Abstract

   Purpose: Bladder cancer (BCa) is generally considered one of the most
   prevalent deadly diseases worldwide. Patients suffering from
   muscle-invasive bladder cancer (MIBC) possess dismal prognoses, while
   those with non-muscle-invasive bladder cancer (NMIBC) generally have a
   favorable outcome after local treatment. However, some NMIBCs relapse
   and progress to MIBC, with an unclarified mechanism. Hence, insight
   into the genetic drivers of BCa progression has tremendous potential
   benefits for precision therapeutics, risk stratification, and molecular
   diagnosis.

   Methods: In this study, three cohorts profile datasets ([34]GSE13507,
   [35]GSE32584, and [36]GSE89) consisting of NMIBC and MIBC samples were
   integrated to address the differently expressed genes (DEGs).
   Subsequently, the protein-protein interaction (PPI) network and pathway
   enrichment analysis of DGEs were performed.

   Results: Six collagen members (COL1A1, COL1A2, COL5A2, COL6A1, COL6A2,
   and COL6A3) were up-regulated and gathered in the ECM-receptor
   interaction signal pathway identified by KEGG pathway analysis and
   GSEA. Evidence derived from the Oncomine and TCGA databases indicated
   that the 6 collagen genes promote the progression of BCa and are
   negatively associated with patient prognosis. Moreover, taking COL1A1
   as a further research object, the results showed that COL1A1 was
   up-regulated in MIBC and its knockdown significantly inhibited the
   proliferation, migration, and invasion of 5637 and T24 cells by
   inhibiting epithelial-mesenchymal transition (EMT) process and the
   TGF-β signaling pathway.

   Conclusion: With integrated bioinformatic analysis and cell
   experiments, we showed that 6 collagen family members are high
   progression risk factors and that they can be used as independent
   effective diagnostic and prognostic biomarkers for BCa.

   Keywords: genomic analysis, bladder cancer, invasion, diagnostic
   marker, prognostic markers

Introduction

   BCa is a common malignant urologic cancer worldwide, with approximately
   76960 new cases and 16390 deaths in 2016 due to its poor clinical
   outcome.[37]^1^,[38]^2 Approximately 80% of BCa patients have
   non-muscle invasive tumors (Ta, T1, and Tis).[39]^3 Unfortunately, 10
   to 30% of NMIBCs progress to MIBCs(T2~T4).[40]^4 On account of the
   involvement of multistep accumulation of genetic and epigenetic factors
   and external elements, such as tobacco smoking,
   neutrophil-to-lymphocyte ratio, body mass index, residual T1 high-grade
   (HG)/G3 tumors, or other carcinogens,[41]^5^–[42]^8 BCa can progress
   from a retrievable form of NMIBC into MIBC, an advanced crippling
   morphology compared to the former.[43]^9 In order to prevent
   postoperative bladder cancer recurrence and progression, some adjuvant
   means are applied such as chemotherapy, Chinese medicine, vaccine,
   etc.[44]^10^–[45]^13

   Many resources and tools have empowered in-depth studies on the
   mechanisms accounting for BCa progression, which provide more
   possibilities in providing ideal and reliable methods to identify
   important genetic or epigenetic modifications in cancer progression. At
   present, novel abnormally expressed genes and key signaling pathways
   can be addressed by advanced bioinformatics analysis, rendering many
   clinical evaluations for BCa studies.[46]^14 The field of identifying
   novel cancer susceptibility loci and some individual SNPs that account
   for the heritability of BCa has been advanced by GWAS (genome-wide
   association studies).[47]^15 Recently, using TCGA analysis, researchers
   found that MIBC has high overall mutation rates and some recurrent
   alterations in genes including TP53, PIK3CA, and FGFR3 genes, and also
   somatic TERT promoter mutations that present in the early process of
   BCa.[48]^16

   BCa entails a complex process, through which a primary tumor progresses
   to a disseminated metastatic disease. Non-cellular surroundings, the
   extracellular matrix (ECM), interact with cancer cells at each step of
   the metastatic process.[49]^17 During cancer progression, the ECM
   regulates numerous cell functions, including proliferation, migration,
   invasion and protein synthesis.[50]^18 The ECM comprises approximately
   300 proteins, of which collagen, elastin, and fibronectin are
   common.[51]^19 The ECM interacts with cells, and these interactions are
   mediated by transmembrane receptors, such as integrins, syndecans,
   CD44, discoidin domain receptor, and dystoglycan.[52]^20^,[53]^21 In
   breast cancer, ECM proteins appear to be involved in the maintenance of
   tumor cell shape, migration and invasion by regulating the expression
   of the CD44 protein, known as a tumor prognostic factor, thus acting on
   tumor progression and metastasis.[54]^22 However, although ECM is
   closely related to tumor metastasis, the role of ECM proteins,
   especially collagens in the progression of non-invasive BCa into
   invasive cancer has not been extensively studied.

