Journal of Cancer Research and Therapeutics

ORIGINAL ARTICLE
Year
: 2020  |  Volume : 16  |  Issue : 4  |  Page : 793--799

Identification of activated pathways in lung adenocarcinoma based on network strategy


Bo Yu1, Tao Li2, Juan Chen1, Feng-Qiang Wang1, Jian-Hua Fu2, Shu-Mei Liu1, Yan Wang1, Xin Zhang1, Hai-Tao Yang1,  
1 Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng 252000, China
2 Department of Thoracic Surgery, People's Hospital of Liaocheng, Liaocheng 252000, China

Correspondence Address:
Juan Chen
Department of Respiratory Medicine, People's Hospital of Liaocheng, No. 67, East Dongchang Road, Liaocheng 252000, Shandong Province
China

Abstract

Background: Lung adenocarcinoma has increased incidence over the past years and is the cause for almost 50% of deaths attributable to lung cancer. The objective of this paper is to identify activated pathways associated with lung adenocarcinoma based on gene co-expression network analysis. Materials and Methods: Kyoto Encyclopedia of Genes and Genomes pathway analysis of dysregulated genes was performed based on Expression Analysis Systematic Explorer test to illuminate the biological pathways. Co-expression networks of lung adenocarcinoma in different tumor Stages (IA, IB, IIA, IIB, IIIA, IIIB, and IV) were constructed by Empirical Bayes approach to reweight gene pair scores. Pathway activity analysis was conducted to compute the distribution of pathways in different stages and to identify “activated” pathways in lung adenocarcinoma. Results: We evaluated 211 dysregulated genes between lung adenocarcinoma patients and normal controls. Pathway activity analysis was performed and P values of pathways, which obtained from co-expression networks (Stage IA, IB, IIA, IIB, IIIA, IIIB, and IV), were calculated. Cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis were activated during all stages in lung adenocarcinoma. Conclusions: We successfully identified three activated pathways (cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis) in different Stages (IA, IB, IIA, IIB, IIIA, IIIB, and IV) of lung adenocarcinoma.



How to cite this article:
Yu B, Li T, Chen J, Wang FQ, Fu JH, Liu SM, Wang Y, Zhang X, Yang HT. Identification of activated pathways in lung adenocarcinoma based on network strategy.J Can Res Ther 2020;16:793-799


How to cite this URL:
Yu B, Li T, Chen J, Wang FQ, Fu JH, Liu SM, Wang Y, Zhang X, Yang HT. Identification of activated pathways in lung adenocarcinoma based on network strategy. J Can Res Ther [serial online] 2020 [cited 2020 Oct 26 ];16:793-799
Available from: https://www.cancerjournal.net/text.asp?2020/16/4/793/199458


Full Text



 Introduction



Lung cancer, of which nonsmall-cell lung cancer (NSCLC) accounts for approximately 85%, is the most common cause of cancer-related death in both developing and developed regions.[1] Lung adenocarcinoma, a major histological subtype of NSCLC, has increased incidence over the past years and is the cause for almost 50% of deaths attributable to lung cancer.[2] Generally, it results from small bronchi, bronchioles, or alveolar epithelial cells and is typically peripherally located as reviewed elsewhere.[3]

Clinically, lung adenocarcinoma patients are categorized into seven Stages (IA, IB, IIA, IIB, IIIA, IIIB, and IV) based on the tumor size, local invasion, and presence of nodal and distant metastases.[4] Multiple clinical trials have demonstrated that surgical resection is considered curative in patients with Stage I lung cancer and adjuvant chemotherapy significantly improved the survival of the patients in Stage IB–IIIA.[5],[6] With the development of bioinformatics, researchers pay more attention to gene expression profiles study and identify several individual genes associated with lung adenocarcinoma stages. For example, several assays showed the gene signature of Stage I alone could not reveal differences in outcome in patients with Stage II disease.[7],[8]

However, previous works focus on effects of individual gene ignore that genes are not only encode as individual genes or proteins but also as subnetworks of interactions within a larger human genes network.[9] Network-based approaches offer an effective means to at least partially solve this challenge with providing potential malignancy diagnostic molecular and connecting them together.[10] Besides, the number of human genes has not yet been assigned to definitive pathways, scoring pathways based on network strategy becomes a more reliable analyzing approach. At present, the success of network-based pathway identification and classification supports the notion that cancer is indeed a “disease of pathways,” and that the keys for understanding at least some of these pathways are encoded in the protein networks.[11] For instance, lung adenocarcinoma lacks activation of any pathway posing a challenge for prognosis and treatment although previous studies have uncovered that activation of epidermal growth factor receptor, Kirsten rat sarcoma, and anaplastic large cell lymphoma kinase oncogenes define three different pathways of pathogenesis.[12] Further understanding of lung adenocarcinoma pathogenesis would be needed to unravel other activated pathways that play important roles in the development of this major subtype of lung cancer.

