|Year : 2016 | Volume
| Issue : 1 | Page : 121-125
Exploring the mechanism of non-small-cell lung cancer cell lines resistant to epidermal growth factor receptor tyrosine kinase inhibitor
Yongkang Yu1, Yaohui Luo2, Yifeng Zheng1, Xiushan Zheng1, Wei Li1, Lie Yang1, Jianqing Jiang1
1 Department of Thoracic Surgery, General Hospital of Chengdu Military Region of People's Liberation Army, Chengdu, China
2 Department of Neurosurgery, General Hospital of Chengdu Military Region of People's Liberation Army, Chengdu, China
|Date of Web Publication||13-Apr-2016|
Department of Thoracic Surgery, General Hospital of Chengdu Military Region of People's Liberation Army, Rong Road No. 270, Jinniu, Chengdu - 610 083
Source of Support: None, Conflict of Interest: None
Purpose: Here we aimed to explore the possible mechanism and potential regulatory relationships in which the non.small.cell lung cancer. (NSCLC)-resisted epidermal growth factor receptor. (EGFR) tyrosine kinase inhibitor erlotinib.
Materials and Methods: GSE38310, the gene expression profiles of NSCLC cell lines treated with dimethylsulfoxide or erlotinib, including HCC827, ER3, and T15-2, were downloaded from Gene Expression Omnibus database and preprocessed by normalization. Basing on the regulatory relationships of transcriptional factors obtained from University of California Santa Cruz. (UCSC) database, the differentially expressed genes. (DEGs) were screened using limma package in R with. |logFC| >1 and P < 0.05, and regulatory networks of these DEGs were built with supervised inference of regulatory networks (SIRENE). Subsequently, differentially regulatory networks were compared basing on Limit Fold Change. (LFC) method.
Results: Totally 24,380 genes were obtained, 1,531 DEGs were identified in HCC827 cell lines, 37 DEGs in ER3 cell lines, 156 DEGs in T15-2 cell lines. After removing the redundancy genes, 1,575 differentially expressed genes were got at last. Basing on three regulatory networks of HCC827 cell lines, ER3 cell lines and T15-2 cell lies, sex-determining region Y (SRY).related high mobility group-box gene 9. (SOX9) and Suppressor of cytokine signaling 3 (STAT3) were identified by comparing with HCC827 and ER3 networks. And suppressor of cytokine signaling 5 B (STAT5B), early growth response-1 (EGR1) and STAT6 were obtained in comparison of HCC827 and T15-2 networks.
Conclusions: The regulatory edges with remarkable changes between HCC827 and ER3, HCC827 and T15.2 included some transcription factors and genes. (e. g., STAT3 and SOX9). STAT3, SOX9, STAT5B, EGR1, and STAT6 might affect the resistance of NSCLC to erlotinib.
Keywords: Epidermal growth factor receptor, erlotinib, non-small-cell lung cancer, tyrosine kinase inhibitor
|How to cite this article:|
Yu Y, Luo Y, Zheng Y, Zheng X, Li W, Yang L, Jiang J. Exploring the mechanism of non-small-cell lung cancer cell lines resistant to epidermal growth factor receptor tyrosine kinase inhibitor. J Can Res Ther 2016;12:121-5
|How to cite this URL:|
Yu Y, Luo Y, Zheng Y, Zheng X, Li W, Yang L, Jiang J. Exploring the mechanism of non-small-cell lung cancer cell lines resistant to epidermal growth factor receptor tyrosine kinase inhibitor. J Can Res Ther [serial online] 2016 [cited 2018 Oct 20];12:121-5. Available from: http://www.cancerjournal.net/text.asp?2016/12/1/121/151425
| > Introduction|| |
Lung cancer is a major cause of cancer-related mortality worldwide  and non-small cell lung cancer (NSCLC) represents <80% of lung cancer diagnoses. The main clinical manifestations of lung cancer are: Fatigue (100%), loss of appetite (97%), shortness of breath (95%), cough (93%), pain (92%), and blood in sputum (63%). NSCLC is increasingly recognized as a heterogeneous set of diseases at the molecular level and these differences can drive therapeutic decision making.
