Anoikis-related Genes Impact Prognosis and Tumor Immune Microenvironment in Bladder Cancer

doi:10.21203/rs.3.rs-3448719/v1

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Anoikis-related Genes Impact Prognosis and Tumor Immune Microenvironment in Bladder Cancer

https://doi.org/10.21203/rs.3.rs-3448719/v1

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Anoikis tolerance is an important biological process of tumor colonization and metastasis outside the primary tumor. In recent years, the role and mechanism of anoikis in tumor metastasis have been continuously clarified in different solid tumors. However, the mechanism of anoikis in bladder cancer and its impact on the immune microenvironment is still unclear. We established a 5-gene anoikis score (EVT7, NGF, SCD, LAMC1, and CASP6) and divided them into high and low-risk groups according to the median score based on the TCGA database. We also found that SCD can promote the proliferation of Bca cells in vitro. Combining anoikis score and clinicopathological characteristics to construct a prognostic nomogram can well predict the prognosis of patients. In the analysis of immune cells, it was found that the infiltration of Treg cells and M2 macrophages increased in the high anoikis group, while the infiltration of CD8 T cells decreased. This study evaluates the role of anoikis score in predicting patient prognosis, immune cell infiltration, and drug response, which may provide potential directions for the treatment of bladder cancer.

Anoikis

bladder cancer

immune microenvironment

drug response

Bladder cancer is one of the common urinary tumors. Globally, the incidence of bladder cancer is 82290[1]. In China, with the improvement in living standards, the incidence of bladder cancer is also increasing year by year[2]. 75% of bladder cancer patients are diagnosed with non-muscle invasive bladder (NMIBC) clinically[3]. 25% of bladder cancer patients are diagnosed with muscle-invasive bladder cancer (MIBC). The biological characteristics of MIBC are easy to progress, easy to metastasize, and poor prognosis[4]. Therefore, finding the mechanism of bladder cancer metastasis and progression is an urgent clinical and basic research problem.

Lymphatic metastasis and lung metastasis of bladder cancer are important biological processes for malignant tumor progression[5, 6]. In the process of cell metastasis, cell anoikis resistance will promote tumor metastasis and progression [7, 8]. Therefore, anoikis resistance may be an important mechanism for solid tumor metastasis including bladder cancer metastasis. Anoikis is a special form of programmed cell death induced by loss of cell adhesion to extracellular matrix (ECM) or improper cell adhesion. This mode of death plays an important role in body development, disease occurrence, and tumor metastasis[9, 10]. In renal cancer, Sakamoto et al found that doxazosin can promote renal cancer cell anoikis by targeting adhesion signaling pathways, and is a potential drug for the treatment of renal cancer[11]. In prostate cancer, Yu et al. found that circ_0004585 can activate autophagy through the TM9SF5/mTOR axis, thereby promoting anoikis resistance in PCa cells[12]. However, the mechanism of anoikis and its impact on the immune microenvironment in bladder cancer is still unclear.

In this study, we analyzed the value of anoikis and its related genes in evaluating the prognosis of bladder cancer patients and further analyzed the effect of anoikis on immune cell infiltration in the bladder cancer microenvironment. We constructed a nomogram based on anoikis-related genes. At the same time, we verified the potential biological function of anoikis-related genes in vitro experiments.

Cell Culture

Bladder cancer cell lines T24, UMUC3, J82, 5637, and RT4, and human immortalized normal bladder epithelial cell line SV-HUC-1 were purchased from the Cell Center of Shanghai Institutes for Biological Sciences. T24, UMUC3, and 5637 were cultured in RPMI-1640 medium, RT4 in Mc Coy's 5A medium, J82 in DMEM medium, and SV-HUC-1 in F12K medium. All cell lines were cultured with 10% fetal bovine serum (FBS) (Gibco, USA) and 1% penicillin and streptomycin and cultured in an incubator at 37 ° C with 5% CO₂.

Data Collection and Processing

The clinical information and RNA sequencing data of 400 patients with bladder cancer were obtained from The Cancer Genome Atlas (TCGA-BLCA) database (https://portal.gdc.cancer.gov/), and differentially expressed genes (DEGs) were screened and identified in the TCGA cohort according to Log (FC) > = 2 and P < 0.05. 446 genes related to anoikis (ANRGs) were obtained from the GeneCard database (https://www.genecards.org/). Further, genes related to anoikis and closely related to prognosis were screened and identified from the TCGA DEGs (Fig. 1).

