+/HER2− tumors (HR = 1.67; 95% CI = 1.49–1.88; P = 5.1 × 10−18) as well as in ER+ and HER2− tumors (Supplementary Fig. S3).

After adjusting for estimated tumor proliferation rates, PGRMC1, TMEM97, and TK1 were not associated with relapse-free survival (HR = 1.02; 95% CI = 0.91–1.14; HR = 1.04; 95% CI = 0.92–1.18; and HR = 1.05; 95% CI = 0.95–1.15, respectively; Figs. 2C and 3C; Supplementary Fig. S4]. This suggests that the associations of PGRMC1, TMEM97, and TK1 with relapse-free survival are each mediated by their respective associations with cellular proliferation.

PGRMC1 expression is weakly associated with TK1 expression

As the expression of PGRMC1 and TK1 are associated with proliferation in human breast cancers, we next asked whether PGRMC1 expression was associated with the expression of TK1. PGRMC1 exhibited a significant correlation with TK1 when all cancers were combined, although the magnitude of these associations was weak (r = 0.154; P = 9 × 10−08; Table 2). Somewhat stronger associations were observed between PGRMC1 and TK1 expression within the basal subtype (r = 0.241; P = 1.2 × 10−12) and within ER−/HER2− breast cancers (r = 0.256; P = 9.2 × 10−13); however, the magnitude of these associations was smaller than the correlation between PGRMC1 and proliferation scores within these same subsets of patients. This suggests that the association between PGRMC1 and cellular proliferation is largely independent of the association between PGRMC1 expression and expression of TK1.

Table 2

PGRMC1 is weakly associated with TK1. PGRMC1 exhibits a significant correlation with TK1 when all cancers are combined, although the magnitude of these associations is weak. PGRMC1 vs. TK1

StrataCorrelation coefficientP value
All 0.154 9.00E08 
ER+ 0.016 0.66 
ER− 0.214 7.90E13 
HER2+ 0.102 0.013 
HER2− 0.141 8.50E07 
ER+/HER2+ 0.009 0.88 
ER+/HER2− −0.037 0.059 
ER−/HER2+ 0.147 0.021 
ER−/HER2− 0.256 9.20E13 
Node+ 0.158 0.00017 
Node 0.105 1.50E07 
Basal 0.241 1.20E12 
ERBB2+ 0.212 8.50E08 
Luminal A −0.063 0.15 
Luminal B 0.009 0.79 
Normal-like −0.015 0.73 
StrataCorrelation coefficientP value
All 0.154 9.00E08 
ER+ 0.016 0.66 
ER− 0.214 7.90E13 
HER2+ 0.102 0.013 
HER2− 0.141 8.50E07 
ER+/HER2+ 0.009 0.88 
ER+/HER2− −0.037 0.059 
ER−/HER2+ 0.147 0.021 
ER−/HER2− 0.256 9.20E13 
Node+ 0.158 0.00017 
Node 0.105 1.50E07 
Basal 0.241 1.20E12 
ERBB2+ 0.212 8.50E08 
Luminal A −0.063 0.15 
Luminal B 0.009 0.79 
Normal-like −0.015 0.73 

P value < 0.05.

PGRMC1 expression is associated with the activation of cell-cycle pathways

TCGA data containing 1,019 breast cancers were analyzed in an exploratory fashion to evaluate expression patterns associated with PGRMC1. A total of 20,531 available genes were tested for correlation with PGRMC1. Within this dataset, 461 genes were significantly associated with PGRMC1 with a coefficient of at least 0.25 (Supplementary Fig. S5A). These genes were analyzed for enrichment of pathways and targets for upstream regulators using Ingenuity Pathway Analysis. PGRMC1 was associated with CCND1 (cyclin D1) and MYC target pathway activities (z = 2.45; P = 8 × 10−5 and z = 1.95; P = 10−6, respectively) and RICTOR target pathway inhibition (z = −4; P = 3 × 10−5; Supplementary Table S4). Exploratory pathway enrichment analysis revealed an overrepresentation of genes significantly correlated with PGRMC1 that were related to mitochondrial dysfunction, ubiquitination, DNA damage, and oxidative phosphorylation pathways (Supplementary Fig. S5B).

Prolif224 is strongly related to PAM50, and the prognostic value is similar to the current clinical standard-of-care recurrence risk scores

We tested whether our proliferation score, Prolif224, was related to Oncotype DX and/or PAM50. We hypothesized that there would be some correlation because each recurrence score has proliferation as a strong component. Prolif224 was strongly related to PAM50 ROR (0.82, P = 5.7 × 10−36) and greatest in ER+/HER2− (r = 0.85; P = 1.5 × 10−157) and HER2− disease (r = 0.86; P = 0; Supplementary Table S5). The correlation with Oncotype DX was somewhat weaker at 0.7 overall (P = 1.4 × 10−30; Supplementary Table S5). We tested a derived PAM50, Oncotype DX score, and our proliferation signature as predictive biomarkers. This established the predictive value of our signature as compared with standard clinical risk scores. The concordance index (C-Index) is a commonly used metric for assessing the association between a continuous variable (e.g., signature scores) and time-to-recurrence data. It is not affected by the scale of continuous variables and deals with censored observations. It was used in the Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge (79), in which the best model among 300 international teams achieved a C-Index of ∼0.75. In that context, the research version of the 70-gene MammaPrint signature was reported to have a C-Index of ∼0.6. The CIs for PAM50, Oncotype DX, and our new proliferation signature were from 0.63 to 0.66, which is moderate and reasonable, consistent with these prior reported data (Fig. 4). LDLR and PGRMC1 did not demonstrate a moderate or strong correlation with PAM50 ROR. TK1 and TMEM97 were moderately to strongly correlated. In both cases when comparing ER+, ER−, ER+/HER2−, and ER−/HER2−, the correlation was strongest in ER+/HER2− disease (TK1: r = 0.70; P = 6.4 × 10−105 and TMEM97: r = 0.46; P = 2.1 × 10−137; Supplementary Table S6).

Figure 4

PAM50, derived Oncotype DX, and a new proliferation score (Prolif224) were all similarly related to RFS. JRH, John Radcliffe Hospital; RFS, recurrence-free survival.