Therefore, the objective of this paper is to identify activated pathways based on the network approach in lung adenocarcinoma. To achieve this, we combined a given database (the Search Tool for the Retrieval of Interacting Genes/Proteins, STRING) method with Empirical Bayes (EB) approach and utilizing permutation strategy to investigate activated pathways in lung adenocarcinoma. The specific pipeline was:First, differentially expressed (DE) genes existed in STRING database were identified as dysregulated genes and pathway analysis was performed of these genes; then we reweighted gene pairs in different stages by EB approach relied on the backbone of STRING protein–protein interaction (PPI) network; finally, pathway activity analysis was conducted on the basis of reweighting gene pairs, and pathways within threshold were defined as activate pathway in lung adenocarcinoma.

 Materials and Methods



In this paper, a dataset of literature-curated, all human PPIs were acquired from STRING database comprising 16730 genes and 787,896 interactions. Here, STRING provides a critical assessment and integration of PPIs including direct (physical) as well as indirect (functional) associations.[13] Subsequently, dysregulated genes were identified by integrating PPI data with DE genes from gene expression data based on EB approach. Finally, activated pathways were detected by pathway enrichment analysis and permutation strategy analysis.

Gene expression data

The microarray expression profiles of E-GEOD-10072,[14] E-GEOD-20189,[15] E-GEOD-31210,[12] and E-GEOD-40791[16] from ArrayExpress database were selected for identify DE genes of lung adenocarcinoma. The characteristics of the four datasets were listed in [Table 1]. A total of 451 lung adenocarcinoma samples and 249 normal samples were collected. Before analysis, we performed the standard methods for preprocessing the gene expression profile of each dataset including background correction,[17] normalization,[18] probe match correction,[19] and summarization of expression.[17]{Table 1}

For the purpose of integrating the four datasets into a single group and removing the batch effects caused by the use of different experimentation plans and methodologies, the GENENORM method was utilized in an intuitive manner.[20] The modified gene expression value [INSIDE:1] was given by the expression:

[INLINE:1]

where Xij indicated each gene expression value in each study; indicated mean gene expression value in the dataset; K indicated the number of the studies; indicated the standard deviation of gene expression value. Finally, we obtained a merged gene expression data for further analysis.

Identifying differentially expressed genes

To identify DE genes between lung adenocarcinoma samples and normal controls, we utilized linear models for microarray data (Limma) package.[21] All genes were manipulated with t-test and F-test, and then Linear fit, EB statistics, and false discovery rate correction were performed to the data using lmFit function.[22] Genes which met to the thresholds of P < 0.05 and | log2 FoldChange | >2 were identified as DE genes of lung adenocarcinoma.

Scoring subnetwork and exploring dysregulated genes

The preprocessed DE genes and STRING gene symbol were mapped to Entrez Gene. For STRING, self-loops were removed, and proteins without expression value were also removed. The remaining largest connected component was kept as the PPI network, and expression matrix was reduced to genes presented in the PPI network and processed for subnetwork analyses. In this paper, a subnetwork of different Stages (IA, IB, IIA, IIB, IIIA, IIIB, and IV) was defined as a gene set that induced a single connected component in the PPI network. DE genes presented in the subnetworks were identified as dysregulated genes in our experiments.

Reweighting gene pairs by empirical Bayes approach

EB approach was selected to reweight gene pairs which obtained from PPI network based on dysregulated genes of lung adenocarcinoma.[23] An m-by-n matrix of expression values, where m was the number of genes (or probes) under consideration and n was the total number of microarrays overall conditions. These values should be normalized and obtained the X. The conditions array of length was n. The members of this array should take values in 1 − K, where K was the total number of conditions. Using ebPatterns function to define equal co-expression/differential co-expression (DC) classes.

From X and the conditions array, we calculated intragroup correlations for all l = m*(m − 1)/2 gene pairs, resulting D matrix of correlations was l-by-K. The initialization of the hyperparameters was performed to find the component normal mixture model that best fitted the correlations of D after transformation. Expectation maximization calculations were not conducted and instead used the initial estimates of the hyperparameters to generate posterior probabilities of DC.[24] DC score (DCS) (probabilities of DC) over 95% of the null distribution was recognized as DC gene pairs.