Colony formation and EdU assay

For the colony formation assay, after cell counting, a cell suspension of 1000 cells per well was added to a 6-well plate. After 2 weeks, the cell colonies were washed three times with precooled PBS, fixed with 4% paraformaldehyde for 15 minutes, and then air dried, and stained with 0.1% crystal violet for 15 minutes. The colonies were then counted and photographed.

For the EdU experiment, a cell suspension of 30,000 cells per well was added to the 6-well plate, and the cell climbing plate was added in advance to the 6-well plate. After 24, the EDU reagent was added and incubated for two hours. Next, the cell climbing plate was removed, and washed 3 times in PBS, and the experiment was performed according to the EdU Imaging Kits (Cy3, K1075, APExBIO, Shanghai, China) instructions. The experimental results were photographed using a confocal microscope.

Cell cycle and apoptosis assay

For cell cycle detection, 500,000 cells were seeded in 6-well plates, cells were collected after 24 h and fixed overnight in 500 µl of precooled 70% ethanol, ethanol was discarded by centrifugation the next day, washed three times with PBS, 25 µl of propidium iodide (PI) and 10 µl of RNase were added for staining (C1052, Beyotime, Shanghai, China), and 0.5 ml of staining buffer was added. After 15 minutes, the samples were detected using flow cytometry at the excitation wavelength of 488nm wavelength.

For apoptosis experiments, cells were collected by centrifugation at 4 ° C 300 g for 5 min after digestion with pancreatic enzyme without EDTA, washed 3 times with PBS, stained with 5 µL Annexin V/PE and 10 µL 7-AAD (40310ES20, Yeasen, Shanghai, China) for 15 min, bandwidth evaluation to 0.5 ml using staining buffer, and apoptosis level was detected by flow cytometry.

Western blot

Total cell protein was extracted by RIPA kit (20115ES60, Yeasen, Shanghai, China) and quantified by BCA kit (20201ES76, Yeasen, Shanghai, China). 40 ug of protein was transferred to nitrocellulose membrane (NC membrane) by electrophoresis and membrane transfer. After blocking with 5% skim milk for 1 h, SCD1 (ab236868, Abcam) antibody was incubated overnight. The next day, after washing three times with PBS, the horseradish peroxide-labeled secondary antibody was incubated for 1 h, and an ECL imaging system was used to scan the results.

Quantitative real-time PCR (qPCR)

Total RNA was extracted from bladder cancer cell lines by Trizol reagent (Invitgen, USA), using reverse transcription of RNA to cDNA according to the manufacturer's instructions (Vazyme, China). Next, qPCR was performed using the Hief UNICON ® qPCR SYBR Green Master Mix Kit (Yeasen, China). The primers are as follows: SCD Forward: 5'-CTGGCTTTGGGGGGTATTCT-3'; and reverse: 5 ′ -ATCAGAAAGAGCCGTAGGGG-3 ′.GAPDH Forward: 5 ′ -GGAGCGAGATCCCTCCAAAAT-3'; and Reverse5'-GGCTGTTGTCATACTTCTCATGG-3'.

Construction and evaluation of a prognostic model of anoikis-related genes

Minimum absolute shrinkage and selection operator (LASSO) Cox regression, random survival forest analysis was used to evaluate the relationship between training anoikis-related gene expression and overall survival (OS) and finally identified 5 key genes. The risk score formula is Risk score = (-0.027 * expression of CASP6) + (0.001 * expression of SCD) +

(0.070 * expression of NGF) + (-0.031 * expression of ETV7) + (0.010 * expression of LAMC1).Meanwhile, the sample was divided into a high-risk group and a low-risk group according to the median risk score.

Drug responses

The R package “pRRophetic” was used to study the correlation between anoikis-related genes and drug sensitivity. Pearson correlation analysis was used to evaluate the relationship between RNA expression levels of anoikis-related genes and drug responses (chemotherapeutic drugs, targeted drugs and immunotherapeutic drugs) in the database.