   While many studies have been carried out, BCa progression is still
   poorly understood, with some more esoteric theories remaining
   unexplored. In this study, to address the potential markers and risk
   factors of BCa, the expression profiles of MIBC tissue and NMIBC tissue
   obtained from three GEO datasets were analyzed by using the limma
   package. Through KEGG pathway analysis and GSEA, the ECM–receptor
   interaction signaling pathway was identified. By further analyzing the
   Oncomine database, it was determined that 6 collagen family members
   that are located in the ECM–receptor interaction signaling pathway were
   positively correlated with BCa progression. Analyzing the Oncomine and
   the TCGA databases indicated that 6 collagen genes overexpressed in
   MIBC are significantly correlated with BCa progression, overall
   survival, and recurrence-free survival in patients with BCa. The
   pivotal protein COL1A1 is further disposed with expression silencing to
   determine its more profound functions or roles in the tumor cell
   growth, proliferation, invasion and migration in BCa. The results
   revealed that the 6 collagen family members and the ECM-receptor
   interaction signaling pathway play a significant role and that the 6
   collagen family members may be effective, independent prognostic
   biomarkers of BCa progression.

Materials and methods

Microarray data information and degs identification

   NMIBC and MIBC tissue gene expression profiles of [55]GSE13507,
   [56]GSE32584 and [57]GSE89 were all obtained from NCBI-GEO
   ([58]https://www.ncbi.nlm.nih.gov/geo/). The array data for
   [59]GSE13507 consisted of 103 NMIBC tissue samples and 61 MIBC tissue
   samples.[60]^23 The array data for [61]GSE32548 contained 92 NMIBC
   tissue samples and 38 MIBC tissue samples.[62]^24 The [63]GSE89 dataset
   contained 30 NMIBC tissue samples and 10 MIBC tissue samples.[64]^25
   Then, the DEGs were identified with the independent t-test in the limma
   package, with p<0.05 and [logFC]>0.75 as the cut-off criterion. The
   robust multi-array average (RMA) method in the limma package
   ([65]http://www.bioconductor.org/packages/2.9/bioc/html/limma.html) was
   used to preprocess the raw CEL data and to perform data normalization
   and quartile data normalization.

Construction of protein-protein interaction (PPI) network

   PPI analysis was used to search core genes and gene modules related to
   carcinogenesis. DEGs were committed to STRING version 10.5 (Search Tool
   for the Retrieval of Interacting Genes; string.embl.de) to find
   interaction associations of the proteins with the confidence score>0.4
   used as the threshold criterion.[66]^26 Then, Cytoscape software
   (cytoscape.org) was performed to construct a PPI network of DEGs and
   analyze the interaction relationship of DEGs encoding proteins in
   BCa.[67]^27

Functional and pathway enrichment analysis

   Gene ontology (GO) analysis is a common method for analyzing the
   functions of many genes.[68]^28 Kyoto Encyclopedia of Genes and Genomes
   (KEGG) is the major recognized pathway-related database resource for
   biological interpretation of high-throughput data. GO and KEGG analyses
   are available in the DAVID database ([69]https://david.ncifcrf.gov/).
   The cut-off value was a p -value <0.05.

Gene set enrichment (GSEA) analysis

   GSEA is a method to identify classes of genes in a big set of genes
   that may have a correlation with disease phenotypes. These genes are
   grouped together by their involvement in the same biological pathway or
   by proximal location on a chromosome. Generally, the enrichment score
   (ES) indicates the degree to which the genes are over-represented at
   either the top or bottom of the list, corresponding to the largest
   differences in genes expression between the MIBC and NMIBC tissues, is
   first calculated. Then, the estimated statistical significance of the
   ES before the enrichment scores for each set are normalized, and a
   false discovery rate is counted.[70]^29 The normalized enrichment score
   (NES) and nominal p-value were applied to rank the pathways enriched in
   each phenotype.

Oncomine and TCGA analysis

   Oncomine database ([71]http://www.oncomine.org) was applied to explore
   the mRNA expression differences of the 7 DEGs in the ECM pathway
   between MIBC and NMIBC tissues in BCa. BCa gene expression data (mRNA,
   normalized RNAseq FPKM-UQ) was obtained from the TCGA database
   (provisional) using cBioPortal ([72]http://www.cbioportal.org/). For
   the survival analyses, BCa patients with high expression levels of the
   7 DEGs in the ECM pathway (the lower 25%, n=102) were compared with
   those with low expression levels of the genes (the upper 25%, n=102).