Pathway enrichment analysis

Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. In this study, KEGG pathway enrichment analysis for dysregulated genes was performed using the online tool the Database for Annotation, Visualization, and Integrated Discovery (DAVID).[25] KEGG pathways with P < 0.05 were selected based on Expression Analysis Systematic Explored (EASE) test implemented in the DAVID.[26] The principle of EASE was shown as follows:

[INLINE:2]

where n is the number of background genes; a' is the gene number of one gene set in the gene lists; a' + b is the number of genes in the gene list including at least one gene set; a' + c is the gene number of one gene list in the background genes; a' is replaced with a = a' − 1.

Pathway activity analysis

After the gene pairs were mapped to pathways, an activity score for each pathway as the summary of the expression values of all gene pairs belonging to this pathway was defined.[27] To assess the activity of the identified pathways in different Stages (IA, IB, IIA, IIB, IIIA, IIIB, and IV), the permutation strategy was used. First, gene–gene interactions in the weighted network and candidate interaction in a specific pathway subnet were regarded as background set and objective set, respectively. Next, vectors of individual gene–gene interactions in the network were randomly permuted. The same search procedure over 1000 random trials, in which the values of co-expression scores of individual gene pairs were randomly permuted on the network. Such permutation disrupted the mean value of permuted values of DCSs. Third, we tested whether the mean value of DCS for each pair in each pathway with the real network was stronger than that obtained with random assignments. The significance of the observed DCS was measured by nominal P, which was estimated by comparing the observed DCS with a set of randomized DCS. The score for each pathway was indexed on the null distribution of all random DCS. If the score was <0.05, the pathway was identified as “activated.”[28]

 Results



Dysregulated genes

There were 12493 genes in microarray gene expression profiles of E-GEOD-10072 and E-GEOD-20189, and 20109 genes in expression profiles of E-GEOD-31210 and E-GEOD-40791. After merging by GENENORM method, a total of 12493 genes were gained in the integrated dataset for further exploitation. With the thresholds of P < 0.05 and | log2 FoldChange | >2, we obtained 340 DE genes across patients and normal controls. Further, intersections of 340 DE genes and the backbone of STRING PPI network were performed and 211 dysregulated genes between lung adenocarcinoma patients and normal controls were identified for the following analysis.

Reweighted gene pairs

In this study, we obtained 22155 co-expressed gene pairs and their co-expression scores by EB approach based on 211 dysregulated genes. Co-expression networks in different Stages (IA, IB, IIA, IIB, IIIA, IIIB, and IV) were shown in [Figure 1], [Figure 2], [Figure 3], [Figure 4], respectively. For the same stage (Stage I, II and III), edges of Stage B were more than that of A, particularly for Stage II. Comparing Stage IA, IIA, and IIIA, Stage IA possessed more edges than others. However, no change rule was found among edges of Stage I, II, III, and IV in lung adenocarcinoma.{Figure 1}{Figure 2}{Figure 3}{Figure 4}

Pathway enrichment analysis

The KEGG pathway database is a collection of manually drawn pathway maps for metabolism, genetic information processing, environmental information processing such as signal transduction, various other cellular processes, and human diseases. Our results revealed that 211 dysregulated genes were significantly enriched in 10 terms [Table 2]. Cell cycle(P = 6.12E − 04), progesterone-mediated oocyte maturation (P = 1.23E − 03), oocyte meiosis (P = 5.02E − 03), extracellular matrix (ECM)-receptor interaction (P = 5.32E − 03), and vascular smooth muscle contraction (P = 5.54E − 03) were top five significant terms. Meanwhile, pathways in cancer (13) and neuroactive ligand-receptor interaction (12) possessed higher counts than others.{Table 2}

Activated pathways

The outcome of pathway activity analysis was shown in [Figure 5], we found that the outcomes in cell cycle progesterone-mediated oocyte maturation and oocyte meiosis pathway of Stage IA, IB, IIA, IIB, IIIA, IIIB, and IV were <0.05, thus the three pathways were commonly activated. Pathways in cancer and p53 signaling pathway were activated during lung adenocarcinoma process except Stage IIB, whereas renin–angiotensin system was not activated in any stage. For Stage IA, IIA, IIIB, and IV, there were nine activated pathways, respectively. There were five activated pathways (cell cycle progesterone-mediated oocyte maturation, oocyte meiosis, pathways in cancer, and p53 signaling pathway) in Stage IB. Only three common activated pathways were activated during Stage IIB, P value of the others was all more than 0.05 and was not activated.{Figure 5}

