# CASC1 Expression in Bladder Cancer Is Regulated by Exosomal miRNA-150: A Comprehensive Pan-Cancer and Bioinformatics Study

Authors: Huarong Luo; Chengdang Xu; Bujun Ge; Tianru Wang
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

This study explored the role of cancer susceptibility 1 (CASC1) in tumorigenesis and development as well as the key pathways affecting bladder cancer progression. CASC1 was examined in various normal tissues in humans using the HPA database to quantify its expression level and subcellular localization. CASC1 is abundantly expressed in tumor tissues, primarily in cytoplasmic vesicles and stroma. TIMER2 was used to analyze the correlation betweenCASC1 expression levels and the types of infiltrates associated with immune cells and immunosuppressive cells. MDSC, Treg, M2, and CAF were significantly correlated with CASC1 expression in various tumors. Comparing patients with and without CASC1 mutation, those with CASC1 mutation had worse overall survival, progression-free survival, and disease-free survival. The correlation between has-miR-150 and CASC1 (for the case of bladder cancer) was then analyzed, and the related ceRNA network was mapped. A negative relationship between CASC1 expression and has-miR-150 expression was found in cases of bladder cancer. And the presence of miR-150-targeted CASC1 may be associated with bladder cancer progression. CASC1 is expressed at elevated levels in various tumor tissues, and it is associated with tumorigenesis and development. Exosomes containing miR-150-targeted CASC1 may affect the progression of bladder cancer.


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## Body

## 1. Introduction

A total of 430,000 new cases of bladder cancer are reported every year, with 165,000 deaths related to the disease [1]. Based on the 2015 China Cancer Data, the incidence and mortality related to bladder cancer are on the rise every year [2, 3]. The prognosis for patients with bladder cancer remains poor despite the development of new treatments due to high recurrence and metastasis rates [4]. Therefore, the development of new therapeutic targets requires an understanding of the molecular mechanisms associated with bladder cancer.Cancer susceptibility 1 (CASC1) regulates microtubule dynamics and plays an important role in mitosis in tumor cells [5]. It has been demonstrated that DEGs upregulated by bladder cancer are frequently linked to mitotic spindle assembly checkpoints [5]. RNAi inhibition of CASC1 expression can reduce tumor growth in vivo and increase survival rates for patients [6–9]. To improve the treatment methods and prognosis of bladder cancer patients, we need to explore the regulatory pathway of CASC1, which affects the progression of bladder cancer.The specific mechanism associated with the regulation pathway ofCASC1 that affects the progression of bladder cancer is still unknown. A study found that CASC1 influenced the function of pulmonary adenoma susceptibility 1 locus (PAS1), which is the regulator of lung tumors [10]. CASC1 was found to be upregulated in malignant tumors [11]. Proteins that target CASC1 and the genes related to CASC1 can be potentially important molecules that influence cancer incidence and development.There is increasing evidence that the ceRNA regulatory network plays a role in cancer development and occurrence [12–14]. It might be possible to uncover the mechanism by which CASC1 influences bladder cancer progression by studying the ceRNA network. The bioinformatics analysis technique can be used to investigate the role of ceRNA regulatory network in cancer development. In previous research, bioinformatics analysis was used to explore how ceRNAs contribute to the development of drug-resistant non-small cell lung cancers (NSCLCs) [15]. Therefore, bioinformatics analysis of ceRNA regulatory networks could provide more insight into the mechanisms whereby CASC1 causes bladder cancer.MicroRNAs (miRNAs) influence various physiological and pathological processes in the body through ceRNA regulatory networks [16–20]. Moreover, miRNAs may be released by cells and loaded into exosomes, acting as messengers for transmitting information between cells and participating in tumor development [21, 22]. A number of studies have shown that exosomal miRNAs play a crucial role in the development of bladder cancer [23]. Researchers have found that miRNA-150 affects bladder cancer prognosis, and we speculate that CASC1 may be associated with miRNA-150 [24]. By analyzing pan-cancer data and using bioinformatics, we are attempting to uncover how CASC1 affects bladder cancer progression. As part of our research, we hope to identify potential therapeutic targets, prevent bladder cancer progression, and improve patient outcomes.


