Abstract

Background:

   Tenascin-C (TNC) is a large glycoprotein of the extracellular matrix
   which associated with poor clinical outcomes in several malignancies.
   TNC over-expression is repeatedly observed in several cancer tissues
   and promotes several processes in tumor progression. Until quite
   recently, more needs to be known about the potential mechanisms of TNC
   as a key player in cancer progression and metastasis.

Materials and Methods:

   In the present study, we performed a bioinformatics analysis of breast
   and colorectal cancer expression microarray data to survey TNC role and
   function with holistic view. Gene expression profiles were analyzed to
   identify differentially expressed genes (DEGs) between normal samples
   and cancer biopsy samples. The protein-protein interaction (PPI)
   networks of the DEGs with CluePedia plugin of Cytoscape software were
   constructed. Furthermore, after PPI network construction,
   gene-regulatory networks analysis was performed to predict long
   noncoding RNAs and microRNAs associated with TNC and cluster analysis
   was performed. Using the Clue gene ontology (GO) plugin of Cytoscape
   software, the GO and pathway enrichment analysis were performed.

Results:

   PPI and DEGs-miRNA-lncRNA regulatory networks showed TNC is a
   significant node in a huge network, and one of the main gene with high
   centrality parameters. Furthermore, from the regulatory level
   perspective, TNC could be significantly impressed by miR-335-5p. GO
   analysis results showed that TNC was significantly enriched in
   cancer-related biological processes.

Conclusions:

   It is important to identify the TNC underlying molecular mechanisms in
   cancer progression, which may be clinically useful for tumor-targeting
   strategies. Bioinformatics analysis provides an insight into the
   significant roles that TNC plays in cancer progression scenarios.

   Keywords: Gene regulatory network, microarray analysis, protein
   interaction maps, Tenascin-C

Introduction

   The tumor microenvironment is highly heterogeneous meshwork,
   encompassing immune cells, fibroblasts, adipocytes, and endothelial
   cells, and a complex network termed the extracellular matrix
   (ECM).[[27]1] The ECM's composition and organization are highly
   regulated and control cell behavior, but the evidence is increasing on
   the crucial role of the altered ECM components in providing oncogenic
   signals that promote cancer cell proliferation, invasion, new
   angiogenesis, and metastasis. Growing evidence showed that the
   composition and organization of ECM are underlying numerous changes in
   cancer when compared to normal tissue.[[28]2,[29]3]

   Tenascin-C (TNC) is a large hexameric glycoprotein that is located
   primarily in the ECM and interacts with cell surface receptors and
   other ECM proteins. TNC is a highly conserved ECM protein, without
   noticeable mutational hotspots, reflecting its importance for the
   maintenance of tissue homeostasis. TNC expression is tightly regulated,
   and TNC presents in stem cell niche regions and at sites of
   epithelial-mesenchymal interaction in adults and is frequently
   up-regulated in a variety of pathological conditions including chronic
   inflammation and cancer.[[30]2,[31]4,[32]5] Its expression in several
   cancer tissues of breast, lung, colon, glioblastoma, liver, prostate,
   gastric cancer, pancreatic cancer, and gastrointestinal tract have been
   well documented in previous studies.[[33]6,[34]7]

   TNC expression in tumors is significantly higher than in normal tissue
   and correlated with distant metastasis and invasiveness, and tumor
   metastases can also express TNC.[[35]7] Because of the abundance of TNC
   in tumor stroma and its association with cancer progression, invasion,
   angiogenesis, and metastasis, identification of the TNC-related
   pathways may be clinically useful for representing the cancer
   progression process and tumor-targeting.

   Systems biology approaches have a holistic view of the cancer molecular
   mechanisms and in the present study, to identify TNC-related pathways
   in cancer, we performed a bioinformatics analysis on breast and
   colorectal cancer expression microarray data.