 Discussion



The goal of this paper is to identify activated pathways associated with lung adenocarcinoma based on gene interaction network analysis and pathway activity analysis. In this paper, we proposed a novel method to identify activated pathways in different stages of lung adenocarcinoma. Unlike the existing methods, our method considers the functional pathways by calculating the DCS (differentially co-expressed gene–gene interactions in networks) for each pathway using a permutation strategy. Benchmarking our method on different stages of lung adenocarcinoma demonstrates the effectiveness of the proposed method.

A total of 211 dysregulated genes for lung adenocarcinoma were identified. Co-expression networks of lung adenocarcinoma in different tumor Stages (IA, IB, IIA, IIB, IIIA, IIIB, and IV) were constructed using EB approach to reweight gene–gene interaction scores. Pathway activity analysis was performed, and the results showed that cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis were activated during all stages in lung adenocarcinoma. Meanwhile, pathways in cancer and p53 signaling pathway were activated during lung adenocarcinoma process except Stage IIB, whereas renin–angiotensin system was not activated in any stage. However, only three common activated pathways were activated during Stage IIB. It possibly resulted from the differences of samples in the gene expression data and the other reasonable explanation was the Stage IIB was special for lung adenocarcinoma progression. No matter which reason was further researches should be carried out to study this phenomenon.

Cell cycle is the series of events that take place in a cell leading to its division and duplication, and a dysregulation of the cell cycle components such as genes (such as cell cycle inhibitors, RB, and p53) may lead to tumor formation.[29] It had been reported that alterations in activated proteins (cyclins and cyclin-dependent kinases, etc.,) led to failure of cell cycle arrest, and thus served as markers of the more malignant phenotype and cell cycle-related genes aided in discrimination of atypical adenomatous hyperplasia from early adenocarcinoma.[30],[31] Chen et al. demonstrated that cell-cycle progression effects on nuclear factor-κB activity represented a molecular basis underlying the aggressive tumor behavior in lung adenocarcinoma.[32] No prognostic gene classes characterized the importance of cell cycle-related genes in prognostic signatures for lung adenocarcinoma patients and identified a specific signature likely to survive additional validation.[33] Recently, genomic profiling was applied to comprehensively identify alterations in the process of cell cycling, 40 genes of the 624 cell cycle genes on the microarray filters were predicted to be DE in lung adenocarcinoma.[34] We conducted further researches based on DE genes and verified that cell cycle was activated pathway in lung adenocarcinoma, which in accordance with studies referred to above.

The other two common activated pathways across seven stages of lung adenocarcinoma were progesterone-mediated oocyte maturation and oocyte meiosis. During meiosis, a single round of DNA replication is followed by two rounds of chromosome segregation (Meiosis I and Meiosis II).[35] Oocytes are naturally arrested at G2 of meiosis I, exposure to the steroid hormone progesterone breaks this arrest and induce resumption of the two meiotic division cycles and maturation of the oocyte.[36],[37] Hence, we might infer that the dysregulations of oocyte maturation and meiosis impacted the cell cycle process, and further cell cycle alteration affected the normal activities in the human body which increased the risks of suffering from cancer. In addition, roles of sex hormones in the development of lung cancer had attracted substantial interest, the results demonstrated that membrane progesterone receptor α was expressed in a lung adenocarcinoma cell line,[38] which was similar to our results. Above all, cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis were activated and played significant roles in the progression of lung adenocarcinoma from Stage IA to Stage IV.

There were two pathways (pathways in cancer and p53 signaling pathway) were activated in Stage IA, IB, IIA, IIIA, IIIB, and IV. Extensive investigations over the past decade have uncovered many of the important mechanistic pathways underlying cancer.[39] Pathways in cancer had been defined as an intrinsic one (driven by genetic events that cause neoplasia) and an extrinsic one (driven by inflammatory or other conditions which predispose to cancer)[40] such as apoptotic pathway in cancer and signaling pathway in cancer including p53 signaling pathway.[40],[41] As a member of cancer, lung adenocarcinoma was inevitable to relate to pathways in cancer. Besides, genetic alterations in lung adenocarcinoma frequently occurred in genes of the p53 signaling and cell cycle.[42] Liu et al. had demonstrated that p53-regulated cell death was correlated to lung cancer.[43] Therefore, the two pathways were significant to the tumor.