## 2. Methods

### 2.1. Mutants, Location-Related Diseases, and Tissue Specificity of CASC1

The Human Protein Atlas (HPA) (https://www.proteinatlas.org/) consists of a dataset analyzed to map proteins in human tissue, organ, and cell. Based on the HPA database, we investigated the expression levels of CASC1 gene in different normal human tissues and the localization of CASC1 protein in JURKAT, SuSa, and U-2 OS cell lines. The OPENTARGET platform (https://www.targetvalidation.org/) integrated genetics and multiple omics methods were used to identify the role of the genes in the progression and occurrence of the disease. We used the OPENTARGET platform to mine disease networks associated with CASC1. GPS-Prot (http://gpsprot.org/index.php) is an online platform that helps visualize protein-protein interactions (PPIs). We used the GPS-PORT database to reveal the PPI network associated with CASC1 protein. PROTTER (https://wlab.ethz.ch/protter/help/) is an interactive protein characterization visualization tool, the data of which can be found in the UniprotKB database. The PROTTER database was used to visualize the topological structure of the CASC1 protein.


### 2.2. Differential Expression and Prognostic Value ofCASC1 in Tumors

RNA-seq data was obtained from the TCGA and Genotype-Tissue Expression (GTEx) databases, and clinical matching data was acquired from the TCGA database [25, 26]. The Wilcoxon rank-sum test was conducted to compare the expression levels of CASC1 observed in tumor and normal groups. It was also conducted to compare the expression levels observed at different pathological stages. And cancer samples of each type were divided into two high–low expression groups according to the expression level of CASC1.The Kaplan-Meier (KM) plot was plotted using the Surminer (version 0.4.9) package, and the log-rank test was conducted to compare the survival differences between the two groups. The survival package (version 3.2-10) was used to conduct the univariate Cox proportional risk regression analysis. All analyses were based on R software (version 3.6.3).


### 2.3. The Relationship betweenCASC1 Expression Levels and Tumor Immunity

TIMER2 (http://timer.cistrome.org/) was used to analyze the correlation between CASC1 expression level and the six types of immune cells, such as B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells (DC). Additionally, we studied the relationship between CASC1 expression levels and four immunosuppressive cells (that promote T cell rejection), including marrow-derived suppressor cells (MDSCs), cancer-associated fibroblasts (CAFs), tumor-associated macrophages M2 subtypes (M2-tams), and regulatory T cells (Treg). A Spearman’s rank correlation test was used to calculate the partial correlation (cor) and p values. Additionally, the TIDE database (http://tide.dfci.harvard.edu/) was used to compare the efficiency of CASC1 and standard biomarkers in predicting the immune state during treatment response to immune checkpoint inhibitors (ICBs).


### 2.4. Mutation Characteristics ofCASC1

CBioPortal (http://www.cbioportal.org) is a cancer genomics database, which was used to observe the mutation frequencies and mutation sites of CASC1 associated with various cancer types. Furthermore, we investigated the relationship between CASC1 mutations and patient survival rates (OS, DFS, and PFS). The expression levels of CASC1 were also studied in relation to the different copy number variation types.


### 2.5. Analysis of Related Genes and Functions Associated withCASC1 and Bladder Cancer

STRING (https://string-db.org/) database was used to obtain the names of the organism and protein. Detailed information about protein interactions was obtained from STRING database. A minimum interaction score of 0.4 was required (medium confidence).GEPIA2 (http://gepia2.cancer-pku.cn/#index) is an online tool for understanding TCGA gene expression and survival analysis. For bladder cancer, we used the GEPIA2 Similar Gene Detection module to identify the top 100 genes associated with CASC1. Using the correlation analysis module, the correlation scatter diagram (Pearson correlation coefficient) representing the relationship between CASC1 and the five most related genes was generated. We further analyzed the first 100 genes associated with CASC1 using the clusterProfiler package. GO terms with q-values less than 0.05 were considered significantly enriched. The scatter plot representing the correlation between has-miR-150 and CASC1 (for bladder cancer) was created using ggploT2, ggExtra, and ggpubr.In order to predict lncRNAs targeted by has-miR-150, we used the StarBase database (version 3), and Cytoscape (version 3.8.0) to plot the ceRNA network. For CASC1 and miR-150 expression, we divided the samples by the median expression levels calculated for bladder cancer. The clusterProfiler was used to perform the KEGG gene set enrichment analysis (GSEA).


