Identification of genes associated with matrix metalloproteinases in invasive lung adenocarcinoma
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- Published online on: May 10, 2018 https://doi.org/10.3892/ol.2018.8683
- Pages: 123-130
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Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Lung cancer is a leading cause of cancer mortalities worldwide, with 226,160 new cases and 160,340 mortalities in 2012 (1). As a type of lung cancer, non-small cell lung carcinoma (NSCLC) consists of adenocarcinoma (AC), squamous cell carcinoma and large cell carcinoma (2). Lung AC, a heterogeneous group of tumors ranging in aggressiveness from noninvasive bronchioloalveolar carcinoma (BAC) to microinvasive tumors, mixed-type tumors and pure invasive AC, is the most common histological type of lung cancer (3,4). The first treatment for patients with early-stage lung AC is surgical resection, but the 5-year overall survival rate remains at ~80% for stage IA disease (5). Notably, patients with invasive lung AC have a lower mean 5-year survival rate compared with that of patients with BAC (59 vs. 100%, respectively) (6). Therefore, it is important to better understand the molecular mechanisms of invasive lung AC in order to develop effective preventive and curative strategies.
Cancer invasion is a highly complicated process, with the early events being the proteolytic degradation of extracellular matrix (ECM) components to provide room for infiltration, followed by migration into the adjacent tissues (7,8). Recent studies suggest that matrix metalloproteinases (MMPs), members of the matrixin subfamily of zinc metalloproteinases, are involved in the breakdown of the ECM (9,10). Over-expression of MMPs has been detected in a number of tumor types, including invasive lung AC (11). MMP2, MMP7, MMP9 and tissue inhibitor of metalloproteinase 2 are upregulated in the lung tissue of patients with primary spontaneous pneumothorax (PSP), and the imbalance of their expression may be implicated in the pathogenesis of PSP (12). The signal module and sequence variant module appearing in lung AC, in which the expression of MMP12 is upregulated but that of MMP11 is downregulated, can promote the invasion of cancer cells (13). Chemotherapy drugs can markedly inhibit the invasive ability of human lung AC cells via reducing the expression of MMP2 and MMP9 (14,15). Additionally, rosuvastatin and simvastatin can function in the treatment of lung cancer by regulating the expression of MMP2, MMP9, RAS and nuclear factor-κB-p65 (16). However, the current number of screened MMPs is limited, and further studies are required to identify genes with a similar function to that of MMPs.
In addition, it has been reported that the transcription factor signal transducer and activator of transcription 3 (STAT3) can regulate the transcription of MMP2 and promote melanoma cell invasion (17). Blocking STAT3 signaling significantly inhibits the invasion of melanoma cells (17). Storz et al (17) have demonstrated that forkhead box O3 promotes invasion and progression in numerous human solid tumors by inducing the expression of MMP9 and MMP13. However, the regulatory mechanisms of MMPs are not well understood in lung AC.
The present study aimed to identify differentially expressed genes (DEGs), particularly MMPs-associated genes, using gene expression profile data of invasive lung AC and adjacent normal samples collected from a publicly available database. Besides, the transcriptional factors regulating MMPs-associated genes were also investigated.
Materials and methods
Ethical statement
The present study was approved by the Ethics Committees of the Beijing Shijitan Hospital (Beijing, China) and the Chest Hospital Affiliated to Shanghai Jiaotong University (Shanghai, China), and it was performed in accordance with the ethical standards (18). In addition, written informed consent was obtained from all patients, prior to enrollment in the present study.
Microarray data
The gene expression dataset GSE2514, which is based on two platforms [GPL81 (MG_U74Av2) Affymetrix Murine Genome U74A Version 2 Array and GPL8300 (HG_U95Av2) Affymetrix Human Genome U95 Version 2 Array], was downloaded from Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database (19). Microarray data obtained with the platform GPL8300 (HG_U95Av2; Affymetrix Human Genome U95 Version 2 Array; Affymetrix, Inc., Santa Clara, CA, USA) was downloaded from the GEO database (19) in our study. It contained 20 lung AC samples and 19 adjacent lung samples (~1 cm away from the tumor site), which were obtained from 5 male and 5 female patients undergoing lobectomy (9) and wedge resection (1). Of these patients, 9 had a history of tobacco smoking. The ages of the patients ranged from 45 to 73 years. Their tumors were all invasive lung AC. The majority of tumors were low-to-intermediate grade and low stage, although 2 stage III tumors were included in the analysis. Probe annotation files were also acquired.