Tissue samples and immunohistochemistry (IHC) staining

   In this study, a total of 30 tisuue samples(15 NMIBC and 15 MIBC) were
   collected from BCa patients who received surgery at the Third
   Affiliated Hospital, Guangzhou Medical University. All patients
   provided wirtten informed consent for the use of their tissue for
   research. All patients were confirmed to have BCa via histopathological
   evaluations. None of the patients received treatment before surgery.
   This study was approved by the Internal Research Ethics Board at the
   Third Affiliated Hospital of Guangzhou Medical University and was
   conducted in accordance with the Declaration of Helsinki.
   Paraffin-embedded sections (3 µm) from 30 BCa samples were
   deparaffinized, rehydrated and blocked as previously described[73]^30
   After being incubated with a goat anti-human monoclonal COL1A1 antibody
   (1:200; ab138492, Abcam, Cambridge, UK), sections were incubated with a
   biotin-labeled rabbit anti-goat antibody. Sections were visualized with
   streptavidin-conjugated horseradish peroxidase and 3,
   3-diaminobenzidine (DAB). According to the number of cells stained (≤5%
   positive cells, 0; 6–25% positive cells,1; 25–50% positive cells,2;
   50–80% positive cells,3; >80% positive cells, 4) and the staining
   intensity (no staining, 0; mild staining, 1; moderate staining, 2;
   severe staining, 3), each sample was scored. The final score was the
   sum of the positive cell percentage score and the staining intensity
   score.

Cell culture and RNAi

   5637 and T24 BCa cells were purchased from the Institute of Cell
   Research, Chinese Academy of Sciences, Shanghai, China. The 5637 and
   T24 cells were cultured in RPMI-1640 medium (Invitrogen, Carlsbad, CA,
   USA) with 10% fetal bovine serum. Plates were then placed at 37 °C in a
   humidified atmosphere of 5% CO2 in an incubator. The siRNAs against
   COL1A1 gene with RNAiMAX (Invitrogen) were transfected into 5637 and
   T24 cells for 48h. The following siRNA sequences were used: COL1A1#NC,
   sense: 5‘-ACUGCCGUUGUUAAGGUGUTT-3′, antisense:
   5′-ACACCUUAACAACGGCAGUTT-3′, COL1A1#1, sense:
   5′-GUACGUCCGGUUGUAUGUATT-3′, antisense: 5′-UACAUACAACCGGACGUACTT-3′,
   COL1A1#2, sense:5′-GGUGUUGUGCGAUGACGUGTT-3′, antisense:
   5′-CACGUCAUCGCACAACACCTT-3′.

qRT-PCR

   Total RNA was extracted from Treated 5637 and T24 cells using Trizol
   lysis method. cDNA synthesis was performed using the
   PrimeScript™Reverse Transcription System (Takara). RNA levels of
   COL1A1were detected using qRT-PCR according to the manufacturer’s
   protocol. Expression of COL1A1 and GAPDH were measured using the 2-ΔΔCt
   method. The following primer sequences were used: COL1A1, forward:
   GAGACCTGCGTGTACCCCACT, reverse: GTCATGCTCTCGCCGAACCAG; GAPDH, forward:
   CCCACTCCTCCACCTTTGAC, reverse: TCTTCCTCTTGTGCTCTTGC.

Western blotting

   Total cell lysates were separated by 8% or 10% SDS-PAGE and then
   electroblotted and transferred onto PVDF membranes.[74]^31 After
   incubation with primary antibodies specific for COL1A1 (1:1000;
   sc-293182, Santa Cruz, TX, USA), CD44 (1:2000; #37259, CST, Boston,
   USA), E-cadherin (1:2000; #14472, CST, Boston, USA), vimentin (1:2000;
   #5741, CST, Boston, USA), TGFBI(1:1000; 10188–1-AP, proteintech, IL,
   USA), MMP9 (1:2000; #15561, CST, Boston, USA) and β-actin
   (1:4000,sc-81178, Santa Cruz, TX, USA) at 4 °C overnight, the blots
   were incubated with the corresponding secondary antibodies at room
   temperature, and the proteins were detected with an ECL kit.

Cell proliferation assay

   5637 and T24 BCa cells were transfected with the indicated siRNAs for
   48 h; 1×10^4 cells were seeded into 24-well plates. The cells were
   counted for the number of viable cells at 24, 48, 72, 96, and 120 h.
   After being transfected with the indicated siRNAs for 48 h, the cells
   were analyzed with an EdU Kit (C10310-1, RiboBio) according to the
   manufacturer’s protocol. The results were detected with an inverted
   fluorescence microscope (Nikon).

Colony formation assays

   For colony formation assays, 500 5637 or T24 cells were seeded and
   cultured in RPMI 1640 medium containing 10% FBS for 12 or 7 days,
   respectively. Clones were fixed with methanol and stained with 0.5%
   crystal violet.

