0115 966 7955 Today's Opening Times 10:00 - 20:00 (BST)
Place an Order
Instant price

Struggling with your work?

Get it right the first time & learn smarter today

Place an Order
Banner ad for Viper plagiarism checker

Non Small Cell Lung Cancer Pathway Crosstalk Analysis

Disclaimer: This work has been submitted by a student. This is not an example of the work written by our professional academic writers. You can view samples of our professional work here.

Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.

Published: Mon, 04 Jun 2018

Pathway Crosstalk Analysis of Non-Small Cell Lung Cancer Based on Microarray Gene Expression Profiling

Chunyan Xing,MM, Ronghua Zhang,MM, Jiyun Cui,MM, Yonghong Li,BM, Guanhua Li,MM, Yanna Yang,MM, Longbin Pang,MM, Xiaoyun Ruan,MM, Jun Li*,MM

Abstract

Aims and background: Lung cancer is a disease characterized by uncontrolled cell growth in tissue of the lung. A major challenge in current cancer research is the biological interpretation of the complexity of cancer somatic mutation profiles. The aim of this study was to analyze the important function of pathway crosstalk in the metastasis process of lung cancer cells based on DNA microarray analysis.

Methods: We downloaded the gene expression profile GSE10096 from Gene Expression Omnibus database. Compared with control samples, the probe-level data were pro-processed. The differentially expressed genes (DEGs) were identified with packages in R language and gene functions of the selected DEGs were further analyzed. After KEGG pathway analysis, the dysfunctional pathways and dysfunctional crosstalk between pathways in the two types of lung cancer cells (low metastasis M1 and high metastasis M5) was analyzed.

Results: A total of 13433 genes were filtered as DEGs, and after pathway analysis, a total of 108 signaling pathways related to cancer signaling pathways were screened, including a host pathway hsa05223 and 79 neighbor pathways. It can be concluded from the dysfunctional crosstalk analysis of pathways that the pathway crosstalk dysfunction of low metastasis M1 and the high metastasis M5 lung cancer cell mainly occurred between hsa05223 (Non-small cell lung cancer) and hsa04310 (Wnt signaling pathway), and between Non-small cell lung cancer and hsa04520 (Adherens junction), respectively. In addition, the significant pathway crosstalk dysfunction also existed between Adherens junction and other classic signaling pathways such as hsa04110 (Cell cycle), hsa04310 (Wnt signaling pathway), hsa04350 (TGF-beta signaling pathway) and hsa04630 (Jak-STAT signaling pathway).

Conclusions: The discovery helps us to elucidate the molecular mechanism of the high metastasis and cancerization of lung cancer cells. In addition, it will pave the way to develop effective therapies for lung cancer.

Key words: non-small cell lung cancer, pathway data mining, PPI network, pathway crosstalk, pathway crosstalk dysfunction.

Introduction

In 2013, approximatedly 220 thousands new cases of lung cancer are reported in USA in 2013, among which 160 thousands cases are dead, consequently 1, 2. These updated findings provide more evidence that extensive efforts should be made for prevention and treatment of the lung cancer. As the primary type of Lung cancer, non-small cell lung cancer (NSCLC) has widely studied in various terms such as diagnosis, clinical presentation and treatment 5.

Diagnosis based on symptoms to detect early lung cancer is not very effective. If the early pathological changes can be detected, the five-year survival rate for lung cancer will raise to 60-80 percent 6. Therefore, studying the pathogenesis of lung adenocarcinoma and exploring biomarker and new therapeutic targets related to diagnostic and prognostic have an important practical significance on looking for better methods for diagnosis and treatment 7.However, further study in the mechanism of lung cancer is still needed. It has been reported that type 1 insulin-like growth factor receptor (IGF-1R) might play a crital role in NSCLC and is considerred as a promising target for cancer treatment 10. It has been confirmed that the development of lung cancer is a multi-step and multi-stage process in which many genes participate 11. In addition to functioning alone, two or more pathways might cooperate to perform importance biological function in the complicated biological process12. The crosstalk between c-Met and EGFR has been reported to be associated with the proliferation and motility of NSCLC cells 13, 14. To provide more insights concerning the pathway crosstalk in NSCLC, the study performed dysfunctional pathways and dysfunctional crosstalk between pathways with a gene expression profile (GSE10096) consisting of non-metastatic cell samples and cell samples at differernt metastatic levels. Crucial pathway crosstalk dysfuntion in low or high metastasis lung cancer was unveiled, respectively. Our study would provide more clues regarding the metastasis molecular mechanism of NSCLC and identify more potential biological biomarkers for early diagnosis and treatment for it.

