Skip to main content
  • Research article
  • Open access
  • Published:

NILCO biomarkers in breast cancer from Chinese patients



Notch, IL-1 and leptin are known pro-angiogenic factors linked to breast cancer development, tumor aggressiveness and poor prognosis. A complex crosstalk between these molecules (NILCO) has been reported in breast cancer cell lines. However, whether NILCO biomarkers are differentially expressed in estrogen responsive (ER+), unresponsive (ER-) and triple negative (TNBC) breast cancer tissues is unknown.


Expression levels of nine NILCO and targets [Notch1, Notch4, JAG1, DLL4, VEGF, VEGFR2 (FLK-1), leptin, leptin receptor (OB-R) and interleukin-1 receptor type I (IL-1R tI)] were examined via immunohistochemistry in breast cancer tissue microarrays from Chinese patients (ER+, n=33; ER-, n=21; TNBC, n=13) and non-malignant breast tissue (n=5; Pantomics, Inc.) using a semi-quantitative analysis of intensity staining, HSCORE.


Categorical expression of NILCO and targets (+ or -) was similar among all cancer tissues. However, TNBC showed differential localization pattern of NILCO. TNBC showed fewer nuclei and cytoplasms positive for Notch4 and JAG1, but more cytoplasms positive for leptin. In addition, fewer TNBC stromas were positive for Notch1 and Notch4, but 100% of TNBC stromas were positive for VEGFR2. Moreover, TNBC had lower DLL4 and IL-1R tI expression. TNBC and ER- showed higher expression of EGFR, but lower expression of AR. Leptin and OB-R were detected in more than 61% of samples. Leptin positively correlated to OB-R, JAG1, VEGF, and marginally to IL-1R tI. Notch1 positively correlated to IL-1R tI. EGFR and Ki67 were positively associated to Notch1, but no associations of NILCO and targets with p53 were found.


Present data suggest that NILCO components are differentially expressed in breast cancer. TNBC showed distinctive patterns for NILCO expression and localization. The complex crosstalk between leptin, IL-1 and Notch could differentially drive breast cancer growth and angiogenesis. Furthermore, the analysis of NILCO and targets using Pathway Studio9 software (Ariadine Genomics) showed multiple molecular relationships that suggest NILCO has potential prognostic biomarker value in breast cancer.

Peer Review reports


Breast cancer is a heterogeneous disease with four major genetic-signature subtypes [1]. However, breast cancer can be broadly divided into two main groups: 1) Estrogen receptor positive (ER+) and triple negative breast cancer (TNBC: ER-, PR- and HER2-). The majority of breast cancers are ER+, respond to estrogens, and are commonly treated with anti-hormonal and HER2 (ErbB-2) targeted therapies. ER+ positive and HER2/neu+ breast cancer cells show suppressed Notch signaling, which is probably limited by the overwhelming proliferative and survival effects of ER and HER2-dependent pathways [2].

In contrast, TNBC is mostly dependent on growth factors [i.e., insulin, insulin-like growth factor-I (IGF-I) and adipokines] [3, 4]. This aggressive form of the disease accounts for 15% of all invasive breast cancers showing an acutely early onset. TNBC is associated with poor survival and resistance to common therapeutic treatments. This difficult-to-treat form of breast cancer shows a tendency to overcome drug effectiveness [5].

Notch signaling is a hallmark of breast cancer [6, 7]. The role of Notch in breast cancer development has long been studied, particularly relative to its effects on angiogenesis [8]. Notch expression correlates to poor prognosis and drug resistance of breast cancer patients [9, 10]. A particular feature of Notch signaling is its variable outcomes, which are dependent on cell and microenvironment types [7]. Ductal and lobular breast carcinomas show variable levels of Notch expression [2].

Notch signaling is a key mediator of proliferation, survival, and possibly malignant invasion of TNBC. These data suggest that TNBC is heavily dependent on Notch signaling [11]. In line with this notion, TNBC seems to differentially-activate Notch. Indeed, Notch1 and Notch4 are overexpressed in TNBC [12]. Moreover, in contrast to normal and ER+ breast cancer tissues, the activation of Notch in ER– breast cancer is linked to survivin upregulation (an apoptosis inhibitor and cell cycle regulator), which suggests ER- breast cancer cells are dependent on Notch-survivin signaling [13].

Recent data indicate that breast cancer development is likely related to lifestyle and the result of being overweight. Obesity is associated with more than 100,000 incidents of cancer in the United States every year, particularly cancers of the breast, colon, and endometrium. The specific molecular mechanisms involved in the development of obesity-related breast cancers are unknown. However, the general picture suggests that obesity-related breast cancer is the consequence of multi-factorial causes [14, 15]. Several molecules with altered patterns of expression in obesity are involved in breast cancer (i.e., insulin and IGF-1, and adipokines) [16]. Leptin, the major adipokine secreted by adipose tissue, is also produced by malignant cells, and linked to increased levels of Notch and survivin in breast cancer [1719], and can affect tumor angiogenesis [20]. Leptin signaling can influence pro-angiogenic, inflammatory and mitogenic events in breast cancer [2124].

