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Estrogen receptors promote NSCLC progression by modulating the membrane receptor signaling network: a systems biology perspective
Journal of Translational Medicinevolume 17, Article number: 308 (2019)
Estrogen receptors (ERs) are thought to play an important role in non-small cell lung cancer (NSCLC). However, the effect of ERs in NSCLC is still controversial and needs further investigation. A new consideration is that ERs may affect NSCLC progression through complicated molecular signaling networks rather than individual targets. Therefore, this study aims to explore the effect of ERs in NSCLC from the perspective of cancer systems biology.
The gene expression profile of NSCLC samples in TCGA dataset was analyzed by bioinformatics method. Variations of cell behaviors and protein expression were detected in vitro. The kinetic process of molecular signaling network was illustrated by a systemic computational model. At last, immunohistochemical (IHC) and survival analysis was applied to evaluate the clinical relevance and prognostic effect of key receptors in NSCLC.
Bioinformatics analysis revealed that ERs might affect many cancer-related molecular events and pathways in NSCLC, particularly membrane receptor activation and signal transduction, which might ultimately lead to changes in cell behaviors. Experimental results confirmed that ERs could regulate cell behaviors including cell proliferation, apoptosis, invasion and migration; ERs also regulated the expression or activation of key members in membrane receptor signaling pathways such as epidermal growth factor receptor (EGFR), Notch1 and Glycogen synthase kinase-3β/β-Catenin (GSK3β/β-Catenin) pathways. Modeling results illustrated that the promotive effect of ERs in NSCLC was implemented by modulating the signaling network composed of EGFR, Notch1 and GSK3β/β-Catenin pathways; ERs maintained and enhanced the output of oncogenic signals by adding redundant and positive-feedback paths into the network. IHC results echoed that high expression of ERs, EGFR and Notch1 had a synergistic effect on poor prognosis of advanced NSCLC.
This study indicated that ERs were likely to promote NSCLC progression by modulating the integrated membrane receptor signaling network composed of EGFR, Notch1 and GSK3β/β-Catenin pathways and then affecting tumor cell behaviors. It also complemented the molecular mechanisms underlying the progression of NSCLC and provided new opportunities for optimizing therapeutic scheme of NSCLC.
Lung cancer is the most common cancer and is the leading cause of cancer deaths for both men and women worldwide [1, 2]. Epidemiological data suggest that sex hormones are associated with the incidence, therapeutic response and clinical outcomes of lung cancer . And in most cases, sex hormones play a role in lung cancer by binding to their corresponding receptors. ERs are important sex hormone receptors, which are proposed to affect the development and progression of lung cancer, particularly in NSCLC.
It is generally agreed that ERs are expressed both in the cytoplasm and nucleus of NSCLC cells and exert their effects through both the genomic and non-genomic mechanisms [4, 5]. The genomic mechanism is mediated by estrogen-responsive elements or AP-1 [6, 7]. Non-genomic mechanism involves crosstalks between ERs and growth factor receptor pathways, such as epidermal growth factor receptor (EGFR) pathway . However, the role of ERs in NSCLC prognosis still remains controversial. Some studies have reported that high expression of estrogen receptor α (ERα) and/or estrogen receptor β (ERβ) correlates with poor prognosis in NSCLC [9,10,11]. There are also some reports suggesting that ERs are favorable prognostic factors for NSCLC patients [12, 13]. These controversies indicate that the molecular mechanisms of ERs in NSCLC are probably more complicated than reported. Therefore we will attempt to reconsider the effect of ERs in NSCLC from a more comprehensive perspective.
Recently, there has been accumulating evidence supporting that cancer is a complex systemic disease involving dysregulation of multiple pathways and loss of homeostasis at multiple levels [14,15,16]. Given the complexity of cancer, it can be inferred that the effects of ERs on NSCLC are likely to be mediated by multiple pathways interacted with each other rather than some individual targets. Therefore, it may be reasonable to explore the role of ERs in NSCLC from a perspective of cancer systems biology [17, 18], which is an emerging approach to investigate the complexity of cancer origin and evolution from a holistic view. Bioinformatics method and high-throughput database facilitate a more comprehensive understanding of the influence of ERs variation on genomes and pathways in NSCLC. And computational model can be used to illustrate the interaction among the signaling networks, which are composed of genes and pathways influenced by ERs. Furthermore, prediction capabilities of systemic models will help solve practical problems such as acquired resistance and therapeutic strategy optimization.
In this study, we intend to integrate bioinformatics method, experimental approach, computational modeling and IHC analysis, to explore the role of ERs in NSCLC from the perspective of cancer systems biology.
Materials and methods
Cell lines and cell culture
The human NSCLC cell lines PC9, H1299, A549, H1975, HCC827 were purchased from the Cell Bank of Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China). The Gefitinib-resistant cell line PC9/G was generated as described previously . All cell lines were cultured in recommend medium supplemented with 10% FBS and 1% penicillin–streptomycin and were cultured at 37 °C in a 5% CO2 incubator as protocols described. All cell lines were authenticated by STR DNA profiling.