Materials and Methods

Derivation of genetic data

The gene expression profile of GSE10096 was extracted from GEO (Gene Expression Omnibus) database, which included a control group and 3 experimental groups. The control group was comprised of 4 non-metastatic cell samples. The 3 experimental groups consisted of metastatic cell samples in M1 (low metastasis), M4 (moderate metastasis) and M5 (high metastasis) according to the metastasis ability, and three repeats of cell lines samples were involved in each group. In order to investigate the differences between the high-and low-metastatic samples of NSCLC, metastatic samples of M1 and M5 was selected in this study.Platform information was GPL571 [HG-U133A_2] Affymetrix Human Genome U133A 2.0 Array.

Data preprocessing

R affy package (v. 2.15.3) 16 was used for microarray data analysis. The probe-level data in CEL files were converted into expression measures and performed background correction and quartile data normalization by the robust multiarray average (RMA) 17 algorithm with defaulted parameters. The limma package 18 was used to do differential analysis, and the Benjamin and Hochberg (BH) method 19 was used to adjust the raw P-values into false discovery rate (FDR). Finally we got 13433 differential p-values of gene expressions.

Kyoto Encyclopedia of Genes and Genomes pathway analysis and protein-protein interaction network construction

Bases on data mining with Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we constructed a lung cancer-related pathway collection by selecting the key signaling pathway, hsa05223 Non-small cell lung cancer pathway as the host pathway, and the pathways which shared at least one overlap gene with the host pathway as neighbor pathways. Out of all genes involved in the pathway collection, pairs of genes with protein-protein interaction (PPI) relations were screened out according to the PPI database which was composed by the intersection of the three databases including mint 20, hprd 21 and grid 22 were screened. We finally constructed PPI network by using these PPI pairs of genes for subsequent analyses. The proteins in the PPI network were represented by the ‘nodes’. Each interaction between proteins is represented by an edge. The degree of a node was in accordance with the number of edges of a protein.

Correlation analysis of co-expressed genes

To determine the function of co-expressed genes, the gene expression profile was mapped to the PPI network, and the Pearson’s correlation coefficient (PCC) of each differentially co-expressed PPI pair was calculated. The p-value of the correlation coefficient was chosen as the reference value of significant difference. The following formula was used to calculate protein interaction score 15:

Where diff(x) and diff(y) are differential expression assessments of gene x and gene y, respectively. And corr(x ‚ y) represents their correlation between gene x and gene y. Where k =3, p1and p2 are the p-values of differential expression of two nodes, and p3 is the p-value of their co-expression.

Pathway crosstalk analysis

Crosstalk between pathways were evaluated by the overlapping score C, accumulation of the scores of all overlapping edges between two pathways, according to a previous study 15. The total score C was calculated in the following fomula:

Where Pi and Pj are the two pathways; S (e) is the total score of all overlapping edges.

To appraise the significance of the crosstalk, two random pathways in the same size as that of Pi and Pj pathways in the edges of pathway network were selected 106 times by the computer, and the overlap score between the two random pathways was calculated. The frequence of the score higher than C were deemed as the p-value of the crosstalk significance between two pathways. The p-value represented the interaction significance between two pathways.

The dysfunction analysis of relevant pathway

The dysfunction significance of a pathway was evaluated by calculating the Sp, the summation of all the scores of edges in the pathway 15. The total score Sp of each pathway was obtained by accumulating all the edge scores of a single pathway. Sp was computed with the following fomula:

Where S (e) represents the total amount of all the edge scores of a given pathway.

The pathway which has the same size of pathway P was generated randomly by the computer, and the score of the generated pathway was calculated 10 6 times. The frequency of the score larger than Sp was selected as the p-value of the pathway dysfunction significance.

Results

Lung cancer related pathway data mining and PPI network construction

We obtained 80 signaling pathways related to cancer signaling pathways from data mining, including a host pathway associated with human cancer (hsa05223, Non -small cell lung cancer) and 79 neighbor pathways. All 3601genes in the 80 pathways were extracted, and 4866 PPI pairs related to all genes in PPI database (excluding protein itself interaction, the two interacted proteins were from those proteins composed of the 80 pathways) were screened. Finally the PPI network was constructed (fig 1).