Preprocessing and differential analysis
The raw array data in the CEL file (Affymetrix, Inc.) were transformed into recognizable gene expression data using the robust multi-array average algorithm from the affy package in R (20). Upon normalization, probes were mapped to genes according to the annotation files. The levels of probes corresponding to one gene were averaged and used as the final gene expression value. DEGs were screened using the limma package (http://www.bioconductor.org/packages/release/bioc/html/limma.html) (21) in R based on the cut-offs of P<0.05 and |log fold-change| >1.
Cluster analysis
Cluster analysis of DEGs was performed with Cluster 3.0 (http://bonsai.ims.u-tokyo.ac.jp/mdehoon/software/cluster) using the K-means clustering algorithm (22), which was conducted on data with K (the number of clusters)=5.
Pearson correlation analysis
The method of Pearson correlation analysis (23) in the cor function in R (https://www.r-project.org/) was used to perform the correlation analysis between the expression of DEGs and MMPs. A correlation coefficient of >0.8 was used as the cut-off criterion.
Western blot analysis
A total of 6 lung AC and matched adjacent lung tissue biopsy samples were obtained in June 2014 from Shanghai Lung Cancer Center, Chest Hospital Affiliated to Shanghai Jiaotong University (Shanghai, China). Tissues were washed with ice-cold PBS and lysed in ice-cold radioimmunoprecipitation assay lysis buffer (Sangon Biotech Co., Ltd., Shanghai, China). Extracted proteins were quantified using a BCA Protein Assay kit (Sangon Biotech Co., Ltd., Shanghai, China) and separated with SDS-PAGE with a 10% separating gel and a 5% stacking gel. Subsequently, proteins were transferred onto polyvinylidene difluoride membranes (EMD Millipore, Billerica, MA, USA). Then, membranes were blocked in 5% nonfat dry milk and probed with primary rabbit antibodies against MMPs (anti-MMP1, cat. no. sc-58377; anti-MMP7, cat. no. sc-80205; anti-MMP9, cat. no. sc-13520; and anti-MMP12, cat. no. sc-8839; all diluted to 1:500; Santa Cruz Biotechnology, Inc., Dallas, TX, USA) overnight at 4°C. Horseradish peroxidase-conjugated anti-rabbit immunoglobulin G (cat. no. 111-035-003; 1:5,000; Jackson ImmunoResearch Laboratories, Inc., West Grove, PA, USA) served as a secondary antibody and was incubated for 1 h at 4°C. The anti-β-actin antibody (cat. no. 8227; 1:10,000; Abcam, Cambridge, CA, USA) was used as the control. The position of protein bands was developed with ECL chemoluminescence kit (Merck Millipore) and visualized under a ChemiDoc MP imaging system (Bio-Rad Laboratories, Inc., Hercules, CA, USA).
Functional and pathway enrichment analysis
Gene Ontology (GO; http://www.geneontology.org/) (24) and Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) (25) pathway enrichment analyses were performed for the MMPs-associated genes using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (26) to reveal altered biological functions in lung AC. P<0.05 and a false discovery rate (FDR) adjusted by the Benjamini and Hochberg method (27) of <0.01 were set as the thresholds.
Selection of disease-associated genes
Diseases associated with the DEGs were identified by gene set enrichment analysis against the Genetic Association Database Disease Class (http://geneticassociationdb.nih.gov/) using the annotation server DAVID (P<0.05) (26).
Construction of protein-protein interaction (PPI) network
PPI network analysis for the DEGs was carried out with the Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org/), and the PPI network involving MMPs-associated genes was selected. Interaction associations with a combined score of ≥0.4 were retained and the PPI network was visualized by Cytoscape software (version 2.8; http://www.cytoscape.org) (28).
Transcriptional regulatory network analysis
Transcriptional regulatory network analysis was performed with DAVID (26) for the group of DEGs including MMPs, and the transcriptional regulatory network was visualized by Cytoscape software (version 2.8; http://www.cytoscape.org) (28).