Molecular profiling of a vast number of human tumors has already led to several targeted therapy regimens, such as inhibition of the vascular endothelial growth factor receptor, epidermal growth factor receptor (EGFR), echinoderm microtubule-associated protein like 4-anaplastic lymphoma kinase (EML4-ALK) gene fusion, and others. EGFR-mutantin NSCLC was first recognized in 2004 as a distinct, clinically relevant molecular of lung cancer. Ten percent of North American patients with NSCLC have somatic mutations in the gene of the EGFR. For EML4-ALK, the rearrangements of the anaplastic lymphoma kinase (ALK) gene were initially identified in anaplastic large cell lymphoma that the 5′ end of the EML4 gene was fused to the 3′ portion of ALK was identified in NSCLC. In the past years, people find that the resistance to erlotinib become more and more common in NSCLC. Erlotinibis a kind of oral drugs based on quinazoline, which can inhibit EGFR-TK reversibly. Its inhibition action is mainly through inducing the retention of cell cycle to interdict cell multiplication and prompt apoptosis., Erlotinib mainly acts on cancer cells by stopping the intracellular phosphorylation of EGFR's tyrosinekinase. Therefore, exploring the mechanism of NSCLC on resistance to EGFR tyrosine kinase inhibitor is of great importance in preventing and treatment of lung cancer.
Therefore, in this study, the gene expression profile of NSCLC cell lines treated with dimethylsulfoxide, the control group (DMSO) and erlotinib (erlotinibhydrochloride tablets as the EGFR tyrosine kinase inhibitor, the experimental group) were downloaded from Gene Expression Omnibus (GEO) database. And we analyzed the differentially expressed genes of three cell lines and mined the difference among these regulatory relationships, basing on the regulatory networks of these DEGs in three cell lines. We anticipate that our research could provide new insights into the resistant mechanism of NSCLC to anti-cancer drugs, and finding new clinical diagnosis and treatment of NSCLC.
| > Materials and Methods|| |
The data of expression profile GSE38310 were downloaded from GEO (http://www.ncbi.nlm.nih.gov/geo/). There are three cell lines in this dataset, including HCC827, ER3, and T15-2. HCC827 is a kind of NSCLC which has the mutation of EGFR and is highly sensitive to erlotinib. ER3 and T15-2 are highly resistant to erlotinib. Then, DMSO and erlotinib were applied to treat for the three cell lines, respectively. Three HCC827 samples with DMSO, three HCC827 samples with erlotinib, three ER3 samples with DMSO, three ER3 samples with erlotinib, three T15-2 samples with DMSO, and three T15-2 samples with erlotinib were obtained, at 12 hours after incubation, ribonucleic acid (RNA) was extracted from the three cell lines. Then the probe in the expression profile were turned into corresponding gene symbol through the corresponding relationship between the probe and gene on the chip platform GPL6947 platform data (Illumina Human HT-12 whole-genome expression BeadChips). Because one gene corresponds to a lot of probes, we got many expression values from one gene and we calculated their average. The data was normalized by normalization. At last, 24,380 genes were obtained.
Getting the regulation relationship of transcriptional factors
The data of human h18's transcription factors and the information of banding sites of genes were downloaded from the University of California Santa Cruz (UCSC) Genome Browser Database. The transcription factor, which binding site was located between 1 kb upstream and 0.5 kb downstream of the transcription start site (TSS) of one gene, was selected and defined as the transcription factors of this gene. At last, 214,608 pairs of regulatory relationships between 216 transcription factors and 16,863 target genes were obtained.
Screening differentially expressed genes
Basing on the genes we obtained before, the limma package  in R was used to identify differentially expressed genes among different samples. We assumed that the gene with P < 0.05 and |logFC|>1 was identified as differentially expressed gene. Then, the genes of each cell lines were analyzed between experimental group and normal group.