Tumor Microenvironment Analysis and Pathway Enrichment Analysis

We evaluated the impact of anoikis-related genes on the immune landscape of bladder cancer. ESTIMATE and single-sample GSEA (ssGSEA) databases were used to assess and analyze the proportion of immune cell subsets in different Riskscores. Meanwhile, gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze changes in differential pathways. These analyses sought to find correlations between different anoikis scores and different biological functions and processes.

Prognostic nomogram construction and calibration

The differences of clinicopathological variables between the low- and high-risk groups were visualized by a heatmap and examined by Chi-square test. Subsequently, univariate and multivariate Cox regression analyses were performed to assess whether the risk scores were independent prognostic indicators of BLCA. A nomogram model was constructed by combining the risk score and clinicopathological factors to estimate the 1-, 3-, and 5-year OS by the “rms” R package. The Kaplan–Meier and ROC curve evaluated the ability to predict OS.

Statistical analysis

The whole study used R software (4.1.2 version) for data analytics mapping by using limma, ggplot2, Kaplan-Meier curve, GSEA, and other R packages. The correlation between variables was calculated using Pearson and Spearman correlation analysis, p < 0.05 was considered statistically significant.

Identified prognostic anoikis-related genes

Based on the TCGA database, 9187 differential genes between tumor and normal tissue were analyzed using the “limma“ package and displayed in the form of a volcano map (Fig. 2A, logFC > 2 & p < 0.05). By collecting previous studies, we obtained a total of 446 anoikis-related genes. After taking the intersection, the Venn diagram shows that there are 146 co-expressed genes (Fig. 2B). Furthermore, we included the prognosis information of these patients and selected 36 genes that were significantly related to prognosis among 146 genes (Fig. 2C). Meanwhile, we found that EVT7 and NGF were significantly associated with bladder cancer prognosis (Fig. 2D, E).

Establishment of an anoikis-related gene prognosis model and functional enrichment

Further screening of the above prognostic-related genes by random forest, we found that a model with 5 genes (CASP6, SCD, NGF, ETV7, LAMC1) had the best accuracy (Fig. 3A, B). Based on the influence coefficients of the five genes, we established anoikis-related prognostic models and calculated the risk score with the following formula: Risk score = (-0.027*expression of CASP6) + (0.001*expression of SCD) + (0.070*expression of NGF) + (-0.031*expression of ETV7) + (0.010*expression of LAMC1) (Fig. 3C). The scatter plot shows that the risk score of patients in the high-risk group is significantly higher than that of patients in the low-risk group (Fig. 3D). The heat map showed that the expression of ETV7 and CASP6 was low in the high-risk group while the expression of SCD, NGF and LAMC1 was high (Fig. 3E). The distribution of high- and low-risk groups can be clearly distinguished with 3D-PCA (Fig. 3F). Finally, we performed a prognostic analysis, and the results showed that the prognosis of the high-risk group was significantly worse than that of the low-risk group (Fig. 3G). ln order to explore the function and pathway of this model, we carried out GO and KEGG enrichment analysis. Go enrichment shows that this model is associated with membrane regulation, MAPK signaling pathway and PI3K-Akt signaling pathway (Fig. 4A, B). KEGG enrichment revealed antibacterial humoral response, Th17 cell differentiation, PD-L1 expression and the PD-1 checkpoint pathway in cancer may be related to anoikis mechanism (Fig. 4C, D).

Anoikis-related gene SCD can significantly promote the malignant progression of bladder cancer

In order to further analyze the role of anoikis-related genes in bladder cancer, we found that SCD was significantly highly expressed in bladder cancer patients through TCGA heat map analysis, compared with the other four genes. (Fig. 5A) Further, we found that the expression of SCD was closely related to N stage (Fig. 5B, C). Prognostic analysis showed that high expression of SCD often predicted a worse prognosis (Fig. 5D).

Therefore, in vitro experiments, we found that compared with normal urothelial cells SV-HUC-1, the expression of SCD in bladder cancer was significantly up-regulated (Fig. 5E). We constructed a stable transgenic cell line that knockdown SCD, and qPCR and Western showed that the knockdown efficiency of sh1 was the most significant (Fig. 5F, G). We used the sh1 stable transgenic cell line to further verify the effect of knockdown of SCD on bladder cancer cell lines.