Results
DEGs analysis
Upon normalization of the raw data (Fig. 1), the DEGs were screened using the limma package in R. As a result, 269 DEGs were identified between lung AC and adjacent normal lung samples, including 78 upregulated and 191 downregulated genes.
MMPs-associated genes screening
MMP1, MMP7, MMP9 and MMP12 were clustered into one group with a number of other DEGs (Table I), which were upregulated in lung AC compared with their expression in normal lung tissues. These results were further confirmed by Pearson correlation analysis and western blotting. The present findings also demonstrated that MMP9 expression exhibited a significant correlation with DSP (correlation coefficient=0.8309295) and CLDN3 (correlation coefficient=0.8058015) expression. Additionally, MMP12 expression was significantly correlated with DSP expression (correlation coefficient=0.8127249). The expression of MMP1, MMP7, MMP9 and MMP12 was also observed to be upregulated in lung AC compared with that in adjacent lung tissues (Fig. 2).
Functional and pathway enrichment analysis
Functional enrichment analysis was applied for the above group of DEGs with the DAVID online tool. As shown in Table II, collagen metabolism (P=4.37×10−6) and multicellular organism macromolecule metabolic processes (P=5.98×10−6) were enriched for these DEGs.
Disease-relevant genes selection
Relevant diseases to the MMPs-associated DEGs were retrieved with DAVID. Under the Genetic Association DB Disease Class, MMP9, NAD(P)H quinone oxidoreductase 1 (NQO1), MMP12 and MMP1 were linked with lung AC, while MMP9, MMP12 and MMP1 were associated with lung function (Table III).
PPI network analysis
A PPI network was constructed for the MMPs-associated DEGs using STRING (Fig. 3). The results revealed that interaction associations existed among MMP9, NQO1 and MMP7. In addition, the other genes clustered into the MMPs group also interacted with each other.
Transcriptional regulatory network analysis
Transcriptional regulatory network analysis indicated that 9 genes, including MMP1, MMP9 and NQO1, were commonly regulated by the CCAAT/enhancer binding protein (C/EBP) α (CEBPA) transcription factor (Fig. 4).
Discussion
In the present study, cluster analysis demonstrated that 4 MMPs, including MMP1, MMP7, MMP9 and MMP12, were upregulated in lung AC samples, and may be involved in ECM metabolic processes, thus promoting cancer invasion. These results were validated by western blotting and were in accordance with previous studies (29–32). MMP1 is the most highly expressed interstitial collagenase for degrading fibrillar collagens (33). Overexpression of MMP1 has been associated with tumor invasion and metastasis by modulating the polarization of T helper (Th)1/Th2 inflammatory responses (29). Downregulation of MMP-7 mediated by antisense oligonucleotide changes the ultrastructure of lung AC A549 cells, leading to decreased microvilli, endoplasmic reticulum dilation, swelling of mitochondria and formation of apoptotic bodies, which eventually inhibits invasion in lung AC A549 cells (30). Similarly, the expression levels of MMP9 and MMP12 were observed to be higher in NSCLC than in normal samples (31). Upregulation of MMP12 and MMP9 may be one of the mechanisms to promote lung cancer cell invasion (32).