We have previously unveiled a complex crosstalk between Notch, IL-1 and leptin (NILCO) in breast cancer cell lines, which could be essential for leptin-induced proliferation, inflammation and angiogenesis [17]. Moreover, a functional Notch-leptin axis is found in mouse carcinogenic-induced [18] and syngeneic breast cancer [19]. In these mouse models, leptin signaling induces both the expression and activation of Notch. However, it is unknown whether NILCO and its targets could correlate and/or are differentially-expressed in human TNBC, ER-, and ER+ breast cancer tissues.

We propose that specific associations between NILCO biomarkers occur in breast cancer, which may differ in TNBC. To this end, we examined the expression and cellular localization of NILCO, and targets, via immunohistochemistry in a commercial array of breast cancer biopsies from Chinese patients. Data were also analyzed in silico via Pathway Studio9 software (Ariadine Genomics, MD) [4]. Data analyses suggest that significant associations exist between NILCO and targets in breast cancer tissues. Higher levels of leptin and Notch1 were found in malignant compared to non-malignant tissues. TNBC showed lower levels of DLL4 and IL-1R tI compared to ER- and ER+ breast cancer. TNBC and their stromas showed differential cellular localization of Notch1, Notch4, JAG1, leptin and VEGFR2. Taken together, these results suggest that differential patterns of NILCO and targets are found in TNBC versus ER- and ER+ breast cancer. Present data support the idea for the potential use of NILCO and related molecules as biomarkers in breast cancer.


Reagents and antibodies

Polyclonal antibodies for Notch4, OB-R amino terminus, DLL4, IL-1 R tI, VEGF, VEGFR2, Jagged1 (JAG1) and leptin were obtained from Santa Cruz Biotechnology, Inc. (Santa Cruz, CA). Polyclonal anti-Notch2 and -Notch3 were from Abcam Inc. (Cambridge, MA). Monoclonal anti-Notch1 antibody and other chemicals were purchased from Sigma-Aldrich (St. Louis, MO). Vectastin ABC-APK and Vectamount were obtained from Vector Laboratories (Burlingame, CA). Hematoxilyn was purchased from Dako Corporation, Carpinteria, CA.

Tissue microarray

Breast cancer tissue arrays from female Chinese were obtained from Pantomics, Inc. (Richmond, CA). Biopsies features included age, grading, TNM staging, and receptor status of estrogen (ER), androgen (AR), progesterone (PR), epidermal growth factor receptors (ErbB1/EGFR/HER1 and ErbB2/HER2), and p53 and Ki67 expression data. However, no information on body weight of patients was available. Each slide contained 150 cores, including 75 cases in duplicate of normal/hyperplastic specimens (n=3), fibroadenomas (n=2), ductal carcinoma in situ (DCIS, n=2), Paget’s disease (n=1) and invasive carcinomas (ER+, n=33; ER-, n=21 and TNBC, n=13) showing diverse levels of PR and HER2 expression. The studies were focused on non-malignant (n=5) and invasive carcinoma samples (ER+, ER- and TNBC; n=67).

Immunohistochemisty (IHC)

IHC staining was performed on 12 separate microarray slides. The following specific antibodies were used to analyze nine antigens: anti-Notch1, Notch4, DLL4, JAG1, leptin, leptin receptor (OB-R), VEGF, VEGFR2 (FLK-1) and IL-1R tI. Staining patterns of 1206 tissue samples were evaluated by two independent observers in a blind manner. Three slides were used for negative controls (no primary antibody) incubated with secondary antibodies (anti-rabbit; anti-mouse and anti-goat-HRP, respectively; Vector Lab.).

HSCORE determination

Staining intensity of cells in tissue arrays was evaluated as negative or positive in three different bright fields (≥100 cells/field). Semi-quantitative HSCORE was calculated for each antigen using the following equation: HSCORE = ∑ pi(i +1), where “i” was the intensity with a value of 0, 1, 2, or 3 (negative, weak, moderate or strong, respectively) and “pi” was the percentage of stained cells for each intensity [25, 26].

In silicoanalysis of NILCO and targets interaction networks in breast cancer

Pathway Studio9 software (Elsevier, Ariadine Genomics, MD) was used to evaluate NILCO and its targets’ interactions in breast cancer tissue arrays. HSCORE of antigens showing significantly relationships in breast cancer were imported into the pathway software and analyzed.


HSCOREs for each antigen were determined twice, averaged, named A_HSCORE and used in the analyses. Pearson or Spearman correlation coefficients were used to compare the concordance between results from duplicate breast tissue samples and pairwise correlation between A_HSCOREs from the nine antigens analyzed in the microarray. The outcome was defined for three types of breast cancers according to the expression of ER, PR and HER2 (ER+, ER-and TNBC). Predictors were defined for A_HSCORE of nine antigens (continuous; 1–4) and dichotomized A_HSCORE (categorical: negative if HSCORE=1; positive otherwise). Covariate analyses were performed for p53, EGFR, Ki67, AR, grade, stage and age. The patients’ clinicopathological and histology characteristics, and the categorical and continuous A_HSCOREs were summarized for breast cancer and non-malignant disease patients. Univariate association between categorical HSCOREs and continuous A_HSCOREs for ER, PR, HER2, EGFR, AR, Ki67 and p53 expressions and grade and stage were compared by Chi-square or Fisher’s exact test. ANOVA (analysis of variance) was used to analyze age and Notch1 expression. Kruskal-Wallis test was also used to analyze continuous HSCOREs. All analyses were done using SAS 9.3 (SAS Institute, Inc.) with a significance level of 0.05.