Cell viability assay
Cells were seeded into 96-well plates at a density of 3000 per well. After 24 h incubation, cells were treated with different concentration of β-Estradiol (17β-E2, Sigma-Aldrich, USA) for 72 h. 10% CCK-8 (Zoman Bio, China) diluted in normal culture medium was added to each well and incubated for an additional 1.5 h. The absorbance was measured spectrophotometrically at 450 nm. Each experiment was performed at least three times independently.
ERα siRNA (sc-29305, sc-44204) and ERβ siRNA (sc-35325) were purchased from Santa Cruz Biotechnology (Dallas, USA). Cells were incubated in 6-well plates supplemented with antibiotic-free normal growth medium until the cells were 60–80% confluent. Negative control siRNA (si-NC), si-ERα or si-ERβ (100 nM) were mixed with Lipofectamine® 3000 Reagent (Invitrogen, USA), and were added to the siRNA transfection medium (Opti-MEM, Gibco, USA). This transfection mixture was added to each plate for 6 h and then replaced by normal growth medium according to the manufacturer’s instructions.
Cell apoptosis analysis
Cells were seeded into 6-well plates and treated with Gefitinib (AstraZeneca) after transfection or combined with Fulvestrant (ICI 182,780, Sigma-Aldrich) for 48 h. Cells in each well were harvested, washed and resuspended in 1× binding buffer, and then stained with Annexin V-FITC and PI (Sungene Biotech, China) for 10 min in the dark, respectively. Data acquisition was performed on a flow cytometry (Becton–Dickinson, USA) with the CellQuest software (BD Biosciences, USA).
Cell migration and invasion assay
After transfection, cells were resuspended in serum-free medium containing Gefitinib or not and were seeded into the upper chambers of Transwell inserts (Corning Costar, USA) with (for invasion assays) or without (for migration assays) Matrigel (BD Biosciences, USA). Normal culture medium was added into each of the bottom chambers. After 24 h (for migration assays) or 48 h (for invasion assays) incubation, cells on the surface of the bottom chamber were stained with 0.1% crystal violet (Google biotechnology, China). The stained cells were photographed and counted under an inverted microscope at 400× magnification.
Western blot analysis
Cells were harvested after transfection or drug treatment. Total intracellular protein was extracted, quantified and denatured. Equal amounts of protein were fractionated by SDS-PAGE gels and transferred onto PVDF membranes. The membranes were first incubated with the corresponding primary antibodies overnight and then incubated with horseradish peroxidase-conjugated secondary antibodies for another 1 h. Protein bands were visualized and analyzed using chemiluminescence system (Pierce Biotechnology, USA) and Gel-Pro Analyser (Media Cybernetics Inc., USA). Details about antibodies were listed in Additional file 1: Table S1.
Human tissue samples and IHC analysis
NSCLC tissue microarrays (Cat. No. HLugA180Su02; National Human Genetic Resources Sharing Service Platform, Shanghai, China) annotated with clinical information were collected from 93 patients who underwent surgical resections from July 2004 to June 2009. This study was approved by the Institutional Review Board for Clinical Research of Tongji Medical College, Huazhong University of Science and Technology, with informed consent from all patients. Protein expressions of EGFR, ERα, ERβ and Notch1 were detected by IHC analysis proceeded as the manufacturer’s instructions. The median values of final IHC scores were applied as the cut-off criterion. Antibodies used in this analysis were list in Additional file 1: Table S2.
Dataset and bioinformatics analysis
The gene expression data of NSCLC tumor tissues were download from the UCSC Xena (dataset ID: TCGA.LUNG.sampleMap/HiSeqV2; samples: 1129; unit: log2(norm_count + 1)) [20, 21]. In this study, we would focus on the expression characteristics of ESR1 and ESR2 genes in NSCLC tumor tissues, so data from 110 normal tissue samples were excluded. Genes with an average expression value < 2 were excluded because the very low-expression genes might not participate in a specific process in the cell. The final data set was an expression matrix of 17,489 genes from 1019 tumor samples. The R package “limma”  was used for differential expression analysis. Fold change > 2 and p < 0.05 was utilized to identify differently expressed genes (DEGs). Gene Ontology (GO)  and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway  enrichment analysis were performed using the DAVID online tool , p < 0.05 was set as the cut-off criterion. Visualization of the DEGs in this dataset was carried out by using the ‘clustergram’ function in MATLAB® software (version: R2016a, 64-bit), the dissimilarity metric was Euclidean distance.
Computational modeling the molecular signaling network
The laws that governed the biochemical reactions used in our model were based on Henri-Michaelis–Menten kinetics, mass action law and irreversible constant flux law. The biological dynamic signaling transduction process inside the NSCLC cells was modeled by a set of ordinary differential equations. The MATLAB toolbox PottersWheel  was used for model constructions, parameter estimations and simulations.
Statistical analysis was performed using GraphPad Prism 7 (GraphPad Software, USA). χ2 test and two-tailed Student’s t-test were applied to determined statistical significance. Survival analysis was estimated by the Kaplan–Meier method.