Building regulatory networks
As we all know, SIRENE  is a method which assumes that the target genes of same transcription factor should have similar expression features basing on the theory of support vector machine. According to the DEGs and their expression information in their own cell lines we obtained above, the 214,608 pairs of regulatory relationships from UCSC were considered as the known regulatory training set, we used SIRENE to build differentially regulatory networks of HCC827 cell lines, ER3 cell lines, and T15-2 cell lines.
Comparisons between different regulatory networks
To find the differences between the regulatory networks of erlotinib-sentensive cell lines and erlotinib-resistence cell lines, the method of Limit Fold Change (LFC) was used in this study. Firstly, three regulatory networks of three cell lines were constructed into the networks with same number of nodes and edges, but we gave weight to every edge in different networks. If one edge did not exist in one regulatory network, then it will be built artificially and given a tiny weight. In other words, three networks were built with the same topological structure but with different weights of edges, then comparing the differences among the three networks were performed.
The comparison of regulatory networks of HCC827 and ER3were named compare I, the comparison of regulatory networks of HCC827 and T15-2 were named compare II. At first, we combined the edges of two networks in compare I, if one edge was seen only in one network, then we added it to the other network, but gave it a very low value 0.0001. In a similar way, the combination of compare II was obtained. In the two networks, we usually pay more attention to the edges which regulatory relationships changed in the other network. Therefore, the combined edges were divided into two groups: differentially regulatory edges with consistent changes (the regulatory relations which are positive or negative in both the two regulatory networks) and differentially regulatory edges with inconsistent changes (the regulatory which is positive in one networks but negative in the other networks), then the edges which regulatory relations had a significant change in the two networks were picked out by the method of LFC.
| > Results|| |
Screening differential expression genes
After pre-processing of the data, 24380 genes were got. Using limma package in R software, the gene with P < 0.05 and |logFC|>1 was identified as differentially expressed genes, and 1531 DEGs were screened out in HCC827 cell lines, 37 DEGs in ER3 cell lines, 156 DEGs in T15-2 cell lines with DMSO and erlotinib.
Building regulatory networks
After screening the differential expression genes, the DEGs of three cell lines were combined and the redundancy was cut out, then 1,575 DEGs were got. The expression information of these 1,575 DEGs in the three cell lines was extracted. Subsequently, SIRENE was used to build differential regulatory networks and three regulatory networks of HCC827 cell lines, ER3 cell lines, and T15-2 cell lines were obtained. HCC827 regulatory network had 1,261 regulation edges including 20 transcription factors and 772 target genes [Figure 1]. ER3 regulatory network possessed 176 regulation edges including 19 transcription factors and 171 target genes [Figure 2]. T15-2 regulatory network consisted of 409 regulation edges including 19 transcription factors and 356 target genes [Figure 3].
|Figure 1: HCC827 regulation network. Note: The yellow dots show the transcription factors, the blue ones show the target genes, the red ones show that the transcription regulate the target genes positively, the green ones show that the transcription factors regulate the target gene negatively. HCC827 regulation network has 1261 regulation edges including 20 transcription factors and 772 target genes|
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|Figure 2: ER3 regulation network. Note: The yellow dots show the transcription factors, the blue ones show the target genes, the red ones show that the transcription regulate the target genes positively, the green ones show that the transcription factors regulate the target gene negatively. ER3 regulation network has 176 regulation edges including 19 transcription factors and 171 target genes|
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|Figure 3: T15-2 regulation network. Note: The yellow dots show the transcription factors, the blue ones show the target genes, the red ones show that the transcription regulate the target genes positively, the green ones show that the transcription factors regulate the target gene negatively. T15-2 regulation network has 409 regulation edges including 19 transcription factors and 356 target genes|
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Comparisons between different regulating networks
By comparing the networks of HCC827 and ER3, there were 69 regulatory edges with consistent changes including 17 transcription factors and 56 target genes, and 56 edges with inconsistent changes including 16 transcription factors and 41 target genes [Table 1]. By the comparison of HCC827 and T15-2 [Table 2], there were 70 edges with consistent changes including 16 transcription factors and 59 target genes, 65 edges with inconsistent changes including 16 transcription factors and 47 target genes.