Breast cancer tissue arrays

The clinicopathological and histological characteristics of non-malignant and breast cancer patients are summarized in Table 1. Invasive breast adenocarcinomas were assigned to three main groups according their expression of ER, PR and HER2 (i.e., ER+, ER- and TNBC). Age, grade, stage, Ki67, and p53 expression statuses were well-balanced among breast cancer tissues in the three groups (ER+, ER- and TNBC). However, ER, PR, HER2, EGFR and AR statuses were significantly different among them.

Table 1 Clinicopathological and histology characteristics of breast cancer tissue microarray samples

The majority of TNBC were grade III and stage III invasive ductal carcinomas. ER+ and ER- breast cancers were mainly invasive ductal carcinomas grade III and stage II (see Table 1). Approximately one third of TNBC and ER- breast cancers were positive for EGFR1. Notably, ER+ breast cancers were negative for EGFR1. Additionally, p53 expression was low in all breast cancers. In contrast, more than 50% of breast cancer samples were positive for Ki67 proliferation marker, which differed from non-malignant tissues (Table 1).

Detection of NILCO and targets in breast cancer tissue arrays

Figure 1 shows representative images of staining of the nine antigens detected in invasive breast carcinomas [n=67; TNBC (n=13; 19%) and ER+(n=33; 49%) and ER- breast cancers (n=21; 31%)] and non-malignant breast tissues (n=5). Other breast cancer types were not included. NILCO and targets were detected in invasive breast adenocarcinomas and non-malignant breast tissues. Majority of breast cancers were positive for Notch1, DLL4 and VEGF.

Figure 1
figure 1

Immunohistochemical (IHC) detection of NILCO and targets in tissue arrays from breast cancer and non-malignant patients. Representative pictures of immunohistochemical staining from breast cancer TNBC (A), ER+ (B), ER- (C) and non-malignant tissues (D) and, IHC negative controls (E) for the expression of NILCO and targets. Arrows show positive staining of NILCO components: Notch1 (a), Notch4 (b), DLL4 (c), JAG1 (d), leptin (e), OB-R (f), IL-1R tI (g) and targets VEGF (h) and VEGFR2 (i), respectively. No staining was found in IHC-negative controls using anti-mouse (j), anti-rabbit (k) and anti-goat (l) secondary antibodies. Magnification ×10.

Continuous A_HSCORE (positive or negative) of Notch1, Notch4, JAG1, OB-R, VEGF and, VEGFR2 were not significantly different among breast cancers irrespective of their expression of ER, HER2 and PR. Notch1, DLL-4 and, VEGF were detected in most malignant tissues. Leptin, OB-R, Notch4, JAG1 and, IL-1R tI were detected in more than 60% of breast cancer (ER+, ER- and, TNBC). In contrast, approximately 30% of these breast cancers showed immunoreactivity for VEGFR2. Leptin, OB-R, Notch1 and VEGFR2 were significantly lower in non-malignant tissues.

Remarkably, cellular localization of NILCO and targets was significantly different in TNBC compared to ER- and ER+ breast cancer (Table 2). A lower number of TNBC showed nuclear and cytoplasmatic staining for Notch4 and JAG1, but more TNBC cells showed cytoplasmatic staining for leptin. Additionally, VEGFR2 was mainly found in TNBC stromas, but fewer showed Notch1 and Notch4 staining (Table 2).

Table 2 Cellular localization of NILCO and targets within TNBC, ER+ and ER- breast cancer tissues

Associations of HSCOREs of NILCO and targets with breast cancer type

Evaluation of A_HSCOREs showed diverse grade of expression of NILCO and targets in ER+, ER- and, TNBC (Table 3). A_HSCOREs of antigens in malignant and non-malignant tissues were used to calculate their univariate associations with breast cancer types.

Table 3 Univariate associations of A_HSCORE of NILCO and targets with TNBC, ER+and ER- breast cancer tissues

Expression of DLL4 and IL-1R tI were significantly different among the three breast cancer types (p=0.028 and 0.027, respectively; Table 3). Remarkably, TNBC showed the lowest levels of DLL4 and IL-1R tI.

Correlations of NILCO and targets in breast cancer

Table 4 shows the pairwise analysis of continuous A_HSCOREs for the nine antigens in all invasive breast cancer tissues evaluated. Several correlations between NILCO and its targets were found in breast cancer tissue arrays. Positive detection of leptin within breast cancer tissues significantly correlated to higher levels of IL-1R tI, VEGF, and, OB-R. Additionally, OB-R was positively correlated to VEGF. Similarly, Notch ligands JAG1 and DLL4 positively correlated to leptin, OB-R, and, VEGF and OB-R expression, respectively. Furthermore, Notch receptors (Notch1 and Notch4) correlated to IL-1R tI and DLL4, respectively, whereas leptin was negatively correlated to VEGFR2.