   The differentially expressed genes (DEGs) in these datasets were
   analyzed and their interaction with TNC was investigated by
   protein-protein interaction (PPI) network analysis. In addition, TNC
   gene regulatory networks (GRN) were constructed to identify possible
   regulatory mechanisms at the transcriptional level. Moreover, possible
   functions of TNC and its related protein were evaluated by gene and
   pathway enrichment analyses. Therefore, we anticipated that the
   findings of the present study may provide further insight into
   TNC-related pathways in cancer pathogenesis and development at the
   molecular level.

Materials and Methods

Acquisition of microarray data

   Gene expression profiles [36]GSE37364, which was composed of 104 breast
   tumor samples and 17 normal breast tissue samples and [37]GSE42568
   included 36 samples from normal colonic biopsy and 55 samples related
   to different stages of colorectal cancer, were downloaded from the NCBI
   gene expression omnibus (GEO) database
   ([38]http://www.ncbi.nlm.nih.gov/geo/). Both datasets were based on the
   Affymetrix [39]GPL570 platform (Affymetrix Human Genome U133 Plus 2.0
   Array). The reason for choosing these gene expression profiles was the
   appropriate number of cancers and control of human samples, also based
   on previous studies which represent the role of TNC in breast and
   colorectal cancer.

Data processing and DE genes determination

   The quality control assessment of these datasets was assigned using
   prcomp function and “ggplot2” package[[40]8] of R software.[[41]9] The
   GEO2R, ([42]http://www.ncbi.nlm.nih.gov/geo/geo2r/), the web tool of
   GEO, was used to identify genes that are differentially expressed
   across experimental conditions. The adjusted P values (adj. p) were
   applied to correct the false-positive results by default
   Benjamini-Hochberg false discovery rate method. The adjusted P ≤ 0.05
   was considered as the cutoff value.

Protein-protein interaction network analysis

   The PPI networks were constructed with CluePedia plugin V1.5.4[[43]10]
   of Cytoscape software V3.6.2.[[44]11] STRING v11 database
   ([45]https://string-db.org) was used for retrieving interactions. The
   topological properties of the networks were calculated by the Network
   Analyzer tool based on undirected interactions.

Gene-regulatory networks and clustering analysis

   The Enrichr database ([46]http://amp.pharm.mssm. edu/Enrichr/) was used
   to predict long noncoding RNAs (lncRNAs) associated with DEGs. The
   lncHUB lncRNA Co-Expression based on Recount2 library was used. With
   the Network Analyst database,[[47]12] experimentally validated
   miRNA-gene interactions were obtained from Tar Base and miRTarBase
   databases. The Cytoscape was utilized to build and visualize the GRN.
   Cytoscape merge tool was used for the construction of comprehensive
   intercoms gene regulators and genes obtained from PPI network analysis.
   Furthermore, the plug-in of molecular complex detection (MCODE) (Bader
   and Hogue, 2003) in Cytoscape software was applied to explore the
   significant modules in GRN networks and, with a graph-theoretic
   clustering algorithm, identifies densely connected regions and
   interconnected clusters in the networks. The advanced options set as
   degree cutofF = 2, K-Core = 2, and Node Score CutofF = 0.2. Moreover,
   the function and pathway enrichment analysis was performed for genes in
   the modules.

Gene ontology and functional analysis

   Function and pathway enrichment analysis was performed for DEGs in the
   modules using Clue gene ontology (GO) plugin V2.5.4,[[48]13] a
   Cytoscape plug-in. For exploring functions and pathways related to
   DEGs, Kyoto Encyclopedia of Genes and Genomes (KEGG) and GO databases
   were used to identify their biological meaning. Enriched terms with >3
   genes and adjusted P ≤ 0.05 was considered to be statistically
   significant.

Results

Identification of differentially expressed genes

   Gene expression datasets [49]GSE42568 and [50]GSE37364 were downloaded
   from GEO datasets. DEGs were identified between normal tissue and
   cancerous samples based on the GEO2R analysis, using the adjusted P ≤
   0.05 and | logFC| ≥1 as the cutoff criteria.