Clone formation and EdU experiments showed that knockdown of SCD could significantly reduce the proliferation ability of cells (Fig. 5H, I). Cell cycle detection showed that knockdown of SCD could cause G0G1 phase arrest of cells (Fig. 5J). Flow cytometry also showed that knockdown of SCD could increase the proportion of cell apoptosis and promote tumor apoptosis(Fig. 5K).

Anoikis-related genes are closely related to immune cell infiltration

We used the CIBERSORT database to analyse the correlation between the proportion of different immune cells and the risk score of anoikis-related genes. We found that the higher the risk score, the higher the infiltration ratio of B cell naive. When analysing T cell subsets, we found that CD8⁺ T cells were significantly decreased and CD4⁺ T cells were significantly increased in high-riskscore groups (p < 0.05). In addition, T regulation cells (Treg) were significantly decreased in high-riskscore groups. In the microenvironment of bladder cancer, another type of immune cell, M2 macrophages, was significantly increased in high-riskscore groups and predicted a poorer prognosis for this group (Fig. 6A).

At the same time, we further found in the estimate database that the estimate score and stromal score increased significantly and tumorpurity decreased significantly in high-riskscore groups (Fig. 6B-E). In the TIMER database, both DC cells and macrophages were significantly increased (Fig. 5F-G, p < 0.05).

Construction and Evaluation of the Nomogram Model

Univariate regression analysis showed that age, T, N, M, stage, papillary and risk score were associated with bladder cancer prognosis (Fig. 7A). Multivariate regression analysis confirmed that the risk score and age were independent risk factors for bladder cancer (Fig. 7B). On the basis of these important clinical factors, we have developed a nomogram for the prediction of 1-, 3- and 5-year survival in bladder cancer (Fig. 7C). Moreover, calibration curve analysis revealed that this nomogram was highly consistent with the ideal model (Fig. 7D). The ROC curve shows that the model has good predictive power, with 1-, 3- and 5- year area under curves (AUCs) of 0.741, 0.714, and 0.719 respectively (Fig. 7E). The Kaplan-Meier curve also showed a significant difference in prognosis between the high-risk and low-risk groups (Fig. 7F).

Anoikis-related genes may affect drug responses

As shown in Fig. 8, we investigated the response of anoikis related subgroups to chemotherapy and immunotherapy drugs by IC50 values by using “pRRophetic”. It was found that the IC50 value of Imatinib, Pazopanib, Midostaurin, Embelin, Axitinib and Bexarotene was significantly lower in the high-riskscore group. Methotrexate, Tipifarnib, and Etoposide were more suitable for low-risk patients. Therefore, this score may provide potential drugs for clinical treatment.

In this study, we analyzed the significance of anoikis, a biological process closely related to metastasis, in the development of bladder cancer. At the same time, we analyzed the genes related to anoikis (ETV7, CASP6, SCD, NGF, LAMC1) based on the TCGA database. We also found SCD can promote the progression of Bca. Then, we constructed a risk score based on lasso regression analysis. This model was used to analyze the immune cell infiltration and drug sensitivity of patients. Further, we constructed a nomogram to evaluate the prognosis of bladder cancer based on the clinicopathological data of patients.

Anoikis is a key mechanism for organisms to prevent exfoliated cells from re-adhering to new substrates in the wrong location[13, 14]. However, in the tumor microenvironment, tumor cells will form a unique survival phenotype, which enables them to do the exfoliation of ECM[15]. Without initiating the programmed death mechanism of anoikis, it will be more conducive to cell migration to other organs or lymphoid tissues and will aggregate to form metastases, eventually leading to lymphatic metastasis, hematogenous metastasis, and further metastasis[16, 17]. When anoikis tolerance occurs, the expression of fibronectin, collagen IV, and other proteins is significantly decreased, while the expression of laminin and metalloproteinases 9 is significantly increased [18, 19]. Therefore, maintaining the integrity of cell anoikis function will be an important means to prevent tumor metastasis in future treatments. In our study, we also found a series of changes in mRNA levels that are closely related to anoikis. Changes in these genes may regulate anoikis tolerance in bladder cancer cells. Interestingly, among these genes, we found that LAMC1 can be translated to produce laminin. Our results also show that LAMC1 expression is increased in bladder cancer and is associated with poor prognosis, which may all be related to LAMC1-induced anoikis tolerance.