In addition, the present study identified several DEGs that were clustered into one group with MMPs, thus indicating the same function in cancer cell invasion. A number of these genes have been linked to lung cancer in the following studies. NQO1 is a member of the NQO family, and altered expression of this protein has been reported in numerous tumors (34). Mutations in this gene contribute to susceptibility to various forms of cancer, including lung cancer (35,36). S100 calcium-binding protein P (S100P) is a member of the S100 family of proteins containing two EF-hand calcium-binding motifs, which are involved in the regulation of cell cycle progression and differentiation (37). Bartling et al (31) reported that S100P expression is mainly increased in AC, and that S100P upregulation is detected in early rather than in advanced tumor stages. Overexpression of S100P may lead to cancer cell invasion by changing the expression levels of several cytoskeletal proteins (38). Serine peptidase inhibitor, Kazal type 1 (SPINK1) is a secreted serine protease inhibitor (39). Overexpression of SPINK1 is associated with aggressiveness of prostate cancer (40). A previous study by Soon et al (34) revealed that SPINK1 is an invasion factor associated with prognosis in breast cancer patients. However, its role in lung cancer remains unknown. The present study suggested that SPINK1 may also be involved in lung AC invasion. Ubiquitin D (UBD, also known as F-associated transcript 10) is a member of the ubiquitin-like modifier family, and appears to be upregulated in hepatocellular carcinoma, as well as in gastrointestinal and gynecological cancers (41). Increased cytoplasmic UBD is significantly associated with depth of colon cancer invasion (42). Few studies have investigated the roles of UBD in lung AC invasion, including the present study. Collagen type XI α I (COL11A1) has also been demonstrated to be overexpressed in NSCLC, and has been identified as a potential invasion-associated gene in cancer (43). However, its mechanism of function is not understood yet. The present study revealed that COL11A1 could interact with the osteoblast-specific factor periostin (POSTN), although this requires further experimental validation. In addition, the associations between MMPs and various genes (including MMP9-CLDN3, MMP9-DSP and MMP12-DSP) were also further confirmed by Pearson correlation analysis. CLDN3, a component of tight junctions, has been reported to be upregulated in NSCLC, and may be important in invasion (44). Agarwal et al (39) reported that CLDN3-mediated increased invasion may be accomplished through the activation of MMP2. In the present study, CLDN3 was significantly correlated with MMP9 via Pearson correlation analysis, indicating a potential link between CLDN3 and MMP9 in lung AC cell invasion. DSP acts as a tumor suppressor via inhibiting the Wnt/β-catenin signaling pathway in NSCLC (45). Thus, the expression patterns of DSP and MMPs are theoretically opposite, which has been demonstrated in the human epithelial carcinoma cell line A431 during the epithelial-mesenchymal transition, with upregulated MMPs and downregulated DSP levels (46).
According to the present transcriptional regulatory network analysis, MMPs-associated genes were regulated by CEBPA. CEBPA is a basic leucine zipper domain transcription factor that not only can bind to certain promoters and enhancers as a homodimer, but can also form heterodimers with the associated proteins CEBPB and CEBPG (47). It has been reported that CEBPB is an important mediator in the activation of MMP genes (including MMP1, MMP3 and MMP10) in A549 lung carcinoma cells stimulated with the inflammatory cytokine interleukin-1β (48). Therefore, it could be speculated that CEBPA may also serve an important role in upregulating the expression of MMP1 and MPP9, thus affecting the invasion of lung cancer. Although the present results indicated that CEBPA could modulate the expression of fibroblast activation protein α, CLDN3, collagen type III α 1, COL11A1, TOX high mobility group box family member 3, NQO1 and POSTN, no experimental evidence could be obtained.