   A total of 9341 and 12240 DEGs were identified from [51]GSE42568 and
   [52]GSE37364 datasets, respectively, and Venn diagram results indicate
   that intersection included 6609 genes and TNC is common in both
   datasets [[53]Figure 1a and Supplementary Table 1 available on the
   website].

Figure 1.

   Figure 1
   [54]Open in a new tab

   (a) The intersection of the differentially expressed genes from
   [55]GSE37364 and [56]GSE42568 datasets. The intersection included 6609
   genes. (b) The principle component analysis results indicate the
   acceptable quality of the [57]GSE37364 and [58]GSE42568 datasets

   In the quality assay step, the samples were segregated based on their
   states, indicate the acceptable quality of these datasets [[59]Figure
   1b].

Protein-protein interaction network construction and analysis

   Cytoscape was applied to evaluate the primary assay of the PPI
   interactions, protein interactome was investigated based on the STRING
   database. Activation, expression, inhibition, posttranslation
   modification, and binding interactions were retrieved.

   Network topology was measured based on the graph theory concepts such
   as degree, betweenness, and closeness centrality. It is remarkable, TNC
   is one-fourth of the genes with high centrality. DEGs interacted
   directly with TNC in PPI networks are listed in [60]Table 1.

Table 1.

   Protein-protein interaction networks analysis. Tenascin-C included the
   main node in protein-protein interaction networks, with high centrality
   parameters. [61]GSE42568 and [62]GSE37364 datasets, respectively
   [63]GSE37364
     __________________________________________________________________

   Gene Degree CC Eccentricity
   EDN1 201 0.39 6
   FN1 113 0.37 6
   TNC 58 0.35 6
   ITGAX 31 0.32 6
   NOTCH1 304 0.42 6
   VWF 61 0.35 6
   COL2A1 60 0.34 6
   EDNRA 62 0.34 6
   ITGA11 74 0.33 6
   ITGA6 113 0.36 6
   ITGA1 152 0.36 6
   ITGA2 109 0.35 6
   ITGA2B 130 0.36 6
   ITGA3 94 0.34 6
   ITGA4 98 0.35 6
   ITGA5 108 0.34 6
   ITGA7 84 0.34 6
   ITGAV 146 0.37 6
   ITGB1 265 0.38 6
   ITGB3 178 0.37 6
   ITGB5 162 0.36 6
   ITGB7 137 0.35 6
   ITGB8 115 0.35 6
   PTK2 299 0.39 6
   ITGA8 86 0.34 6
   IL1A 57 0.35 6
   MMP9 222 0.40 6
   VEGFA 539 0.43 5
   FHL2 64 0.38 6
   EGFR 594 0.44 6
   MAPK1 1195 0.44 5
   MMP2 142 0.39 5
   SAT1 17 0.31 6
   MMP1 88 0.36 6
   CDH1 341 0.41 5
   WNT7B 85 0.32 6
   IL6 463 0.42 5
   JUN 484 0.43 6
   CAV1 215 0.41 5
   PTPRZ1 99 0.35 6
   ITGA10 71 0.33 6
   FGF2 260 0.40 5
   KHDRBS1 56 0.35 6
   VCAN 18 0.33 6
   ACAN 23 0.32 6
   F3 54 0.35 5
   NOTCH2 139 0.37 6
   PRRX1 4 0.27 7
   BCAN 6 0.31 7
   SDC4 70 0.35 6
   ITGAE 38 0.32 7
   ITGAX 35 0.31 7
   TNC 38 0.29 7
   ITGAL 43 0.32 7
   TNN 34 0.29 7
   ITGA2 100 0.34 6
   ITGA3 92 0.33 7
   ITGA7 82 0.32 7
   ITGA9 81 0.32 7
   ITGB4 103 0.35 7
   ITGB8 77 0.33 7
   ITGA8 91 0.33 7
   ITGA1 133 0.35 6
   ITGB1 196 0.36 6
   PTK2 244 0.37 6
   SDC1 65 0.33 7
   ITGB5 107 0.34 7
   SDC4 58 0.34 6
   VCAN 13 0.31 7
   F12 15 0.29 7
   GPLD1 16 0.29 7
   CD209 15 0.31 7
   MASP1 10 0.27 7
   SVEP1 10 0.30 7
   FRAS1 9 0.32 7
   ADAMTS3 5 0.29 7
   [64]Open in a new tab