GO and KEGG analysis revealed that ERs affected some membrane receptor signaling pathways
To outline the effect of ERs in NSCLC, the gene expression data of NSCLC in TCGA database were analyzed. Using the mRNA level of ESR1, which encoded the ERα protein, as the phenotypic label, 1019 tumor tissue samples were divided into the high-ESR1 group (N = 509) and the low-ESR1 group (N = 510), the median value of ESR1 expression level was used as cut-off criterion. Similarly, for ESR2, which encoded the ERβ protein, these 1019 samples were also divided into the high-ESR2 group (N = 509) and the low-ESR2 group (N = 510). Differential expression analysis showed that there were 1237 DEGs between the low- and high-ESR1 group, of which 769 genes were upgraded in the high-ESR1 group and 468 genes were downgraded. For ESR2, there were 102 DEGs between the low- and high-ESR2 group, of which 95 genes were upgraded in the high-ESR2 group and 7 genes were downgraded. Hierarchical clustering showed systematic variations in the expression of DEGs in NSCLC (Fig. 1).
The DEGs were uploaded to DAVID to identify overrepresented GO categories and KEGG pathways. Results of enrichment analysis for 769 upgraded and 468 downgraded DEGs in the high-ESR1 group were listed in Table 1 and Additional file 2. The upgraded DEGs in the high-ESR1 group were mainly enriched in the terms of signal transduction, immune and inflammatory responses, cell adhesion, receptor binding and activation. The downgraded DEGs in the high-ESR1 group were mainly enriched in the terms of transcriptional regulation, oxidation–reduction process, calcium ion binding, CYP450-related metabolism. For ESR2, no significant term was found for enrichment analysis of these 7 downgraded DEGs. The upgraded DEGs in the high-ESR2 group were mainly enriched in the terms of cell adhesion, embryonic limb morphogenesis, epithelial cell differentiation, calcium ion binding, structural molecule activity and GABAergic synapses (Table 2 and Additional file 2). For cell component of GO analysis, both of DEGs in ESR1 and ESR2 group were mainly enriched in plasma membrane and extracellular space. Taken together,the results suggested that variations of ESR1/2 expression might affect many important molecular events and pathways, especially cell communication, including receptor activation, signal transduction, cell adhesion, immune response, which might predominate in ESR1/2-mediated regulation in NSCLC.
Moreover, DEGs enriched in the above terms of GO and KEGG pathway were involved in many membrane receptor signaling pathways. As shown in Tables 1, 2 and Additional file 2, DEGs were involved in the following pathways: growth factor signaling pathway (such as FGF12, FGFR2, IGFBP2, PDGFD and PIK3CG), Wnt/GSK/β-Catenin pathway (such as FZD10, LRP4, MARK1, SFRP4, WISP2, WNT2B and WNT3A) and Notch pathway (such as Jag1, MSI1, NRARP and TP63). These results indicated that ESR1/2 might directly or indirectly regulate these pathways, which were also important oncogenic signals in NSCLC.
ERs induced cell proliferation, migration, invasion and apoptosis escape
As shown in the results of bioinformatics analysis, ESR1/2 might regulate many genes and pathways involved in the development and progression of NSCLC. Therefore, before investigating the molecular mechanisms of ERs in NSCLC, we first verified the promote role of ERs in NSCLC at cellular level by detecting the effects of ERs on NSCLC cell phenotypes.
To assess the effects of ERs on cell proliferation, the viability of cells treated with 17β-E2 was detected by CCK assay. The proliferation of PC9/G, PC9 and H1299 cells was increased after 17β-E2 stimulation, while the proliferation of A549, H1975 and HCC827 cells was not affected by 17β-E2 (Additional file 3: Fig. S1A). ERs expression in NSCLC cells was also detected. ERα and ERβ were highly expressed in PC9/G, PC9 and H1299 cells, while ERs expression were relatively low in A549, H1975 and HCC827 cells (Additional file 3: Fig. S1B). The results suggested that high expression of ERs induced NSCLC cells proliferation after 17β-E2 stimulation. It also indicated that the proliferative effect of 17β-E2 was ERs-dependent. Hence we chose PC9/G and H1299 cell lines, which with high expression of ERs, for further study.
PC9/G and H1299 cells were transfected with si-ERα or si-ERβ. After silencing ERs, ERα and ERβ expression was inhibited (Fig. 2d and Additional file 4: Fig. S2D), PC9/G and H1299 cell migration and invasion (Fig. 2a, b and Additional file 4: Fig. S2A, B) were inhibited, and cell apoptosis was increased (Fig. 2c and Additional file 4: Fig. S2C). The effects of ERs silencing on key molecular markers associated with cell migration, invasion and apoptosis were also detected. The results showed that the expression of mesenchymal markers N-Cadherin, Fibronectin, ZEB1, Vimentin and Snail was decreased by ERs silencing, and the expression of epithelial markers E-Cadherin was restored (Fig. 2e and Additional file 4: Fig. S2E). ERs silencing also decreased the expression of the anti-apoptotic proteins Survivin and Bcl-2, and increased the expression of the pro-apoptotic proteins Cleaved Caspase3 and Bim (Fig. 2f and Additional file 4: Fig.S2F). The results suggested that the reduction of cell migration and invasion caused by ERs silencing was probably due to the suppression of epithelial–mesenchymal transition (EMT) process. It also suggested that ERs silencing could induce NSCLC cell apoptosis by regulating the expression of anti- and pro-apoptotic proteins.