Figure 4

PAM50, derived Oncotype DX, and a new proliferation score (Prolif224) were all similarly related to RFS. JRH, John Radcliffe Hospital; RFS, recurrence-free survival.

Close modal

We report for the first time an analysis of the individual components of the putative PGRMC1–σ2R/TMEM97–LDLR complex in human breast cancer as a function of receptor subtype, molecular subtype, and proliferation. Our studies reveal that each component is differentially expressed across breast cancer molecular subtypes, with the highest levels of expression observed within ER− disease. In addition, each protein in the complex has higher expression in high-grade tumors, and all three are positively associated with tumor cell proliferation rates, with the strongest association seen with TMEM97. Furthermore, we demonstrate that PGRMC1 and TMEM97 expression are associated with an increased risk of tumor recurrence within the first 5 years following breast cancer diagnosis, in a manner that seems to be mediated by their association with cellular proliferation. In the case of TMEM97, this is only applicable in ER+ disease. Our prognostic findings are supported by prior work noting that PGRMC1 is associated with tumor aggressiveness (14, 80, 81) and an analysis of a small patient subset (69 tumors) demonstrating that PGRMC1 overexpression is associated with breast cancer recurrence and decreased survival when untreated tumor expression is dichotomized into positive and negative PGRMC1 IHC staining (82). There is also prior work demonstrating that patients with increased PGRMC1 have decreased overall survival (HR = 1.7; P = 0.029; ref. 83), but the latter publication did not account for the association of PGRMC1 with proliferation, which is known to correlate with worse survival outcomes in patients with breast cancer.

The most important finding in our study is that the impact of TMEM97 on recurrence-free survival seems to be mediated by its association with proliferation. This suggests a mechanism to explain a decade of data demonstrating that σ2R/TMEM97 correlates with worse outcomes in a variety of solid tumors, including gastric (84), non–small cell lung (85, 86), squamous cell lung (87), and ovarian cancers (88). The association of TMEM97 with proliferation in breast cancer is consistent with in vitro cell culture studies (33, 89, 90) as well as studies in mice utilizing a highly selective, optically labeled (fluorescent) σ2R ligand probe, SW120, wherein SW120 binding was positively correlated with the cell proliferation marker Ki-67 (91). Additionally, the association of σ2R/TMEM97 with proliferation indicates that the σ2R-selective in vivo radioligand imaging agent 18F-ISO-1 may be a useful marker for breast cancer imaging that could have utility in targeted cell-cycle therapy selection and evaluating response to therapy. Supporting this, a clinical trial correlated 18F-ISO-1 uptake in vivo with Ki-67 in ER+ breast cancer (39), notably the same IHC subset in which the correlation between σ2R/TMEM97 and proliferation is the strongest and the same subset in which the association with early relapse is the highest. In particular, 18F-ISO-1 may provide information distinct from, and possibly complementary to another novel radiotracer, 18F-FLT. Unlike Ki-67 and 18F-ISO-1, 18F-FLT is trapped exclusively during the S-phase and not during G1, M, or G2. Furthermore, 18F-FLT has high background uptake in bone marrow, making it impossible to monitor bone metastasis, which is especially important in patients with breast cancer with receptor-positive disease. In an early human study for 18F-ISO-1, bone marrow uptake was noted to be low, making this a possible imaging agent for bone metastasis (34).

Table 1 shows an association between PGRMC1, TMEM97 and proliferation. This is consistent with previous literature showing high expression of these two proteins in rapidly proliferating cells. However, Table 1 also reveals only a weak correlation between LDLR and proliferation. As these proteins form a ternary complex, it is important to explain why all three proteins are not strongly correlated with proliferation. We propose the following explanation: The Nobel Prize in Physiology or Medicine in 1985 was awarded jointly to Michael S. Brown and Joseph L. Goldstein for their discoveries about the regulation of cholesterol metabolism, which includes LDLR-mediated internalization. In normal tissues and quiescent tumor cells, this mechanism explains how cells take up LDL. However, in proliferating tumor cells, the demand for cholesterol surpasses the capacity of the Brown and Goldstein mechanism.

As a result, tumor cells have developed an alternative mechanism that increases the internalization rate of LDL. This is when the sigma-2–PGRMC1–LDL complex becomes crucial, as it can enhance the rate of internalization by up to tenfold (Fig. 5). Thus, we propose a revision to the Brown and Goldstein mechanism to include a secondary pathway for cholesterol internalization utilized by rapidly proliferating cells. We refer to this as the “skip-the-line” mechanism because the ternary complex offers cholesterol a modified pathway of receptor mediated endocytosis to provide the heightened demand for cholesterol to support cell proliferation. TMEM97 and PGRMC1 are upregulated whereas the LDLR is not since the balance between the Brown and Goldstein mechanism and the "skip the line" mechanism is determined by the density of TMEM97 and PGRMC1 in the cell membrane. This observation aligns with prior studies showing that the activation of LDLR-mediated cholesterol influx is linked to cancer cell growth (92). The mechanisms underlying cholesterol biosynthesis and uptake in relation to cancer progression remain largely unclear. Therefore, further mechanistic studies, both in vivo and in vitro, are needed in addition to population-based epidemiologic data to better understand the role of cholesterol in cancer development.

Figure 5

Proposed mechanism of a secondary pathway for cholesterol internalization utilized by rapidly proliferating cells demonstrating an “accelerated rate mechanism” of receptor mediated endocytosis. Created in BioRender. McDonald, E. (2024) https://BioRender.com/i39x933.

Figure 5

Proposed mechanism of a secondary pathway for cholesterol internalization utilized by rapidly proliferating cells demonstrating an “accelerated rate mechanism” of receptor mediated endocytosis. Created in BioRender. McDonald, E. (2024) https://BioRender.com/i39x933.