   TNC: Tenascin-C

Identification of Tenascin-C related regulatory network

   After enrichment of genes at regulatory levels, GRNs were constructed
   and analyzed. Cluster analysis to determine significant modules was
   performed. Modules consider as protein complexes which are highly
   interconnected subnetworks. The results of module detection showed, In
   the [65]GSE42568 dataset, TNC is clustered in MCODE Cluster 9 (score:
   10.824) and in [66]GSE37364 in MCODE Cluster 10 (score: 11.157). They
   are important clusters of the networks. Furthermore, the analytic
   results from lncRNAs and miRNAs prediction and DEGs-miRNA-lncRNA
   regulatory network construction indicated that TNC could be potentially
   modulated by hsa-mir-335-5p also by LINC01326, LINC00603, and TENM3-AS1
   [Figure [67]2a and [68]b].

Figure 2.

   Figure 2
   [69]Open in a new tab

   Gene regulatory networks. Tenascin-C is a component of key modules from
   [70]GSE42568 (a) and [71]GSE37364 (b) datasets gene regulatory networks
   analysis, respectively

Biological classification and pathway enrichment analysis

   Aiming to evaluate the functions of identified DEGs, we uploaded DEGs
   clustered in modules to Clue GO plugin V2.5.4 to find significant GO
   categories and KEGG pathways. GO analysis and KEGG pathway analysis
   indicated that the TNC was significantly enriched in biological
   process, including cellular response to stress, focal adhesion,
   extracellular structure organization, PI3K-Akt signaling pathway,
   positive regulation of cell population proliferation, and regulation of
   cell adhesion [72]Table 2.

Table 2.

   Pathway enrichment analysis
   [73]GSE37364
     __________________________________________________________________