Migration and invasion assays

   For transwell migration assays, 1×10^5 5637 or T24 cells were plated in
   the top transwell chamber (8-µm pore size, BD, NY, USA) and were
   cultured in RPMI 1640 medium containing 0.1% FBS for 36 or 24 h,
   respectively. The chambers were placed in RPMI 1640 medium containing
   10% FBS. Then, the chambers were fixed with methanol and stained with
   0.5% crystal violet. The results were detected with an inverted
   microscope (Nikon).

   For invasion assays, chamber inserts were coated with 100 μl/ml of
   Matrigel (BD, Biosciences, MA, USA) with RPMI 1640 media and dried for
   1 h at 37°C. Then, 20×10^4 5637 or T24 cells were plated in the top
   transwell chamber and were cultured in RPMI 1640 medium containing 0.1%
   FBS for 96 or 48 h, respectively. The same staining and detecting
   method for the migration assay was used.

Statistics

   Statistical analysis was performed using GraphPad Prism 5. The survival
   curve between the groups was described by the Kaplan-Meier plot and was
   evaluated using the log-rank test. Differences in quantitative data
   were compared between the two groups using Student’s t-test. All
   experiments were performed three times independently. *p<0.05,
   **p<0.01, or ***p<0.001 was considered to be significant.

Results

The ECM-receptor interaction signal pathway may promote BCa progression

   By utilizing gene expression microarray analysis, DEGs (491 in
   [75]GSE13507, 418 in [76]GSE32584, and 529 in [77]GSE89) were
   identified, and 45 genes were found in all three datasets ([78]Figure
   1A). These 45 genes were shown in [79]Table 1, including 30
   up-regulated genes and 15 down-regulated genes in the MIBC tissues
   compared with NMIBC tissues ([80]Table 1). PPI network was performed
   using the STRING database, and the results are presented in [81]Figure
   1B. Analysis of the PPI sub-network demonstrated that COL1A1, COL1A2,
   COL5A2, COL6A1, COL6A2, COL6A3, MMP9, THY1, TAGLN, VIM, CTGF, PDGFRB,
   ACTA2, SPP1, and TPM2 interacted closely with each other ([82]Figure
   1B).

Figure 1.

   [83]Figure 1
   [84]Open in a new tab

   The ECM-receptor interaction signal pathway may promote BCa
   progression. (A) Venn diagram of DEGs from the three mRNA expression
   profiling datasets [85]GSE89, [86]GSE13507, and [87]GSE32548. (B) The
   protein–protein interaction (PPI) network of DEGs was constructed using
   the String website (available online: [88]www.string-db.org) and
   Cytoscape software (red represents upregulated genes, and blue
   represents downregulated genes). (C) KEGG pathway enrichment analysis
   of DEGs. (D) Venn diagram of the top 20 gene sets enriched by GSEA in
   the MIBC phenotype compared to NMIBC. (E) Venn diagram of the
   intersection of GSEA and KEGG analysis. (F) GSEA results showing the
   ECM-receptor interaction pathway.

Table 1.

   Forty-five DEGs were identified from [89]GSE89, [90]GSE13507, and
   [91]GSE32548 expression profile datasets
   DEGs Genes Name
   Up-regulated ACTA2, ACTN1, AIF1, C1QB, CD14, CDC20, COL1A1, COL1A2,
   COL5A2, COL6A1, COL6A2, COL6A3, CRIP1, CTGF, DPYSL2, LGALS1, MGP, MMP9,
   MT2A, PDGFRB, PLEK, RBP1, RGS1, SLC2A3, SPP1, TAGLN, TGFBI, THY1, TPM2,
   VIM
   Down-regulated CTSE, CYP2J2, CYP4B1, DHRS2, FABP6, FOXA1, GPX2, HMGCS2,
   HSD17B2, ID3, PTK6,SLC14A1,SMAD6, SORL1, SPINK1
   [92]Open in a new tab

   For further analyzing the candidate DEGs’ functions and pathway
   enrichment, both GO and KEGG analyses were conducted using DAVID. All
   DGEs were classified into three groups, cellular component (CC),
   biological processes (BP) and molecular function (MF), and the results
   are shown in [93]Table 2. Specifically, regarding the cellular
   component (CC), the upregulated DEGs were enriched in proteinaceous
   extracellular matrix, extracellular exosomes, and collagen. As for
   biological processes (BP), the upregulated DEGs were mainly involved in
   extracellular matrix organization, cell adhesion, and skeletal system
   development. Moreover, for molecular function, the upregulated DEGs
   were mainly enriched in extracellular matrix structural constituent and
   platelet-derived growth factor binding. In addition, the downregulated
   DEGs were mainly enriched in steroid metabolic processes and electron
   carrier activity. Furthermore, KEGG pathway analysis revealed that the
   upregulated DEGs were mainly involved in focal adhesion and
   ECM-receptor interaction ([94]Figure 1C), and both of the former two
   signaling pathways contained 7 common genes, COL6A1, COL6A2, COL6A3,
   COL5A2, COL1A1, COL1A2, and SPP1(osteopontin) ([95]Table 3). However,
   the downregulated DEGs were not enriched.