Table 4 Pairwise correlation for NILCO in breast cancer tissue array

Associations of A_HSCOREs of NILCO and targets with EGFR, AR, Ki67 and p53 expression

Table 5 shows the analysis of univariate associations of categorized and continuous A-HSCOREs of NILCO and targets in breast cancer tissue arrays with EGFR and AR expression. Notch1 expression was associated to EGFR1 expression (p=0.018; Table 5). However, the expression of EGFR1 was not significantly associated with the positive or negative detection (categorized A_HSCOREs) of NILCO or targets. DLL4, Notch1, IL-1R tI and VEGF were expressed in almost all breast cancer tissues irrespective of AR status (Table 5). In contrast, IL-1R tI expression was associated with AR expression (p=0.026; Table 5). All TNBC tissues analyzed were negative for AR.

Table 5 Univariate associations of HSCORE for NILCO and targets with EGFR and AR

Table 6 shows the analysis of univariate associations of categorized and continuous A-HSCOREs of NILCO and targets in breast cancer tissue arrays with Ki67 and p53 expression. The analysis of univariate association of categorized and continuous A_HSCOREs of NILCO and targets showed that Notch1 and JAG1 expression were significantly higher in breast cancers positive for Ki67 (p=0.01 and p=0.004 respectively; Table 6). In contrast, continuous A_HSCORE of VEGF was marginally higher and negatively associated to Ki67 expression (p=0.056, Table 6). An inverse association of VEGFR2 with Ki67 positive staining was found in breast cancer tissue arrays (p=0.026, Table 6). Univariate association analysis of A_HSCOREs of NILCO and its targets did not show significant differences with p53 expression in breast tissue arrays (Table 6). Nevertheless, no differences in Ki67 or p53 reactivity were found among ER+, ER- and, TNBC (see Table 1).

Table 6 Univariate associations of HSCORE for NILCO and targets with Ki67 and p53

Pathway studio analyses

In silico analysis of relationships between NILCO and its targets were performed using Pathway Studio9 software. Analysis of data published on expression of Notch, leptin, OB-R, IL-1R tI, and VEGF/VEGFR2 in breast cancer showed several correlations with tumor progression/angiogenesis. The software identified 1626 references reporting 160 connectivity hits that include regulation, biomarker, quantitative, and state changes (Figure 2). Further, analysis of EGFR, AR, Ki67, Notch, leptin, IL-1, VEGF and VEGFR2 genes showed their involvement in the regulation of Notch1, leptin, JAG1, and VEGF in carcinogenesis (1064 references; see Additional file 1).

Figure 2
figure 2

Processes and outcomes involving leptin, OB-R, Notch1, Notch4, IL-1R tI, JAG1, DLL4, VEGF, VEGFR2 (KDR), EGFR, AR and Ki67 (MKi67 gene) in the development of breast neoplasms. Breast cancers showed a quantitative increase in the expression of leptin, OB-R, Notch1, Notch4, DLL4, JAG1, IL-1R tI, VEGF, VEGFR2, EGFR, AR and Ki67. Increased expression of leptin, Notch1, Notch4, JAG1, and VEGF upregulated the development of breast neoplasms. Elevated levels of leptin, OB-R, Notch1, DLL4, VEGF, AR, EGFR and Ki67 proteins are reported to be biomarkers for breast cancers (data obtained from 1064 references; see Additional file 1; Pathway Studio9, Ariadine Genomics).


Notch signaling is a hallmark of breast cancer that is frequently identified as an indicator of poor prognosis and advanced disease. Therefore, Notch signaling is being targeted for breast cancer treatment [7, 27]. Additionally, increased leptin signaling has also been related to breast cancer growth, angiogenesis and poor outcomes [24]. Leptin increased the expression and activation of several members of the Notch family of proteins in breast cancer cells and derived tumors [1719]. VEGF and VEGFR2 can be regulated by leptin-Notch crosstalk, which was also affected by IL-1 signaling. Therefore, Notch, IL-1 and leptin crosstalk outcome (NILCO) could be essential for the integration of leptin’s proangiogenic, pro-inflammatory and proliferative actions in breast cancer [17]. Leptin could also be involved in the development of drug resistance, metastasis and relapse of breast cancer, which are related to cancer stem cells [24, 28]. Furthermore, leptin transactivated and induced the expression of ER [29], EGFR [30], HER2 [31, 32] and IGF-1R [33] in breast cancer.

The abrogation of leptin signaling impaired the growth of tumors and expression of angiogenic biomarkers in human breast cancer xenografts [34, 35], and in mouse carcinogenic-induced [18] and syngeneic mammary tumors, which was more evident in obese contexts [19, 21]. Moreover, accumulated evidence from these pre-clinical studies in mice reinforces the idea that leptin-Notch crosstalk plays an important role in breast cancer. Nevertheless, whether NILCO and targets are differentially-expressed in human breast cancer tissues, in relation to ER, PR and HER2 as well as EGFR and AR statuses, is unknown.

Here we show that NILCO components (Notch1, Notch4, JAG1, DLL4, leptin, OB-R, IL-1R tI) and target molecules (VEGF and VEGFR2) were co-expressed in breast cancer tissues, irrespective of ER, PR and, HER2 statuses. Remarkably, TNBC shows a differential pattern of expression and cellular localization of NILCO. TNBC showed lower protein levels of IL-1R tI and DLL4, and fewer nuclei and cytoplasms were positive for Notch4 and JAG1. In contrast, more TNBC cytoplasms were positive for leptin. Moreover, TNBC stromas showed distinctive patterns of Notch1 and VEGFR2 immnoreactivities. Notch1 and Notch4 expression were lower, but VEGFR2 expression was higher in stromas from TNBC compared with ER- and ER+ breast cancer stromas.