Close modal

The second important finding is that PGRMC1 is associated with proliferation. The clinical significance of the association of PGRMC1 with proliferation includes the ongoing investigations into why one arm of the Women’s Health Initiative, including women treated with combination estrogen/progestin, had an increased risk for developing breast cancer versus the estrogen-only arm (93). PGRMC1 involvement in steroidogenesis, P4 responses in the nervous system, and cells associated with the female reproductive system are extensively established (9496), and it has been postulated that PGRMC1 mediated the increased risk of breast cancer in the estrogen/progesterone arm via activation by synthetic progestin. There is evidence from cultured breast cancer cells and xenograft studies in mice to support this hypothesis (13, 16, 9799). Interestingly, PGRMC1 shows a stronger correlation with proliferation in ER− cells, whereas TMEM97 is more closely associated with proliferation in ER+ cells. This raises questions about how ER status fits into the broader context of tumor proliferation and cholesterol transport. Notably, TMEM97 is generally more strongly correlated with proliferation, and variations in proliferation rates are likely more significant in ER+ tumors, as these tumors exhibit a wide range of proliferation rates that can influence both treatment response and survival outcomes. One potential mechanism by which the more aggressive ER+ subgroup may overcome barriers to proliferation could involve cholesterol transport, a mechanism that might be less critical in ER− tumors. In contrast, TNBC tumors tend to have more uniform proliferation rates.

In a prior publication (36), we demonstrated in cells that there is more PGRMC1 not complexed with TMEM97 than TMEM97 not complexed with PGRMC1. This observation suggests that PGRMC1 performs multiple functions within the cell, with its complex formation with TMEM97 and the LDLR representing just one of its roles. In contrast, TMEM97 may primarily function in conjunction with PGRMC1 and LDLR to facilitate LDL transport, which could explain why TMEM97 has a stronger correlation with proliferation. This hypothesis warrants further investigation, as the specific functions of both proteins remain poorly understood. The role of PGRMC1 in supporting increased cholesterol demand may be more tightly tied to proliferation than the other functions of PGRMC1.

The role of cholesterol trafficking in the proliferation of human breast cancer is poorly understood, with some evidence that SERM inhibit angiogenesis independent of ERs, with that mechanism being partially attributed to inhibiting cholesterol trafficking in endothelial cells (9). Our data demonstrate that differential expression of PGRMC1 in human breast cancer is a function of cell proliferation, as well as breast cancer receptor and molecular subtypes, and further reveal an association between PGRMC1 and cell-cycle markers. Although the mechanisms underlying the association of PGRMC1 with proliferation are unknown, potential effector pathways associated with PGRMC1 expression in breast cancer provide a possible explanation (Supplementary Table S4). For example, increased cyclin D1 and MYC pathway activities were each correlated with PGRMC1 expression. Cyclin D1 is an oncogene that is frequently amplified in human breast cancer, regulates cell-cycle progression, and is associated with chemoresistance (100) and decreased overall survival in patients with ER+ breast cancers (101). Like cyclin D1, c-MYC is an oncogene that regulates cell growth and cell proliferation at the G1 transition (102), and its amplification is associated with aggressive tumor behavior and poor outcome in patients with breast cancer (103). PGRMC1 was also associated with fourfold lower levels of RICTOR pathway activity. RICTOR is a subunit of the mTOR complex 2 that promotes proliferation through Akt/PKB signaling (104), which in turn regulates mTORC1, a cell-cycle progression factor implicated in resistance to endocrine therapy (105, 106).

A strength of this study is that publicly available data were leveraged to analyze a large number of invasive breast cancers, with the power to detect correlations that can guide further studies at the protein level. Limitations of our study include that mRNA expression may not accurately reflect protein levels, which have greater biological significance, that proteins may undergo posttranslational modifications, such as phosphorylation, that could affect ligand binding, and that protein subcellular localization might differ in tumors compared with normal tissue.

In summary, each component of the PGRMC1, TMEM97, and LDLR complex is a breast cancer biomarker associated with cellular proliferation. This should help guide in vitro and in vivo studies exploring them in the context of additional markers of proliferation. These data also inform the clinical use of 18F-ISO-1 in breast cancer, in which 18F-ISO-1 correlated with Ki-67, providing independent clinical trial data supporting the association of a component of the trimeric complex with breast cancer proliferation (39). 18F-FLT and 18F-ISO-1 PET/CT have the potential to serve as a clinically translatable approach for predicting and monitoring response to combinatorial CDK4/6 inhibitors and endocrine therapy in patients with ER+ breast cancer, with 18F-FLT measuring immediate changes in the S-phase as a predominate effect of targeting CDK4/6, providing a very early prediction of tumor response, and 18F-ISO-1 assessing delayed changes reflecting cell-cycle arrest and transition to quiescence (35). This work exploring the role of PGRMC1–TMEM97–LDLR in breast cancer demonstrates the importance of further research evaluating how proliferation interplays with cholesterol metabolism in malignant transformation or propagation.

E.S. McDonald reports grants from the Susan G. Komen Foundation, American Roentgen Ray Society, and Abramson Cancer Center Pilot Grant during the conduct of the study, as well as grants from NCI, Pennsylvania Breast Cancer Coalition, and Department of Defense office of the Congressionally Directed Medical Research Programs. R.H. Mach reports being a cofounder of Accuronix Therapeutics, a small business that is commercializing a therapeutic targeting the sigma-2 receptor/TMEM97 protein. No disclosures were reported by the other authors.

E.S. McDonald: Conceptualization, resources, data curation, supervision, funding acquisition, writing–original draft, project administration, writing–review and editing. T. C. Pan: Data curation, formal analysis, writing–original draft. D.K. Pant: Data curation, formal analysis, writing–original draft. M.A. Troester: Validation, investigation, writing–review and editing. A.V. Kossenkov: Data curation, formal analysis, writing–original draft. D.A. Mankoff: Conceptualization, resources, writing–review and editing. R.H. Mach: Conceptualization, investigation, methodology, writing–review and editing. L.A. Chodosh: Resources, data curation, supervision, funding acquisition, writing–review and editing.

This work was supported by NCI grants CA164490, CA148774, CA98371, CA127917, CA277541, CA259037 and CA143296; P30 CA016520; Komen Leadership grant SAC140060 and Komen CCR16376362; PA Breast Cancer Coalition 2023 Award; Department of Energy training grant DE-SE0012476; and the National Center for Research Resources and the National Center for Advancing Translational Sciences, NIH, through grant UL1TR000003. The results published here are in part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/.