Table 2.

   The significant GO terms analysis of DEGs
   Category Term Count P-value
   Up-regulated (Top20)
   GOTERM_CC_FAT GO:0005578~proteinaceous extracellular matrix 11 1.97E-10
   GOTERM_CC_FAT GO:0031012~extracellular matrix 11 4.11E-10
   GOTERM_CC_FAT GO:0044421~extracellular region part 13 6.26E-08
   GOTERM_CC_FAT GO:0005581~collagen 5 5.72E-07
   GOTERM_CC_FAT GO:0005576~extracellular region 16 5.73E-07
   GOTERM_MF_FAT GO:0048407~platelet-derived growth factor binding 4
   1.63E-06
   GOTERM_BP_FAT GO:0030198~extracellular matrix organization 6 2.08E-06
   GOTERM_BP_FAT GO:0001501~skeletal system development 8 2.94E-06
   GOTERM_BP_FAT GO:0007155~cell adhesion 10 7.17E-06
   GOTERM_BP_FAT GO:0022610~biological adhesion 10 7.26E-06
   GOTERM_BP_FAT GO:0043062~extracellular structure organization 6
   1.88E-05
   GOTERM_MF_FAT GO:0005201~extracellular matrix structural constituent 5
   3.76E-05
   GOTERM_CC_FAT GO:0044420~extracellular matrix part 5 7.27E-05
   GOTERM_MF_FAT GO:0019838~growth factor binding 5 8.21E-05
   GOTERM_BP_FAT GO:0001503~ossification 5 8.67E-05
   GOTERM_BP_FAT GO:0060348~bone development 5 1.13E-04
   GOTERM_CC_FAT GO:0005583~fibrillar collagen 3 2.40E-04
   GOTERM_CC_FAT GO:0005615~extracellular space 8 2.53E-04
   GOTERM_MF_FAT GO:0005178~integrin binding 4 2.99E-04
   GOTERM_BP_FAT GO:0048705~skeletal system morphogenesis 4 1.56E-03
   Down-regulated
   GOTERM_BP_FAT GO:0008202~steroid metabolic process 5 3.10474E-05
   GOTERM_BP_FAT GO:0055114~oxidation reduction 5 0.002505005
   GOTERM_MF_FAT GO:0009055~electron carrier activity 3 0.022942517
   GOTERM_MF_FAT GO:0070330~aromatase activity 2 0.026636667
   GOTERM_MF_FAT GO:0016712~oxidoreductase activity 2 0.031884301
   [96]Open in a new tab

Table 3.

   The significant signaling pathway enrichment analysis of DEGs functions
   Pathway Name Gene Count p-value Genes
   Focal adhesion 9 8.9E-07 COL6A3,COL6A2, COL1A2, COL6A1, ACTN1, PDGFRB,
   COL1A1, COL5A2, SPP1
   PI3K-Akt signaling pathway 8 2.6E-04
   COL6A3,COL6A2,COL1A2,COL6A1,PDGFRB, COL1A1, COL5A2, SPP1
   ECM-receptor interaction 7 8.9E-07 COL6A3,COL6A2,COL1A2,COL6A1, COL1A1,
   COL5A2, SPP1
   Protein digestion and absorption 6 1.7E-05 COL6A3,COL6A2,COL1A2,COL6A1,
   COL1A1, COL5A2
   Amoebiasis 5 7.5E-04 COL1A2, ACTN1, COL1A1, COL5A2, CD14
   [97]Open in a new tab

   To further identify signaling pathways that are obviously activated in
   MIBC compared to NMIBC, we performed GSEA in the three expression
   datasets ([98]GSE13507, [99]GSE32584, and [100]GSE89). Compared to the
   NMIBC phenotype, the top 20 positively correlated gene signatures for
   NES values were enriched in MIBC. Among them, 4 gene signatures were
   found in all three datasets, including ECM-receptor interaction,
   cytokine-receptor interaction, complement, coagulation cascades and
   NOD-like receptor signaling pathways ([101]Figure 1D). Among them, the
   ECM-receptor interaction signaling pathway was found in GSEA and KEGG
   analysis in common ([102]Figure 1E). The ECM-receptor interaction
   signaling pathway was differentially enriched in the NMIBC phenotype
   based on the [103]GSE13507, [104]GSE32584, and [105]GSE89 gene sets
   ([106]Figure 1F and [107]Figure S1).

Figure S1.

   [108]Figure S1
   [109]Open in a new tab

   Blue-pink O’gram of the ECM-receptor interaction pathway in the three
   datasets [110]GSE89, [111]GSE13507, and [112]GSE32548.

