Hypopharyngeal squamous cell carcinoma (HSCC) is one of the most common head and neck malignancies worldwide. Due to its frequent regional lymphatic metastasis and delayed diagnosis, the prognosis of HSCC is the worst among head and neck malignancies. Besides, compared with other head and neck cancers, second primary esophageal squamous cell carcinoma (ESCC) is the most common in HSCC patients, with an incidence of 10% to 50%.[2,3] Even with aggressive treatment, the prognosis of patients with double primary tumors remains poor, with a 5-year overall survival of only 9% to 11%.[4,5] Thus, it is crucial to develop more efficacious diagnostic and therapeutic strategies.
In recent years, intensive work has been made to illuminate the etiology, the common risk factors containing cigarette, alcohol, and betel nut have been proven to may trigger field cancerization in the hypopharynx and esophagus.[6,7] However, the common molecular mechanisms involved in the carcinogenesis of HSCC and ESCC remains unclear. With the advent of microarray and high throughput sequencing technology, bioinformatic analysis has been widely used to identify the differentially expressed genes (DEGs) and functional pathways involved in the carcinogenesis and progression of tumors, which may contribute to developing effective diagnostic and therapeutic strategies. Therefore, we conducted the study to seek to find the common gene signatures and key functional pathways associated with oncogenesis and treatment in HSCC and ESCC by bioinformatic analysis.
2 Materials and methods
2.1 Microarray datasets search
Datasets containing gene expression differences between HSCC, ESCC, and normal tissues were retrieved from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo), which offers massive public available gene expression data to conduct comprehensive genes analysis. To control the heterogeneity, all included datasets must meet the following inclusion criteria: search term: hypopharyngeal squamous cell carcinoma or esophageal squamous cell carcinoma; sample source: homo sapiens; sample size ≥30; study type: expression profiling by array; publication date: January 1, 2005 to January 1, 2020.
2.2 Identification of DEGs
The DEGs between HSCC, ESCC, and normal tissues were extracted and analyzed by GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/). GEO2R is an integrative web analysis tool that contributes to identifying DEGs between different groups of samples. The cut-off values of DEGs were defined as adjusted P value <.05 and |log fold-change| (|logFC|) values >2. Afterwards, a Venn diagram was drawn to obtain the common DEGs.
2.3 GO annotation and KEGG pathways enrichment analyses of DEGs
Gene ontology (GO) analysis is an important part of functional genomics research, which is applied to annotate the biological process (BP), cellular composition (CC), and molecular function (MF) of all genes in the genome. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was applied to clarify relevant signaling pathways of the DEGs involved. The Database for Annotation, Visualization and Integrated Discovery (DAVID; version 6.7; http://david.ncifcrf.gov) is an online bioinformatic database that provides integrated biometric annotation information of genes. To analyze the biological function of DEGs, GO annotation and KEGG pathway enrichment analyses were conducted by DAVID, and P-value < .05 was considered statistically significant.
2.4 PPI network construction and key module analysis
Analysis of the functional interactions between proteins could provide important insights for the carcinogenesis and progression of tumors. Thus, the Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org/) was applied to construct the protein–protein interaction (PPI) network of DEGs with a confidence score ≥0.4. Afterwards, the PPI network was visualized using Cytoscape software version 3.7.0 (Cytoscape Consortium, San Diego, CA, USA), which is a public platform for visualizing molecular interaction networks from attributing data. And the key module in the PPI networks was identified using the Molecular Complex Detection (MCODE) plugin of Cytoscape with degree cut-off = 10, Max depth = 100, node score cut-off = 0.2, and k-score = 2.
2.5 Hub genes selection and analysis
The genes with degree ≥10 in the PPI network were defined as hub genes. Subsequently, the cBioPortal online platform (http://www.cbioportal.org) was utilized to define the coexpressed genes of hub genes according to spearman correlation coefficient >0.8, and the coexpression network was constructed using Cytoscape.[16,17] Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn) is an interactive web server for analyzing gene expression profiling of cancer and normal tissues. The GEPIA was applied to verify the expression differences of hub genes between tumor samples with normal tissues, and conduct the survival analyses of hub genes using the Kaplan–Meier method. Tumor Immune Estimation Resource (TIMER; https://cistrome.shinyapps.io/timer/) is a public web server for comprehensive analysis of immunological, clinical, and genomic features of diverse malignancies. The expression levels of hub genes in multiple malignancies were explored using TIMER.