Note: Supplementary data for this article are available at Cancer Research Communications Online (https://aacrjournals.org/cancerrescommun/).

1.
Gabitova
L
,
Gorin
A
,
Astsaturov
I
.
Molecular pathways: sterols and receptor signaling in cancer
.
Clin Cancer Res
2014
;
20
:
28
34
.
2.
Cedo
L
,
Reddy
ST
,
Mato
E
,
Blanco-Vaca
F
,
Escola-Gil
JC
.
HDL and LDL: potential new players in breast cancer development
.
J Clin Med
2019
;
8
:
853
.
3.
Johnson
KE
,
Siewert
KM
,
Klarin
D
,
Damrauer
SM
,
VA Million Veteran Program
;
Chang
KM
,
Tsao
PS
, et al
.
The relationship between circulating lipids and breast cancer risk: a Mendelian randomization study
.
PLoS Med
2020
;
17
:
e1003302
.
4.
Liu
B
,
Yi
Z
,
Guan
X
,
Zeng
Y-X
,
Ma
F
.
The relationship between statins and breast cancer prognosis varies by statin type and exposure time: a meta-analysis
.
Breast Cancer Res Treat
2017
;
164
:
1
11
.
5.
Zhao
G
,
Ji
Y
,
Ye
Q
,
Ye
X
,
Wo
G
,
Chen
X
, et al
.
Effect of statins use on risk and prognosis of breast cancer: a meta-analysis
.
Anticancer Drugs
2022
;
33
:
e507
18
.
6.
Chen
Z
,
Wu
P
,
Wang
J
,
Chen
P
,
Fang
Z
,
Luo
F
.
The association of statin therapy and cancer: a meta-analysis
.
Lipids Health Dis
2023
;
22
:
192
.
7.
McKechnie
T
,
Brown
Z
,
Lovrics
O
,
Yang
S
,
Kazi
T
,
Eskicioglu
C
, et al
.
Concurrent use of statins in patients undergoing curative intent treatment for triple negative breast cancer: a systematic review and meta-analysis
.
Clin Breast Cancer
2024
;
24
:
e103
115
.
8.
Blackwell
KL
,
Haroon
ZA
,
Shan
S
,
Saito
W
,
Broadwater
G
,
Greenberg
CS
, et al
.
Tamoxifen inhibits angiogenesis in estrogen receptor-negative animal models
.
Clin Cancer Res
2000
;
6
:
4359
64
.
9.
Shim
JS
,
Li
R-J
,
Lv
J
,
Head
SA
,
Yang
EJ
,
Liu
JO
.
Inhibition of angiogenesis by selective estrogen receptor modulators through blockade of cholesterol trafficking rather than estrogen receptor antagonism
.
Cancer Lett
2015
;
362
:
106
15
.
10.
Xu
J
,
Dang
Y
,
Ren
YR
,
Liu
JO
.
Cholesterol trafficking is required for mTOR activation in endothelial cells
.
Proc Natl Acad Sci U S A
2010
;
107
:
4764
9
.
11.
Fang
L
,
Choi
S-H
,
Baek
JS
,
Liu
C
,
Almazan
F
,
Ulrich
F
, et al
.
Control of angiogenesis by AIBP-mediated cholesterol efflux
.
Nature
2013
;
498
:
118
22
.
12.
Lyu
J
,
Yang
EJ
,
Shim
JS
.
Cholesterol trafficking: an emerging therapeutic target for angiogenesis and cancer
.
Cells
2019
;
8
:
389
.
13.
Pru
JK
.
Pleiotropic actions of PGRMC proteins in cancer
.
Endocrinology
2022
;
163
:
bqac078
.
14.
Clark
NC
,
Friel
AM
,
Pru
CA
,
Zhang
L
,
Shioda
T
,
Rueda
BR
, et al
.
Progesterone receptor membrane component 1 promotes survival of human breast cancer cells and the growth of xenograft tumors
.
Cancer Biol Ther
2016
:
17
,
262
71
.
15.
Zhou
J
,
Yu
Q
,
Chen
R
,
Seeger
H
,
Fehm
T
,
Cahill
MA
, et al
.
Medroxyprogesterone acetate-driven increase in breast cancer risk might be mediated via cross-talk with growth factors in the presence of progesterone receptor membrane component-1
.
Maturitas
2013
;
76
:
129
33
.
16.
Neubauer
H
,
Ruan
X
,
Schneck
H
,
Seeger
H
,
Cahill
MA
,
Liang
Y
, et al
.
Overexpression of progesterone receptor membrane component 1: possible mechanism for increased breast cancer risk with norethisterone in hormone therapy
.
Menopause
2013
;
20
:
504
10
.
17.
Hellewell
SB
,
Bruce
A
,
Feinstein
G
,
Orringer
J
,
Williams
W
,
Bowen
WD
.
Rat liver and kidney contain high densities of sigma 1 and sigma 2 receptors: characterization by ligand binding and photoaffinity labeling
.
Eur J Pharmacol
1994
;
268
:
9
18
.
18.
Gebreselassie
D
,
Bowen
WD
.
Sigma-2 receptors are specifically localized to lipid rafts in rat liver membranes
.
Eur J Pharmacol
2004
;
493
:
19
28
.
19.
Zeng
C
,
Vangveravong
S
,
Xu
J
,
Chang
KC
,
Hotchkiss
RS
,
Wheeler
KT
, et al
.
Subcellular localization of sigma-2 receptors in breast cancer cells using two-photon and confocal microscopy
.
Cancer Res
2007
;
67
:
6708
16
.
20.
Bem
WT
,
Thomas
GE
,
Mamone
JY
,
Homan
SM
,
Levy
BK
,
Johnson
FE
, et al
.
Overexpression of sigma receptors in nonneural human tumors
.
Cancer Res
1991
;
51
:
6558
62
.
21.
Vilner
BJ
,
Bowen
WD
.
Sigma receptor-active neuroleptics are cytotoxic to C6 glioma cells in culture
.
Eur J Pharmacol
1993
;
244
:
199
201
.