ERs regulated Notch1 and GSK3β/β-Catenin pathway
The effects of ERs on some representative carcinogenesis-related membrane receptor pathways were detected next. EGFR is likely to interact with ERs has been reported . Besides EGFR, we further investigated whether ERs regulated Notch1 pathway and GSK3β/β-Catenin pathway, which was downstream of the EGFR and Wnt pathways. After ERs silencing, the expression of Notch1 (transmembrane domain of Notch1 receptor), NICD (intracellular domain of Notch1 receptor), Hes1 and β-Catenin was downregulated in both PC9/G (Fig. 3a) and H1299 cells (Fig. 3b). The results suggested that ERs activated Notch1 pathway and inhibited β-Catenin degradation. GSK3β expression was decreased and GSK3β phosphorylation was elevated by ERs silencing in H1299 cells (Fig. 3b). However, the expression and phosphorylation of GSK3β were seemly not affected by ERs silencing in PC9/G cells (Fig. 3a). It indicated that ERs could inhibit β-Catenin degradation by inducing GSK3β phosphorylation. However, ERs might also bypass GSK3β to regulate the expression of β-Catenin.
To verify the effect of ERs silencing on Notch1 and GSK3β/β-Catenin pathways, 17β-E2 and Fulvestrant, the stimulant and inhibitor of ERs, respectively, were used to treat PC9/G cells. After treated with 17β-E2, ERα and ERβ expression was increased (Fig. 3c, E2 group). Consistent with elevated ERs expression, the expression of Notch1, NICD and Hes1 was also increased (Fig. 3d, E2 group). For GSK3β/β-Catenin signal, β-Catenin expression was slightly elevated, while the expression of GSK3β was not changed in PC9/G cells (Fig. 3e, E2 group). The effects of Fulvestrant on ERs, Notch1 and GSK3β/β-Catenin pathways were opposite to that of 17β-E2. After treated with Fulvestrant, the expression of ERα, ERβ, Notch1, NICD, Hes1 and β-Catenin was significantly decreased (Fig. 3c–e, Ful group). The results suggested that fluctuations in ERs expression could affect the expression or activation of key members in Notch1 pathway and affect the accumulation of β-Catenin.
Computational modeling illustrated the dynamic process of the integrated signaling network
Combined with the information reported in [27,28,29,30] and the results in this study, a kinetic model was constructed to illustrate the dynamic process of the integrated signaling network (Additional file 5: Fig. S3). For simplicity, we made the following assumptions: ERK/SOS feedback was removed because ERK was continuously activated after EGF stimulation (Additional file 6: Fig. S4B), the overactivation of EGFR, ERs and Notch1 pathways was initiated by high amount of EGF, 17β-E2 and Dll1, respectively; pAkt, pERK, β-Catenin and Hes1 were regarded as the output indicators of the model; cell proliferation, apoptosis and mobility-associated molecules were regulated by these model outputs and then affected cell behaviors. The time-dependent expression data of key proteins (Additional file 6: Fig. S4) were used to estimate and modify model parameters. Dynamic variables, rate equations and parameters of the model could be found in Additional file 7: Tables S3–S5.
The effects of overactivation of different pathways on the output indicators were shown in Fig. 4a. When EGF, 17β-E2 and Dll1 were all at low levels (All-Low group), the network was in an inactive state, and the outputs were maintained at low levels. When EGFR pathway was activated (High-EGF group), Akt and ERK were highly phosphorylated, but β-Catenin and Hes1 were not significantly affected. When Notch1 pathway was activated (High-Dll1 group), rapid and transient activation of Hes1 appeared, and the pAkt level was slightly elevated due to the Hes1/PTEN/PIP3 signaling. 17β-E2 (High-E2 group) could directly activate EGFR, then caused high activation of Akt and ERK. Moreover, the expression of β-Catenin and Hes1 was elevated by increased ERs expression and activation. If all pathways were activated (All-High group), the four output indicators would reach the highest levels. These results indicated that ERs activation did not show significant advantage when EGFR and Notch1 had been both highly activated, but if the two pathways were inhibited, ERs would reactivate the signaling network and induce high-level outputs.
According to the modeling result, it could be predicted that even if EGFR pathway was inhibited, hyperactivation of ERs would reactive the EGFR pathway (High-E2 group in Fig. 4a). This prediction was confirmed by our experiments: the combination treatment of Gefitinib and Fulvestrant to NSCLC cells showed better tumor suppression effects (Fig. 4b); for Gefitinib-resistant PC9/G and H1299 cells, ERs silencing could also enhance the tumor suppression effects of Gefitinib (Fig. 2a–c and Additional file 4: Fig. S2A–C). It indicated potential strategies for overcoming drug resistance of NSCLC.