2.6 miRNA-hub gene network construction
The aberrant expression of miRNAs plays a crucial role in the coordinate regulation of target gene expression. To further study the interactions between miRNAs and hub genes, the Encyclopedia of RNA Interactomes (ENCORI) (http://starbase.sysu.edu.cn/; version 3.0) was utilized to predict the targeted miRNAs of hub genes. In the study, the targeted miRNAs of hub genes were defined according to the positive results of ≥3 miRNA-target predicting databases, including TargetScan, miRanda, PicTar, and PITA.[20,21] Finally, the interaction network of miRNAs and hub genes was constructed using Cytoscape.
3.1 Datasets for research
After the rigorous screening of all relevant datasets, 3 gene expression datasets including GSE2379, GSE20347, and GSE75241 met the inclusion criteria. Among them, GSE2379 used 38 samples to detect the gene expression difference between hypopharyngeal squamous cell carcinoma and normal tissues. While the others concentrate on the gene expression differences between esophageal squamous cell carcinoma and normal esophageal tissues. The baseline characteristics of the 3 included datasets are summarized in Table 1.
3.2 Identification of DEGs
The original data of 3 datasets (GSE2379, GSE20347, GSE75241) were obtained from GEO and then subjected to differential expression analysis by the GEO2R online platform. Based on the predefined cut-off values, DEGs (254 in GSE2379, 292 in GSE20347, and 257 in GSE75241) were identified (Fig. 1A–C). Afterwards, the overlapping DEGs among 3 datasets were identified by drawing a Venn diagram (Fig. 1D), comprising 25 upregulated genes and 18 downregulated genes (Table 2).
3.3 GO and KEGG enrichment analyses of DEGs
In order to analyze the biological function of DEGs, GO annotation and KEGG pathways enrichment analysis were conducted by DAVID. The biological processes of DEGs were mainly involved in the extracellular matrix organization, collagen metabolic process, multicellular organismal metabolic process, epidermis development, and cell adhesion (Fig. 2A). The cellular compositions of DEGs mainly include extracellular matrix, extracellular space, extracellular region, collagen, secretory granule, basement membrane (Fig. 2B). The changes in molecular function (MF) of DEGs were mainly concentrated on peptidase activity, polysaccharide binding, pattern binding, extracellular matrix structural constituent, and platelet-derived growth factor binding (Fig. 2C). As for the KEGG functional pathways, DEGs mainly involved in the extracellular matrix-receptor interaction and focal adhesion (Fig. 2D).
3.4 PPI network construction and key module analysis
To analyzing the functional interactions between DEGs, the PPI network constructed by STRING was visualized using Cytoscape (Fig. 3A), consisting of 33 nodes and 129 edges. Afterwards, the most key module of PPI network was extracted using MCODE (Fig. 3B), and 12 upregulated genes (SERPINE1, SPP1, LUM, POSTN, COL1A1, COL1A2, COL3A1, MMP1, MMP13, FN1, SPARC, VCAN) involved in the module were identified as hub genes based on the predefined criteria. Subsequently, the GO annotation demonstrated the biological processes of hub genes were mainly concentrated on extracellular matrix organization, collagen fibril organization, cellular response to growth factor stimulus, response to cytokine, regulation of cell-substrate adhesion, and regulation of cell migration (Fig. 3C). Finally, the coexpression network of hub genes obtained from cbioportal was constructed using Cytoscape (Fig. 4).
3.5 Survival analysis and verification of hub genes
Thorny difficulties were encountered in verifying hub genes due to limited data associated with HSCC in public databases. Referring to previous studies, the Head-Neck cancer dataset was used to evaluate hub genes in HSCC. In the present study, the expression differences of hub genes between tumors and normal tissues were verified using GEPIA. As shown in Fig. 5, the expression levels of hub genes in tumor samples were significantly elevated, which was consistent with our results. Moreover, survival analysis conducted by GEPIA demonstrated that SERPINE1 and SPP1 were closely related to poor prognosis of patients with HSCC and ESCC (Fig. 6), and the expression levels of SERPINE1 and SPP1 were found to be upregulated in multiple malignancies including breast carcinoma, esophageal carcinoma, kidney clear cell carcinoma, hepatocellular carcinoma, stomach adenocarcinoma, colorectal adenocarcinoma, thyroid carcinoma, and head and neck cancer (Fig. 7).