22.
Vilner
BJ
,
John
CS
,
Bowen
WD
.
Sigma-1 and sigma-2 receptors are expressed in a wide variety of human and rodent tumor cell lines
.
Cancer Res
1995
;
55
:
408
13
.
23.
Sun
T
,
Wang
Y
,
Wang
Y
,
Xu
J
,
Zhao
X
,
Vangveravong
S
, et al
.
Using SV119-gold nanocage conjugates to eradicate cancer stem cells through a combination of photothermal and chemo therapies
.
Adv Healthc Mater
2014
;
3
:
1283
91
.
24.
Mir
SU
,
Ahmed
IS
,
Arnold
S
,
Craven
RJ
.
Elevated progesterone receptor membrane component 1/sigma-2 receptor levels in lung tumors and plasma from lung cancer patients
.
Int J Cancer
2012
;
131
:
E1
9
.
25.
Hashim
YM
,
Spitzer
D
,
Vangveravong
S
,
Hornick
MC
,
Garg
G
,
Hornick
JR
, et al
.
Targeted pancreatic cancer therapy with the small molecule drug conjugate SW IV-134
.
Mol Oncol
2014
;
8
:
956
67
.
26.
Hashim
YM
,
Vangveravong
S
,
Sankpal
NV
,
Binder
PS
,
Liu
J
,
Goedegebuure
SP
, et al
.
The targeted SMAC mimetic SW IV-134 is a strong enhancer of standard chemotherapy in pancreatic cancer
.
J Exp Clin Cancer Res
2017
;
36
:
14
.
27.
Kashiwagi
H
,
McDunn
JE
,
Simon
PO
Jr
,
Goedegebuure
PS
,
Vangveravong
S
,
Chang
K
, et al
.
Sigma-2 receptor ligands potentiate conventional chemotherapies and improve survival in models of pancreatic adenocarcinoma
.
J Transl Med
2009
;
7
:
24
.
28.
Jin
J
,
Arbez
N
,
Sahn
JJ
,
Lu
Y
,
Linkens
KT
,
Hodges
TR
, et al
.
Neuroprotective effects of σ2R/TMEM97 receptor modulators in the neuronal model of Huntington’s disease
.
ACS Chem Neurosci
2022
;
13
:
2852
62
.
29.
Sahn
JJ
,
Mejia
GL
,
Ray
PR
,
Martin
SF
,
Price
TJ
.
Sigma 2 receptor/Tmem97 agonists produce long lasting antineuropathic pain effects in mice
.
ACS Chem Neurosci
2017
;
8
:
1801
11
.
30.
Yi
B
,
Sahn
JJ
,
Ardestani
PM
,
Evans
AK
,
Scott
LL
,
Chan
JZ
, et al
.
Small molecule modulator of sigma 2 receptor is neuroprotective and reduces cognitive deficits and neuroinflammation in experimental models of Alzheimer’s disease
.
J Neurochem
2017
;
140
:
561
75
.
31.
Weng
CC
,
Riad
A
,
Lieberman
BP
,
Xu
K
,
Peng
X
,
Mikitsh
JL
, et al
.
Characterization of sigma-2 receptor-specific binding sites using [(3)H]DTG and [(125)I]RHM-4
.
Pharmaceuticals (Basel)
2022
;
15
,
1564
.
32.
Sai
KK
,
Jones
LA
,
Mach
RH
.
Development of (18)F-labeled PET probes for imaging cell proliferation
.
Curr Top Med Chem
2013
;
13
:
892
908
.
33.
Shoghi
KI
,
Xu
J
,
Su
Y
,
He
J
,
Rowland
D
,
Yan
Y
, et al
.
Quantitative receptor-based imaging of tumor proliferation with the sigma-2 ligand [(18)F]ISO-1
.
PLoS One
2013
;
8
:
e74188
.
34.
Dehdashti
F
,
Laforest
R
,
Gao
F
,
Shoghi
KI
,
Aft
RL
,
Nussenbaum
B
, et al
.
Assessment of cellular proliferation in tumors by PET using 18F-ISO-1
.
J Nucl Med
2013
;
54
:
350
7
.
35.
Elmi
A
,
Makvandi
M
,
Weng
CC
,
Hou
C
,
Clark
AS
,
Mach
RH
, et al
.
Cell-proliferation imaging for monitoring response to CDK4/6 inhibition combined with endocrine-therapy in breast cancer: comparison of [18F]FLT and [18F]ISO-1 PET/CT
.
Clin Cancer Res
2019
;
25
:
3063
73
.
36.
Riad
A
,
Zeng
C
,
Weng
C-C
,
Winters
H
,
Xu
K
,
Makvandi
M
, et al
.
Sigma-2 receptor/TMEM97 and PGRMC-1 increase the rate of internalization of LDL by LDL receptor through the formation of a ternary complex
.
Sci Rep
2018
;
8
:
16845
.
37.
Riad
A
,
Lengyel-Zhand
Z
,
Zeng
C
,
Weng
CC
,
Lee
VM
,
Trojanowski
JQ
, et al
.
The sigma-2 receptor/TMEM97, PGRMC1, and LDL receptor complex are responsible for the cellular uptake of Aβ42 and its protein aggregates
.
Mol Neurobiol
2020
;
57
:
3803
13
.
38.
Bartz
F
,
Kern
L
,
Erz
D
,
Zhu
M
,
Gilbert
D
,
Meinhof
T
, et al
.
Identification of cholesterol-regulating genes by targeted RNAi screening
.
Cell Metab
2009
;
10
:
63
75
.
39.
McDonald
ES
,
Doot
RK
,
Young
AJ
,
Schubert
EK
,
Tchou
J
,
Pryma
DA
, et al
.
Breast cancer 18F-ISO-1 uptake as a marker of proliferation status
.
J Nucl Med
2020
;
61
:
665
70
.
40.
Parker
JS
,
Mullins
M
,
Cheang
MCU
,
Leung
S
,
Voduc
D
,
Vickery
T
, et al
.
Supervised risk predictor of breast cancer based on intrinsic subtypes
.
J Clin Oncol
2009
;
27
:
1160
7
.
41.
Whitfield
ML
,
Sherlock
G
,
Saldanha
AJ
,
Murray
JI
,
Ball
CA
,
Alexander
KE
, et al
.
Identification of genes periodically expressed in the human cell cycle and their expression in tumors
.
Mol Biol Cell