Meanwhile, neuroactive ligand-receptor interaction and ECM-receptor interaction were activated in Stage IA, IIA, IIIB, and IV. In detail, neuroactive ligand-receptor interaction was upregulated and involved in the etiology of NSCLC.[44] There is rare research to focus on activation of neuroactive ligand-receptor interaction in different stages of lung adenocarcinoma, and thus it is the first time to uncover the relationship between this pathway and lung cancer. For ECM-receptor interaction, Douglas et al. discovered that ECM-receptor interaction and focal adhesion pathways were implicated in Stage I and Stage II/III lung tumors which indicated the increased emphasis on cell migration and invasion in the later stage tumors.[45] This finding gave a great hand for our study and strengthened the feasibility of present analysis methods. However, the result was not an exact match with ours since the division of stages was different, and the samples also were different.

 Conclusions



We identified three activated pathways (cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis) in different Stages (Stage IA, IB, IIA, IIB, IIIA, IIIB, and IV) of lung adenocarcinoma based on the method which considered the DC interactions within the pathways in the networks.

Acknowledgments

This research received no specific grants from any funding agency in public, commercial, or not-for-profit sectors.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

1Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin 2011;61:69-90.
2Herbst RS, Heymach JV, Lippman SM. Lung cancer. N Engl J Med 2008;359:1367-80.
3Kadara H, Kabbout M, Wistuba II. Pulmonary adenocarcinoma: A renewed entity in 2011. Respirology 2012;17:50-65.
4Rami-Porta R, Crowley JJ, Goldstraw P. The revised TNM staging system for lung cancer. Ann Thorac Cardiovasc Surg 2009;15:4-9.
5Wood AJ, Spira A, Ettinger DS. Multidisciplinary management of lung cancer. N Engl J Med 2004;350:379-92.
6Booth CM, Shepherd FA. Adjuvant chemotherapy for resected non-small cell lung cancer. J Thorac Oncol 2006;1:180-7.
7Bianchi F, Nuciforo P, Vecchi M, Bernard L, Tizzoni L, Marchetti A, et al. Survival prediction of stage I lung adenocarcinomas by expression of 10 genes. J Clin Invest 2007;117:3436-44.
8Lau SK, Boutros PC, Pintilie M, Blackhall FH, Zhu CQ, Strumpf D, et al. Three-gene prognostic classifier for early-stage non small-cell lung cancer. J Clin Oncol 2007;25:5562-9.
9Vinayagam A, Zirin J, Roesel C, Hu Y, Yilmazel B, Samsonova AA, et al. Integrating protein-protein interaction networks with phenotypes reveals signs of interactions. Nat Methods 2014;11:94-9.
10Nitsch D, Tranchevent LC, Gonçalves JP, Vogt JK, Madeira SC, Moreau Y. PINTA: A web server for network-based gene prioritization from expression data. Nucleic Acids Res 2011;39:W334-8.
11Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol 2007;3:140.
12Okayama H, Kohno T, Ishii Y, Shimada Y, Shiraishi K, Iwakawa R, et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res 2012;72:100-11.
13Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 2015;43:D447-52.
14Shiraishi T, Matsuyama S, Kitano H. Large-scale analysis of network bistability for human cancers. PLoS Comput Biol 2010;6:e1000851.
15Rotunno M, Hu N, Su H, Wang C, Goldstein AM, Bergen AW, et al. Agene expression signature from peripheral whole blood for stage I lung adenocarcinoma. Cancer Prev Res (Phila) 2011;4:1599-608.
16Zhang Y, Foreman O, Wigle DA, Kosari F, Vasmatzis G, Salisbury JL, et al. USP44 regulates centrosome positioning to prevent aneuploidy and suppress tumorigenesis. J Clin Invest 2012;122:4362-74.
17Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP. Summaries of affymetrix GeneChip probe level data. Nucleic Acids Res 2003;31:e15.
18Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003;19:185-93.
19Bolstad B. Affy: Built in Processing Methods; 2013. Available form: http://www. bioconductor.org/packages/release/bioc/vignettes/affy/inst/doc/builtinMethods. pdf, 2013.