   GO term Associated genes found
   Cellular response to stress ABI2, ACKR3, ACTR2, ACTR5, ADAM8, APEX1,
   ARFRP1, ARHGEF5, ATRX, AVPR1A, BABAM1, BRCA2, BRSK1, CAMK2A, CAMK2B,
   CAMK2D, CAMK2G, CCR4, CD74, CDC14B, CHEK2, COPS4, COPS7A, COPS8,
   CTDSP2, CXCL12, DCLRE1A, DCLRE1B, DDX6, DTL, EDN1, EIF4A2, EME1, EPHA4,
   FAAP100, FAAP20, FAAP24, FANCA, FANCB, FANCE, FANCF, FANCG, FANCL,
   FGF1, FGF19, FSCN1, GPS1, GRB2, GUCY1B1, HDAC2, HSP90AB1, HSP90B1,
   HSPA5, HSPA9, HSPH 1, INO80B, INO80D, INO80E, ITCH, KDM6B, KMT5A, LIG1,
   MAPK9, MYEF2, NCOR1, NOTCH1, PAK3, PALB2, PCLAF, PDGFRA, PEBP1, PIAS4,
   PIK3R1, PMPCB, PTGER4, PTK2B, PTPN1, PTPN2, RAD18, RAD51AP1, RAP1A,
   RBL1, RBL2, RHOB, RNF8, RTEL1, SEH1L, SFN, SIRT1, SLX4, SMURF1, SPP1,
   SUV39H2, TAB1, TAF9B, TFDP2, TFPT, THBS1, TNC, TPM1, UBE2D3, UBE2G2,
   UBE2N, UBE2T, UIMC1, UQCRFS1, USP10, USP3, USP51, VPS51, VPS52, WDR1,
   YAP1
   Focal adhesion ERBB2, FLNA, FLNB, FLT1, FN1, GRB2, MAPK9, MYL12A, MYL5,
   PAK3, PARVA, PARVB, PDGFRA, PIK3R1, RAP1A, SPP1, THBS1, TLN2, TNC, VWF
   PI3K-Akt signaling pathway CSF1R, ERBB2, F2R, FGF1, FGF18, FGF19,
   FGF20, FLT1, FN1, GNB5, GRB2, HSP90AB1, HSP90B1, KIT, LPAR5, NRAS,
   PDGFRA, PIK3R1, RBL2, SPP1, TGFA, THBS1, TNC, VWF, YWHAQ
   Extracellular structure organization ABI2, ADAM8, COL11A1, COL27A1,
   COL5A1, COL5A2, COL5A3, COL7A1, COL8A1, FN1, FSCN1, GLMN, ITGAL, ITGAX,
   NFKB2, NOTCH1, PDGFRA, PMPCA, SH3PXD2A, SH3PXD2B, SPP1, THBS1, TNC,
   VCAM1, VWF, WDR1
   Extracellular matrix organization ADAM8, COL11A1, COL27A1, COL5A1,
   COL5A2, COL5A3, COL7A1, COL8A1, FN1, FSCN1, GLMN, ITGAL, ITGAX, NFKB2,
   NOTCH1, PDGFRA, SH3PXD2A, SH3PXD2B, SPP1, THBS1, TNC, VCAM1, VWF
   Response to wounding ADRA2A, CCR1, CCR2, COL5A1, EDN1, EPHA4, ERBB2,
   F2R, F5, FERMT3, FLNA, FN1, GLMN, GNAS, GSN, HDAC1, HDAC2, MCFD2,
   MYL12A, NOTCH1, NOTCH4, PDGFRA, PEBP1, PIK3R1, PMPCA, RHOC, SERPINA1,
   SPP1, TGFA, THBS1, TNC, TPM1, TSPAN32, VPS52, VWF, YAP1
   Response to antibiotic ACTR2, ADCY2, APEX1, ARHGEF5, CCR4, DRD4, EDN1,
   EPS8, GRIN3A, GSN, GUCY2C, HDAC2, HSPA5, KDM6B, PEBP1, PTK2B, RANBP1,
   RHOB, SIRT1, STAT3, TNC, UQCRFS1, VCAM1
   Response to fibroblast growth factor ESRP1, FGF1, FGF18, FGF19, FGF20,
   GRB2, GUCY2C, RAB14, RBFOX2, THBS1, TIA1, TIAL1, TNC
   Response to ketone ADCY2, AVPR1A, EDN1, EIF4A2, GNAS, GPS1, GUCY2C,
   OXTR, PEBP1, PTGER4, PTK2B, SIRT1, SPP1, THBS1, TNC, YAP1
   Wound healing ADRA2A, COL5A1, EDN1, ERBB2, F2R, F5, FERMT3, FLNA, FN1,
   GLMN, GNAS, GSN, HDAC1, HDAC2, MCFD2, MYL12A, NOTCH1, NOTCH4, PDGFRA,
   PIK3R1, PMPCA, RHOC, SERPINA1, TGFA, THBS1, TNC, TPM1, TSPAN32, VWF,
   YAP1
     __________________________________________________________________

   [74]GSE42568
     __________________________________________________________________

   GO term Associated genes found
     __________________________________________________________________