The analysis of dysfunctional pathway and crosstalk among pathways

The rank of dysfunctional pathways in M1 and M5 was shown in table 1 and table 2, respectively. Table 3 and 4 were lists of pathway crosstalk in M1 and M5, respectively. The hsa05223 pathway (Non-small cell lung cancer) was screened as the host pathway, and the neighbor pathways with significant pathway crosstalk (p<0.05) were selected. Cytoscape software 23 was used to build pathway crosstalk network as shown in fig 2. Compared with the control, the pathway crosstalk dysfunction of low metastasis M1 lung cancer cell mainly occurred in the crosstalk between hsa05223 (Non-small cell lung cancer) and hsa04310 (Wnt signaling pathway) (p-value=0.003) (Table 3), and the pathway crosstalk dysfunction of high metastasis M5 lung cancer cell mainly occurred in the crosstalk between Non-small cell lung cancer and hsa04520 (Adherens junction) (p-value=0.00047) (Table 4). In addition, the significant pathway crosstalk dysfunction in M5 lung cancer also existed between Adherens junction and other classic signaling pathways such as hsa04110 (Cell cycle), hsa04310 (Wnt signaling pathway), hsa04350 (TGF-beta signaling pathway) and hsa04630 (Jak-STAT signaling pathway).

Discussion

There is evidence supportive of the high incidence of lung cancer in differernt countries 24. Therefore, there is an urgent need to explore the mechanism of lung cancer for developing an effective prevention strategy against the cancer. In this paper, we analyzed the dysfunctional pathways and pathway crosstalk dysfunction in the two types of lung cancer cells (low metastasis M1 and high metastasis M5) by using a system biology approach. Our study showed that the pathway crosstalk dysfunction of low metastasis M1 and high metastasis M5 lung cancer cell mainly occurred inthe interactions between hsa05223 (Non-small cell lung cancer) and hsa04310 (Wnt signaling pathway), and between hsa05223 and hsa04520 (Adherens junction), respectively. In addition, the significant pathway crosstalk dysfunctions in M5 also existed between Adherens junction and other classic signaling pathways such as hsa04110 (Cell cycle), hsa04310 (Wnt signaling pathway), hsa04350 (TGF-beta signaling pathway) and hsa04630 (Jak-STAT signaling pathway).

It has been well-documented that genes and pathways are important participators in tumor formation or progression. Cyclin –dependent kinase 4 (CDK4), which was involved in the hsa05223 (Non-small cell lung cancer) pathway in our study, is part of the cyclin-dependent kinase family. The upregulation of CDK4 in lung cancer has been reported and might be involved in the G1-S transition of cancer cells 25. The role of CDK4 in lung cancer has been further unveiled by a study showing that p21 might mediate the regulation of cell cycle by CDK4 and CDK4 is recommended as a promisingnegative prognostic factor of patients with the cancer 26. Our study provided evidence that CDK4 might affect the tumorigenesis of lung cancer via Non-small cell lung cancer pathway. These findings revealed the complicated mechanism underlying the role of CDK4 in lung cancer.

Wnt signaling pathway (hsa04310) has also been reported to be a critial player in lung carcinogenesis 27-30. There is evidence that tts activation is positively asociated with reoccurance of Stage I NSCLC 31 . Casein kinase 1 A1(CSNK1A1), which is involved in Wnt signaling pathway is significantly down regulated in hypoxia induced lung cancer and might be a potentialsignature biomarker for lung cancer 33. Our result revealed that the interaction of Wnt signaling pathway and Non-small cell lung cancer pathway might play a key role in the occurrence of lung cancer. Adherens junction signaling pathway (hsa04520) has been reported to be involved in the progression of lung cancer, and its important component of Adherens junction pathway, E-Cadherin, has been suggested as prognostic biomarker of NSCLC 36, 37. Moreover, Casein kinase 2, alpha (CSNK2α), which is also involved in the pathway, has been found to be overexpressed and might serve as an oncogene in lung cancer 38. Consistenly, this study showed that there was significant dysfunction in the crosstalk between hsa05223 (Non-small cell lung cancer) and hsa04520 (Adherens junction), which suggested that Adherens junction signaling pathway might play an role in the metastasis of lung cancer cells by interaction with Non-small cell lung cancer pathway 39, 40.