Over-expression of 6 collagen genes in muscle-invasive bca

   Based on GSEA and KEGG analysis, we hypothesized that up-regulation of
   genes located in the ECM-receptor interaction signal pathway may be
   important in the progression of NMIBC into MIBC. In order to ascertain
   the roles collagen genes played and investigate whether the genes
   belonging to the ECM-receptor interaction signal pathway are amplified
   in MIBC, mRNA levels in both types of BCa were analyzed using the
   Oncomine database. Significantly higher mRNA levels of all 6 genes were
   found in MIBC tissues compared to NMIBC tissues ([113]Figure 2A).
   Similarly, higher mRNA levels were also found in MIBC tissues in the
   Sanchez-Carbayo bladder database ([114]Figure 2B). These observations
   indicate that the COL6A3, COL6A2, COL6A1, COL5A2, COL1A2, and COL1A1
   genes are amplified in MIBC. Collectively, these results demonstrate
   that the ECM-receptor interaction signaling pathway collagen genes may
   be critical for BCa progression, even having the possibility of
   clinical treatment.

Figure 2.

   Figure 2
   [115]Open in a new tab

   Over-expression of COL6A3, COL6A2, COL6A1, COL5A2, COL1A2, and COL1A1
   genes in MIBC tissues compared to NMIBC tissues. (A) COL6A3, COL6A2,
   COL6A1, COL5A2, COL1A2, and COL1A1 mRNA levels were up-regulated in
   MIBC compared to NMIBC based on the Dyrskjot bladder database from
   Oncomine. (B) COL6A3, COL6A2, COL6A1, COL5A2, COL1A2, and COL1A1 mRNA
   levels were increased in the MIBC compared to NMIBC in the
   Sanchez-Carbayo bladder database.

Higher expression of 6 collagen genes involved in the ECM–receptor
interaction signaling pathway promoted mortality in BCa patients

   By comparing the 6 collagen gene expression levels between the MIBC and
   NMIBC tissues, all mRNA levels turned out to be extraordinary similar
   with the former exhibiting a higher level relative to the latter. This
   indicated that higher expression levels of the genes located in the
   ECM-receptor interaction signal pathway might increase the progression
   possibility of NMIBC into MINBC. However, correlation of the
   upregulation of these 6 genes with the recurrence-free survival and
   overall survival for BCa patients remained unclear. The results showed
   that the overall survival rates of BCa patients were negatively
   correlated with the levels of all 6 collagen genes ([116]Figure 3A),
   and the same was observed for the recurrence-free survival ([117]Figure
   3B). The results showed that 6 collagen genes were negatively
   correlated with the prognosis of BCa patients, and may become an
   independent prognostic marker.

Figure 3.

   [118]Figure 3
   [119]Open in a new tab

   Increased COL6A3, COL6A2, COL6A1, COL5A2, COL1A2 and COL1A1 mRNA levels
   correlate with poor survival rates in BCa patients. (A) Kaplan-Meier
   analysis of overall survival for BCa patients with high (the upper 25%)
   or low (the lower 25%) COL6A3, COL6A2, COL6A1, COL5A2, COL1A2 and
   COL1A1 expression (102 patients per subgroup). (B) Kaplan-Meier
   analysis of recurrence-free survival for BCa patients with high (the
   upper 25%) or low (the lower 25%) COL6A3, COL6A2, COL6A1, COL5A2,
   COL1A2 and COL1A1 expression (102 patients per subgroup).

Upregulation of COL1A1 protein is positively correlated with the progression
of NMIBC to MIBC

   We evaluated the expression level of 6 collagen genes involved in the
   ECM–receptor interaction signaling pathway with GAPDH as the internal
   control, based on the [120]GSE13507, [121]GSE32584, and [122]GSE89 gene
   sets. The result showed that the mRNA level of COL1A1 was the highest,
   relative to the other five collagen genes, in MIBC, leading us to
   speculate that COL1A1 plays a more key role in the progression of BCa
   ([123]Figure S2). Therefore, we regard it as a future research object.
   To examine the role of COL1A1 in the progression of BCa, COL1A1 levels
   in 15 NMIBC tissues and 15 MIBC tissues were analyzed with IHC staining
   ([124]Figure 4A and [125]B). We found that 73.33% (11/15) of the MIBC
   tissues were stained positively forCOL1A1, including 2 cases of
   severely positive samples, 2 cases of moderately positive samples and 7
   cases of highly positive samples but only 40.0% (6 of 15) of the NMIBC
   tissues were positive ([126]Figure 4B).

Figure S2.

   [127]Figure S2
   [128]Open in a new tab

   Analysis of the three datasets ([129]GSE89, [130]GSE13507, and
   [131]GSE32548) for mRNA expression levels of COL6A3, COL6A2, COL6A1,
   COL5A2, COL1A2 and COL1A1 in the MIBC and NMIBC samples.