In our study, we found that bladder cancer cell loss is closely related to SCD, a key molecule in the cellular process of ROS [20]. Metabolic differences between normal and cancer cells, the mitochondrial TCA cycle, and the shift from oxidative phosphorylation to glycolysis play an important role in tumor progression. Lu et al. report that cells disengaged from ECM induces anoikis tolerance by reducing glucose oxidation and increasing their antioxidant capacity [21].

In the past five years, immunotherapy has played a significant role in advanced or progressive bladder cancer. The landscape changes of the immune microenvironment of bladder cancer can also effectively reflect the efficacy of immunotherapy. Anoikis tolerance is a change in the microenvironment of bladder cancer, making tumor cells easier to colonize and metastasize [22]. Tsai et al. also reported that IL-17A can enhance anoikis resistance in breast cancer, regulate the immune landscape of the tumor microenvironment, and promote cancer metastasis [23]. Tang et al. found that mesenchymal stem cells secrete IL-8, which enhances the resistance of osteosarcoma cells to anoikis and lung metastasis through the CXCR1/Akt axis[24]. In our study, we found that anoikis-related gene scores are also closely related to immunity. On the one hand, the higher the Riskscore score, the lower the regulatory T cells (Treg), which may benefit patients from immunotherapy[25, 26]. Treg is a subset of cells with significant immunosuppressive effects. On the other hand, the higher the Riskscore, the lower the proportion of CD8⁺ T cells and the higher the proportion of M2 macrophages, which is often a manifestation of insensitivity to immunotherapy[27, 28]. Of course, the interpretation of this result may require a more detailed subset analysis of CD8⁺ T cells and macrophages.

There are still some limitations that need to be pointed out in this study. This study was based on the TCGA database for excavation studies, missing external validation cohorts, and multi-center validation. These issues need to be resolved in future studies, and we must also evaluate whether this model is applicable to the Chinese population. Another issue of concern is that the molecular mechanism of anoikis tolerance regulating the development of bladder cancer needs further research. In subsequent experiments, we will continue to conduct in vivo experimental verification.

To sum up, anoikis tolerance is an important phenomenon in bladder cancer metastasis. The risk score constructed based on anoikis tolerance is an independent marker for predicting the prognosis of patients with BCa. At the same time, we constructed a nomogram based on patient clinical data and risk score. In addition, the same anoikis tolerance score can also analyze the infiltration of immune cells in the bladder cancer microenvironment and predict drug response, hoping to provide preliminary guidance for doctors to evaluate the prognosis of bladder cancer patients and choose treatment strategies.

Acknowledgments

This work was supported in part by a grant from the Natural Science Foundation of Shandong（ZR2020QH241 and ZR2019PH095）

Authors’ Contributions

(I) Conception and design: LSW; (II) Provision of study materials or patients: TZ; (III) Collection and assembly of data: SNQ; (IV) Data analysis and interpretation: TZ (V) Manuscript writing: LSW; (VI) Final approval of manuscript: All authors.

Conflicts of Interest

The authors declare that they have no conﬂicts of interest.

Data Availability

The data could be acquired from the TCGA dataset ( https:// portal.gdc.cancer.gov/ ) and The data on drug sensitivity could be acquired from the CellMiner database ( https://discover.nci.nih.gov/cellminer ). The code used in the study is available from the corresponding author upon reasonable request.

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Consent for publication: Not applicable.

Competing interests: The authors declare that they have no competing interests.

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No competing interests reported.

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Anoikis-related Genes Impact Prognosis and Tumor Immune Microenvironment in Bladder Cancer

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Version 1

Abstract

Figures

Introduction

Methods

Cell Culture

Data Collection and Processing

Colony formation and EdU assay

Cell cycle and apoptosis assay

Western blot

Quantitative real-time PCR (qPCR)

Construction and evaluation of a prognostic model of anoikis-related genes

Drug responses

Tumor Microenvironment Analysis and Pathway Enrichment Analysis

Prognostic nomogram construction and calibration

Statistical analysis

Results

Identified prognostic anoikis-related genes

Establishment of an anoikis-related gene prognosis model and functional enrichment

Anoikis-related gene SCD can significantly promote the malignant progression of bladder cancer

Anoikis-related genes are closely related to immune cell infiltration

Construction and Evaluation of the Nomogram Model

Anoikis-related genes may affect drug responses

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

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