|Table 1: The opposite sign regulation edges with significant differences in the comparison between HCC827 regulatory network and ER3 regulatory network|
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|Table 2: The opposite sign regulation edges with significant differences in the comparison between HCC827 regulation network and T15-2 regulation network|
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By comparing HCC827 and ER3 network [Table 1], there were four transcription factors and six target genes such as transforming growth factor 1 (TGIF1), SRY-related high mobility group-box gene9 (SOX9), and Suppressor of cytokine signaling 3 (STAT3). In the comparison of HCC827 and T15-2 [Table 2], there are 24 regulatory edges involving 10 transcription factors and 22 target genes such as early growth response-1 (EGR1), TGIF1, and Suppressor of cytokine signaling 5B (STAT5B). At last, six regulation edges with inconsistent changes were obtained in the comparison of HCC827 and ER3, and 24 regulatory edges with inconsistent changes in the comparison of HCC827 and T15-2 were got.
| > Discussion|| |
The occurrence and development of cancer is a complex process. The research development of Molecular Biology and Genetics makes the special sites where the cancer occur and develop become the targets of the new anti-cancer drugs. Many target drugs have been used in the research before clinic or in clinic.
Lung cancer is the most common cancer in the world today (12.3% of all new cases), with an estimated 1.2 million new cases and 1.1 million deaths (17.8% of all cancer deaths) worldwide in 2000. However, people find that the resistance to erlotinib usually happens in NSCLC, so exploring the potential mechanism of NSCLC resistance to drugs will be urgent for further treatment of NSCLC.
In the present study, we utilized the gene expression profile downloaded from GEO to explore the mechanism of the erlotinib-resistance in NSCLC lines. A total of 1,575 DEGs were identified in the HCC827 cell lines, ER3 cell lines, T15-2 cell lines treated with erlotinib. Then, regulatory networks were screened out and the differences among these networks were detected. In the three networks, HCC827 cell lines are sensitive to erlotinid, whereas ER3 and T15-2 are resistant to erlotinid.
By comparing HCC827 and ER3 network, some transcription factors and target genes were found including STAT3, and SOX9,, and some specific documents have showed the effect and the mechanism of tumorigenicity of the two regulatory factors. The STAT family of transcription factors consists of seven proteins in humans (STAT1–STAT4, STAT5A, STAT5B, and STAT6) that are encoded by separate genes. STAT3 and STAT5 are the STAT most often implicated in human cancer progression. STAT3 has been the subject of more investigations than STAT5. SOX9 might contribute to gain of tumor growth potential, possibly acting through affecting the expression of cell cycle regulators p21 and CDK4. In the comparison of HCC827 and T15-2, some regulatory factors and target genes were found including STAT5B and STAT3, and there are documents showing that they are related to erlotinib-resistant in NSCLC. STAT3 were found both in the two comparisons. STAT3 was less suppressed compared to EGFR, suggesting signals from upstream might activate STAT3 even in EGFR-driven lung cancer. And some studies showed that early growth response-1 (EGR1) is related to the resistance of anti-cancer drugs like gefitinib. In addition, EGR1 and STAT6 were reported to be related to NSCLC. EGR1 expression was strongly correlated with phosphatase and tensin homolog deleted on chromosome ten (PTEN) expression (P < 0.0001), and patients with high levels of EGR1 had better overall and disease-free survival compared with patients with low levels of EGR1 (P = 0.040 and P = 0.096, respectively).
Exploring the mechanism of the resistance to EGFR-tyrosine kinase inhibitor in NSCLC is fascinating in lung cancer research. Our research might provide a new strategy in the medical therapy of lung cancer, including some important transcription factors and target genes, such as STAT3, SOX9, STAT5B, EGR1, and STAT6, which might be related to the resistance of NSCLC to erlotinib. However, further experimental verification is required, and the mechanism of NSCLC for resistance to EGFR-tyrosine kinase inhibitor needs to be further explored and researched.
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[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2]