Figure 4.

   [132]Figure 4
   [133]Open in a new tab

   Compared with NMIBC tissue samples, the COL1A1 protein was upregulated
   in MIBC tissue samples. (A) Representative IHC images of COL1A1
   expression in the NMIBC and MIBC tissue samples. (B) Differences in
   COL1A1 expression scores between NMIBC tissues (n=15) and MIBC tissues
   (n=15) are shown as a scatter plot.

Silencing of COL1A1 inhibits both migration and invasion of tumors in vitro

   Features of tumor progression generally consist of the dysregulation of
   cell proliferation, invasiveness, and metastasis.[134]^32^,[135]^33 To
   further investigate the role of COL1A1 in the progression of BCa,
   COL1A1 was targeted with RNA interference in 5637 and T24 cells. The
   results showed that both mRNA and protein expression levels of COL1A1in
   5637 and T24 cells were significantly reduced compared to the control
   groups ([136]Figure 5A and [137]B). We also found that silencing of
   COL1A1 downregulated the expression level of CD44 in both cells
   ([138]Figure 5B). During tumor metastasis, epithelial-mesenchymal
   transition (EMT) is a key process. E-cadherin and vimentin are regarded
   as improtant EMT markers.[139]^34 We demonstrated that silencing COL1A1
   downregulated the expression levels of E-cadherin and Vimentin. Binding
   of collagen to integrin is mediated by TGFBI.[140]^35 In addition,
   TGFBI can activate matrix metalloproteinase (MMP) secretion and promote
   invasion.[141]^36 We found that silencing COL1A1 down-regulated the
   expression levels of TGFBI and MMP9. We found that silencing COL1A1 in
   5637 and T24 cells inhibited cell growth ([142]Figure 5C and [143]D).
   Silencing COL1A1 also inhibited cell colony formation in 5637 and T24
   cells ([144]Figure 5E). Silencing COL1A1 caused various degrees of
   reduction in both cell lines’ invasion and migration ([145]Figure 5F).
   Taken together, we demonstrated that COL1A1 promotes BCa cell malignant
   phenotypes, which matches the poor prognosis in BCa patients.

Figure 5.

   Figure 5
   [146]Open in a new tab

   COL1A1 is required for tumorigenesis and metastasis of BCa cells. (A)
   The mRNA levels of COL1A1 in COL1A1-silenced 5637 and T24 cells. (B)
   5637 and T24 cells were transfected with 100 nM anti-COL1A1 siRNA, and
   the protein levels of COL1A1, CD44, E-cadherin, vimentin, Snail and
   TGFBI were analyzed by western blotting. (C) The effects of COL1A1
   silencing on 5637 and T24 cell growth after being transfected with the
   indicated siRNAs for 48 h. (D) Cell proliferation was measured using
   EdU immunofluorescence assays in COL1A1-silenced 5637 and T24 cells.(E)
   Colony formation assays of COL1A1 silenced 5637 and T24 cells. (F)
   Migration and invasion of COL1A1-silenced 5637 and T24 cells.

Discussion

   Plenty of studies have been implemented to clarify the unique
   mechanisms of BCa formation and progression, whereas the clinical
   incidence and mortality of BCa still maintains a severe status. Since
   most efforts have concentrated on a individual genetic research, little
   attention has been paid to integrated processes for radical exploration
   by systematic bioinformatics methods and medical molecular biology
   technologies. In the present study, gene expression profiling was
   utilized to investigate the molecular mechanisms underlying BCa
   progression. A set of 30 up-regulated and 15 down-regulated DEGs were
   identified between the NMIBC samples and MIBC samples. Furthermore,
   results from the KEGG analysis and GSEA collectively focused on the
   ECM–receptor interaction signaling pathway. ECM, as a key component in
   the modulation of cancer cell invasion, is considered as necessary to
   maintain the integrity of the impermeable bladder surface.[147]^37 The
   interaction between membrane receptors of tumor cells and ECM proteins
   plays a crucial role in tumor invasion and metastasis.[148]^38