Notch1 was found in the majority of breast cancer tissues evaluated. This data was in agreement with a previous report that showed 100% of Notch1 expression in TNBC [36]. Notch4 expression was previously found in 73% of TNBC cases (n=29) [36]. Present data show that fewer TNBCs were positive for Notch4 (58%) compared to ER- (75%) and ER+ (84%), but the differences were not significant. However, nuclear localization of Notch4 was significantly lower in TNBC malignant cells and stroma.

Present data further confirm previous findings showing that TNBC cells in culture (MDA-MB231) secreted more leptin (approximately four-fold) than ER+ breast cancer cells (MCF-7) [34]. Notch-induced transcriptional activity was not previously correlated with Notch receptor levels in breast cancer cell cultures, but Notch-induced gene transcription was highest in TNBC cells [2]. The global biological relevance of these findings is unclear. Nevertheless, present findings might indicate that TNBC could greatly depend on leptin’s actions, which could underline the role of NILCO in this breast cancer type.

Notch1 expression was also associated with the cell proliferation marker, Ki67. This marker was detected in approximately 60% of breast cancer independently of the expression of hormone receptors. It was previously reported that Ki67 is found in 90% of TNBC [12] and its expression correlates to Notch4, which is induced by Notch1 in breast cancer samples [2].

The transformation of normal breast epithelial cells by increased Notch signaling was previously linked to the repression of apoptosis in vitro[9]. Notch NICD1 interacted and mediated p53 inactivation through phosphorylation in vitro[37]. Additionally, it was also suggested that Notch signaling regulated apoptosis specifically caused by p53-induced expression of Puma and Noxa in vitro[6]. However, our present data suggest that p53 was not associated with the expression of NILCO and its targets, and is independent of hormonal receptor status. This data may also suggest that Notch-induced apoptosis in breast cancer in vivo may not always be p53 dependent.

TNBC and ER- breast cancers are not responsive to steroid hormones, but are highly aggressive tumors that respond to several other growth factor-related signals [5]. TNBC frequently show EGFR expression and resistance to EGFR drugs that could be driven by the Notch pathway [38]. In these cancers, Notch, leptin and OB-R could further contribute to tumor growth via increased the survival of breast cancer stem cells [24]. Indeed, the abrogation of Notch can negatively affect stem cells [10, 27]. Moreover, inhibition of OB-R significantly reduces the expression of several stem cell self-renewal transcription factors (NANOG, SOX2, and OCT4), and induces a mesenchymal-to-epithelial transition in TNBC cells [39]. Our present investigations show that EGFR expression was found in more ER- and TNBC (five-fold) than ER+ tumors. Additionally, EGFR was associated with higher expression of Notch1. Interestingly, leptin-induced activation of EGFR was suggested as a potential mechanism that promotes metastasis as well as invasion and, migration of breast cancer [33].

Obesity could affect breast carcinogenesis by autocrine and paracrine actions mediated by two major adipokines: leptin and adiponectin [24]. Obese breast cancer patients show poor prognosis, higher aggressiveness, and drug resistance [4042]. Accumulated evidence suggests that obesity could induce Notch signaling. Indeed, an intact leptin-Notch axis could be involved in obesity-related breast cancer [18, 19]. However, diverse factors from adipose and other organs could also influence breast carcinogenesis and tumor growth. Therefore, more investigations are necessary to understand obesity-related breast cancer causes and mechanisms [43, 44]. All tissue samples used in this investigation were from Asiatic women (mean age of approximately 50 years). Obesity in China is currently a health problem. In 2002, the prevalence of obesity in China was relatively low (overweight prevalence at about 22.8% and for obesity, 7.1%) compared with Western countries, but the rapid increase in obesity is alarming [45]. Unfortunately, body weight and obesity data were not available for the breast cancer tissues used in this study.


For the first time, we are reporting on a comprehensive data analysis on protein levels of NILCO and targets in three major groups of breast cancer: TNBC, ER-, and ER+. TNBC showed distinctive patterns of expression and localization of NILCO, which suggests these molecules may be useful as markers for disease progression and aggressiveness. It is known that inhibition of Notch [9] and leptin signaling [17, 21, 34, 39] can revert the transformed phenotype of human breast cancer cell lines. Thus, treatments aimed to abrogate NILCO could provide the development of novel therapeutic interventions. More research is needed to establish the biomarker and potential therapeutic values of NILCO, and target expression in breast cancer, particularly in obese contexts.



Leptin receptor

IL-1R tI:

Interleukin 1 receptor type I


Notch interleukin 1 leptin crosstalk outcome


Epidermal growth factor receptor


A homeobox protein and transcription factor


Sex determining region Y-box 2 protein


Octamer-binding transcription factor 4


Insulin like growth factor receptor 1


Estrogen receptor


Androgen receptor


Progesterone receptor


Epidermal growth factor receptor 2


Delta like notch ligand 4


Jagged 1 protein (a Notch ligand)


Vascular endothelial growth factor


Vascular endothelial growth factor receptor 2 or KDR


Triple negative breast cancer cells


A semi-quantitative analysis of intensity staining.