   PI3K-Akt signaling pathway COL1A1, COL1A2, COL4A1, COL4A2, COL4A3,
   COL4A5, ERBB2, ERBB3, FGF1, FGF18, FGF2, FGFR1, FGFR3, FGFR4, IBSP,
   IRS1, JAK2, KRAS, MAP2K1, MAP2K2, MAPK1, MAPK3, RAF1, TNC, TNN
   Focal adhesion ACTN1, ACTN4, BCAR1, BRAF, COL1A1, COL1A2, COL4A1,
   COL4A2, COL4A3, COL4A5, ERBB2, IBSP, MAP2K1, MAPK1, MAPK3, RAF1, TNC,
   TNN
   Response to fibroblast growth factor COL1A1, CXCL8, FGF1, FGF18, FGF2,
   FGFR1, FGFR3, FGFR4, KDM5B, MAPK1, MAPK3, TNC, WASF1
   Osteoblast differentiation ACVR1, ACVR2A, ACVR2B, BMP2, BMP4, COL1A1,
   IBSP, SMAD1, SMAD3, SMAD5, SMAD6, SMAD9, SYNCRIP, TNC, TNN
   ECM-receptor interaction COL1A1, COL1A2, COL4A1, COL4A2, COL4A3,
   COL4A5, IBSP, TNC, TNN
   Morphogenesis of a branching epithelium ACVR1, ARHGEF5, BMP2, BMP4,
   COL4A1, FGF1, FGF2, FGFR1, KDM5B, KRAS, SMAD4, TNC
   MicroRNAs in cancer ERBB2, ERBB3, FGFR3, IRS1, KRAS, MAP2K1, MAP2K2,
   MAPK1, RAF1, TNC, TNN, UBE2I
   [75]Open in a new tab

   ECM: Extracellular matrix, GO: Gene ontology, TNC: Tenascin-C

Discussion

   Previously data demonstrated that TNC promotes several procedures in
   tumor progression and involved in a wide range of cellular processes
   including cell proliferation, migration, invasiveness, angiogenesis,
   and immunomodulation.[[76]7,[77]14] TNC is one of the most highly
   expressed proteins in the tumor ECM which has poor prognostic value for
   patient survival in a variety of malignant tumors.

   Despite advances in current molecular methods, the potential mechanism
   of TNC as a major player in cancer progression and metastasis was not
   well known.[[78]1,[79]2] Therefore, it's meaningful to explore the
   TNC-related underlying molecular mechanisms in cancer progression
   procedure.

   In the present study, we downloaded gene expression profiles from GEO
   to identify DEGs associated with breast cancer and colorectal cancer.

   To explore the TNC protein interaction in these cancers, the PPI and
   GRN networks were constructed, and central parameters analyzed. The
   MCODE was applied to explore the significant modules in GRNs and TNC
   included in the main modules that were selected for further analysis.

   Previous data reported that TNC induces epithelial-mesenchymal
   transition (EMT) in cancer tissues at the initial steps of cancer cell
   invasion, during which carcinoma cells lose their epithelial polarity
   and intercellular connections, which leads to cancer cell escape from
   the surrounding epithelium.[[80]4,[81]5,[82]6] Data extracted from
   previous studies showed that binding of avb6 and avb1 integrins to TNC
   as their essential ligand induces EMT.[[83]15] In addition, via
   bioinformatics analysis, we show some other integrin's family members
   are co-express with TNC, which included ITGA7, ITGB8, ITGAV, ITGBL1,
   ITGA5, and ITGA6. The co-expression of these integrins with TNC can
   involve in some biological processes such as ECM organization,
   extracellular structure organization, cell-matrix adhesion, blood
   vessel development, cell adhesion molecule binding, blood vessel
   morphogenesis, angiogenesis, integrin-mediated signaling pathway, and
   cell-cell adhesion. These pathways are significantly related to cancer
   metastasis, invasion, and angiogenesis.[[84]7,[85]16,[86]17]

   The versican (VCAN) a large aggregating chondroitin sulfate
   proteoglycan, is an important ECM component and associated with cell
   survival, growth, migration, invasion, angiogenesis, and metastasis in
   several cancers.[[87]18] The PPI networks indicate the co-expression of
   TNC and VCAN both in breast and colorectal cancer.