3.6 miRNA-hub gene network construction
To illustrate the regulatory relationships between miRNA and hub genes, the interaction network of miRNAs and hub genes constructed by ENCORI was visualized using Cytoscape. As illustrated in Fig. 8, the interaction network consists of 11 hub genes and 116 miRNAs. After analyzing the network, all miRNAs and hub genes were ranked by degree score. Among them, COL1A2 (degree score = 32), SERPINE1 (degree score = 30), COL3A1 (degree score = 22), SPARC (degree score = 22), and MMP1 (degree score = 20) were confirmed as the top 5 interactive hub genes. Meanwhile, hsa-miR-29c-3p (degree score = 7), hsa-miR-29a-3p (degree score = 6), and hsa-miR-29b-3p (degree score = 6) were confirmed as the top 3 interactive miRNAs that target the most hub genes. Previously, Qiu et al reported that miR-29a/b could promote cell invasion and migration by inducing SPARC and COL3A1 gene expression in nasopharyngeal carcinoma. Similarly, our results also confirmed that miR-29a/b can regulate the expression of the SPARC and COL3A1. Therefore, the interaction network may contribute to understanding the molecular mechanisms involved in the carcinogenesis and progression of HSCC and ESCC.
Hypopharyngeal and esophageal squamous cell carcinoma are the most common double primary tumors worldwide. Even with aggressive treatment, the prognosis remains poor. Recently, genomic studies have demonstrated that the genomic changes in ESCC are similar to those in HSCC based on the identified common risk factors.[26,27] However, the common carcinogenic mechanisms remain unclear, as no relative study has been carried out. With the development of pan-cancer research and high throughput sequencing technology, the bioinformatic analysis may contribute to identifying the common DEGs and functional pathways involved in the carcinogenesis and progression of HSCC and ESCC.
In the study, 3 datasets were included to identify the common DEGs between HSCC, ESCC, and normal tissues to offset the false-positive rates in independent datasets analysis. As a result, a total of 43 DEGs were obtained, consisting of 25 upregulated genes and 18 downregulated genes. Subsequently, GO annotation, KEGG pathways enrichment analysis, and PPI network construction were conducted to explore interactions between the DEGs. The results indicate that the DEGs are mainly involved in the extracellular matrix organization, collagen metabolic process, extracellular matrix–receptor interaction, focal adhesion, and epidermis development. As reported, the extracellular matrix as the medium of cell communication exerts a significant impact on the carcinogenesis and progression of tumors, and activation of the extracellular matrix is also regarded as a landmark event for the formation of tumors. In addition, aberrant adhesion of tumor cells to the extracellular matrix also plays a critical role in tumor invasion and metastasis.
To conduct deeper research, 12 hub genes involved in the key module were identified based on the predefined criteria. After verification, the expression levels of hub genes were significantly elevated in tumor samples. Subsequently, we conduct survival analysis and found SERPINE1 and SPP1 were closely related to poor prognosis. SERPINE1 is known as the plasminogen activator inhibitor, which plays a crucial role in enhancing tumor cell migration and invasion through the PI3K-Akt pathway, promoting angiogenesis, protecting tumor cells from Fas/Fas-L mediated apoptosis.[29–31] Meanwhile, SERPINE1 overexpression was proven to be strongly associated with poor prognosis in multiple cancers including breast cancer, fibrosarcoma, esophageal cancer, and head and neck cancer. At present, SERPINE1 has been established as a prognostic marker in patients with early lymph-node negative breast cancer.[33,34] In addition, previous studies have demonstrated SERPINE1 was closely related to chemoradiotherapy resistance in head and neck cancer. One hand, the expression level of SERPINE1 is significantly upregulated after reactive oxygen exposure or irradiation, which activates hypoxia-related factors, thereby contributing to radiation resistance. On the other hand, the overexpression of SERPINE1 could protect tumor cells from inducing apoptosis after cisplatin treatment, which is mediated by PI3K-Akt pathway activation. In the context, the antitumor activity of several small molecules inhibitors targeting SERPINE1 is currently being evaluated. Among them, Tiplaxtinin is proven to block tumor cells’ growth and induce apoptosis in head and neck cancer. However, more preclinical and clinical trials are necessary to explore the application of the specific SERPINE1 inhibitors in the treatment of patients with HSCC and ESCC. SPP1, also known as osteopontin, is a secreted glycophosphoprotein. Kim et al found that the ectopic overexpression of SPP1 could activate ITGB1/FAK/AKT pathway, thereby enhancing the metastatic ability of head and neck cancer cells. Meanwhile, SPP1 overexpression was also reported to be involved in tumor proliferation, invasion, and angiogenesis in multiple malignancies including lung, breast, colorectal, and head and neck cancer. Moreover, our results demonstrated SPP1 was closely related to poor prognosis in patients with HSCC and ESCC. Overall, SERPINE1 and SPP1 play vital roles in the carcinogenesis and progression of HSCC and ESCC, which may present therapeutic targets and prognostic markers for patients with HSCC and ESCC in the future.