  1. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lønning PE, Børresen-Dale AL: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001, 98: 10869-10874. 10.1073/pnas.191367098.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Rizzo P, Miao H, D’Souza G, Osipo C, Song LL, Yun J, Zhao H, Mascarenhas J, Wyatt D, Antico G, Hao L, Yao K, Rajan P, Hicks C, Siziopikou K, Selvaggi S, Bashir A, Bhandari D, Marchese A, Lendahl U, Qin JZ, Tonetti DA, Albain K, Nickoloff BJ, Miele L: Cross-talk between notch and the estrogen receptor in breast cancer suggests novel therapeutic approaches. Cancer Res. 2008, 68: 5226-5235. 10.1158/0008-5472.CAN-07-5744.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rose DP, Vona-Davis L: Interaction between menopausal status and obesity in affecting breast cancer risk. Maturitas. 2010, 66: 33-38. 10.1016/j.maturitas.2010.01.019.

    Article  PubMed  Google Scholar 

  4. Newman G, Gonzalez-Perez RR: Leptin-cytokine crosstalk in breast cancer. Mol Cell Endocrinol. 2013, 382 (25): 570-582.

    PubMed  Google Scholar 

  5. Mohamed A, Krajewski K, Cakar B, Ma CX: Targeted therapy for breast cancer. Am J Pathol. 2013, 183: 1096-1112. 10.1016/j.ajpath.2013.07.005.

    Article  CAS  PubMed  Google Scholar 

  6. Stylianou S, Clarke BR, Brennan K: Aberrant activation of notch signaling in human breast cancer. Cancer Res. 2006, 66: 1517-1525. 10.1158/0008-5472.CAN-05-3054.

    Article  CAS  PubMed  Google Scholar 

  7. Guo S, Liu M, Gonzalez-Perez RR: Role of notch and its oncogenic signaling crosstalk in breast cancer. Biochim Biophys Acta. 1815, 2011: 197-213.

    Google Scholar 

  8. Shi W, Harris AL: Notch signaling in breast cancer and tumor angiogenesis: cross-talk and therapeutic potentials. J Mammary Gland Biol Neoplasia. 2006, 11: 41-52. 10.1007/s10911-006-9011-7.

    Article  CAS  PubMed  Google Scholar 

  9. Reedijk M, Odorcic S, Chang L, Zhang H, Miller N, McCready DR, Lockwood G, Egan SE: High-level coexpression of JAG1 and NOTCH1 is observed in human breast cancer and is associated with poor overall survival. Cancer Res. 2005, 65: 8530-8537. 10.1158/0008-5472.CAN-05-1069.

    Article  CAS  PubMed  Google Scholar 

  10. Wang Z, Li Y, Ahmad A, Azmi AS, Banerjee S, Kong D, Sarkar FH: Targeting notch signaling pathway to overcome drug resistance for cancer therapy. Biochim Biophys Acta. 1806, 2010: 258-267.

    Google Scholar 

  11. Hao L, Rizzo P, Osipo C, Pannuti A, Wyatt D, Cheung LW, Sonenshein G, Osborne BA, Miele L: Notch-1 activates estrogen receptor-alpha-dependent transcription via IKKalpha in breast cancer cells. Oncogene. 2010, 29: 201-213. 10.1038/onc.2009.323.

    Article  CAS  PubMed  Google Scholar 

  12. Speiser J, Foreman K, Drinka E, Godellas C, Perez C, Salhadar A, Erşahin Ç, Rajan P: Notch-1 and Notch-4 biomarker expression in triple-negative breast cancer. Int J Surg Pathol. 2012, 20: 139-145.

    Article  PubMed  Google Scholar 

  13. Lee CW, Simin K, Liu Q, Plescia J, Guha M, Khan A, Hsieh C-C, Altieri DC: A functional Notchsurvivin gene signature in basal breast cancer. Breast Cancer Res. 2008, 10: R97-10.1186/bcr2200.

    Article  PubMed  PubMed Central  Google Scholar 

  14. WHO: World Health Organization Fact Sheet for World Wide Prevalence of Obesity. 2006,,

    Google Scholar 

  15. Nichols HB, Trentham-Dietz A, Egan KM, Titus-Ernstoff L, Holmes MD, Bersch AJ, Holick CN, Hampton JM, Stampfer MJ, Willett WC, Newcomb PA: Body mass index before and after breast cancer diagnosis: associations with All-cause, breast cancer, and cardiovascular disease mortality. Cancer Epidemiol Biomarkers Prev. 2009, 18: 1403-1409. 10.1158/1055-9965.EPI-08-1094.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Khandekar MJ, Cohen P, Spiegelman BM: Molecular mechanisms of cancer development in obesity. Nat Rev Cancer. 2011, 11: 886-895. 10.1038/nrc3174.