High ERα, ERβ, EGFR and Notch1 expression correlated with poor prognosis of advanced NSCLC
Here we also evaluated the clinical relevance of these four membrane receptors ERα, ERβ, EGFR and Notch1 in NSCLC patients. Demographic information of all patients was shown in Additional file 8: Table S6. IHC results showed that the expressions of ERα, ERβ, EGFR and Notch1 in tumor tissues were higher than those in adjacent normal tissues (Fig. 5a, b). However, there were no significant results in the Kaplan–Meier analysis for correlations between the expression levels of each receptor and survival outcomes (data not shown). Considering that the degree of malignancy served as one of the most important predictive factors for NSCLC prognosis, the samples were divided into early-stage group (stage I) and late-stage group (stage II–IV). Survival analysis demonstrated that high levels of ERα, ERβ, or EGFR were significantly correlated with worse 10-year overall survival for the late-stage NSCLC patients (Fig. 5c). But for the early-stage NSCLC patients, there was still no significant results in survival analysis (data not shown). For the late-stage NSCLC group, the survival outcomes of patients with grouped high-expression receptors were further analyzed. As shown in Fig. 5d, the more receptors that were highly expressed, the worse the patients’ survival were. Moreover, in the subgroup with at least two high-expression receptors in Fig. 5d, patients with high-expression Notch1 displayed worse survival outcomes (Fig. 5e). These results suggested that ERs, EGFR and Notch1 were poor prognostic factors for advanced NSCLC, and that these receptors had a synergistic effect on poor prognosis of advanced NSCLC.
Over the past few decades, many reports have proposed that ERs play an important role in NSCLC [1,2,3]. However, the effect of ERs in NSCLC is still controversial. The mechanisms of ERs in NSCLC are also not clear enough. So this study, we reconsidered the role of ERs in NSCLC from the perspective of cancer systems biology. And we suggested that ERs promoted NSCLC progression through modulating the integrated membrane receptor signaling network rather than individual targets to maintain and enhance the tumor cell behaviors. These results also indicated that during the evolution of cancer such as NSCLC, various carcinogenic factors interact with each other to maintain tumor phenotypes. This opinion was also supported by other researchers [31, 32].
To gain a preliminary understanding of ERs in NSCLC, effects of ESR1/2 expression fluctuations on the expressions and functions of the genome in NSCLC patients were first analyzed. GO and KEGG analysis showed that activation and transduction of the membrane receptor signaling pathways were likely to be affected by ESR1/2 variation. DEGs were involved the growth factor signaling pathway, Wnt/GSK/β-Catenin pathway and Notch pathway. These pathways had been reported to play an vital role in the development and progression of NSCLC. As the important therapeutic targets of NSCLC, EGFR , fibroblast growth factor receptor (FGFR)  and type 1 insulin-like growth factor receptor (IGF1R)  pathways were also reported to crosstalk with ERs signals [8, 36, 37]. It was also reported that Notch  and Wnt/β-Catenin  pathways could participate in NSCLC progression. However, the relationship between ERs and Notch, Wnt/β-Catenin pathways in NSCLC was rarely discussed. Therefore, we subsequently investigated the effects of ERs on key proteins of Notch1 and GSK3β/β-Catenin pathways and on the membrane receptor signal network composed of EGFR, Notch1 and GSK3β/β-Catenin pathways.
In addition to the membrane receptor signaling pathways, DEGs were also involved in the following genes: complement system members (C3, C7, etc.), chemokines and their receptors (CCL13, CCR2, CX3CR1, CXCL2, etc.), CD molecules (CD2, CD4, CD22, etc.), tumor necrosis factor receptor superfamily (TNFRSF10C, TNFSF8, etc.), cadherin related members (CDH8, CDHR1, CDHR4, etc.) and so on. All of these terms could be attributed to the tumor microenvironment, suggesting that regulating the integrated balance of tumor microenvironment might also be one of the potential mechanisms of ERs in NSCLC [40,41,42,43]. Since the tumor microenvironment is not the focus of this study, we plan to incorporate immune-related pathways, especially chemokine receptors, into the signal network model in our future research.
As shown in the results of bioinformatics analysis, ESR1/2 might regulate many genes and pathways such as EGFR, Notch and Wnt/β-Catenin pathways, most of which were involved in the development and progression of NSCLC. Activation of EGFR could lead to autophosphorylation of receptor tyrosine kinase and subsequent regulate cell proliferation, differentiation and survival . Notch and β-Catenin signals were proved to be important promoting factors of NSCLC metastasis [45, 46]. Therefore, ERs might promote NSCLC cell proliferation, migration, invasion and apoptosis escape by regulating these pathways. Before investigating the molecular mechanisms of ERs in NSCLC, we first investigated the influence of ERs in NSCLC at cellular level by detecting the effects of ERs on NSCLC cell phenotypes.
In this study, it was emphasized that ERs could promote cell migration and invasion by regulating EMT. The expression of epithelial and mesenchymal markers was regulated by ERs (Fig. 2e and Additional file 4: Fig. S2E). Among them, the key markers E-Cadherin and N-Cadherin were important cell adhesion molecules. Cell adhesion was also significant enriched in GO and KEGG analysis, which is the molecular basis of various physiological and pathological processes such as signaling transduction, cell differentiation, invasion and migration . EMT reduced the adhesion capacity of tumor cells and lead to cell invasion and migration, which might eventually cause tumor metastasis . Besides tumor metastasis, Hamilton et al. also reported that ERα could induce chemotherapy resistance by promoting EMT .