Although the other hub genes were not found to be directly associated with prognosis in our study, they are still proved to be involved in the carcinogenesis and progression of tumors. COL1A1 and COL1A2 encode the pro-α1 chain and pro-α2 chain of type I collagen, respectively, which is a key structural component of the extracellular matrix. Previous studies have demonstrated ESCC cells could produce COL1A1 endogenously, and miR-133a-3p could inhibit the ESCC cell proliferation, invasion, and migration by targeting COL1A1. However, the specific roles of COL1A2 in various tumors remain controversial. In bladder and colorectal cancer, COL1A2 was significantly downregulated and mainly plays an anticarcinogenic role in tumor development.[42,43] However, in other malignancies such as ovarian cancer, pancreatic cancer, and head and neck cancer, overexpressed COL1A2 was found to promote tumor cell invasion and migration.[44,45] Thus, the unique roles of COL1A2 in various cancers may be partially attributed to specific genetic characteristics of the different malignancies. Matrix metalloproteinases (MMPs) are zinc-dependent endopeptidase involved in the degradation of extracellular matrix and basement membrane, which were regarded as strong predictors of tumor metastasis. In the study, MMP1 and MMP13 were screened out as DEGs of HSCC and ESCC, implying that they may play important roles in tumor progression. Previous studies have demonstrated that MMP1 could facilitate tumor cells metastasize into the blood or lymphatic circulation by degrading interstitial collagen and vascular endothelium. In addition, it also supports tumor angiogenesis by activating protease-activated receptor-1. However, distinct from the function of MMP1 in angiogenesis, MMP13 not only promoted capillary tube formation but also induced vascular endothelial growth factor-A (VEGF-A) secretion from endothelial cells, which can indirectly stimulate tumor angiogenesis.[49,50]
FN1, an extracellular matrix glycoprotein, mainly mediates the interaction between tumor cells and extracellular matrix. Previous studies have reported that FN1 could activate the PI3K/Akt pathway to stimulate tumor cell proliferation and invasion through binding to α5β1 integrin receptors.[52,53] Meanwhile, POSTN has also been proven to be able to bind to multiple integrin receptors including αvβ3, αvβ5, and α6β4, thereby regulating the intracellular PI3K/Akt signaling pathway.[54,55] Abnormal activation of PI3K/Akt signaling pathway could directly mediate the epithelial-mesenchymal transition, which is crucial for tumors to obtain malignant properties.[56,57] Thus, targeting PI3K/Akt signaling pathway may be a promising strategy for anti-cancer therapy. VCAN belongs to the large chondroitin sulfate proteoglycans family and plays a vital role in the formation of tumor-specific extracellular matrices. As reported, VCAN could induce tumorigenesis by inhibiting TNF signaling-mediated apoptosis and promote tumor invasion and metastasis through inducing MMPs expression.[58,59] SPARC, also termed osteonectin, mainly regulates cell–matrix interactions and cell adhesion. Che et al have demonstrated that overexpressed SPARC is closely related to lymph node metastasis and distant metastasis in ESCC patients. Meanwhile, as a vital downstream target of SPARC, the expression level of COL3A1 has been found to be positively regulated by SPARC. Moreover, consistent with previous studies, our results also confirmed that miR-29a/b can regulate the expression of the SPARC and COL3A1. As for LUM, conflicting data have been reported in the previous studies with regard to its exact role in tumor progression. For instance, LUM could modulate the expression of MMP-9 and MMP14 to inhibit tumor cell migration in breast cancer, which mainly acts as the anticancer effector. However, in pancreatic carcinoma, LUM overexpression plays a carcinogenic effect by promoting cell invasion and metastasis. Thus, the exact role of LUM in ESCC and HSCC deserves further exploration.