In conclusion, the present study identified several genes such as SPINK1 and UBD that may serve important roles in lung AC invasion with MMPs (including MMP1, MMP7, MMP9 and MMP12). Several MMPs-associated genes were observed to regulated by the CEBPA transcription factor. These findings may provide various underlying targets for prevention of lung AC invasion. However, further experimental investigations or studies on other datasets are required to validate the present observations.
References
Siegel R, Naishadham D and Jemal A: Cancer statistics, 2012. CA Cancer J Clin. 62:10–29. 2012. View Article : Google Scholar : PubMed/NCBI | |
Lazar V, Suo C, Orear C, van den Oord J, Balogh Z, Guegan J, Job B, Meurice G, Ripoche H, Calza S, et al: Integrated molecular portrait of non-small cell lung cancers. BMC Med Genomics. 6:532013. View Article : Google Scholar : PubMed/NCBI | |
Devesa SS, Bray F, Vizcaino AP and Parkin DM: International lung cancer trends by histologic type: Male:female differences diminishing and adenocarcinoma rates rising. Int J Cancer. 117:294–299. 2005. View Article : Google Scholar : PubMed/NCBI | |
Borczuk AC, Qian F, Kazeros A, Eleazar J, Assaad A, Sonett JR, Ginsburg M, Gorenstein L and Powell CA: Invasive size is an independent predictor of survival in pulmonary adenocarcinoma. Am J Surg Pathol. 33:462–469. 2009. View Article : Google Scholar : PubMed/NCBI | |
van Rens MT, de la Rivière AB, Elbers HR and van Den Bosch JM: Prognostic assessment of 2,361 patients who underwent pulmonary resection for non-small cell lung cancer, stage I, II and IIIA. Chest. 117:374–379. 2000. View Article : Google Scholar : PubMed/NCBI | |
Sakurai H, Maeshima A, Watanabe S, Suzuki K, Tsuchiya R, Maeshima AM, Matsuno Y and Asamura H: Grade of stromal invasion in small adenocarcinoma of the lung: Histopathological minimal invasion and prognosis. Am J Surg Pathol. 28:198–206. 2004. View Article : Google Scholar : PubMed/NCBI | |
Veiseh O, Kievit FM, Ellenbogen RG and Zhang M: Cancer cell invasion: Treatment and monitoring opportunities in nanomedicine. Adv Drug Deliv Rev. 63:582–596. 2011. View Article : Google Scholar : PubMed/NCBI | |
Kam Y, Rejniak KA and Anderson AR: Cellular modeling of cancer invasion: Integration of in silico and in vitro approaches. J Cell Physiol. 227:431–438. 2012. View Article : Google Scholar : PubMed/NCBI | |
Freije JM, Balbin M, Pendás AM, Sánchez LM, Puente XS and López-Otin C: Matrix metalloproteinases and tumor progression. Adv Exp Med Biol. 532:91–107. 2003. View Article : Google Scholar : PubMed/NCBI | |
Kleiner DE and Stetler-Stevenson WG: Matrix metalloproteinases and metastasis. Cancer Chemother Pharmacol. 43 Suppl:S42–S51. 1999. View Article : Google Scholar : PubMed/NCBI | |
Kumaki F, Matsui K, Kawai T, Ozeki Y, Yu ZX, Ferrans VJ and Travis WD: Expression of matrix metalloproteinases in invasive pulmonary adenocarcinoma with bronchioloalveolar component and atypical adenomatous hyperplasia. Am J Pathol. 159:2125–2135. 2001. View Article : Google Scholar : PubMed/NCBI | |
Chen CK, Chen PR, Huang HC, Lin YS and Fang HY: Overexpression of matrix metalloproteinases in lung tissue of patients with primary spontaneous pneumothorax. Respiration. 88:418–425. 2014. View Article : Google Scholar : PubMed/NCBI | |
Sun Y, Wang L, Jiang M, Huang J, Liu Z and Wolfl S: Secreted phosphoprotein 1 upstream invasive network construction and analysis of lung adenocarcinoma compared with human normal adjacent tissues by integrative biocomputation. Cell Biochem Biophys. 56:59–71. 2010. View Article : Google Scholar : PubMed/NCBI | |
Yan KH, Lee LM, Yan SH, Huang HC, Li CC, Lin HT and Chen PS: Tomatidine inhibits invasion of human lung adenocarcinoma cell A549 by reducing matrix metalloproteinases expression. Chem Biol Interact. 203:580–587. 2013. View Article : Google Scholar : PubMed/NCBI | |