2002
;
13
:
1977
2000
.
42.
Chang
HY
,
Sneddon
JB
,
Alizadeh
AA
,
Sood
R
,
West
RB
,
Montgomery
K
, et al
.
Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds
.
PLoS Biol
2004
;
2
:
E7
.
43.
Baldi
P
,
Long
AD
.
A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes
.
Bioinformatics
2001
;
17
:
509
19
.
44.
Wertheim
GBW
,
Yang
TW
,
Pan
T-C
,
Ramne
A
,
Liu
Z
,
Gardner
HP
, et al
.
The Snf1-related kinase, Hunk, is essential for mammary tumor metastasis
.
Proc Natl Acad Sci U S A
2009
;
106
:
15855
60
.
45.
Ramasamy
A
,
Mondry
A
,
Holmes
CC
,
Altman
DG
.
Key issues in conducting a meta-analysis of gene expression microarray datasets
.
PLoS Med
2008
;
5
:
e184
.
46.
Cochran
WG
.
The combination of estimates from different experiments
.
Biometrics
1954
;
10
:
101
.
47.
DerSimonian
R
,
Laird
N
.
Meta-analysis in clinical trials
.
Control Clin Trials
1986
;
7
:
177
88
.
48.
Whitehead
A
,
Whitehead
J
.
A general parametric approach to the meta-analysis of randomized clinical trials
.
Stat Med
1991
;
10
:
1665
77
.
49.
Rubin
MA
,
Chinnaiyan
AM
.
Bioinformatics approach leads to the discovery of the TMPRSS2:ETS gene fusion in prostate cancer
.
Lab Invest
2006
;
86
:
1099
102
.
50.
Chin
K
,
DeVries
S
,
Fridlyand
J
,
Spellman
PT
,
Roydasgupta
R
,
Kuo
W-L
, et al
.
Genomic and transcriptional aberrations linked to breast cancer pathophysiologies
.
Cancer Cell
2006
;
10
:
529
41
.
51.
Esserman
LJ
,
Berry
DA
,
Cheang
MC
,
Yau
C
,
Perou
CM
,
Carey
L
, et al
.
Chemotherapy response and recurrence-free survival in neoadjuvant breast cancer depends on biomarker profiles: results from the I-SPY 1 TRIAL (CALGB 150007/150012; ACRIN 6657)
.
Breast Cancer Res Treat
2012
;
132
:
1049
62
.
52.
Hess
KR
,
Anderson
K
,
Symmans
WF
,
Valero
V
,
Ibrahim
N
,
Mejia
JA
, et al
.
Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer
.
J Clin Oncol
2006
;
24
:
4236
44
.
53.
Popovici
V
,
Chen
W
,
Gallas
BG
,
Hatzis
C
,
Shi
W
,
Samuelson
FW
, et al
.
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors
.
Breast Cancer Res
2010
;
12
:
R5
.
54.
Saal
LH
,
Johansson
P
,
Holm
K
,
Gruvberger-Saal
SK
,
She
QB
,
Maurer
M
, et al
.
Poor prognosis in carcinoma is associated with a gene expression signature of aberrant PTEN tumor suppressor pathway activity
.
Proc Natl Acad Sci U S A
2007
;
104
:
7564
9
.
55.
Chang
HY
,
Nuyten
DS
,
Sneddon
JB
,
Hastie
T
,
Tibshirani
R
,
Sørlie
T
, et al
.
Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival
.
Proc Natl Acad Sci U S A
2005
;
102
:
3738
43
.
56.
Chanrion
M
,
Negre
V
,
Fontaine
H
,
Salvetat
N
,
Bibeau
F
,
Mac Grogan
G
, et al
.
A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer
.
Clin Cancer Res
2008
;
14
:
1744
52
.
57.
Curtis
C
,
Shah
SP
,
Chin
SF
,
Turashvili
G
,
Rueda
OM
,
Dunning
MJ
, et al
.
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
.
Nature
2012
;
486
:
346
52
.
58.
Desmedt
C
,
Piette
F
,
Loi
S
,
Wang
Y
,
Lallemand
F
,
Haibe-Kains
B
, et al
.
Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series
.
Clin Cancer Res
2007
;
13
:
3207
14
.
59.
Ivshina
AV
,
George
J
,
Senko
O
,
Mow
B
,
Putti
TC
,
Smeds
J
, et al
.
Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer
.
Cancer Res
2006
;
66
:
10292
301
.
60.
Ma
XJ
,
Wang
Z
,
Ryan
PD
,
Isakoff
SJ
,
Barmettler
A
,
Fuller
A
, et al
.
A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen
.
Cancer Cell
2004
;
5
:
607
16
.
61.
Minn
AJ
,
Gupta
GP
,
Padua
D
,
Bos
P
,
Nguyen
DX
,
Nuyten
D
, et al
.
Lung metastasis genes couple breast tumor size and metastatic spread
.
Proc Natl Acad Sci U S A
2007
;
104
:
6740
5
.
62.
Oh
DS
,
Troester
MA
,
Usary
J
,
Hu
Z
,
He
X
,
Fan
C
, et al
.
Estrogen-regulated genes predict survival in hormone receptor-positive breast cancers
.
J Clin Oncol
2006
;
24
:
1656
64
.
63.
Pawitan
Y
,
Bjöhle
J
,
Amler
L
,
Borg
AL
,
Egyhazi
S
,
Hall
P
, et al
.
Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts
.
Breast Cancer Res
2005
;
7
:
R953
64
.
64.
Sabatier
R
,
Finetti
P
,
Cervera
N
,
Lambaudie
E
,
Esterni
B
,
Mamessier
E
, et al
.
A gene expression signature identifies two prognostic subgroups of basal breast cancer
.
Breast Cancer Res Treat
2011
;
126
:
407
20
.
65.
Schmidt
M
,
Böhm
D
,
von Törne
C
,
Steiner
E
,
Puhl
A
,
Pilch
H
, et al
.