Apoptosis escape was one of the reasons for uncontrolled growth of tumor cells, which in turn leads to continuous evolution of tumors . Promoting apoptosis has been considered as an effective strategy for oncotherapy . We found that ERs could inhibit cell apoptosis by regulating the expression of anti- and pro-apoptotic proteins to inhibit cell apoptosis. In addition, ERs inhibition combined with EGFR tyrosine kinase inhibitor resistance (TKI) such as Gefitinib could increase the amount of cell apoptosis, consistent with the results of Stabile et al. [8, 52]. Another research reported that ERβ from the mitochondrial fraction could exert its apoptosis-inhibition function by disrupting Bad-Bcl-XL and Bad-Bcl-2 interactions . Besides, GO analysis showed that calcium ion binding was a significant enrichment term (Tables 1, 2 and Additional file 2). Some researchers pointed out that pro- and anti-apoptotic proteins regulated the intracellular calcium homeostasis, which could affect the efficiency of various apoptosis inducing agents. And this regulation process was ER-associated . Anyhow, it was consistent with the opinion that ERs regulated the balance between pro- and anti-apoptotic proteins through multiple signals.
In our research, ERs were suggested to regulate the expression and activation of key proteins in EGFR, Notch1 and GSK3β/β-Catenin pathways (Fig. 3 and Additional file 6: Fig. S4). The results also suggested that ERs modulated the signaling network through the following nodes: phosphorylate EGFR; phosphorylate GSK3β by pAkt/pERK and then reduce the phosphorylation and degradation of β-catenin; upregulated β-catenin expression bypass GSK3β signal; increase Notch1 expression and activate NICD signal. That is, ERs modulated the signaling network by adding redundant and positive-feedback paths to maintain the stable outputs of carcinogenic signals, and then to promote tumor phenotypic stability and tumor progression. The redundancy and feedback effects of signaling networks were main reasons for the complexity and refractory of cancers, which had been widely discussed [55, 56]. It was also one of the important reasons for acquired resistance of molecular targeted drugs . Our results confirmed that ERs lead to NSCLC resistance by reactivate the redundant pathways in the signaling network. Targeting ERs could alleviate EGFR TKI had been reported by Stabile et al. [8, 52], which suggests potential strategies for overcoming drug resistance of NSCLC.
Our modeling results suggested that these membrane receptor pathways constituted an integrated network to cooperatively promote NSCLC progression. The IHC results echoed that the four membrane receptors ERα, ERβ, EGFR and Notch1 had a synergistic effect on poor prognostic effect of advanced NSCLC. But for the early-stage NSCLC, no significant results were observed. These results were supported by the idea that mutations and abnormal expressions of the genome in the advanced stage of a cancer were much more frequent than those in the early stage.
It should be noted that this study has some limitations. Because of some assumptions made for simplification, our model should be considered as an approximate description rather than an exact definition of the signaling network. In addition, several pathways such as EGFR and Notch1 were involved to illustrate the regulation of ER on molecular networks in our research. However, signals involved in tumor progression of NSCLC go far beyond those. We provided a research prototype here, and we hoped to gradually refine the signaling network model of NSCLC in the future.
In summary, this study aimed to explore the tumor-promoted mechanism of ERs in NSCLC from the perspective of cancer systems biology. ERs might affect many cancer-related molecular events and pathways in NSCLC, particularly the membrane receptor signaling pathways, which might ultimately lead to changes in cell behaviors. The promotive effect of ERs in NSCLC progression was achieved by modulating the signaling network composed of EGFR, Notch1, GSK3β/β-Catenin pathways, and then regulating cell proliferation, mobility and apoptosis. IHC analysis echoed that ERs, EGFR and Notch1 had a synergistic effect on poor prognosis of advanced NSCLC. Overall, this study suggested that ERs were likely to facilitate NSCLC progression by modulating the integrated signaling network and maintaining the stable outputs of oncogenic signals. It also complemented the molecular mechanisms underlying the progression of NSCLC and provided new opportunities for optimizing therapeutic scheme and for improving clinical outcomes in NSCLC. On the other hand, this study encouraged systemic and comprehensive perspectives of cancer and had made some new attempts at the methodology of cancer research.
Availability of data and materials
The TCGA dataset (ID: TCGA.LUNG.sampleMap/HiSeqV2) was free to download from the UCSC Xena (https://xenabrowser.net/datapages/). Other data generated or analyzed during this study were included in this published article and its additional files.
The Database for Annotation, Visualization and Integrated Discovery online tool
differently expressed genes
epidermal growth factor receptor
fibroblast growth factor receptor
glycogen synthase kinase-3β
type 1 insulin-like growth factor receptor (IGF1R)
Kyoto Encyclopedia of Genes and Genomes
intracellular domain of Notch1 receptor
non-small cell lung cancer
tyrosine kinase inhibitor
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA A Cancer J Clin. 2018;68:7–30.
Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J. Cancer statistics in China, 2015. CA A Cancer J Clin. 2016;66:115–32.
Stabile LP, Burns TF. Sex-Specific Differences in Lung Cancer. In: Hemnes AR, editor. Gender, sex hormones and respiratory disease: a comprehensive guide. Cham: Springer International Publishing; 2016. p. 147–71.
Burns TF, Stabile LP. Targeting the estrogen pathway for the treatment and prevention of lung cancer. Lung Cancer Manag. 2014;3:43–52.
Siegfried JM, Hershberger PA, Stabile LP. Estrogen receptor signaling in lung cancer. Semin Oncol. 2009;36:524–31.