Kao SJ, Su JL, Chen CK, Yu MC, Bai KJ, Chang JH, Bien MY, Yang SF and Chien MH: Osthole inhibits the invasive ability of human lung adenocarcinoma cells via suppression of NF-κB-mediated matrix metalloproteinase-9 expression. Toxicol Appl Pharmacol. 261:105–115. 2012. View Article : Google Scholar : PubMed/NCBI | |
Falcone D, Gallelli L, Di Virgilio A, Tucci L, Scaramuzzino M, Terracciano R, Pelaia G and Savino R: Effects of simvastatin and rosuvastatin on RAS protein, matrix metalloproteinases and NF-κB in lung cancer and in normal pulmonary tissues. Cell Prolif. 46:172–182. 2013. View Article : Google Scholar : PubMed/NCBI | |
Storz P, Döppler H, Copland JA, Simpson KJ and Toker A: FOXO3a promotes tumor cell invasion through the induction of matrix metalloproteinases. Mol Cell Biol. 29:4906–4917. 2009. View Article : Google Scholar : PubMed/NCBI | |
Assembly WMAG: World medical association declaration of Helsinki: Ethical principles for medical research involving human subjects. Chinese J Integrative Med. 310:2191–2194. 2001. | |
Stearman RS, Dwyer-Nield L, Zerbe L, Blaine SA, Chan Z, Bunn PA Jr, Johnson GL, Hirsch FR, Merrick DT, Franklin WA, et al: Analysis of orthologous gene expression between human pulmonary adenocarcinoma and a carcinogen-induced murine model. Am J Pathol. 167:1763–1775. 2005. View Article : Google Scholar : PubMed/NCBI | |
Gautier L, Cope L, Bolstad BM and Irizarry RA: Affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI | |
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI | |
Na S, Xumin L and Yong G: Research on k-means clustering algorithm: An improved k-means clustering algorithmProceedings of the Third International Symposium on Intelligent Information Technology and Security Informatics (IITSI). IITSI; Jian: pp. 63S–67S. 2010, View Article : Google Scholar | |
Månsson R, Tsapogas P, Akerlund M, Lagergren A, Gisler R and Sigvardsson M: Pearson correlation analysis of microarray data allows for the identification of genetic targets for early B-cell factor. J Biol Chem. 279:17905–17913. 2004. View Article : Google Scholar : PubMed/NCBI | |
Sherlock G: Gene Ontology: Tool for the unification of biology. Canadian Institute Food Sci Technol J. 22:4152009. | |
Kanehisa M, Sato Y, Kawashima M, Furumichi M and Tanabe M: KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44:D457–D462. 2016. View Article : Google Scholar : PubMed/NCBI | |
da Huang W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protoc. 4:44–57. 2009. View Article : Google Scholar | |
Benjamini Y and Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Royal Statistical Society. 57:289–300. 1995. | |
Yeung N, Cline MS, Kuchinsky A, Smoot ME and Bader GD: Exploring biological networks with Cytoscape software. Curr Protoc Bioinformatics. 23Chapter 8. (8.13): 8.13.1–8.13.20. 2008. View Article : Google Scholar | |
Fanjul-Fernández M, Folgueras AR, Fueyo A, Balbín M, Suárez MF, Fernández-García MS, Shapiro SD, Freije JM and López-Otín C: Matrix metalloproteinase Mmp-1a is dispensable for normal growth and fertility in mice and promotes lung cancer progression by modulating inflammatory responses. J Biol Chem. 288:14647–14656. 2013. View Article : Google Scholar : PubMed/NCBI | |
Yang B, Gao J, Rao Z, Zhang B, Ouyang W and Yang C: Antisense oligonucleotide targeting matrix metalloproteinase-7 (MMP-7) changes the ultrastructure of human A549 lung adenocarcinoma cells. Ultrastruct Pathol. 35:256–259. 2011. View Article : Google Scholar : PubMed/NCBI | |
Bartling B, Rehbein G, Schmitt WD, Hofmann H-S, Silber R-E and Simm A: S100A2–S100P expression profile and diagnosis of non-small cell lung carcinoma: impairment by advanced tumour stages and neoadjuvant chemotherapy. Eur J Cancer. 43:1935–1943. 2007. View Article : Google Scholar : PubMed/NCBI | |
Hung WC, Tseng WL, Shiea J and Chang HC: Skp2 overexpression increases the expression of MMP-2 and MMP-9 and invasion of lung cancer cells. Cancer Lett. 288:156–161. 2010. View Article : Google Scholar : PubMed/NCBI | |
Sauter W, Rosenberger A, Beckmann L, Kropp S, Mittelstrass K, Timofeeva M, Wölke G, Steinwachs A, Scheiner D, Meese E, et al: Matrix metalloproteinase 1 (MMP1) is associated with early-onset lung cancer. Cancer Epidemiol Biomarkers Prev. 17:1127–1135. 2008. View Article : Google Scholar : PubMed/NCBI | |