In conclusion, the present study found 43 common DEGs between ESCC, HSCC, and normal samples using bioinformatics analysis, which were mainly involved in the extracellular matrix–receptor interaction, collagen metabolic, epidermis development, cell adhesion, and PI3K/Akt signaling pathways. Among DEGs, 12 genes (SERPINE1, SPP1, LUM, POSTN, COL1A1, COL1A2, COL3A1, MMP1, MMP13, FN1, SPARC, VCAN) were identified as hub genes, which may serve as diagnostic and therapeutic targets in ESCC and HSCC. In addition, SERPINE1 and SPP1 were proven to be closely related to poor prognosis, which may be potential prognostic biomarkers. Meanwhile, the interaction network of miRNAs and hub genes illustrates the regulatory relationships of the hub genes and miRNA, and hsa-miR-29c-3p, hsa-miR-29a-3p, and hsa-miR-29b-3p were confirmed as the top 3 interactive miRNAs that target the most hub genes. These results provide important ideas for a comprehensive understanding of cancer characteristics, however, further studies are needed to validate the current findings and elucidate the specific molecular mechanisms of these genes in ESCC and HSCC.
Conceptualization: Tao Zhang.
Data curation: Rui Zhou, Denghua Liu.
Formal analysis: Rui Zhou, Denghua Liu.
Software: Rui Zhou, Denghua Liu.
Supervision: Jing Zhu, Tao Zhang.
Validation: Rui Zhou, Denghua Liu. Visualization: Rui Zhou, Denghua Liu.
Writing – original draft: Rui Zhou.
Writing – review & editing: Tao Zhang.
. Monès E, Bertolus C, Salaun PY, et al. Initial staging of squamous cell carcinoma of the oral cavity, larynx and pharynx (excluding nasopharynx). Part 2: Remote extension assessment and exploration for secondary synchronous locations outside of the upper aerodigestive tract. 2012 SFORL guidelines. Eur Ann Otorhinolaryngol Head Neck Dis 2013;130:107–12.
. Nakaminato S, Toriihara A, Makino T, et al. Prevalence of esophageal cancer during the pretreatment of hypopharyngeal cancer patients: routinely performed esophagogastroduodenoscopy and FDG-PET/CT findings. Acta Oncol 2012;51:645–52.
. Huang YC, Lee YC, Tseng PH, et al. Regular screening of esophageal cancer for 248 newly diagnosed hypopharyngeal squamous cell carcinoma by unsedated transnasal esophagogastroduodenoscopy. Oral Oncol 2016;55:55–60.
. Morimoto M, Nishiyama K, Nakamura S, et al. Significance of endoscopic screening and endoscopic resection for esophageal cancer in patients with hypopharyngeal cancer. Jpn J Clin Oncol 2010;40:938–43.
. Watanabe S, Ogino I, Inayama Y, et al. Impact of the early detection of esophageal neoplasms in hypopharyngeal cancer patients treated with concurrent chemoradiotherapy. Asia Pac J Clin Oncol 2017;13:e3–10.
. Hsu WL, Chien YC, Chiang CJ, et al. Lifetime risk of distinct upper aerodigestive tract cancers and consumption of alcohol, betel and cigarette. Int J Cancer 2014;135:1480–6.
. Lee CH, Lee JM, Wu DC, et al. Independent and combined effects of alcohol intake, tobacco smoking and betel quid chewing on the risk of esophageal cancer in Taiwan. Int J Cancer 2005;113:475–82.
. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002;30:207–10.
. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 2013;41:D991–5.
. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000;25:25–9.
. Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci 2020;29:28–35.
. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44–57.
. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019;47:D607–13.
. Smoot ME, Ono K, Ruscheinski J, et al. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011;27:431–2.
. Bandettini WP, Kellman P, Mancini C, et al. MultiContrast Delayed Enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn Reson 2012;14:83.
. Gao JJ, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013;6:pl1.
. Cerami E, Gao JJ, Dogrusoz U, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012;2:401–4.
. Tang ZF, Li CW, Kang BX, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017;45:W98–102.
. Li T, Fan J, Wang B, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 2017;77:e108–10.
. Yang W, Zhao X, Han Y, et al. Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy. Cancer Cell Int 2019;19:142.
. Yang D, He Y, Wu B, et al. Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer. J Ovarian Res 2020;13:10.
. Cromer A, Carles A, Millon R, et al. Identification of genes associated with tumorigenesis and metastatic potential of hypopharyngeal cancer by microarray analysis. Oncogene 2005;23:2484–98.