Klinge CM. Estrogen receptor interaction with estrogen response elements. Nucleic Acids Res. 2001;29:2905–19.
Hershberger PA, Vasquez AC, Kanterewicz B, Land S, Siegfried JM, Nichols M. Regulation of endogenous gene expression in human non-small cell lung cancer cells by estrogen receptor ligands. Can Res. 2005;65:1598–605.
Stabile LP, Lyker JS, Gubish CT, Zhang W, Grandis JR, Siegfried JM. Combined targeting of the estrogen receptor and the epidermal growth factor receptor in non-small cell lung cancer shows enhanced antiproliferative effects. Cancer Res. 2005;65:1459–70.
Raso MG, Behrens C, Herynk MH, Liu S, Prudkin L, Ozburn NC, Woods DM, Tang X, Mehran RJ, Moran C, et al. Immunohistochemical expression of estrogen and progesterone receptors identifies a subset of NSCLCs and correlates with EGFR mutation. Clin Cancer Res. 2009;15:5359–68.
Stabile LP, Dacic S, Land SR, Lenzner DE, Dhir R, Aquafondata M, Landreneau RJ, Grandis JR, Siegfried JM. Combined analysis of estrogen receptor β−1 and progesterone receptor expression identifies lung cancer patients with poor outcome. Clin Cancer Res. 2011;17(1):154–64.
Olivo-Marston SE, Mechanic LE, Mollerup S, Bowman ED, Remaley AT, Forman MR, Skaug V, Zheng Y-L, Haugen A, Harris CC. Serum estrogen and tumor-positive estrogen receptor-alpha are strong prognostic classifiers of non-small-cell lung cancer survival in both men and women. Carcinogenesis. 2010;31:1778–86.
Schwartz AG, Prysak GM, Murphy V, Lonardo F, Pass H, Schwartz J, Brooks S. Nuclear estrogen receptor β in lung cancer: expression and survival differences by sex. Clin Cancer Res. 2005;11:7280–7.
Schwartz AG, Wenzlaff AS, Prysak GM, Murphy V, Cote ML, Brooks SC, Skafar DF, Lonardo F. Reproductive factors, hormone use, estrogen receptor expression and risk of non small-cell lung cancer in women. J Clin Oncol. 2007;25:5785–92.
Hornberg JJ, Bruggeman FJ, Westerhoff HV, Lankelma J. Cancer: a systems biology disease. Biosystems. 2006;83:81–90.
Hejmadi M. How cancer arises. In: Introduction to cancer biology. 2 ed. London: Bookboon; 2009. P. 7–16.
Masoudi-Nejad A, Bidkhori G, Hosseini Ashtiani S, Najafi A, Bozorgmehr JH, Wang E. Cancer systems biology and modeling: microscopic scale and multiscale approaches. Semin Cancer Biol. 2015;30:60–9.
Du W, Elemento O. Cancer systems biology: embracing complexity to develop better anticancer therapeutic strategies. Oncogene. 2014;34:3215.
Werner HMJ, Mills GB, Ram PT. Cancer systems biology: a peek into the future of patient care? Nat Rev Clin Oncol. 2014;11:167.
Zhu Y, He W, Gao X, Li B, Mei C, Xu R, Chen H. Resveratrol overcomes gefitinib resistance by increasing the intracellular gefitinib concentration and triggering apoptosis, autophagy and senescence in PC9/G NSCLC cells. Sci Rep. 2015;5:17730.
Goldman M, Craft B, Zhu J, Swatloski T, Cline M, Haussler D. Abstract 5270: The UCSC Xena system for integrating and visualizing functional genomics. Cancer Res. 2016;76:5270.
Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemporary Oncology. 2015;19:A68–77.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.
Gene Ontology C. The Gene Ontology (GO) project in 2006. Nucleic Acids Res. 2006;34:D322–6.
Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999;27:29–34.
Dennis G, Sherman B, Hosack D, Yang J, Gao W, Lane H, Lempicki R. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4:P3.
Maiwald T, Timmer J. Dynamical modeling and multi-experiment fitting with PottersWheel. Bioinformatics. 2008;24:2037–43.
Brown KS, Hill CC, Calero GA, Myers CR, Lee KH, Sethna JP, Cerione RA. The statistical mechanics of complex signaling networks: nerve growth factor signaling. Phys Biol. 2004;1:184–95.
Orton RJ, Adriaens ME, Gormand A, Sturm OE, Kolch W, Gilbert DR. Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway. BMC Syst Biol. 2009;3:100.
Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB. Cancerous perturbations within the ERK, PI3 K/Akt, and Wnt/β-catenin signaling network constitutively activate inter-pathway positive feedback loops. Mol BioSyst. 2017;13:830–40.
Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S. A systems biology approach to model neural stem cell regulation by notch, Shh, Wnt, and EGF signaling pathways. OMICS J Integr Biol. 2011;15:729–37.
Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009;27:199–204.
Ashworth A, Lord Christopher J, Reis-Filho Jorge S. Genetic interactions in cancer progression and treatment. Cell. 2011;145:30–8.
da Cunha Santos G, Shepherd FA, Tsao MS. EGFR mutations and lung cancer. Annu Rev Pathol. 2011;6:49–69.