Soon WW, Miller LD, Black MA, Dalmasso C, Chan XB, Pang B, Ong CW, SaltoTellez M, Desai KV and Liu ET: Combined genomic and phenotype screening reveals secretory factor SPINK1 as an invasion and survival factor associated with patient prognosis in breast cancer. EMBO Mol Med. 3:451–464. 2011. View Article : Google Scholar : PubMed/NCBI | |
Sunaga N, Kohno T, Yanagitani N, Sugimura H, Kunitoh H, Tamura T, Takei Y, Tsuchiya S, Saito R and Yokota J: Contribution of the NQO1 and GSTT1 polymorphisms to lung adenocarcinoma susceptibility. Cancer Epidemiol Biomarkers Prev. 11:730–738. 2002.PubMed/NCBI | |
Chao C, Zhang ZF, Berthiller J, Boffetta P and Hashibe M: NAD(P)H:quinone oxidoreductase 1 (NQO1) Pro187Ser polymorphism and the risk of lung, bladder, and colorectal cancers: A meta-analysis. Cancer Epidemiol Biomarkers Prev. 15:979–987. 2006. View Article : Google Scholar : PubMed/NCBI | |
Du M, Wang G, Barraclough R and Rudland P: P0017 S100P regulates cytoskeleton dynamics to promote cell migration and metastasis. Eur J Cancer. 51:e6–e7. 2015. View Article : Google Scholar | |
Whiteman HJ, Weeks ME, Dowen SE, Barry S, Timms JF, Lemoine NR and Crnogorac-Jurcevic T: The role of S100P in the invasion of pancreatic cancer cells is mediated through cytoskeletal changes and regulation of cathepsin D. Cancer Res. 67:8633–8642. 2007. View Article : Google Scholar : PubMed/NCBI | |
Agarwal R, D'Souza T and Morin PJ: Claudin-3 and claudin-4 expression in ovarian epithelial cells enhances invasion and is associated with increased matrix metalloproteinase-2 activity. Cancer Res. 65:7378–7385. 2005. View Article : Google Scholar : PubMed/NCBI | |
Ateeq B, Tomlins SA, Laxman B, Asangani IA, Cao Q, Cao X, Li Y, Wang X, Feng FY, Pienta KJ, et al: Therapeutic targeting of SPINK1-positive prostate cancer. Sci Transl Med. 3:72ra172011. View Article : Google Scholar : PubMed/NCBI | |
Lee CG, Ren J, Cheong IS, Ban KH, Ooi LL, Tan Yong S, Kan A, Nuchprayoon I, Jin R, Lee KH, et al: Expression of the FAT10 gene is highly upregulated in hepatocellular carcinoma and other gastrointestinal and gynecological cancers. Oncogene. 22:2592–2603. 2003. View Article : Google Scholar : PubMed/NCBI | |
Yan DW, Li DW, Yang YX, Xia J, Wang XL, Zhou CZ, Fan JW, Wen YG, Sun HC, Wang Q, et al: Ubiquitin D is correlated with colon cancer progression and predicts recurrence for stage II-III disease after curative surgery. Br J Cancer. 103:961–969. 2010. View Article : Google Scholar : PubMed/NCBI | |
Kim H, Watkinson J, Varadan V and Anastassiou D: Multi-cancer computational analysis reveals invasion-associated variant of desmoplastic reaction involving INHBA, THBS2 and COL11A1. BMC Med Genomics. 3:512010. View Article : Google Scholar : PubMed/NCBI | |
Li J, Tu Y, Jiang L, Xu H and Zhang S: Expression and significance of Snail and Claudin-3 in non-small cell lung cancer. Zhongguo Fei Ai Za Zhi. 15:583–590. 2012.(In Chinese). PubMed/NCBI | |
Yang L, Chen Y, Cui T, Knösel T, Zhang Q, Albring KF, Huber O and Petersen I: Desmoplakin acts as a tumor suppressor by inhibition of the Wnt/β-catenin signaling pathway in human lung cancer. Carcinogenesis. 33:1863–1870. 2012. View Article : Google Scholar : PubMed/NCBI | |
Lin CY, Tsai PH, Kandaswami CC, Lee PP, Huang CJ, Hwang JJ and Lee MT: Matrix metalloproteinase-9 cooperates with transcription factor Snail to induce epithelial-mesenchymal transition. Cancer Sci. 102:815–827. 2011. View Article : Google Scholar : PubMed/NCBI | |
Nerlov C: The C/EBP family of transcription factors: A paradigm for interaction between gene expression and proliferation control. Trends Cell Biol. 17:318–324. 2007. View Article : Google Scholar : PubMed/NCBI | |
Armstrong DA, Phelps LN and Vincenti MP: CCAAT enhancer binding protein-beta regulates matrix metalloproteinase-1 expression in interleukin-1beta-stimulated A549 lung carcinoma cells. Mol Cancer Res. 7:1517–1524. 2009. View Article : Google Scholar : PubMed/NCBI |