    Article  CAS  PubMed  Google Scholar 

  17. Guo S, Gonzalez-Perez RR: Notch, IL-1 and leptin crosstalk outcome (NILCO) is critical for leptin-induced proliferation, migration and VEGF/VEGFR-2 expression in breast cancer. PloS One. 2011, 6: e21467-10.1371/journal.pone.0021467.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gillespie C, Quarshie A, Penichet M, Gonzalez-Perez RR: Potential role of leptin signaling in DMBA-induced mammary tumors by Non-responsive C57BL/6 J mice Fed a high-Fat diet. J Carcinogene Mutagene. 2012, 3: 132-

    Google Scholar 

  19. Battle M, Gillespie C, Quarshie A, Lanier V, Torroella-Kouri M, Gonzalez-Perez RR: Obesity induced leptin-Notch signaling axis in breast cancer. Int J Cancer. 2014, 134: 1606-1616.

    Article  Google Scholar 

  20. Gonzalez-Perez RR, Lanier V, Newman G: Leptin’s proangiogenic signature in breast cancer. Cancers. 2013, 5: 1140-1162. 10.3390/cancers5031140.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gonzalez RR, Cherfils S, Escobar M, Yoo JH, Carino C, Styer AK, Sullivan BT, Sakamoto H, Olawaiye A, Serikawa T, Lynch MP, Rueda BR: Leptin signaling promotes the growth of mammary tumors and increases the expression of vascular endothelial growth factor (VEGF) and its receptor type two (VEGF-R2). J Biol Chem. 2006, 281: 26320-26328. 10.1074/jbc.M601991200.

    Article  CAS  PubMed  Google Scholar 

  22. Saxena NK, Vertino PM, Anania FA, Sharma D: Leptin-induced growth stimulation of breast cancer cells involves recruitment of histone acetyltransferases and mediator complex to CYCLIN D1 promoter via activation of Stat3. J Biol Chem. 2007, 282: 13316-13325. 10.1074/jbc.M609798200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ferla R, Bonomi M, Otvos L, Surmacz E: Glioblastoma-derived leptin induces tube formation and growth of endothelial cells: comparison with VEGF effects. BMC Cancer. 2011, 11: 303-

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Guo S, Liu M, Wang G, Torroella-Kouri M, Gonzalez-Perez RR: Oncogenic role and therapeutic target of leptin signaling in breast cancer and cancer stem cells. Biochim Biophys Acta. 1825, 2012: 207-222.

    Google Scholar 

  25. Gonzalez RR, Leary K, Petrozza JC, Leavis PC: Leptin regulation of interleukin-1 (IL-1) system in endometrial cells. Mol Hum Reprod. 2003, 9: 151-158. 10.1093/molehr/gag022.

    Article  CAS  PubMed  Google Scholar 

  26. Palomino WA, Fuentes A, Gonzalez RR, Gabler F, Boric MA, Vega M, Devoto L: Differential expression of endometrial integrins and progesterone receptor during the window of implantation in normo-ovulatory women treated with clomiphene citrate. Fertil Steril. 2005, 83: 587-593. 10.1016/j.fertnstert.2004.11.020.

    Article  CAS  PubMed  Google Scholar 

  27. Espinoza I, Miele L: Notch inhibitors for cancer treatment. Pharmacol Ther. 2013, 139: 95-110. 10.1016/j.pharmthera.2013.02.003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chen X, Zha X, Chen W, Zhu T, Qiu J, Røe OD, Li J, Wang Z, Yin Y: Leptin attenuates the anti-estrogen effect of tamoxifen in breast cancer. Biomed Pharmacother. 2013, 67: 22-30.

    Article  CAS  PubMed  Google Scholar 

  29. Catalano S, Mauro L, Marsico S, Giordano C, Rizza P, Rago V, Montanaro D, Maggiolini M, Panno ML, Ando S: Leptin induces, via ERK1/ERK2 signal, functional activation of estrogen receptor alpha in MCF-7 cells. J Biol Chem. 2004, 279: 19908-19915. 10.1074/jbc.M313191200.

    Article  CAS  PubMed  Google Scholar 

  30. Knight BB, Oprea-Ilies GM, Nagalingam A, Yang L, Cohen C, Saxena NK, Sharma D: Survivin upregulation, dependent on leptin-EGFR-Notch1 axis, is essential for leptin induced migration of breast carcinoma cells. Endocr Relat Cancer. 2011, 18: 413-428. 10.1530/ERC-11-0075.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Soma D, Kitayama J, Yamashita H, Miyato H, Ishikawa M, Nagawa H: Leptin augments proliferation of breast cancer cells via transactivation of HER2. Surg Res. 2008, 149: 9-14. 10.1016/j.jss.2007.10.012.

    Article  CAS  Google Scholar 

  32. Giordano C, Vizza D, Panza S, Barone I, Bonofiglio D, Lanzino M, Sisci D, De Amicis F, Fuqua SA, Catalano S, Andò S: Leptin increases HER2 protein levels through a STAT3-mediated up-regulation of Hsp90 in breast cancer cells. Mol Oncol. 2013, 7: 379-391. 10.1016/j.molonc.2012.11.002.