   Collagens, major components of ECM, are involved in regulating tumor
   cell proliferation, migration, and invasion. COL1A1, as a major
   component of collagen type I, has been well-identified by researchers
   as an oncogene in the progression of colon cancer .[149]^39 In
   addition, high COL1A1 and COL1A2 mRNA expression levels are
   significantly associated with survival in patients with NMIBC.[150]^40
   Moreover, researchers have discovered that COL3A1 is involved in many
   clinical diseases and that it could function as one translocation
   partner gene in lipomatous tumors and influence cell migration and
   invasion in nasopharyngeal carcinoma progression.[151]^41^,[152]^42 It
   could also interfere with malignant development in renal cell carcinoma
   and act as one of the etiologically linked genes in aortic dissected
   aneurysm.[153]^43 COL5A2 is also widely studied, and previous evidence
   indicated that COL5A2 might be associated with the pathological
   processes of several human cancers. For example, the expression of
   COL5A2 was determine to be up-regulated in invasive breast cancer
   compared with ductal carcinoma in situ, indicating that COL5A2 is
   related to tumor progression in breast cancer.[154]^44 COL5A2 is also
   considered as a diagnostic marker for osteosarcoma and might affect the
   growth of BC cells by ECM remodeling.[155]^45 COL6A1, an oncogene in
   the progression of cervical cancer, is highly correlated with poor
   prognosis in cervical cancer patients.[156]^46 In this study, we
   demonstrated upregulation of the ECM–receptor interaction signaling
   pathway by 6 collagen genes, COL1A1, COL1A2, COL5A2, COL6A1, COL6A2,
   and COL6A3, favoring NMIBC progression to MIBC. Our data reveal that
   the expression of the 6 collagen genes was highly correlated with high
   pathological stages, poor recurrence-free survival rates, and poor
   overall survival rates. These findings indicated the potentially
   important roles of 6 collagen genes in the diagnosis and prognosis of
   BCa patients. It has been reported that constitutive expression of SPP1
   is involved in tumorigenesis and metastasis of BCa.[157]^47 We also
   found that higher expression of SPP1 was involved in the ECM–receptor
   interaction signaling pathway and was highly correlated with poor
   pathological stages of BCa and poor survival rates of patients with BCa
   ([158]Figure S3).

Figure S3.

   [159]Figure S3
   [160]Open in a new tab

   SPP1 was highly correlated with poor pathological stages of BCa and
   poor survival rates. (A and B) The mRNA levels of SPP1 were upregulated
   in MIBC compared to NMIBC by analyzing the Dyrskjot and the
   Sanchez-Carbayo bladder databases from Oncomine. (C and D) Kaplan-Meier
   analysis of overall survival and recurrence-free survival for BCa
   patients with high (the upper 25%) or low (the lower 25%) SPP1
   expression (102 patients per subgroup).

   To further explore the function and mechanism of collagen genes in
   promoting bladder cancer progression, we used COL1A1 as a further
   research object. The results showed that the suppression of COL1A1
   decreased the growth, migration, and invasion of BCa cells. CD44 plays
   an important role in the ECM-receptor interaction signal pathway and is
   evidently increased in tumor cells cultured in 3D collagen
   scaffolds.[161]^48^,[162]^49 Our results showed that COL1A1 knockdown
   inhibited the expression of CD44. COL1A1 is closely related to EMT and
   the transforming growth factor β (TGF-β, a common stimulator of EMT)
   pathway. COL1A1 directly stimulates EMT of pancreatic,[163]^50
   lung,[164]^51 and breast[165]^52 carcinoma cells via TGF-β signaling.
   TGFBI, which is induced by TGF-β1 protein, is an ECM protein that
   activates matrix metalloproteinase (MMP) secretion. Logan A. et al
   showed that inhibiting the expression of COL1A1 with siRNA inhibited
   EMT and cell migration induced by TGF-β1in HK-2 cells. EMT is
   characterized by changes in the expression of biochemical markers, a
   decrease in epithelial markers like E-cadherin and an increase in
   mesenchymal markers such as vimentin and N-cadherin.[166]^34 First, we
   discovered that COL1A1 is a key effector of CD44 in the regulation of
   BCa progression. Further, we found that inhibiting the expression of
   COL1A1 activated the transcription of E-cadherin and inhibited the
   expression of vimentin. Finally, we determined that inhibiting the
   expression of COL1A1 also inhibited the expression of TGFBI and MMP9.
   Therefore, we believe that COL1A1 is an important EMT mediator and
   effector of TGF-β-induced expression via interacting with CD44 in BCa
   progression. Interestingly, vimentin, TGFBI, and MMP9 are also
   significantly upregulated DEGs. These data are mutually confirmed with
   the results of our cellular experiments. Meanwhile, relevant studies
   have shown that other collagen genes, such as COL1A2, COL5A2, and
   COL6A3, are important EMT mediators.[167]^53^–[168]^55

   Our findings provide novel insights into NMIBC progression to MIBC. In
   summary, the present study determined gene expression profiles and
   identified 6 collagen proreins(COL1A1, COL1A2, COL5A2, COL6A1, COL6A2,
   and COL6A3) involved in the ECM–receptor interaction signaling pathway
   in BCa progression. The 6 collagen genes can be used as independent
   effective prognostic biomarkers for BCa. In addition, the 6 collagen
   genes may be an independent potential indicator of whether NMIBC
   patients need further treatment after surgery to prevent the recurrence
   and progression of the disease.

Acknowledgments