20Bolstad B. Affy: Built in Processing Methods; 2013. http://www. bioconductor. org/packages/release/bioc/vignettes/affy/inst/doc/builtinMethods. pdf, 2013.
21Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004;3:3.
22Diboun I, Wernisch L, Orengo CA, Koltzenburg M. Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics 2006;7:252.
23Dawson JA, Ye S, Kendziorski C. R/EBcoexpress: An empirical Bayesian framework for discovering differential co-expression. Bioinformatics 2012;28:1939-40.
24Moon TK. The expectation-maximization algorithm. IEEE Signal Process Mag 1996;13:47-60.
25Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57.
26Wang X, Simon R. Microarray-based cancer prediction using single genes. BMC Bioinformatics 2011;12:391.
27Zhang HQ, Lin MZ, Shen KY, Ge L, Li JS, Tang MX, et al. Surgical management for multilevel noncontiguous thoracic spinal tuberculosis by single-stage posterior transforaminal thoracic debridement, limited decompression, interbody fusion, and posterior instrumentation (modified TTIF). Arch Orthop Trauma Surg 2012;132:751-7.
28Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: A primer with examples. Hum Brain Mapp 2002;15:1-25.
29Hirt BV. Mathematical Modelling of Cell Cycle and Telomere Dynamics. Nottingham: University of Nottingham; 2013.
30Singhal S, Vachani A, Antin-Ozerkis D, Kaiser LR, Albelda SM. Prognostic implications of cell cycle, apoptosis, and angiogenesis biomarkers in non-small cell lung cancer: A review. Clin Cancer Res 2005;11:3974-86.
31Ho YF, Karsani SA, Yong WK, Abd Malek SN. Induction of apoptosis and cell cycle blockade by helichrysetin in a549 human lung adenocarcinoma cells. Evid Based Complement Alternat Med 2013;2013:857257.
32Chen G, Bhojani MS, Heaford AC, Chang DC, Laxman B, Thomas DG, et al. Phosphorylated FADD induces NF-kappaB, perturbs cell cycle, and is associated with poor outcome in lung adenocarcinomas. Proc Natl Acad Sci U S A 2005;102:12507-12.
33Dancik GM, Theodorescu D. Robust prognostic gene expression signatures in bladder cancer and lung adenocarcinoma depend on cell cycle related genes. PLoS One 2014;9:e85249.
34Singhal S, Amin KM, Kruklitis R, DeLong P, Friscia ME, Litzky LA, et al. Alterations in cell cycle genes in early stage lung adenocarcinoma identified by expression profiling. Cancer Biol Ther 2003;2:291-8.
35Mehlmann LM. Signaling for meiotic resumption in granulosa cells, cumulus cells, and oocyte. In: Oogenesis. Berlin: Springer; 2013. p. 171-82.
36Mahrous E, Yang Q, Clarke HJ. Regulation of mitochondrial DNA accumulation during oocyte growth and meiotic maturation in the mouse. Reproduction 2012;144:177-85.
37Shao H, Li R, Ma C, Chen E, Liu XJ. Xenopus oocyte meiosis lacks spindle assembly checkpoint control. J Cell Biol 2013;201:191-200.
38Xie M, You S, Chen Q, Chen X, Hu C. Progesterone inhibits the migration and invasion of A549 lung cancer cells through membrane progesterone receptor a-mediated mechanisms. Oncol Rep 2013;29:1873-80.
39Del Prete A, Allavena P, Santoro G, Fumarulo R, Corsi MM, Mantovani A. Molecular pathways in cancer-related inflammation. Biochem Med (Zagreb) 2011;21:264-75.
40Dreesen O, Brivanlou AH. Signaling pathways in cancer and embryonic stem cells. Stem Cell Rev 2007;3:7-17.
41Patra SK. Dissecting lipid raft facilitated cell signaling pathways in cancer. Biochim Biophys Acta 2008;1785:182-206.
42Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 2008;455:1069-75.
43Liu YH, Liu GH, Mei JJ, Wang J. The preventive effects of hyperoside on lung cancerin vitro by inducing apoptosis and inhibiting proliferation through Caspase-3 and P53 signaling pathway. Biomed Pharmacother 2016;83:381-91.
44Saito Y, Nagae G, Motoi N, Miyauchi E, Ninomiya H, Uehara H, et al. Prognostic significance of CpG island methylator phenotype in surgically resected small cell lung carcinoma. Cancer Sci 2016;107:320-5.
45Douglas SE, Bethune DC, Xu Z. Mi-croarray analysis identifies pathways in progression of early stage lung adenocarcinoma: The importance of focal adhesion and ECM-receptor interactions. Pulm Res Respir Med Open J 2014;1:21-31.