Identification of differentially expressed genes in gastric cancer
To identify the DEGs in gastric cancer vs. non-cancerous gastric tissues, we analyzed GSE13911, GSE29272, GSE79973, and GSE118916 datasets by GEO2R. Figure 2a shows the volcano plots and the number of the DEGs we detected in each dataset. The four datasets shared 83 DEGs: 38 upregulated- and 45 downregulated- genes (Fig. 2b-d; Additional File 1: Table S2).
Functional annotation clustering and enrichment analysis of differentially expressed genes in gastric cancer
To illuminate the biological functions and the pathways the DEGs enrich, we performed the functional annotation and enrichment analysis of 83 DEGs. The functional annotation clustering with the highest classification stringency in DAVID revealed 3 clusters (Table 1). The cluster with the highest enrichment score included the extracellular matrix-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway.
Functional enrichment analysis of the upregulated genes indicated a key role for ECM organization, ECM remodeling, ECM-receptor interaction, and activation of pro-tumorigenic signaling pathways (Fig. 2e, Additional File 1: Table S3). The most enriched molecular functions were extracellular matrix structural constituent, platelet-derived growth factor binding and, integrin-binding. The top KEGG pathways related to the upregulated genes were ECM-receptor interaction, focal adhesion and, PI3K-Akt signaling (Fig. 2g, Additional File 1: Table S3). The downregulated genes enriched in metabolic processes and ion homeostasis (Fig. 2f-g, Additional File 1: Table S4).
Protein-protein interaction network analysis and identification of the hub genes
To investigate the potential interactions between the protein products of DEGs, we constructed a PPI network in STRING (Fig. 3a). The analysis of this network on Cytoscape 3.8.2. revealed two prominent hubs. The first hub was a larger-scale hub composed almost totally of ECM components. The top six proteins with the highest degree in this hub were: COL3A1 (collagen type III alpha 1 chain), FN1 (fibronectin 1), COL1A2 (collagen type I alpha 2 chain), COL1A1 (collagen type I alpha 1 chain), COL5A1 (collagen type V alpha 1 chain), and SPARC (cysteine-rich acidic matrix-associated protein) respectively (Fig. 3b). Table 2 shows the topological parameters for these proteins. The topological parameters for the whole PPI network are listed in Additional File 1: Table S5. The components of the larger-scale hub were all upregulated genes in gastric tumors, except for COL2A1 (collagen type II alpha 1 chain) (Additional File 2: Fig. S1).
The second hub with the low clustering and connectivity was composed of metallothioneins MT1E, MT1F, MT1G, MT1H, MT1M, and MT1X (Fig. 3a-b), which are proteins responsible for cellular response to metal ions such as zinc and cadmium (21). This finding explains the GO-BP terms "cellular response to zinc ion" and "cellular response to cadmium ion" enriched in the list of downregulated genes (Fig. 2f). The interactions between these genes were based solely on co-expression and protein homology. All components of this hub were downregulated in gastric cancer (Additional File 2: Fig. S2).
Analysis of the network modules
To identify the individual modules in the PPI network, we used the MCODE tool in Cytoscape. MCODE identified three modules. The larger-scale ECM protein hub was represented by modules 1 and 2 and, the small-scale metallothionein hub was represented by module 3 in Fig. 3c. The 5 out of 6 highest degree nodes, COL1A1, COL1A2, COL3A1, COL5A1, and SPARC, were components of module 1, while the FN1 was in module 2.
Then we performed functional enrichment analysis to identify enriched KEGG pathways at each module. "ECM-receptor interaction" and "focal adhesion" were the common enriched KEGG pathways in modules 1 and 2 (Additional File 1: Table S6). “PI3K-Akt signaling pathway” was the third enriched pathway in module 1. The only KEGG pathway detected in module 3 was mineral absorption. Hence the module analysis strengthened the connection of the six hub genes: COL1A1, COL1A2, COL3A1, COL5A1, FN1 and, SPARC, with the 3 KEGG pathways: ECM-receptor interaction, focal adhesion and, PI3K-Akt signaling pathway, in gastric cancer. Since the clustering efficiency and connectivity were very low for the metallothionein module, we focused on the ECM genes in modules 1 and 2 for their potential as prognostic factors and therapeutic targets in gastric cancer.
COL1A1, COL1A2, COL3A1, COL5A1, and FN1 are abundant structural proteins at the ECM. COL1A1 and COL1A2 are produced mainly by fibroblasts and together constitute the type I collagen in the connective tissue. COL3A1 and COL5A1 are the alpha-1 chains of type III and V collagen, which are found in connective tissue together with type I collagen (22, 23). FN1 is a glycoprotein involved in cell adhesion, wound healing, and metastasis. Besides their structural role, these proteins bind to the integrins on the cell membrane, and through focal adhesion kinases, they activate intracellular signaling pathways such as PI3K-Akt and MAPK pathways (24). SPARC encodes the cysteine-rich acidic matrix-associated protein that is an essential protein for ECM remodeling. It binds to collagens and fibronectin; and regulates the interactions of cells with the ECM (25).
We analyzed the first neighbors of these proteins in our PPI network using the Cytohubba tool in Cytoscape. All these proteins highly interacted with other structural ECM components: BGN (biglycan), THBS1/2 (thrombospondin 1/2), and VCAN (versican), or ECM remodeling enzymes like SERPINH1 (serpin family H member 1), SULF1 (sulfatase 1), and TIMP1 (tissue inhibitor matrix metalloproteinase 1) (Fig. 3d).
Verifying the differential expression of six hub genes in gastric cancer
To validate the differential expression of the six hub genes in gastric cancer, we analyzed the TCGA data in UALCAN. The expression of all six genes was significantly higher in stomach adenocarcinoma samples, compatible with our results (Fig. 4a). The significant upregulation of these genes in gastric cancer was also verified on GEPIA2 using gastric cancer data from the GTEx dataset (data not shown).
Prognostic significance of the extracellular matrix hub genes in gastric cancer
To investigate whether the six hub genes are involved in gastric cancer progression, we investigated their differential expression by tumor stage and grade on UALCAN using the TCGA data. For COL1A2, COL3A1, COL5A1, FN1, and SPARC, there was no significant upregulation in stage 1 patients compared to healthy controls (Fig. 4b). However, their expression was significantly higher in stage 2, 3, and 4 samples than the stage 1 samples. Only for COL1A1, the expression was high starting from stage 1. After stage 2, the increase in COL1A1 expression became much more prominent. These findings suggest that COL1A1 may be involved in both tumorigenesis and tumor progression. On the other hand, COL1A2, COL3A1, COL5A1, FN1, and SPARC may be more involved in tumor progression from stage 1 to stage 2, at which the tumor cells gain the ability to invade surrounding tissues.
The expression of COL1A1, COL1A2, COL5A1, and SPARC was significantly high starting from grade 1 (Fig. 4c). The medians of expression for these genes gradually increased in grade 2 and 3 samples. For COL3A1 and FN1, the upregulation in grade 1 disease compared to healthy gastric tissue was not statistically significant due to the high variance in grade1. However, their expression was significantly higher in grade 2 and grade 3 compared to grade 1 disease and normal tissue. These findings suggest that all six genes may be associated with a poorly differentiated phenotype in gastric cancer.
Then we investigated the impact of upregulated COL1A1, COL1A2, COL3A1, COL5A1, FN1, and SPARC on patient survival with KM-survival analysis (Fig. 4d). High expression of COL1A1, COL3A1, COL5A1, FN1, and SPARC was significantly associated with poor survival in gastric cancer patients (p<0.05). Although the survival curves for COL1A2 high vs. low expression samples were different, the difference was not significant enough to suggest that the high expression of COL1A2 is associated with poor survival (p=0.0618). Despite that, the KM-survival curves for COL1A2 high vs. low/medium expression samples from the TCGA database were statistically different (p=0.029) (Additional File 2: Fig. S3). These findings support that COL1A1, COL1A2, COL3A1, COL5A1, FN1, and SPARC are associated with poor prognosis in gastric cancer.
The connection of the hub genes with the cancer-associated fibroblasts
The CAFs are critical components at the cellular compartment of the tumor microenvironment that assemble and remodel the ECM. They secrete numerous ECM proteins, mainly fibrous collagens (type I, III, and V collagens) and fibronectin. They dynamically interact with the cells and signals in the tumor microenvironment; alter the ECM through matrix metalloproteinases (MMPs) which cleave the ECM components, and the lysyl oxidase (LOX) family enzymes which crosslink the collagens. The dynamic remodeling of ECM by CAFs facilitates cancer cell migration and invasion. Additionally, CAFs induce ECM stiffness in the tumor microenvironment, which is associated with poor survival in many cancers (6, 23).
All the six hub genes we detected are CAF markers in several cancers (26). Hence, our network-based analysis implicated a significant CAF infiltration in gastric cancer. Correlation analysis in TIMER2.0 revealed a statistically significant correlation for all six hub genes with CAF infiltration in stomach adenocarcinoma, supporting our hypothesis (Fig. 5). The correlation of these genes with the CAF infiltration was even higher than that of poor prognostic CAF signature genes recently identified in gastric cancer: THBS1, THBS2, INHBA (inhibin A), CXCL12 (C-X-C motif chemokine ligand 12), TGFB2 (transforming growth factor-beta 2), VEGFB (vascular endothelial growth factor B), COL10A1 (collagen type X alpha 1 chain), AREG (amphiregulin) and EFNA5 (ephrin A5) (Additional File 2: Fig. S4) (27-29).
Then we investigated the correlation of the six hub genes with a list of CAF markers at the gene expression level in TCGA stomach adenocarcinoma samples. The CAF marker list included 27 genes; 18 commonly used CAF markers (highlighted blue in Fig. 6a) and 9 CAF-specific markers (highlighted red in Fig. 6a). We also included CXCL12, INHBA, THBS1, and THBS2 since they are CAF markers associated with an aggressive phenotype in gastric cancer (27, 28, 30). In hierarchical clustering analysis of the correlation matrix, the six hub genes were highly correlated and clustered with COL11A1 (collagen type XI alpha1 chain), FAP (fibroblast activation protein), INHBA, MMP11 (matrix metalloproteinase 11), S100A4 (S100 calcium-binding protein A4) and THBS2 (Fig. 6a).
After that, we compared all components of the ECM protein hub with the CAF markers list in terms of identity and gene ontology in Metascape. Eight out of 38 upregulated genes in gastric cancer (ASPN, COL1A1, COL1A2, COL3A1, COL5A1, FAP, FN1, and SPARC) overlapped with CAF markers list (Fig. 6b, upper circos plot). Besides that, 28 out of 38 upregulated genes in gastric cancer and 23 out of 27 CAF markers fell into the same ontology term that is statistically significantly enriched in both lists (Fig. 6b, lower circos plot). The two lists differed in terms of 3 main ontologies. The "peptide cross-linking" and "integrin α4β1 pathway” were enriched in the gastric cancer-upregulated gene list. The "mesenchyme development" was enriched in the CAF markers list (Fig. 6c). Network layouts for enriched ontology clusters given in Fig. 7a-c better demonstrate that the upregulated genes in gastric cancer highly overlap with the CAF markers in terms of gene ontologies. The most striking difference for upregulated genes in gastric cancer was the enrichment of the “integrin α4β1 pathway”.
Prognostic impact of the hub genes on gastric tumors with CAF infiltration
CAFs display a heterogenous gene expression profile in the stroma of distinct cancers. Hence, they play anti-tumor or tumor-promoting roles depending on the tumor type (6). Studies suggest a tumor-promoting role for CAFs in gastric cancer (29, 31). Accordingly, KM-survival analysis on TCGA data showed that CAF infiltration is associated with poor survival with a hazard ratio of 5.24 in stomach adenocarcinoma (Table 3). Moreover, the outcome of CAF infiltration worsens with the increasing tumor stage in gastric cancer (Additional File 2: Fig. S5).
Then, we investigated whether high expression of the six hub genes further worsens the prognosis in gastric tumors with CAF infiltration. We performed Cox proportional hazard regression analysis that considers both gene expression profiles and CAF infiltration in the TCGA stomach adenocarcinoma samples. Out of the six genes, the expression of COL1A1, COL1A2, COL3A1, or COL5A1 increased the z-score and hazards ratio for CAF infiltration (Table 3). The high COL5A1 expression led to the highest increase in the risk for poor survival (z=2.666, HR=8.584). The high FN1 or SPARC expression did not increase the z-score and hazards ratio for CAF infiltration.
After that, we investigated whether the high expression of dual combinations of COL1A1, COL1A2, COL3A1, or COL5A1 exacerbate the outcome of CAF infiltration in stomach adenocarcinoma (Table 3). Concomitant high expression of COL1A1 and COL5A1 increased the hazard ratio most for CAF infiltration in TCGA samples (z=2.924, HR=11.654). The hazard ratio of CAF infiltration with this dual gene combination was even higher than that with the quadruple combination of collagen subunits. We also investigated the impact of CAF markers highly clustered with COL1A1 and COL5A1 in correlation analysis, namely THBS2, FAP, INHBA, S100A4, COL11A1, or MMP11, on the outcome of CAF infiltration in stomach adenocarcinoma. Except for MMP11, all slightly increased the hazard ratio for CAF infiltration (Additional File 1: Table S7).
Recently, Grunberg et. al suggested THBS1, THBS2, and INHBA; and Liu et. al suggested TGFB2, VEGFB, COL10A1, AREG, and EFNA5 as poor prognostic signatures for CAF infiltration in gastric cancer. To compare the prognostic significance of these two gene signatures with that of COL1A1 and COL5A1, we investigated the Cox regression models for these two signatures. The z-scores and hazard ratios for both signatures were lower than those for COL1A1 and COL5A1 (Additional File 1: Table S8). These findings suggested a high potential for COL1A1 and COL5A1 as a poor prognostic signature in CAF infiltrated gastric tumors.
Prognostic significance of COL1A1 and COL5A1 for CAF infiltration in other cancers
At the next step, we asked whether COL1A1 and COL5A1 increase the poor prognostic impact of CAF infiltration in other cancers with a poor outcome profile for the six hub genes like gastric cancer. We investigated the risk scores for COL1A1, COL1A2, COL3A1, COL5A1, FN1, or SPARC at each tumor type in the TCGA dataset. We detected four cancer types with increased risk scores for all six genes: adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), kidney renal papillary cell carcinoma (KIRP), and mesothelioma (MESO) (Fig. 8a). High CAF infiltration was also associated with an increased risk score in all four cancers (Fig. 8b). Despite that, the addition of COL1A1 and COL5A1 to the Cox model led to a decreased risk score for CAF infiltration in adrenocortical carcinoma, kidney renal papillary cell carcinoma, and mesothelioma (Fig. 8c).
Identifying the players for the opposing roles of COL1A1 and COL5A1 in different cancers could reveal new insights into the field. To predict possible players, we extracted the list of genes that correlate with the expression of COL1A1 and COL5A1 in adrenocortical carcinoma, kidney renal papillary cell carcinoma, and mesothelioma. We identified 25 genes that are highly correlated (rho ≥ 0.5) with both COL1A1 and COL5A1 in all three cancers (Fig. 8d). Then we comparatively analyzed this list with the list of genes upregulated in gastric cancer. The two lists shared seven genes (Fig. 8e, upper circos plot). The 18 genes common to the three cancers fell into the same gene ontology as the 27 genes upregulated in gastric cancer (Fig. 8e, lower circos plot). Despite these overlaps, more than ten ontologies are differentially enriched in the list of upregulated genes in gastric cancer (Fig. 8f). Among these, "integrin α4β1 pathway” and “peptide crosslinking” were striking, since they were the two ontologies that were enriched at the upregulated gene list in gastric cancer compared to the CAF markers list (Fig. 6c and 7a-c). This observation emphasized the role of “integrin α4β1 pathway” and “peptide crosslinking” together with CAF infiltration in gastric cancer.
Contribution of Integrin α4β1 pathway to the poor prognostic impact of COL1A1, COL5A1, and CAF infiltration in stomach adenocarcinoma
Integrins are heterodimeric transmembrane proteins that are involved in cell-cell or cell-ECM adhesions. They bind ECM components, mainly collagen, and fibronectin, activate intracellular signaling pathways, and regulate cell survival, proliferation, migration, and differentiation. Integrin α4β1 is a heterodimer of integrin α4 (ITGA4) and integrin β1 (ITGB1). ITGB1 couples with a large variety of α integrin subunits (32). However, ITGA4 couples with integrin β1 or β7 subunits (33, 34).
The integrin α4β1, also known as very late antigen-4 (VLA-4), is expressed on various immune cells, mediating the migration of leukocytes to the inflammatory sites via interaction with VCAM-1 (vascular cell adhesion protein 1) (33). Additionally, it binds to ECM components and takes part in fibronectin assembly (35). Increased expression of α4β1 integrin is associated with tumor progression and chemoresistance in cancer. The interaction of integrin α4β1 on the tumor cell membrane with the VCAM-1 on vascular endothelial cells is involved in metastasis (33). The interaction of α4β1 integrin with fibronectin suppressed apoptosis via FAK-mediated suppression of p53, and PI3K/Akt mediated upregulation of Bcl-2 in myeloma cells. Increased integrin α4β1 expression was associated with increased binding of melanoma cells to collagen I and collagen IV, and invasion through fibronectin (36). Moreover, α4 integrin was suggested to affect a drug efflux mechanism independent from its coupling with β1 integrin (37). However, the mechanisms by which integrin α4β1 heterodimer or α4 integrin monomer contribute to invasion, metastasis, and chemoresistance in cancer are not exactly known.
To understand whether the integrin α4β1 potentiates the poor prognostic impact of CAFs, we analyzed the Cox regression model for CAF infiltration that considers the expression of ITGA4, ITGB1, or both in addition to COL1A1 and COL5A1. The addition of ITGA4 to the model increased the hazard ratio and z-score in stomach adenocarcinoma (z=2.963, HR=12.247) (Table 4). However, only ITGB1 or ITGA4 and ITGB1 slightly decreased the hazard ratio and z-score, which may be due to a less selective coupling of ITGB1 with several integrin α subtypes (Additional File 1: Table S9). These findings supported that ITGA4 may worsen the poor prognostic impact of CAFs in gastric cancer.
ITGA4 interacts with signaling molecules, receptors, and kinases that take part in ECM organization, integrin signaling, and cell-matrix adhesion (Additional File 2: Fig. S6A-B). Among the integrin α4β1 partners, FN1, osteopontin (secreted phosphoprotein1: SPP1), THBS1, and EMILIN-1 (Elastin microfibril interface-located protein 1) are ECM proteins; JAM2 (junctional adhesion molecule 2), JAM3 (junctional adhesion molecule 3), MADCAM1 (mucosal vascular addressin cell adhesion molecule 1), and VCAM1 are membrane-bound proteins. Their interaction with integrin α4β1 makes them potential players for the poor prognostic impact of ITGA4 (35, 38-41). We asked whether FN1, one of the six hub genes we detected in network analysis, strengthens the poor prognostic effect of ITGA4 on CAF infiltration. However, the addition of FN1 to the COL1A1, COL5A1, and ITG4 Cox regression model decreased the poor prognostic impact of CAF in stomach adenocarcinoma (Additional File 1: Table S10). THBS1 acted similarly and decreased the hazard ratio. JAM2, JAM3, MADCAM1, SPP1, and VCAM1 slightly increased the hazard ratio. On the other hand, Emilin1 substantially increased the poor prognostic impact of CAFs in the COL1A1, COL5A1, and ITG4 model, raising the hazard ratio from 12.247 to 28.315 (Table 4).
The EMILIN-1 is a member of the elastin microfibrillar interface proteins (EMILINs) family, expressed as a homotrimer at the ECM (42). Since fibroblasts are the major sources of EMILIN-1 at the ECM, it is accepted as a fibroblast marker (26). The interaction of EMILIN-1 with integrin α4β1 is involved in cell adhesion and migration (43). The increase in the poor prognostic impact of CAFs in stomach adenocarcinoma with the addition of Emilin1 as a covariate to the Cox model was surprising since EMILIN-1 is known as a tumor suppressor that exerts anti-proliferative action via integrin α4β1 in cell and in vivo models (42, 44). Despite that, some reports suggest a pro-tumorigenic role for EMILIN-1 in ovarian serous tumors and osteosarcoma (45, 46).
A recent study reported that the action of EMILIN-1 to inhibit the MAPK pathway and suppress proliferation in gastric cancer cells might depend on Tetraspanin9 (TSPAN9) (47), which is a member of tetraspanin family membrane receptors with four transmembrane domains. These receptors are involved in signal transduction, cell adhesion, invasion, and migration. TSPAN9 is alluded to have anti-cancer effects in gastric cancer, suppressing proliferation, invasion, and migration in gastric cancer cell lines (48, 49). Despite that, adding TSPAN9 to the COL1A1, COL5A1, ITGA4, and Emilin1 Cox model further increased the hazard ratio to 36.813 for CAF infiltration in stomach adenocarcinoma samples (Fig. 9a, Table 4). However, the hazard ratios remained zero with the stepwise addition of ITGA4, Emilin1, and TSPAN9 to the Cox model in adrenocortical carcinoma, kidney renal papillary cell carcinoma, and mesothelioma (Table 4). All this data suggested COL1A1, COL5A1, ITGA4, Emilin1, and TSPAN9 as a poor prognostic CAF signature with high specificity to stomach adenocarcinoma.
We further investigated the expression profiles of the signature genes and their correlation with CAF infiltration (Fig. 9b-e). Mesothelioma and stomach adenocarcinoma displayed a higher expression profile for the COL1A1, COL5A1, ITGA4, and Emilin1, in comparison to adrenocortical carcinoma and kidney renal papillary cell carcinoma. The expression of TSPAN9 was similar in all four cancers (Fig. 9b). Correlation between the COL1A1 or COL5A1 expressions and CAF infiltration was strong in all four cancers (rho>0.5). ITGA4 expression poorly correlated with the CAF infiltration, but the correlation was slightly higher in kidney renal papillary cell carcinoma and stomach adenocarcinoma. Strikingly, the correlation of Emilin1 and TSPAN9 with CAF infiltration was quite strong in stomach adenocarcinoma compared to a poorer correlation in the other three cancers (Fig. 9c).
Further investigation revealed that the expression of ITGA4 increases with stage in stomach adenocarcinoma (Fig. 9d). Although there was a significant decrease in Emilin1 and TSPAN9 levels in stage 1 compared to healthy stomach tissue, their expression increased again at stage 2, reaching the level of or above that of normal tissues (Fig. 9e-f). A similar pattern was not observed for adrenocortical carcinoma, kidney renal papillary cell carcinoma, and mesothelioma (Additional File 2: Fig. S7).
The KM-survival analysis did not indicate a prognostic role for ITGA4, Emilin1, or TSPAN9 in stomach adenocarcinoma per se (Additional File 2: Fig. S8A-C). However, their hazard ratios increased with the stage (Additional File 2: Fig. S8D-F). This was in parallel to the increase in the poor prognostic impact of CAF infiltration by stage in stomach adenocarcinoma (Additional File 2: Fig. S5), suggesting a stage and CAF dependent role for ITGA4, Emilin1, and TSPAN9.
Search on drugs that target COL1A1, COL5A1, and ITGA4
Lastly, we searched for currently available drugs that target COL1A1, COL5A1, and ITGA4. Our search on DrugBank and DGIB revealed three agents which target COL1A1 and COL5A1: collagenase clostridium histolyticum, halofuginone, and ocriplasmin; and three agents which target ITGA4: natalizumab, firategrast, and BIO-1211 (Fig. 10).
Collagenase clostridium histolyticum and ocriplasmin are enzymes that cleave COL1A1 and COL5A1. They also have proteolytic activity on COL3A1 and FN1, respectively (50, 51). Collagenase clostridium histolyticum is used on skin ulcers to hasten wound healing and Dupuytrens’ disease to resolve contractures by digesting collagen (52, 53). Intra-tumoral or intravenous injection of collagenase increased the diffusion of large drug molecules in tumor models (54). Intraperitoneal administration of collagenase was reported to increase the efficacy of chemotherapy by cleaving the tumor stroma in a rat model of colorectal cancer peritoneal metastasis (55). Ocriplasmin is used to remove adhesions in symptomatic vitreomacular adhesion (51). Like our study, another bioinformatics study suggested ocriplasmin as a potential anti-cancer agent (56). But, to the best of our knowledge, ocriplasmin has not been tested in cancer before.
Halofuginone is an alkaloid that suppresses the expression of the COL1A1 gene, cell migration, and ECM formation. Besides its’ antifibrotic and anti-angiogenetic actions, halofuginone shows antiproliferative effects by inhibiting TGFβ/Smad3 signaling (57, 58). Halofuginone, showed an apoptotic effect in prostate cancer and Wilms' tumor cells by inhibiting the transformation of fibroblasts to myofibroblasts (59), which carry similar features with CAFs (6). Halofuginone also acted synergistically with gemcitabine and suppressed tumorigenesis in a mouse pancreatic cancer model by reducing the number of stromal myofibroblasts and generation of ECM (60).
Integrin α4β1 is a significant therapeutic target in chronic inflammatory diseases and cancer. Natalizumab, the monoclonal antibody against integrin α4 subtype, was approved in multiple sclerosis and inflammatory bowel disease. However, its long-term use is associated with progressive multifocal leukoencephalopathy (41). Although abrilumab and vedolizumab are listed as integrin α4 targeting agents, their action is specific to integrin α4β7 heterodimer (61, 62). Besides monoclonal antibodies, small molecule inhibitors that target integrin α4 such as firategrast and BIO-1211 are available (63, 64). To the best of our knowledge, these agents have not been tested for their therapeutic efficacy in cancer yet.