The humoral immune system has a key prognostic impact in node-negative breast cancer
.
Cancer Res
2008
;
68
:
5405
13
.
66.
Sotiriou
C
,
Wirapati
P
,
Loi
S
,
Harris
A
,
Fox
S
,
Smeds
J
, et al
.
Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis
.
J Natl Cancer Inst
2006
;
98
:
262
72
.
67.
Wang
Y
,
Klijn
JG
,
Zhang
Y
,
Sieuwerts
AM
,
Look
MP
,
Yang
F
, et al
.
Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer
.
Lancet
2005
;
365
:
671
9
.
68.
Irizarry
RA
,
Hobbs
B
,
Collin
F
,
Beazer-Barclay
YD
,
Antonellis
KJ
,
Scherf
U
, et al
.
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
.
Biostatistics
2003
;
4
:
249
64
.
69.
Zhou
J
,
He
E
,
Skog
S
.
The proliferation marker thymidine kinase 1 in clinical use
.
Mol Clin Oncol
2013
;
1
:
18
28
.
70.
Brockenbrough
JS
,
Souquet
T
,
Morihara
JK
,
Stern
JE
,
Hawes
SE
,
Rasey
JS
, et al
.
Tumor 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) uptake by PET correlates with thymidine kinase 1 expression: static and kinetic analysis of (18)F-FLT PET studies in lung tumors
.
J Nucl Med
2011
;
52
:
1181
8
.
71.
Rasey
JS
,
Grierson
JR
,
Wiens
LW
,
Kolb
PD
,
Schwartz
JL
.
Validation of FLT uptake as a measure of thymidine kinase-1 activity in A549 carcinoma cells
.
J Nucl Med
2002
;
43
:
1210
7
.
72.
McKinley
ET
,
Ayers
GD
,
Smith
RA
,
Saleh
SA
,
Zhao
P
,
Washington
MK
, et al
.
Limits of [18F]-FLT PET as a biomarker of proliferation in oncology
.
PLoS One
2013
;
8
:
e58938
.
73.
Muzi
M
,
Mankoff
DA
,
Grierson
JR
,
Wells
JM
,
Vesselle
H
,
Krohn
KA
.
Kinetic modeling of 3'-deoxy-3'-fluorothymidine in somatic tumors: mathematical studies
.
J Nucl Med
2005
;
46
:
371
80
.
74.
Vesselle
H
,
Grierson
J
,
Muzi
M
,
Pugsley
JM
,
Schmidt
RA
,
Rabinowitz
P
, et al
.
In vivo validation of 3'deoxy-3'-[(18)F]fluorothymidine ([(18)F]FLT) as a proliferation imaging tracer in humans: correlation of [(18)F]FLT uptake by positron emission tomography with Ki-67 immunohistochemistry and flow cytometry in human lung tumors
.
Clin Cancer Res
2002
;
8
:
3315
23
.
75.
Bading
JR
,
Shields
AF
.
Imaging of cell proliferation: status and prospects
.
J Nucl Med
2008
;
49
(
Suppl 2
):
64S
80S
.
76.
Mankoff
DA
,
Shields
AF
,
Krohn
KA
.
PET imaging of cellular proliferation
.
Radiol Clin North Am
2005
;
43
:
153
67
.
77.
Shields
AF
,
Grierson
JR
,
Dohmen
BM
,
Machulla
HJ
,
Stayanoff
JC
,
Lawhorn-Crews
JM
, et al
.
Imaging proliferation in vivo with [F-18]FLT and positron emission tomography
.
Nat Med
1998
;
4
:
1334
6
.
78.
Kostakoglu
L
,
Duan
F
,
Idowu
MO
,
Jolles
PR
,
Bear
HD
,
Muzi
M
, et al
.
A phase II study of 3'-deoxy-3'-18F-fluorothymidine PET in the assessment of early response of breast cancer to neoadjuvant chemotherapy: results from ACRIN 6688
.
J Nucl Med
2015
;
56
:
1681
9
.
79.
Margolin
AA
,
Bilal
E
,
Huang
E
,
Norman
TC
,
Ottestad
L
,
Mecham
BH
, et al
.
Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer
.
Sci Transl Med
2013
;
5
:
181re1
.
80.
Asperger
H
,
Stamm
N
,
Gierke
B
,
Pawlak
M
,
Hofmann
U
,
Zanger
UM
, et al
.
Progesterone receptor membrane component 1 regulates lipid homeostasis and drives oncogenic signaling resulting in breast cancer progression
.
Breast Cancer Res
2020
;
22
:
75
.
81.
Bai
Y
,
Ludescher
M
,
Poschmann
G
,
Stühler
K
,
Wyrich
M
,
Oles
J
, et al
.
PGRMC1 promotes progestin-dependent proliferation of breast cancer cells by binding prohibitins resulting in activation of ERα signaling
.
Cancers (Basel)
2021
;
13
:
5635
.
82.
Ruan
X
,
Zhang
Y
,
Mueck
AO
,
Willibald
M
,
Seeger
H
,
Fehm
T
, et al
.
Increased expression of progesterone receptor membrane component 1 is associated with aggressive phenotype and poor prognosis in ER-positive and negative breast cancer
.
Menopause
2017
;
24
:
203
9
.
83.
Xu
X
,
Ruan
X
,
Zhang
Y
,
Cai
G
,
Ju
R
,
Yang
Y
, et al
.
Comprehensive analysis of the implication of PGRMC1 in triple-negative breast cancer
.
Front Bioeng Biotechnol
2021
;
9
:
714030
.
84.
Wu
X
,
Zhou
F
,
Ji
X
,
Ren
K
,
Shan
Y
,
Mao
X
, et al
.
The prognostic role of MAC30 in advanced gastric cancer patients receiving platinum-based chemotherapy
.
Future Oncol
2017
;
13
:
2691
6
.
85.
Han
K-Y
,
Gu
X
,
Wang
H-R
,
Liu
D
,
Lv
F-Z
,
Li
J-N
.
Overexpression of MAC30 is associated with poor clinical outcome in human non-small-cell lung cancer
.
Tumour Biol
2013
;
34
:
821