   ADAMTS, which known as adamalysins, is a matrix zinc-dependent
   metalloproteinases and plays an important role in the cell phenotype
   regulation and ECM remodeling through its role in cell adhesion and
   migration. ADAMTS can cleave TNC and generates TNC splice
   variants.[[88]19]

   As same as TNC, E-cadherin is a major contributor to cell-cell
   adhesion. TNC expression leads to the cytoplasmic delocalization of
   E-cadherin and β-catenin by Src-mediated focal adhesion kinase (FAK)
   signaling in combination with ανβ1 and ανβ6 integrins and promotes
   migratory behavior.[[89]20]

   The role of Neuropilin-1 (NRP-1), a multifunctional transmembrane
   protein that interacts with some of signaling receptors, in initiating
   tumorigenic pathways has been shown in several cancer types, including
   breast cancer.[[90]21] TNC is associated with NRP-1-triggered cell
   migration and activation of the FAK/Akt signaling pathway. Upregulation
   of NRP-1 and TNC was also related to considerably increased Vimentin
   expression and downregulation of E-cadherin (mature form) and β-catenin
   that leads to EMT.[[91]22,[92]23]

   In addition, TNC stimulates endothelin receptor type A (EDNRA)
   expression, and EDNRA supports cell rounding by TNC through concomitant
   repression of RhoA and tumor suppressor-like molecule
   tropomyosin-1.[[93]24]

   TNC inhibits the interaction between fibronectin and syndecan-4, a
   transmembrane heparan sulfate proteoglycan, and as a result, activation
   of Rho family GTPases leads to the induction of actin-rich filopodia
   and stimulates cell invasion.[[94]25]

   The GO analysis results indicated that TNC was shown to be mostly
   involved in biological processes, including cellular response to
   stress, focal adhesion, extracellular structure organization, PI3K-Akt
   signaling pathway, positive regulation of cell population
   proliferation, and regulation of cell adhesion.

   Therefore, studying these signaling pathways could facilitate the
   prediction of TNC-related cancer progression mechanisms.

   The result from this study indicates that TNC can be regulated via some
   miRNA and lncRNA at the expression level. DEGs-miRNA-lncRNA regulatory
   network construction also showed that TNC could be significantly
   impressed by miR-335-5p. Previous data report that miR-335-5p playing a
   key role in TGF-β1-induced EMT, and its over-expression can negatively
   regulate cell migration and invasion in nonsmall-cell lung
   cancer.[[95]26] In addition, a recent study showed that miR-335 could
   inhibit proliferation, invasion, and migration of the cancer cells, and
   arrest the cells in S phase.[[96]27,[97]28] This notion is supported by
   a study which demonstrates that miR-335 suppresses breast cancer cell
   migration by negatively regulating the HGF/c-Met pathway.[[98]29] Our
   bioinformatics analysis shows that miR-335 regulates TNC, and this
   interaction may have a key role in TNC-related EMT and TNC-induced
   cancer cell proliferation and invasion. Further analysis and
   comprehensive functional characterization of the TNC-related regulatory
   network can provide valuable insights into the molecular events which
   control TNC expression in tumor cells.

   Obviously, this study has some limitations to consider. More gene
   expression profiles of most common cancer types must be included, and
   in addition, the experimental analysis must be conducted to ensure the
   accuracy and precision of the results obtained from this study. The
   evaluated level of TNC reported in previous studies in most cancer
   types, but the co-expression of TNC with different DEGs should be
   assessed.

Conclusion

   In summary, based on the gene expression profile analysis, these
   screened genes and pathways contribute to the molecular mechanisms
   underlying the TNC-related cancer progression. These data may offer new
   clues for the identification of the potential targets, therapeutic
   agents, and biological mechanisms of breast and colorectal cancer after
   confirmation by further preclinical and clinical studies.

Financial support and sponsorship

   This study was financially supported by grant No 199523 of the
   Biotechnology Development Council of the Islamic Republic of Iran.

Conflicts of interest

   There are no conflicts of interest.

References