Tiseo M, Gelsomino F, Alfieri R, Cavazzoni A, Bozzetti C, De Giorgi AM, Petronini PG, Ardizzoni A. FGFR as potential target in the treatment of squamous non small cell lung cancer. Cancer Treat Rev. 2015;41:527–39.
Scagliotti GV, Novello S. The role of the insulin-like growth factor signaling pathway in non-small cell lung cancer and other solid tumors. Cancer Treat Rev. 2012;38:292–302.
Siegfried JM, Farooqui M, Rothenberger NJ, Dacic S, Stabile LP. Interaction between the estrogen receptor and fibroblast growth factor receptor pathways in non-small cell lung cancer. Oncotarget. 2017;8:24063–76.
Tang H, Liao Y, Chen G, Xu L, Zhang C, Ju S, Zhou S. Estrogen upregulates the IGF-1 signaling pathway in lung cancer through estrogen receptor-β. Med Oncol. 2012;29:2640–8.
Collins BJ, Kleeberger W, Ball DW. Notch in lung development and lung cancer. Semi Cancer Biol. 2004;14:357–64.
Stewart DJ. Wnt signaling pathway in non-small cell lung cancer. J Natl Cancer Inst. 2014;106(1):djt356.
Rothenberger N, Somasundaram A, Stabile L. The role of the estrogen pathway in the tumor microenvironment. Int J Mol Sci. 2018;19:611.
Tang H, Bai Y, Xiong L, Zhang L, Wei Y, Zhu M, Wu X, Long D, Yang J, Yu L. Interaction of estrogen receptor β5 and interleukin 6 receptor in the progression of non-small cell lung cancer. J Cell Biochem. 2019;120:2028–38.
Kovats S. Estrogen receptors regulate innate immune cells and signaling pathways. Cell Immunol. 2015;294:63–9.
Rodriguez-Lara V, Ignacio G-S, Cerbón Cervantes MA. Estrogen induces CXCR43 overexpression and CXCR43/CXL12 pathway activation in lung adenocarcinoma cells in vitro. Endocr Res. 2017;42:219–31.
Herbst RS. Review of epidermal growth factor receptor biology. Int J Radiat Oncol Biol Phys. 2004;59:S21–6.
Yuan X, Wu H, Han N, Xu H, Chu Q, Yu S, Chen Y, Wu K. Notch signaling and EMT in non-small cell lung cancer: biological significance and therapeutic application. J Hematol Oncol. 2014;7:87.
Bremnes RM, Veve R, Hirsch FR, Franklin WA. The E-cadherin cell–cell adhesion complex and lung cancer invasion, metastasis, and prognosis. Lung Cancer. 2002;36:115–24.
Makrilia N, Kollias A, Manolopoulos L, Syrigos K. Cell adhesion molecules: role and clinical significance in cancer. Cancer Investig. 2009;27:1023–37.
Nurwidya F, Takahashi F, Murakami A, Takahashi K. Epithelial mesenchymal transition in drug resistance and metastasis of lung cancer. Cancer Res Treat. 2012;44:151–6.
Hamilton DH, Matthews Griner L, Keller JM, Hu X, Southall N, Marugan J, David JM, Ferrer M, Palena C. Targeting estrogen receptor signaling with fulvestrant enhances immune and chemotherapy-mediated cytotoxicity of human lung cancer. Clin Cancer Res. 2016;22:6204–16.
Shivapurkar N, Reddy J, Chaudhary PM, Gazdar AF. Apoptosis and lung cancer: a review. J Cell Biochem. 2003;88:885–98.
Fesik SW. Promoting apoptosis as a strategy for cancer drug discovery. Nat Rev Cancer. 2005;5:876.
Xu R, Shen H, Guo R, Sun J, Gao W, Shu Y. Combine therapy of gefitinib and fulvestrant enhances antitumor effects on NSCLC cell lines with acquired resistance to gefitinib. Biomed Pharmacother. 2012;66:384–9.
Zhang G, Yanamala N, Lathrop KL, Zhang L, Klein-Seetharaman J, Srinivas H. Ligand-independent antiapoptotic function of estrogen receptor-β in lung cancer cells. Mol Endocrinol. 2010;24:1737–47.
Pinton P, Giorgi C, Siviero R, Zecchini E, Rizzuto R. Calcium and apoptosis: ER-mitochondria Ca2+ transfer in the control of apoptosis. Oncogene. 2008;27:6407.
Sun C, Bernards R. Feedback and redundancy in receptor tyrosine kinase signaling: relevance to cancer therapies. Trends Biochem Sci. 2014;39:465–74.
Logue JS, Morrison DK. Complexity in the signaling network: insights from the use of targeted inhibitors in cancer therapy. Genes Dev. 2012;26:641–50.
Wilson TR, Fridlyand J, Yan Y, Penuel E, Burton L, Chan E, Peng J, Lin E, Wang Y, Sosman J, et al. Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors. Nature. 2012;487:505.
This work was supported by the National Natural Science Foundation of China (NSFC) (Grant 30973586).
Ethics approval and consent to participate
This study was approved by the Institutional Review Board for Clinical Research of Tongji Medical College, Huazhong University of Science and Technology, with informed consent from all patients.
Consent for publication
The authors declare that they have no competing interests.
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