5
.
86.
Ding
H
,
Gui
X
,
Lin
X
,
Chen
R
,
Ma
T
,
Sheng
Y
, et al
.
The prognostic effect of MAC30 expression on patients with non-small cell lung cancer receiving adjuvant chemotherapy
.
Technol Cancer Res Treat
2017
;
16
:
645
53
.
87.
Ding
H
,
Gui
XH
,
Lin
XB
,
Chen
RH
,
Cai
HR
,
Fen
Y
, et al
.
Prognostic value of MAC30 expression in human pure squamous cell carcinomas of the lung
.
Asian Pac J Cancer Prev
2016
;
17
:
2705
10
.
88.
Yang
S
,
Li
H
,
Liu
Y
,
Ning
X
,
Meng
F
,
Xiao
M
, et al
.
Elevated expression of MAC30 predicts lymph node metastasis and unfavorable prognosis in patients with epithelial ovarian cancer
.
Med Oncol
2013
;
30
:
324
.
89.
McDonald
ES
,
Mankoff
DA
,
Mach
RH
.
Novel strategies for breast cancer imaging: new imaging agents to guide treatment
.
J Nucl Med
2016
;
57
(
Suppl 1
):
69S
74S
.
90.
Wheeler
KT
,
Wang
LM
,
Wallen
CA
,
Childers
SR
,
Cline
JM
,
Keng
PC
, et al
.
Sigma-2 receptors as a biomarker of proliferation in solid tumours
.
Br J Cancer
2000
;
82
:
1223
32
.
91.
Zeng
C
,
Vangveravong
S
,
Jones
LA
,
Hyrc
K
,
Chang
KC
,
Xu
J
, et al
.
Characterization and evaluation of two novel fluorescent sigma-2 receptor ligands as proliferation probes
.
Mol Imaging
2011
;
10
:
420
33
.
92.
Ding
X
,
Zhang
W
,
Li
S
,
Yang
H
.
The role of cholesterol metabolism in cancer
.
Am J Cancer Res
2019
;
9
:
219
27
.
93.
Rossouw
JE
,
Anderson
GL
,
Prentice
RL
,
LaCroix
AZ
,
Kooperberg
C
,
Stefanick
ML
, et al
.
Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women’s Health Initiative randomized controlled trial
.
JAMA
2002
;
288
:
321
33
.
94.
Cahill
MA
.
Unde venisti PGRMC? Grand-scale biology from early eukaryotes and eumetazoan animal origins
.
Front Biosci (Landmark Ed)
2022
;
27
:
317
.
95.
Cahill
MA
.
Quo vadis PGRMC? Grand-scale biology in human Health and disease
.
Front Biosci (Landmark Ed)
2022
;
27
:
318
.
96.
Cahill
MA
,
Jazayeri
JA
,
Catalano
SM
,
Toyokuni
S
,
Kovacevic
Z
,
Richardson
DR
.
The emerging role of progesterone receptor membrane component 1 (PGRMC1) in cancer biology
.
Biochim Biophys Acta
2016
;
1866
:
339
49
.
97.
Willibald
M
,
Bayer
G
,
Stahlhut
V
,
Poschmann
G
,
Stühler
K
,
Gierke
B
, et al
.
Progesterone receptor membrane component 1 is phosphorylated upon progestin treatment in breast cancer cells
.
Oncotarget
2017
;
8
:
72480
93
.
98.
Ruan
X
,
Neubauer
H
,
Yang
Y
,
Schneck
H
,
Schultz
S
,
Fehm
T
, et al
.
Progestogens and membrane-initiated effects on the proliferation of human breast cancer cells
.
Climacteric
2012
;
15
:
467
72
.
99.
Stanczyk
FZ
.
Can the increase in breast cancer observed in the estrogen plus progestin arm of the Women’s Health Initiative trial be explained by progesterone receptor membrane component 1?
Menopause
2011
;
18
:
833
4
.
100.
Zalatnai
A
,
Molnár
J
.
Review. Molecular background of chemoresistance in pancreatic cancer
.
In Vivo
2007
;
21
:
339
47
.
101.
Xu
XL
,
Chen
SZ
,
Chen
W
,
Zheng
WH
,
Xia
XH
,
Yang
HJ
, et al
.
The impact of cyclin D1 overexpression on the prognosis of ER-positive breast cancers: a meta-analysis
.
Breast Cancer Res Treat
2013
;
139
:
329
39
.
102.
Gomez-Roman
N
,
Grandori
C
,
Eisenman
RN
,
White
RJ
.
Direct activation of RNA polymerase III transcription by c-Myc
.
Nature
2003
;
421
:
290
4
.
103.
Deming
SL
,
Nass
SJ
,
Dickson
RB
,
Trock
BJ
.
C-myc amplification in breast cancer: a meta-analysis of its occurrence and prognostic relevance
.
Br J Cancer
2000
;
83
:
1688
95
.
104.
McDonald
PC
,
Oloumi
A
,
Mills
J
,
Dobreva
I
,
Maidan
M
,
Gray
V
, et al
.
Rictor and integrin-linked kinase interact and regulate Akt phosphorylation and cancer cell survival
.
Cancer Res
2008
;
68
:
1618
24
.
105.
Gnant
M
.
The role of mammalian target of rapamycin (mTOR) inhibition in the treatment of advanced breast cancer
.
Curr Oncol Rep
2013
;
15
:
14
23
.
106.
Vilquin
P
,
Villedieu
M
,
Grisard
E
,
Ben Larbi
S
,
Ghayad
SE
,
Heudel
PE
, et al
.
Molecular characterization of anastrozole resistance in breast cancer: pivotal role of the Akt/mTOR pathway in the emergence of de novo or acquired resistance and importance of combining the allosteric Akt inhibitor MK-2206 with an aromatase inhibitor
.
Int J Cancer
2013
;
133
:
1589
602
.
This open-access article is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.