. Hu N, Clifford RJ, Yang HH, et al. Genome wide analysis of DNA copy number neutral loss of heterozygosity (CNNLOH) and its relation to gene expression in esophageal squamous cell carcinoma. BMC Genomics 2010;11:576.
. Couto VJ, Nicolau NP, Costa EP, et al. Multi-cancer V-ATPase molecular signatures: a distinctive balance of subunit C isoforms in esophageal carcinoma. EBioMedicine 2020;51:102581.
. Qiu F, Sun R, Deng N, et al. miR-29a/b enhances cell migration and invasion in nasopharyngeal carcinoma progression by regulating SPARC and COL3A1 gene expression. PLoS ONE 2015;10:e0120969.
. Cancer Genome Atlas Network. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 2015;517:576–82.
. Song Y, Li L, Ou Y, et al. Identification of genomic alterations in oesophageal squamous cell cancer. Nature 2014;509:91–5.
. Cretu A, Brooks PC. Impact of the non-cellular tumor microenvironment on metastasis: potential therapeutic and imaging opportunities. J Cell Physiol 2007;213:391–402.
. Pavón MA, Arroyo-Solera I, Céspedes MV, et al. uPA/uPAR and SERPINE1 in head and neck cancer: role in tumor resistance, metastasis, prognosis and therapy. Oncotarget 2016;7:57351–66.
. Pavón MA, Arroyo-Solera I, Téllez-Gabriel M, et al. Enhanced cell migration and apoptosis resistance may underlie the association between high SERPINE1 expression and poor outcome in head and neck carcinoma patients. Oncotarget 2015;6:29016–33.
. Bajou K, Peng H, Laug WE, et al. Plasminogen activator inhibitor-1 protects endothelial cells from FasL-mediated apoptosis. Cancer Cell 2008;14:324–34.
. Balsara RD, Castellino FJ, Ploplis VA. A novel function of plasminogen activator inhibitor-1 in modulation of the AKT pathway in wild-type and plasminogen activator inhibitor-1-deficient endothelial cells. J Biol Chem 2006;281:22527–36.
. Duffy MJ, McGowan PM, Harbeck N, et al. uPA and PAI-1 as biomarkers in breast cancer: validated for clinical use in level-of-evidence-1 studies. Breast Cancer Res 2014;16:428.
. Harbeck N, Schmitt M, Meisner C, et al. Ten-year analysis of the prospective multicentre Chemo-N0 trial validates American Society of Clinical Oncology (ASCO)-recommended biomarkers uPA and PAI-1 for therapy decision making in node-negative breast cancer patients. Eur J Cancer 2013;49:1825–35.
. Sun ZJ, Yu GT, Huang CF, et al. Hypoxia induces TFE3 expression in head and neck squamous cell carcinoma. Oncotarget 2016;7:11651–63.
. Gomes-Giacoia E, Miyake M, Goodison S, et al. Targeting plasminogen activator inhibitor-1 inhibits angiogenesis and tumor growth in a human cancer xenograft model. Mol Cancer Ther 2013;12:2697–708.
. Kim SA, Kwon SM, Kim JA, et al. 5’-Nitro-indirubinoxime, an indirubin derivative, suppresses metastatic ability of human head and neck cancer cells through the inhibition of Integrin (1/FAK/Akt signaling. Cancer Lett 2011;306:197–204.
. Tu Y, Chen C, Fan G. Association between the expression of secreted phosphoprotein - related genes and prognosis of human cancer. BMC Cancer 2019;19:1230.
. Exposito JY, Valcourt U, Cluzel C, et al. The fibrillar collagen family. Int J Mol Sci 2010;11:407–26.
. Fang S, Dai Y, Mei Y, et al. Clinical significance and biological role of cancer-derived Type I collagen in lung and esophageal cancers. Thorac Cancer 2019;10:277–88.
. Yin Y, Du L, Li X, et al. miR-133a-3p suppresses cell proliferation, migration, and invasion and promotes apoptosis in esophageal squamous cell carcinoma. J Cell Physiol 2019;234:12757–70.
. Yu Y, Liu D, Liu Z, et al. The inhibitory effects of COL1A2 on colorectal cancer cell proliferation, migration, and invasion. J Cancer 2018;9:2953–62.