    Article  CAS  PubMed  Google Scholar 

  33. Saxena NK, Taliaferro-Smith L, Knight BB, Merlin D, Anania FA, O’Regan RM, Sharma D: Bidirectional crosstalk between leptin and insulin-like growth factor-I signaling promotes invasion and migration of breast cancer cells via transactivation of epidermal growth factor receptor. Cancer Res. 2008, 68: 9712-9722. 10.1158/0008-5472.CAN-08-1952.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rene Gonzalez R, Watters A, Xu Y, Singh UP, Mann DR, Rueda BR, Penichet ML: Leptin-signaling inhibition results in efficient anti-tumor activity in estrogen receptor positive or negative breast cancer. Breast Cancer Res. 2009, 11: R36-10.1186/bcr2321.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Otvos L, Kovalszky I, Riolfi M, Ferla R, Olah J, Sztodola A, Nama K, Molino A, Piubello Q, Wade JD, Surmacz E: Efficacy of a leptin receptor antagonist peptide in a mouse model of triple-negative breast cancer. Eur J Cancer. 2011, 10: 1578-1584.

    Article  Google Scholar 

  36. Speiser J, Foreman K, Drinka E, Godellas C, Perez C, Salhadar A, Erşahin Ç, Rajan P: Notch-1 and Notch-4 biomarker expression in triple-negative breast cancer. Int J Surg Pathol. 2012, 20: 139-145.

    Article  PubMed  Google Scholar 

  37. Kim SB, Chae GW, Lee J, Park J, Tak H, Chung JH, Park TG, Ahn JK, Joe CO: Activated Notch1 interacts with p53 to inhibit its phosphorylation and transactivation. Cell Death Differ. 2007, 14: 982-991.

    PubMed  Google Scholar 

  38. Dong Y, Li A, Wang J, Weber JD, Michel LS: Synthetic lethality through combined Notch-epidermal growth factor receptor pathway inhibition in basal-like breast cancer. Cancer Res. 2010, 70: 5465-5474. 10.1158/0008-5472.CAN-10-0173.

    Article  CAS  PubMed  Google Scholar 

  39. Zheng Q, Banaszak L, Fracci S, Basali D, Dunlap SM, Hursting SD, Rich JN, Hjelmeland AB, Vasanji A, Berger NA, Lathia JD, Reizes O: Leptin receptor maintains cancer stem-like properties in triple negative breast cancer cells. Endocr Relat Cancer. 2013, 20: 797-808. 10.1530/ERC-13-0329.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ: Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N Engl J Med. 2003, 348: 1625-1638. 10.1056/NEJMoa021423.

    Article  PubMed  Google Scholar 

  41. Feldman DE, Chen C, Punj V, Tsukamoto H, Machida K: Pluripotency factor-mediated expression of the leptin receptor (OB-R) links obesity to oncogenesis through tumor-initiating stem cells. Proc Natl Acad Sci U S A. 2012, 109: 829-834. 10.1073/pnas.1114438109.

    Article  CAS  PubMed  Google Scholar 

  42. Gilbert CA, Slingerland JM: Cytokines, Obesity, and Cancer: New Insights on Mechanisms Linking Obesity to Cancer Risk and Progression. Ann Rev Med. 2013, 64: 45-57. 10.1146/annurev-med-121211-091527.

    Article  CAS  PubMed  Google Scholar 

  43. Prieto-Hontoria PL, Perez-Matute P, Fernandez-Galilea M, Bustos M, Martinez JA, Moreno-Aliaga MJ: Role of obesity-associated dysfunctional adipose tissue in cancer: a molecular nutrition approach. Biochim Biophys Acta. 1807, 2011: 664-678.

    Google Scholar 

  44. Zhang Y, Bellows CF, Kolonin MG: Adipose tissue-derived progenitor cells and cancer. World J Stem Cells. 2010, 2: 103-113. 10.4252/wjsc.v2.i5.103.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Wang Y, Mi J, Shan XY, Wang QJ, Ge KY: Is China facing an obesity epidemic and the consequences? The trends in obesity and chronic disease in China. Int J Obes (Lond). 2007, 1: 177-188.

    Article  Google Scholar 

Pre-publication history

Download references


This work was partially supported by the National Institutes of Health and National Cancer Institute Grant 1SC1CA138658-05 and U54 CA118638, the Georgia Cancer Coalition Distinguished Cancer Scholar Award, and DOD Idea Award BC 123427 to RRGP; a grant from CREDO (MSCR) 2R25RR017694-06A1 to LSC; the Blackstone Academy and NIH/NIDDK Step-up HS Research Program to K.W.; and facilities, and support services at Morehouse School of Medicine (NIH RR03034 and 1C06 RR18386) and NIH/NCRR grant 1G12RR026250-03.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ruben Rene Gonzalez-Perez.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

RRGP designed and wrote up the current study. LSC was involved in the study design, immunohistochemistry assays and HSCORE determinations of NILCO and targets. GN developed the in silico analysis of NILCO relationships using Pathway Studio9 Program and contributed to writing and editing the manuscript. KW, CG and TD developed immunohistochemistry assays and HSCORE determinations of NILCO, and targets. GO, performed immunohistochemical and pathological analyses of tissue samples. SK and YL performed the statistical analysis and interpretation of data. All authors read and approved the final manuscript.

Electronic supplementary material


Additional file 1: List of references reporting relationships between NILCO and its targets in breast cancer.(XLSX 113 KB)

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article

Colbert, L.S., Wilson, K., Kim, S. et al. NILCO biomarkers in breast cancer from Chinese patients. BMC Cancer 14, 249 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: