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. 2021 May 15;14(5):551–566.

Identification of a tumor microenvironment-associated prognostic gene signature in bladder cancer by integrated bioinformatic analysis

Zhengchun Liu 1,2,*, Xiuli Liu 3,*, Rui Cai 2, Meilian Liu 2, Rensheng Wang 1
PMCID: PMC8167492  PMID: 34093942

Abstract

Bladder cancer is a common malignancy in the urinary system. Stromal and immune cells in tumor microenvironments, including those in the bladder cancer microenvironment, can serve as prognostic markers. However, the complex processes of bladder cancer necessitate large-scale evaluation to better understand the underlying mechanisms and identify biomarkers for diagnosis and treatment. We used the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data algorithm to assess the association between stromal and immune cell-related genes and overall survival of patients with bladder cancer. We also identified and evaluated differentially expressed genes between cancer and non-cancer tissues from The Cancer Genome Atlas. Patients were categorized into different prognosis groups according to their stromal/immune scores based on differential gene expression. In addition, the prognostic value of the differentially expressed genes was assessed in a separate validation cohort using the Gene Expression Omnibus microarray dataset GSE13507, which identified nine genes (TNC, CALD1, PALLD, TAGLN, TGFB1I1, HSPB6, RASL12, CPXM2, and CYR61) associated with overall survival. Multivariate regression analysis showed that three genes (TNC, CALD1, and PALLD) were possible independent prognostic markers for patients with bladder cancer. Multiple gene set enrichment analysis of individual genes showed strong correlations with stromal and immune interactions, indicating that these nine genes may be related to carcinogenesis, invasion, and metastasis of bladder cancer. These findings provide useful insight into the molecular mechanisms of bladder cancer development, and suggest candidate biomarkers for prognosis and treatment.

Keywords: TCGA, GEO, tumor microenvironment, ESTIMATE, overall survival, bladder cancer

Introduction

Bladder cancer (BC) is a common malignancy in the human urinary system. It is estimated that approximately 81,400 new cases of BC and 17,980 deaths occurred in the United States in 2020 [1]. Over the past 30 years, despite much progress in therapeutic options for BC, there has been no substantial improvement in the number of BC cases diagnosed and treated, or in the five-year survival rate [2]. Precisely identifying BC patients at high risk of recurrence or metastasis, or with worse clinical outcomes, could benefit treatment decisions. Several clinical characteristics, such as tumor size, muscularis propria invasion, and metastasis, have been recognized as independent prognostic factors in BC [3,4]. However, these clinical data are not specific and cannot predict an individual patient survival. More accurate risk models are urgently needed to screen high-risk BC patients who may benefit from personalized adjuvant therapies.

Accumulating evidence suggests that the tumor microenvironment (TME) significantly affects not only cancer progression but also treatment responses and clinical outcomes [5,6]. While the TME presents antineoplastic potential at the early stages of tumorigenesis, malignancies can tolerate this suppressive effect and transform the TME into one conferring pro-malignancy functions [7]. In addition to cancer cells, the TME contains the surrounding vascular network, extracellular matrix, fibroblasts, and immune infiltrates [8]. For example, cancer-associated fibroblasts, considered as one of the most dominant components in the TME, were shown to facilitate the proliferation of cancer stem cells based on CXC-chemokine ligand 12-CXC-chemokine receptor 4 [9]. CAF-secreted cytokines, such as macrophage colony-stimulating factor 1 and IL-6 promote monocyte recruitment and encourage the differentiation of tumor-associated macrophages in TME [10]. Though the complex interaction of cells in TME is not fully characterized, the molecular profiles of stromal components are of great value in serving as prognostic biomarkers [11]. TME-associated prognostic multi-gene signatures have shown potential value in clinical outcome prediction and treatment guidance in lung cancer [12], glioma [13], and ovarian cancer [14], but no research has been reported in BC.

In the present study, we investigated the association of clinical parameters of BC patients with the stromal and immune scores calculated by Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm [15]. Through integrated bioinformatic analyses, we obtained a list of TME-related genes and several independent prognostic factors. A nine-gene prognostic signature for BC was then established and validated to be clinically significant.

Materials and methods

Data acquisition

The expression profile of BC samples and their corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) (TCGA-BLCA, https://portal.gdc.cancer.gov/repository) [16] and Gene Expression Omnibus (GEO) (GSE13507, https://www.ncbi.nlm.nih.gov/geo) databases. There were 408 BC samples in TCGA and 188 BC samples in GSE13507. The ESTIMATE algorithm was used for stromal and immune scoring, which divided the patients into high-score and low-score groups according to the median.

Differentially expressed genes (DEGs) and functional analysis

DEGs between the high-score and low-score groups were identified using the “limma” package v3.11 (http://www.bioconductor.org/packages/release/bioc/html/limma.html) in R software v3.6.3 (R Foundation for Statistical Computing, Vienna, Austria). The screening criteria of DEGs was |log2 fold change| value > 1.5 and adjusted-P value < 0.05. Heatmap, plotted by “pheatmap” package v1.0.12 (https://cran.r-project.org/web/packages/pheatmap/) was used to display the DEGs. The protein-protein interaction (PPI) network was constructed using STRING database (https://string-db.org/). Functional enrichment analysis of selected DEGs was based on Gene Ontology (GO) terms, including biologic processes, molecular functions, and cellular components, generated in the R packages of clusterProfiler v3.11 and org.Hs.eg.db v3.11 (http://www.bioconductor.org/).

Survival analysis and construction of the risk model

The prognostic values of DEGs were evaluated by the Kaplan-Meier method and log-rank test, and were shown as a survival curve using the R package of “survival” v3.1-12 (https://cran.r-project.org/web/packages/survival/index.html). Univariate and multivariate Cox regression models were used to identify independent prognostic risk factors. For the nine prognostically-significant genes obtained from TCGA and GEO, we conducted univariate Cox regression analysis to obtain regression coefficients. The risk score was calculated by the sum of gene expression levels multiplied by corresponding regression coefficients. We divided all samples into high- and low-risk groups based on the median value of the risk score.

Prediction of immunotherapy and chemotherapeutic response

We used subclass mapping to compare the expression profiles of the high- and low-risk groups with another published dataset, which contains 47 melanoma patients who received immunotherapy [17]. On the basis of the Genomics of Drug Sensitivity in Cancer, we predicted the chemotherapeutic response of each sample for four commonly used chemotherapy drugs in BC (cisplatin, bleomycin, mitomycin C and gemcitabine) using R package “pRRophetic” (https://github.com/paulgeeleher/pRRophetic2). Ridge regression was used to estimate the half-maximal inhibitory concentration (IC50).

Gene set enrichment analysis (GSEA)

The GSEA [18] analysis was conducted using GSEA v4.0.1 (http://software.broadinstitute.org/gsea/downloads.jsp). The referenced gene sets in GSEA were “c2.cp.kegg.v7.1.symbols.gmt” and “c5.all.v7.1.symbols.gmt”. In this study, “permutations = 1000” was set to generate a null distribution for enrichment score and “FDR < 0.05” was applied as the threshold for screening. The other parameters for GSEA were set as defaults.

Statistical analysis

A comparison of the estimated IC50 of the high- and low-risk groups was performed using the Wilcoxon test. The Kaplan-Meier plotter was used to analyze the overall survival in TCGA and GEO, and the statistical differences between different groups were evaluated by log-rank test. Multivariate survival analysis was performed using Cox regression models. Hazard ratios were obtained with respective 95% confidence intervals. A P value < 0.05 was considered significant. The statistical tests were 2-sided and conducted using R software (version 3.5.3).

Results

Stromal and immune scores are associated with clinical traits of BC patients

The workflow chart is shown in Figure 1. Initially, the expression profile (FPKM) of TCGA-BLCA cohort containing 408 BC patients was downloaded to calculate stromal and immune scores. It was found that papillary carcinoma and non-papillary carcinoma, two distinct pathologic types of BC, possessed significantly different stromal and immune scores. Compared with papillary BC, non-papillary BC had higher stromal (P < 0.001, Figure 2A) and immune scores (P < 0.001, Figure 2B). Low-grade and high-grade BC displayed significant differences in stromal (Figure 2C, P < 0.001) and immune scores (Figure 2D, P < 0.001), in which high-grade BC was associated with higher scores. Female patients had higher stromal (Figure 2E, P = 0.032) and immune scores (Figure 2F, P = 0.036) than male patients. This result is consistent with the fact that female patients with higher-grade BC usually had worse clinical outcomes than male patients [2]. These results revealed that patients with different BC subtypes, tumor grades, and genders had distinct TMEs.

Figure 1.

Figure 1

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Workflow chart.

Figure 2.

Figure 2

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Analysis of bladder cancer (BLCA) stromal and immune scores by subtype and gender. A, B. Stromal (1) and immune (2) scores according to BLCA subtype (N = 403). C, D. Stromal (1) and (2) immune scores associated with BLCA grade (N = 405). E, F. Stromal (1) (P = 0.032) and (2) immune scores (P = 0.036) associated with patient gender (N = 408).

Differentially expressed genes between high and low stromal/immune scores

The samples in TCGA-BLCA were divided into two groups of high and low stromal/immune scores according to the median value. A total of 1519 upregulated DEGs (log2 fold change > 1.5, P < 0.05) and 1371 downregulated DEGs (log2 fold change < -1.5, P < 0.05) were identified between the high and low stromal score groups (Figure 3A). Between the high and low immune score groups, 457 upregulated DEGs and 308 downregulated DEGs were identified (Figure 3B). When intersected, a total of 1125 shared upregulated DEGs (Figure 3C) and 209 (Figure 3D) shared downregulated DEGs were obtained. GO enrichment analysis of these DEGs demonstrated that the top five enriched pathways were T cell activation, extracellular matrix organization, cytokine activity, leukocyte cell-cell adhesion, and leukocyte migration (Figure 3E).

Figure 3.

Figure 3

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Comparison of bladder cancer stromal and immune scores with gene expression profiles. The heatmap was drawn using the average line and Pearson distance measurement methods. The differential gene matrix from the heatmap was corrected using the Wilcoxon test method. The more highly expressed genes in bladder cancer are shown in red, and the less highly expressed genes are shown in green. Genes with the same expression level between groups are indicated in black. A. Differentially expressed gene (DEG) heatmap (log2 fold change > 1.5, P < 0.05) comparing groups with high and low stromal scores. B. DEG heatmap comparing groups with high and low immune scores (log2 fold change > 1.5, P < 0.05). C. Venn plot showing upregulated DEGs along with stromal and immune scores. D, E. Venn plot showing downregulated DEGs along with stromal and immune scores related to biologic process, cell component, molecular function, top-10 GO pathway clusters, and the pseudo-discovery rate (P < 0.05). F. Protein-protein interaction (PPI) network of prognostic DEGs. G. Hub genes in the PPI network.

PPI network of DEGs with prognostic value

Kaplan-Meier survival analysis revealed that 204 of the 1125 upregulated DEGs and 70 of the 209 downregulated DEGs were significantly associated with overall survival (data not shown). Using these 274 prognostic DEGs, a PPI network with 92 nodes and 190 edges was constructed in the STRING database (Figure 3F). The central nodes (key genes) were as follows: FN1, ITGB3, GAS6, TGFB3, TNC, VCAN, CYR61, IGF1, LCK, MFGE8, BDKRB2, CHRM2, and CXCL12 (Figure 3G). Survival curves of these key genes are displayed in Supplementary Figure 1.

Validation of prognostic DEGs

GSE13507 datasets in the GEO database were analyzed to verify the prognostic value of these DEGs. Of the 161 prognostic genes identified using TCGA-BLCA, nine genes (TNC, CALD1, PALLD, TAGLN, TGFB1I1, HSPB6, RASL12, CPXM2, and CYR61) were validated to be of prognostic value in GSE13507 (Figure 4). The expression levels of these nine genes in BC and adjacent normal tissues are shown in Supplementary Figure 2. Univariate and multivariate analyses of TCGA-BLCA also revealed that TNC (Supplementary Table 1), CALD1 (Supplementary Table 2), and PALLD (Supplementary Table 3) emerged as independent prognostic markers of BC patients. We then conducted the GSEA to explore the biological processes in which these nine genes potentially participate. It was shown that these nine key genes were mainly involved in chemokine signaling, T cell receptor signaling, focal adhesion, extracellular matrix receptor interaction, and other immune and matrix-related pathways (Supplementary Figure 3). The results of GSEA further indicated that the nine key DEGs with prognostic value were TME-related genes, which is also in accordance with previously published literature [19-33].

Figure 4.

Figure 4

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Validation of overall survival associated with DEGs extracted from The Cancer Genome Atlas using Gene Expression Omnibus data. Kaplan-Meier survival curves between the groups with highly expressed genes (red line) and lowly expressed genes (blue line) (P < 0.05 in the log-rank test). OS, overall survival in years.

Construction and validation of the nine-gene prognostic signature

The shared nine prognostic TME-related DEGs obtained from TCGA-BLCA and GSE13507 were then included in a multivariate Cox regression to acquire coefficients. A TME-associated nine-gene signature was developed: risk score = 0.0029 × TNC + (-0.0120) × CALD1 + 0.0126 × PALLD + (-0.0011) × CYR61 + (-0.0031) × HSPB6 + 0.0384 × CPXM2 + 0.0127 × RASL12 + 0.0615 × TGFB1I1 + (-0.0017) × TAGLN. BC patients were divided into high-risk and low-risk groups according to the median risk score. Survival analysis of TCGA-BLCA showed that compared with the high-risk group, BC patients in the low-risk group had significantly longer overall survival (Figure 5A) and longer progression-free survival (Figure 5B). This nine-gene prognostic signature was then validated in GSE13507, and a satisfactory predictive significance was achieved (Figure 5C and 5D).

Figure 5.

Figure 5

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Survival analysis between high- and low-risk groups in The Cancer Genome Atlas and GSE13507. Kaplan-Meier survival curves show the (A) overall survival (OS), (B) progression-free survival (PFS) and (E) relapse-free survival (RFS) in TCGA, together with (C) OS and (D) PFS in GSE13507.

Prediction of immunotherapeutic and chemotherapeutic response

The expression profiles of the high- and low-risk groups were compared with those in a published dataset containing 47 melanoma patients who responded to immunotherapy by subclass mapping [34] to evaluate immunotherapeutic and chemotherapeutic response of BC patients. The high-risk group showed significantly greater sensitivity to anti-PD-1 treatment (Bonferroni correction, P = 0.048, Figure 6A).

Figure 6.

Figure 6

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Prediction of immunotherapy and chemotherapeutic response between high- and low-risk groups. (A) Prediction response of immune checkpoint inhibitors anti-CTLA4 and anti-PD1 between high- and low-risk groups. The high-risk group showed it was more sensitive to the anti-PD1 inhibitor (Bonferroni-corrected P= 0.048). Estimated IC50 for (B) cisplatin, (C) bleomycin, (D) mitomycin C and (E) gemcitabine.

In addition, considering that chemotherapy is the most common treatment of BC, we evaluated the response of the high- and low-risk groups to four common chemotherapy drugs. There was no significant difference in IC50 estimated by cisplatin between the high- and low-risk groups (P = 0.973, Figure 6B). Significant differences were achieved in IC50 estimated by bleomycin (Figure 6C), mitomycin C (Figure 6D) and gemcitabine (Figure 6E). Compared with the low-risk group, bleomycin, mitomycin C, and gemcitabine showed a greater sensitivity of therapeutic response to BC patients in the high-risk group. Enrichment analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and GO were then conducted to reveal the underlying mechanisms. The gene expression profiles of BC patients in the high-risk group were mainly enriched in various TME-associated processes, such as extracellular matrix receptor interaction, focal adhesion pathway, migration of macrophages, and composition of extracellular matrix (Figure 7).

Figure 7.

Figure 7

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Pathway enrichment analysis between the high- and low-risk groups. (A, B) Kyoto Encyclopedia of Genes and Genomes (KEGG) and (C-E) Gene ontology (GO) enrichment analysis between high- and low-risk groups. NES: normalized enrichment score; FDR: false discovery rate.

Discussion

The incidence and mortality of bladder cancer (BC), a common and complex disease, increased by 2.5 and 1.6 times, respectively, between 1990 and 2013 [35]. There has been significant progress in understanding the association between overall survival and gene expression in BC, including experiments from animal tumor models, in vitro tumor cell lines, and patient tumor samples. However, the complexity of BC and the associated microenvironments necessitate broader analyses using larger cohorts. The present study integrated clinical information associated with BC from TCGA data with stromal and immune scores based on the ESTIMATE algorithm, revealing a set of nine DEGs (TNC, CALD1, PALLD, TAGLN, TGFB1I1, HSPB6, RASL12, CPXM2, and CYR61) in BC tumor microenvironments associated with poor prognosis for patients with BC. The prognostic significance of these genes was verified using an independent GEO cohort.

CALD1, PALLD, CYR61, TGFB1I1, and TAGLN are all widely expressed in smooth muscle cells, and either directly or indirectly participate in cancer cell growth, proliferation, and migration, as well as inhibit immune cells to help cancer cells invade the muscle tissue [36-43]. TNC encodes an extracellular matrix (ECM) glycoprotein that is highly expressed during organogenesis, and is associated with cell proliferation and migration, epithelial-mesenchymal transition, and parenchymal and mesenchymal interactions [44,45]. HSPB6 is also highly expressed in muscle tissues and may be the main mediator of muscle protection signal transduction in tumors invading the muscle layer, although its specific role in cancer is not clear [46]. CALD1 encodes an actin-regulating protein that is usually expressed in smooth muscle and non-muscle cells. CALD1 also plays a role in the actin cytoskeleton and participates in a variety of biologic functions associated with tumors, such as migration, invasion, and proliferation [47]. In patients who underwent colorectal cancer treatment, tumor cell growth was found to significantly decrease, and invasion and migration were inhibited with an increase in CALD1 expression levels [48]. In addition, CALD1 inhibits cell invasion by reducing ECM degradation activity [36], as cell invasion is mediated by podocytes that degrade the ECM by secreting matrix metalloproteinases, allowing cells to pass through the basement membrane and invade tissues, which are signs of cancer progression. Nevertheless, the specific immune function of CALD1 remains uncertain, and further research is needed to better understand its role in cancers. PALLD encodes an actin-related protein expressed in various cell types that is essential for establishing cell morphology and maintaining cytoskeletal organization. PALLD expression is absent or low in peripheral blood mononuclear cells and causes dendritic cells to differentiate into monocytes, whereas its expression is significantly upregulated during cell proliferation. There is also evidence that PALLD promotes phagocytic cup extension and closure [37]. However, the precise mechanisms by which PALLD regulates innate immunity and other biological processes remain unclear. The function and molecular mechanisms of RASL12 and CPXM2 are not known. The recognized roles of these nine DEGs in the BC microenvironment are summarized in Figure 8.

Figure 8.

Figure 8

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Cellular localization and biologic processes associated with the nine DEGs. CALD1, PALLD, TAGLN, TGFB1I1, HSPB6, RASL12, and CPXM2 are mainly expressed in smooth muscle cells (SMCs). TNC and CYR61 are mainly expressed in tumor cells or fibroblasts/myofibroblasts. The BLCA-related biologic processes that they may participate in are as follows: TNC: a, b, c, d; CALD1: e, f, g; PALLD: b; CYR61 (CCN1): h, i, j, l; TGFB1I1: f, g, h, k; TAGLN: e, f, g; and HSPB6, RASL12, and CPXM2: unknown.

The functions of these nine genes in BC should be confirmed clinically and experimentally. Multi-pathway GSEA of each of the nine genes showed that they are jointly involved in calcium, transforming growth factor-beta, mitogen-activated protein kinase, and JAK-STAT signaling, and contribute to cancer progression by regulating cell proliferation, differentiation, apoptosis, and immune regulation. Focal adhesion, ECM receptor interactions, cytokine-cytokine receptor interactions, and other pathways directly related to tumor microenvironments were also implicated. The ECM is a highly dynamic structure that facilitates structural and biochemical signaling to regulate cell function; it is present in all tissues and is constantly undergoing controlled remodeling, and is further considered to be a driver of fibrosis. In addition, the ECM interacts with cells to regulate multiple functions, includingproliferation, migration, and differentiation of tumor cells. During cancer progression, epithelial cells undergo genetic alterations that, along with matrix changes such as ECM remodeling, disrupt homeostasis of the epithelium. For example, parallel tissues composed of matrix ECM fibrils are associated with tumorigenic responses [49-51]. Focal adhesions are the main hubs of cellular mechanical sensing and act as bridges between integrin-ECM and the cytoskeleton. Recent studies have shown that cell proliferation and RhoA GTPase activity control focal adhesion formation through YAP, thereby anchoring the actin cytoskeleton to the cell membrane and determining cell shape, migration, and differentiation. Changes in the signals transmitted through focal adhesions of malignant cells are crucial for the spread of tumor cells [52-54].

Cytokine-cytokine receptor interactions were also prominent functions identified in the present study. Cytokines are multifunctional molecules that can regulate broad-spectrum biologic events related to inflammation, metabolism, cell growth, differentiation, morphogenesis, fibrogenesis, and/or homeostasis. Immune regulation determines the nature and type of immune response [55-57]. Therefore, these nine genes have their own unique functions in BC microenvironments and play important roles in common signaling pathways.

Finally, we systematically analyzed the risk model of the nine-gene signature. The risk coefficients of TNC, PALLD, CPXM2, RASL12, and TGFB1I1 were positive, and the coefficients of CALD1, CYR61, HSPB6, and TAGLN were negative, revealing their different contributions to the risk score. Although the high expression of all nine genes in the signature showed a poor prognosis for BC, the risk score further helped us identify which genes (TNC, PALLD, CPXM2, RASL12 and TGFB1I1) have a greater association with poor prognosis for BC. In addition, the high-risk group showed sensitivity to anti-PD1 immunotherapy and to the conventional chemotherapy drugs bleomycin, mitomycin C, and gemcitabine, all of which help with the treatment and diagnosis of bladder cancer in the future.

Based on our in-depth analysis of BC microenvironment transcriptome data, we confirmed that TNC, CALD, PALLD, CYR61, TGFB1I1, TAGLN, HSPB6, RASL12, and CPXM2 are biologic markers of poor prognosis for patients with BC. The relevance of these genes to BC prognosis was validated in independent BC cohorts. However, the biologic functions and molecular mechanisms associated with HSPB6, RASL12, and CPXM2 in BC microenvironments should be explored in future research.

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (2016GXNSFAA380306), the self-generated project of the Guangxi Zhuang Autonomous Region Health Department (Z20170816), China.

Disclosure of conflict of interest

None.

Supporting Information

ijcep0014-0551-f9.pdf (3.5MB, pdf)

References

  • 1.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70:7–30. doi: 10.3322/caac.21590. [DOI] [PubMed] [Google Scholar]
  • 2.Berdik C. Unlocking bladder cancer. Nature. 2017;551:S34–S35. doi: 10.1038/551S34a. [DOI] [PubMed] [Google Scholar]
  • 3.Gondo T, Nakashima J, Ohno Y, Choichiro O, Horiguchi Y, Namiki K, Yoshioka K, Ohori M, Hatano T, Tachibana M. Prognostic value of neutrophil-to-lymphocyte ratio and establishment of novel preoperative risk stratification model in bladder cancer patients treated with radical cystectomy. Urology. 2012;79:1085–1091. doi: 10.1016/j.urology.2011.11.070. [DOI] [PubMed] [Google Scholar]
  • 4.Kamat AM, Hahn NM, Efstathiou JA, Lerner SP, Malmstrom PU, Choi W, Guo CC, Lotan Y, Kassouf W. Bladder cancer. Lancet. 2016;388:2796–2810. doi: 10.1016/S0140-6736(16)30512-8. [DOI] [PubMed] [Google Scholar]
  • 5.Wu T, Dai Y. Tumor microenvironment and therapeutic response. Cancer Lett. 2017;387:61–68. doi: 10.1016/j.canlet.2016.01.043. [DOI] [PubMed] [Google Scholar]
  • 6.Roma-Rodrigues C, Mendes R, Baptista PV, Fernandes AR. Targeting tumor microenvironment for cancer therapy. Int J Mol Sci. 2019;20:840. doi: 10.3390/ijms20040840. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Maman S, Witz IP. A history of exploring cancer in context. Nat Rev Cancer. 2018;18:359–376. doi: 10.1038/s41568-018-0006-7. [DOI] [PubMed] [Google Scholar]
  • 8.Meurette O, Mehlen P. Notch signaling in the tumor microenvironment. Cancer Cell. 2018;34:536–548. doi: 10.1016/j.ccell.2018.07.009. [DOI] [PubMed] [Google Scholar]
  • 9.Labernadie A, Kato T, Brugués A, Serra-Picamal X, Derzsi S, Arwert E, Weston A, González-Tarragó V, Elosegui-Artola A, Albertazzi L, Alcaraz J, Roca-Cusachs P, Sahai E, Trepat X. A mechanically active heterotypic E-cadherin/N-cadherin adhesion enables fibroblasts to drive cancer cell invasion. Nat Cell Biol. 2017;19:224–237. doi: 10.1038/ncb3478. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Liu T, Han C, Wang S, Fang P, Ma Z, Xu L, Yin R. Cancer-associated fibroblasts: an emerging target of anti-cancer immunotherapy. J Hematol Oncol. 2019;12:86. doi: 10.1186/s13045-019-0770-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Zhang M, Wang X, Chen X, Zhang Q, Hong J. Novel immune-related gene signature for risk stratification and prognosis of survival in lower-grade glioma. Front Genet. 2020;11:363. doi: 10.3389/fgene.2020.00363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Yue C, Ma H, Zhou Y. Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma. PeerJ. 2019;7:e8128. doi: 10.7717/peerj.8128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Su J, Long W, Ma Q, Xiao K, Li Y, Xiao Q, Peng G, Yuan J, Liu Q. Identification of a tumor microenvironment-related eight-gene signature for predicting prognosis in lower-grade gliomas. Front Genet. 2019;10:1143. doi: 10.3389/fgene.2019.01143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Ding Q, Dong S, Wang R, Zhang K, Wang H, Zhou X, Wang J, Wong K, Long Y, Zhu S, Wang W, Ren H, Zeng Y. A nine-gene signature related to tumor microenvironment predicts overall survival with ovarian cancer. Aging (Albany NY) 2020;12:4879–4895. doi: 10.18632/aging.102914. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird PW, Levine DA, Carter SL, Getz G, Stemke-Hale K, Mills GB, Verhaak RG. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. doi: 10.1038/ncomms3612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Comprehensive molecular characterization of urothelial bladder carcinoma. Nature. 2014;507:315–322. doi: 10.1038/nature12965. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Roh W, Chen PL, Reuben A, Spencer CN, Prieto PA, Miller JP, Gopalakrishnan V, Wang F, Cooper ZA, Reddy SM, Gumbs C, Little L, Chang Q, Chen WS, Wani K, De Macedo MP, Chen E, Austin-Breneman JL, Jiang H, Roszik J, Tetzlaff MT, Davies MA, Gershenwald JE, Tawbi H, Lazar AJ, Hwu P, Hwu WJ, Diab A, Glitza IC, Patel SP, Woodman SE, Amaria RN, Prieto VG, Hu J, Sharma P, Allison JP, Chin L, Zhang J, Wargo JA, Futreal PA. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci Transl Med. 2017;9:eaah3560. doi: 10.1126/scitranslmed.aah3560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–15550. doi: 10.1073/pnas.0506580102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Bohn E, Muller S, Lauber J, Geffers R, Speer N, Spieth C, Krejci J, Manncke B, Buer J, Zell A, Autenrieth IB. Gene expression patterns of epithelial cells modulated by pathogenicity factors of Yersinia enterocolitica. Cell Microbiol. 2004;6:129–141. doi: 10.1046/j.1462-5822.2003.00346.x. [DOI] [PubMed] [Google Scholar]
  • 20.de Bock CE, Cools J. T-ALL: home is where the CXCL12 is. Cancer Cell. 2015;27:745–746. doi: 10.1016/j.ccell.2015.05.011. [DOI] [PubMed] [Google Scholar]
  • 21.Emre Y, Imhof BA. Matricellular protein CCN1/CYR61: a new player in inflammation and leukocyte trafficking. Semin Immunopathol. 2014;36:253–259. doi: 10.1007/s00281-014-0420-1. [DOI] [PubMed] [Google Scholar]
  • 22.Hanamura N, Yoshida T, Matsumoto E, Kawarada Y, Sakakura T. Expression of fibronectin and tenascin-C mRNA by myofibroblasts, vascular cells and epithelial cells in human colon adenomas and carcinomas. Int J Cancer. 1997;73:10–15. doi: 10.1002/(sici)1097-0215(19970926)73:1<10::aid-ijc2>3.0.co;2-4. [DOI] [PubMed] [Google Scholar]
  • 23.Kalluri R, Zeisberg M. Fibroblasts in cancer. Nat Rev Cancer. 2006;6:392–401. doi: 10.1038/nrc1877. [DOI] [PubMed] [Google Scholar]
  • 24.Karpova D, Bonig H. Concise review: CXCR4/CXCL12 signaling in immature hematopoiesis--lessons from pharmacological and genetic models. Stem Cells. 2015;33:2391–2399. doi: 10.1002/stem.2054. [DOI] [PubMed] [Google Scholar]
  • 25.Lai CF, Chen YM, Chiang WC, Lin SL, Kuo ML, Tsai TJ. Cysteine-rich protein 61 plays a proinflammatory role in obstructive kidney fibrosis. PLoS One. 2013;8:e56481. doi: 10.1371/journal.pone.0056481. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Liaunardy-Jopeace A, Murton BL, Mahesh M, Chin JW, James JR. Encoding optical control in LCK kinase to quantitatively investigate its activity in live cells. Nat Struct Mol Biol. 2017;24:1155–1163. doi: 10.1038/nsmb.3492. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Palacios EH, Weiss A. Function of the Src-family kinases, Lck and Fyn, in T-cell development and activation. Oncogene. 2004;23:7990–8000. doi: 10.1038/sj.onc.1208074. [DOI] [PubMed] [Google Scholar]
  • 28.Puente Navazo MD, Valmori D, Ruegg C. The alternatively spliced domain TnFnIII A1A2 of the extracellular matrix protein tenascin-C suppresses activation-induced T lymphocyte proliferation and cytokine production. J Immunol. 2001;167:6431–6440. doi: 10.4049/jimmunol.167.11.6431. [DOI] [PubMed] [Google Scholar]
  • 29.Riester M, Taylor JM, Feifer A, Koppie T, Rosenberg JE, Downey RJ, Bochner BH, Michor F. Combination of a novel gene expression signature with a clinical nomogram improves the prediction of survival in high-risk bladder cancer. Clin Cancer Res. 2012;18:1323–1333. doi: 10.1158/1078-0432.CCR-11-2271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Schenk S, Chiquet-Ehrismann R, Battegay EJ. The fibrinogen globe of tenascin-C promotes basic fibroblast growth factor-induced endothelial cell elongation. Mol Biol Cell. 1999;10:2933–2943. doi: 10.1091/mbc.10.9.2933. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Teicher BA, Fricker SP. CXCL12 (SDF-1)/CXCR4 pathway in cancer. Clin Cancer Res. 2010;16:2927–2931. doi: 10.1158/1078-0432.CCR-09-2329. [DOI] [PubMed] [Google Scholar]
  • 32.Wu G, Ma Z, Hu W, Wang D, Gong B, Fan C, Jiang S, Li T, Gao J, Yang Y. Molecular insights of Gas6/TAM in cancer development and therapy. Cell Death Dis. 2017;8:e2700. doi: 10.1038/cddis.2017.113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Yoshida T, Matsumoto E, Hanamura N, Kalembeyi I, Katsuta K, Ishihara A, Sakakura T. Co-expression of tenascin and fibronectin in epithelial and stromal cells of benign lesions and ductal carcinomas in the human breast. J Pathol. 1997;182:421–428. doi: 10.1002/(SICI)1096-9896(199708)182:4<421::AID-PATH886>3.0.CO;2-U. [DOI] [PubMed] [Google Scholar]
  • 34.Hoshida Y, Brunet JP, Tamayo P, Golub TR, Mesirov JP. Subclass mapping: identifying common subtypes in independent disease data sets. PLoS One. 2007;2:e1195. doi: 10.1371/journal.pone.0001195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Dy GW, Gore JL, Forouzanfar MH, Naghavi M, Fitzmaurice C. Global burden of urologic cancers, 1990-2013. Eur Urol. 2017;71:437–446. doi: 10.1016/j.eururo.2016.10.008. [DOI] [PubMed] [Google Scholar]
  • 36.Yoshio T, Morita T, Kimura Y, Tsujii M, Hayashi N, Sobue K. Caldesmon suppresses cancer cell invasion by regulating podosome/invadopodium formation. FEBS Lett. 2007;581:3777–3782. doi: 10.1016/j.febslet.2007.06.073. [DOI] [PubMed] [Google Scholar]
  • 37.Sun HM, Chen XL, Chen XJ, Liu J, Ma L, Wu HY, Huang QH, Xi XD, Yin T, Zhu J, Chen Z, Chen SJ. PALLD regulates phagocytosis by enabling timely actin polymerization and depolymerization. J Immunol. 2017;199:1817–1826. doi: 10.4049/jimmunol.1602018. [DOI] [PubMed] [Google Scholar]
  • 38.Sato D, Tsuchikawa T, Mitsuhashi T, Hatanaka Y, Marukawa K, Morooka A, Nakamura T, Shichinohe T, Matsuno Y, Hirano S. Stromal palladin expression is an independent prognostic factor in pancreatic ductal adenocarcinoma. PLoS One. 2016;11:e0152523. doi: 10.1371/journal.pone.0152523. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Fan Y, Yang X, Zhao J, Sun X, Xie W, Huang Y, Li G, Hao Y, Zhang Z. Cysteine-rich 61 (Cyr61): a biomarker reflecting disease activity in rheumatoid arthritis. Arthritis Res Ther. 2019;21:123. doi: 10.1186/s13075-019-1906-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Pattabiraman PP, Rao PV. Hic-5 regulates actin cytoskeletal reorganization and expression of fibrogenic markers and myocilin in trabecular meshwork cells. Invest Ophthalmol Vis Sci. 2015;56:5656–5669. doi: 10.1167/iovs.15-17204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Dave JM, Abbey CA, Duran CL, Seo H, Johnson GA, Bayless KJ. Hic-5 mediates the initiation of endothelial sprouting by regulating a key surface metalloproteinase. J Cell Sci. 2016;129:743–756. doi: 10.1242/jcs.170571. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Dvorakova M, Nenutil R, Bouchal P. Transgelins, cytoskeletal proteins implicated in different aspects of cancer development. Expert Rev Proteomics. 2014;11:149–165. doi: 10.1586/14789450.2014.860358. [DOI] [PubMed] [Google Scholar]
  • 43.Asano K, Nelson CM, Nandadasa S, Aramaki-Hattori N, Lindner DJ, Alban T, Inagaki J, Ohtsuki T, Oohashi T, Apte SS, Hirohata S. Stromal versican regulates tumor growth by promoting angiogenesis. Sci Rep. 2017;7:17225. doi: 10.1038/s41598-017-17613-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Yang R, Chen Y, Chen D. Biological functions and role of CCN1/Cyr61 in embryogenesis and tumorigenesis in the female reproductive system (Review) Mol Med Rep. 2018;17:3–10. doi: 10.3892/mmr.2017.7880. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Yoshida T, Akatsuka T, Imanaka-Yoshida K. Tenascin-C and integrins in cancer. Cell Adh Migr. 2015;9:96–104. doi: 10.1080/19336918.2015.1008332. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Booth C, Harnden P, Selby PJ, Southgate J. Towards defining roles and relationships for tenascin-C and TGFbeta-1 in the normal and neoplastic urinary bladder. J Pathol. 2002;198:359–368. doi: 10.1002/path.1214. [DOI] [PubMed] [Google Scholar]
  • 47.Pritchard K, Moody CJ. Caldesmon: a calmodulin-binding actin-regulatory protein. Cell Calcium. 1986;7:309–327. doi: 10.1016/0143-4160(86)90035-7. [DOI] [PubMed] [Google Scholar]
  • 48.Yabasin IB, Sanches JGP, Ibrahim MM, Huidan J, Williams W, Lu ZL, Wen Q. Cisatracurium retards cell migration and invasion upon upregulation of p53 and inhibits the aggressiveness of colorectal cancer. Front Physiol. 2018;9:941. doi: 10.3389/fphys.2018.00941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Malik R, Lelkes PI, Cukierman E. Biomechanical and biochemical remodeling of stromal extracellular matrix in cancer. Trends Biotechnol. 2015;33:230–236. doi: 10.1016/j.tibtech.2015.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Herrera J, Henke CA, Bitterman PB. Extracellular matrix as a driver of progressive fibrosis. J Clin Invest. 2018;128:45–53. doi: 10.1172/JCI93557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Bonnans C, Chou J, Werb Z. Remodelling the extracellular matrix in development and disease. Nat Rev Mol Cell Biol. 2014;15:786–801. doi: 10.1038/nrm3904. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Paluch EK, Aspalter IM, Sixt M. Focal adhesion-independent cell migration. Annu Rev Cell Dev Biol. 2016;32:469–490. doi: 10.1146/annurev-cellbio-111315-125341. [DOI] [PubMed] [Google Scholar]
  • 53.Kenific CM, Wittmann T, Debnath J. Autophagy in adhesion and migration. J Cell Sci. 2016;129:3685–3693. doi: 10.1242/jcs.188490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Branis J, Pataki C, Sporrer M, Gerum RC, Mainka A, Cermak V, Goldmann WH, Fabry B, Brabek J, Rosel D. The role of focal adhesion anchoring domains of CAS in mechanotransduction. Sci Rep. 2017;7:46233. doi: 10.1038/srep46233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Elias JA, Zitnik RJ. Cytokine-cytokine interactions in the context of cytokine networking. Am J Respir Cell Mol Biol. 1992;7:365–367. doi: 10.1165/ajrcmb/7.4.365. [DOI] [PubMed] [Google Scholar]
  • 56.Spangler JB, Moraga I, Mendoza JL, Garcia KC. Insights into cytokine-receptor interactions from cytokine engineering. Annu Rev Immunol. 2015;33:139–167. doi: 10.1146/annurev-immunol-032713-120211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Borish LC, Steinke JW. 2. Cytokines and chemokines. J Allergy Clin Immunol. 2003;111:S460–475. doi: 10.1067/mai.2003.108. [DOI] [PubMed] [Google Scholar]

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