. Mori K, Enokida H, Kagara I, et al. CpG hypermethylation of collagen type I alpha 2 contributes to proliferation and migration activity of human bladder cancer. Int J Oncol 2009;34:1593–602.
. Misawa K, Kanazawa T, Misawa Y, et al. Hypermethylation of collagen α2 (I) gene (COL1A2) is an independent predictor of survival in head and neck cancer. Cancer Biomark 2011;10:135–44.
. Shintani Y, Hollingsworth MA, Wheelock MJ, et al. Collagen I promotes metastasis in pancreatic cancer by activating c-Jun NH(2)-terminal kinase 1 and up-regulating N-cadherin expression. Cancer Res 2006;66:11745–53.
. Svaita JK, Varsha VK, Girish HC, et al. Role of MMP1 and MMP10 as potential markers in head and neck squamous cell carcinoma. World J Pharma Res 2019;8:479–86.
. Liu M, Hu Y, Zhang MF, et al. MMP1 promotes tumor growth and metastasis in esophageal squamous cell carcinoma. Cancer Lett 2016;377:97–104.
. Fan HX, Chen Y, Ni BX, et al. Expression of MMP-1/PAR-1 and patterns of invasion in oral squamous cell carcinoma as potential prognostic markers. Onco Targets Ther 2015;8:1619–26.
. Kudo Y, Iizuka S, Yoshida M, et al. Matrix metalloproteinase-13 (MMP-13) directly and indirectly promotes tumor angiogenesis. J Biol Chem 2012;287:38716–28.
. Iizuka S, Ishimaru N, Kudo Y. Matrix metalloproteinases: the gene expression signatures of head and neck cancer progression. Cancers (Basel) 2014;6:396–415.
. Sun Y, Zhao C, Ye Y, et al. High expression of fibronectin 1 indicates poor prognosis in gastric cancer. Oncol Lett 2020;19:93–102.
. Korah R, Boots M, Wieder R. Integrin alpha5beta1 promotes survival of growth-arrested breast cancer cells: an in vitro paradigm for breast cancer dormancy in bone marrow. Cancer Res 2004;64:4514–22.
. Xiao J, Yang W, Xu B, et al. Expression of fibronectin in esophageal squamous cell carcinoma and its role in migration. BMC Cancer 2018;18:976.
. Svaita JK, Varsha VK, Girish HC, et al. Association between periostin and epithelial-mesenchymal transition in esophageal squamous cell carcinoma and its clinical significance. Oncol Lett 2017;14:376–82.
. Bayo P, Jou A, Stenzinger A, et al. Loss of SOX2 expression induces cell motility via vimentin up-regulation and is an unfavorable risk factor for survival of head and neck squamous cell carcinoma. Mol Oncol 2015;9:1704–19.
. Mayer IA, Arteaga CL. The PI3K/AKT pathway as a target for cancer treatment. Annu Rev Med 2016;67:11–28.
. Spangle JM, Roberts TM, Zhao JJ. The emerging role of PI3K/AKT-mediated epigenetic regulation in cancer. Biochim Biophys Acta Rev Cancer 2017;1868:123–31.
. Du WW, Yang W, Yee AJ. Roles of versican in cancer biology--tumorigenesis, progression and metastasis. Histol Histopathol 2013;28:701–13.
. Mitsui Y, Shiina H, Kato T, et al. Versican promotes tumor progression, metastasis and predicts poor prognosis in renal carcinoma. Mol Cancer Res 2017;15:884–95.
. Zhang F, Zhang Y, Da J, et al. Downregulation of SPARC expression decreases cell migration and invasion involving epithelial-mesenchymal transition through the p-FAK/p-ERK pathway in esophageal squamous cell carcinoma. J Cancer 2020;11:414–20.
. Che Y, Luo A, Wang H, et al. The differential expression of SPARC in esophageal squamous cell carcinoma. Int J Mol Med 2006;17:1027–33.
. Brézillon S, Pietraszek K, Maquart FX, et al. Lumican effects in the control of tumour progression and their links with metalloproteinases and integrins. FEBS J 2013;280:2369–81.
. Niewiarowska J, Brézillon S, Sacewicz-Hofman I, et al. Lumican inhibits angiogenesis by interfering with α2β1 receptor activity and downregulating MMP-14 expression. Thromb Res 2011;128:452–7.
. Ping LY, Ishiwata T, Asano G. Lumican expression in alpha cells of islets in pancreas and pancreatic cancer cells. J Pathol 2002;196:324–30.