The significant pathway crosstalk dysfunction also existed between Adherens junction and other classic signaling pathways in additon to Wnt signaling pathway, including Cell-cycle signaling pathway (hsa04110), TGF-beta signaling pathway (hsa04350) and the Jak-STAT signaling pathway (hsa04630). The Cell-cycle signaling pathway (hsa04110) has been reported in variety of tumors, such as lung cancer 41, 42. The TGF-beta signaling pathway (hsa04350) has been also found to be implicated in several types of human cancer including breast, lung, and colon 43, 44, and is related to the sensitivity of lung cancer cells to radiation 45. Cyclin –dependent kinase 4 inhibitor B (CDKN2B) encodes a cyclin-dependent kinase inhibitor that is involved in cell cycle G1 progression through preventing the activation of CDK kinases-encoded proteins. CDKN2B has been reported to be a tumor suppressor gene and frequently deleted in lung cancer cell lines 46. Moreover, it has been found that the expression of CDKN2B is induced by TGF beta 47, suggesting TGF beta might play a part in lung cancer via regulating CDKN2B. The Jak-STAT signaling pathway (hsa04630) has been identified as a important participant in the biology of cancer stem cells 38, 39. It has been reported that p-STAT3 is associated with EGFR mutation status in lung adenocarcinoma and related to adenocarcinoma metastasis 54. Thus, we assume that these classic signaling pathways may promote high metastasis of lung cancer cells by the crosstalk with Adherens junction signaling pathway.

Conclusion

The study unravelled the pathway crosstalk dysfunction between Non-small cell lung cancer and Wnt signaling pathway, and between Non-small cell lung cancer and Adherens junction might play an important role in lung cancer metastasis. Our discovery might provide useful clues regarding the complex interacting mechanisms underlying the cancer metastasis and lay a foundation for the individualized medical therapy of lung cancer.

Figures legends

Figure 1. PPI network of Cancer signaling pathway (hsa05223) and its adjacent signaling pathways.

The red dots in the figure represent the genes involved in the cancer signaling pathway (hsa05223), and the green dots represent the genes involved in its adjacent signaling pathways. Total 1652 nodes and 4806 sides are included.

Figure 2. athway crosstalk dysfunction map(left:M1 vs Control, right: M5 vs Control).

The node size represents the gene numbers involved in the pathway (pathway size), and the larger the node is, the larger the pathway size is. The node color represents pathway dysfunction significance which decreases from red to blue. The edge thickness represents the crosstalk dysfunction significance between pathways. The thicker the edge is, the greater the dysfunction significance of crosstalk between pathways is.

Table 1 Rank of dysfunctional pathways in M1 vs Con

Pathway

p-value

size

description

hsa04310

0.002518

151

Wnt signaling pathway

hsa04520

0.014275

75

Adherens junction

hsa04730

0.019998

69

Long-term depression

hsa05131

0.024458

64

Shigellosis

hsa05416

0.092412

73

Viral myocarditis

hsa05223

0.892467

54

Non-small cell lung cancer

Table 2 Rank of dysfunctional pathways in M5 vs Con

Pathway

p-value

size

description

hsa04310

0.000397

151

Wnt signaling pathway

hsa05416

0.003409

73

Viral myocarditis

hsa04916

0.004396

102

Melanogenesis

hsa04110

0.007106

128

Cell cycle

hsa04630

0.00742

155

Jak-STAT signaling pathway

hsa04520

0.030226

75

Adherens junction

hsa04320

0.034347

25

Dorso-ventral axis formation

hsa04350

0.108996

85

TGF-beta signaling pathway

hsa05223

0.828123

54

Non-small cell lung cancer

Table 3 pathway crosstalk analysis in M1

Pathway

Pathway

crosstalk p-value

hsa05223

hsa04310

0.00304

hsa05416

hsa05131

0.02417

hsa05223

hsa05131

0.02451

hsa04730

hsa05131

0.02465

hsa04310

hsa05131

0.0249

hsa05416

hsa04520

0.02521

hsa04310

hsa04520

0.02563

hsa04730

hsa04520

0.02581

hsa05131

hsa04520

0.02587

hsa05223

hsa04520

0.02594

hsa04730

hsa05416

0.03567

hsa05223

hsa05416

0.03571

hsa04310

hsa05416

0.03586

hsa05223

hsa04730

0.04321

hsa04310

hsa04730

0.04381

Table 4 pathway crosstalk analysis with hsa04520 in M5

Pathway

description

crosstalk p-value

hsa05223

Non-small cell lung cancer

0.00047

hsa04110

Cell cycle

0.00049

hsa04310

Wnt signaling pathway

0.0005

hsa04350

TGF-beta signaling pathway

0.00051

hsa04630

Jak-STAT signaling pathway

0.00055


To export a reference to this article please select a referencing stye below:

Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.