## 2.1. Mutants, Location-Related Diseases, and Tissue Specificity of CASC1

The Human Protein Atlas (HPA) (https://www.proteinatlas.org/) consists of a dataset analyzed to map proteins in human tissue, organ, and cell. Based on the HPA database, we investigated the expression levels of CASC1 gene in different normal human tissues and the localization of CASC1 protein in JURKAT, SuSa, and U-2 OS cell lines. The OPENTARGET platform (https://www.targetvalidation.org/) integrated genetics and multiple omics methods were used to identify the role of the genes in the progression and occurrence of the disease. We used the OPENTARGET platform to mine disease networks associated with CASC1. GPS-Prot (http://gpsprot.org/index.php) is an online platform that helps visualize protein-protein interactions (PPIs). We used the GPS-PORT database to reveal the PPI network associated with CASC1 protein. PROTTER (https://wlab.ethz.ch/protter/help/) is an interactive protein characterization visualization tool, the data of which can be found in the UniprotKB database. The PROTTER database was used to visualize the topological structure of the CASC1 protein.


## 2.2. Differential Expression and Prognostic Value ofCASC1 in Tumors

RNA-seq data was obtained from the TCGA and Genotype-Tissue Expression (GTEx) databases, and clinical matching data was acquired from the TCGA database [25, 26]. The Wilcoxon rank-sum test was conducted to compare the expression levels of CASC1 observed in tumor and normal groups. It was also conducted to compare the expression levels observed at different pathological stages. And cancer samples of each type were divided into two high–low expression groups according to the expression level of CASC1.The Kaplan-Meier (KM) plot was plotted using the Surminer (version 0.4.9) package, and the log-rank test was conducted to compare the survival differences between the two groups. The survival package (version 3.2-10) was used to conduct the univariate Cox proportional risk regression analysis. All analyses were based on R software (version 3.6.3).


## 2.3. The Relationship betweenCASC1 Expression Levels and Tumor Immunity

TIMER2 (http://timer.cistrome.org/) was used to analyze the correlation between CASC1 expression level and the six types of immune cells, such as B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells (DC). Additionally, we studied the relationship between CASC1 expression levels and four immunosuppressive cells (that promote T cell rejection), including marrow-derived suppressor cells (MDSCs), cancer-associated fibroblasts (CAFs), tumor-associated macrophages M2 subtypes (M2-tams), and regulatory T cells (Treg). A Spearman’s rank correlation test was used to calculate the partial correlation (cor) and p values. Additionally, the TIDE database (http://tide.dfci.harvard.edu/) was used to compare the efficiency of CASC1 and standard biomarkers in predicting the immune state during treatment response to immune checkpoint inhibitors (ICBs).


## 2.4. Mutation Characteristics ofCASC1

CBioPortal (http://www.cbioportal.org) is a cancer genomics database, which was used to observe the mutation frequencies and mutation sites of CASC1 associated with various cancer types. Furthermore, we investigated the relationship between CASC1 mutations and patient survival rates (OS, DFS, and PFS). The expression levels of CASC1 were also studied in relation to the different copy number variation types.


## 2.5. Analysis of Related Genes and Functions Associated withCASC1 and Bladder Cancer

STRING (https://string-db.org/) database was used to obtain the names of the organism and protein. Detailed information about protein interactions was obtained from STRING database. A minimum interaction score of 0.4 was required (medium confidence).GEPIA2 (http://gepia2.cancer-pku.cn/#index) is an online tool for understanding TCGA gene expression and survival analysis. For bladder cancer, we used the GEPIA2 Similar Gene Detection module to identify the top 100 genes associated with CASC1. Using the correlation analysis module, the correlation scatter diagram (Pearson correlation coefficient) representing the relationship between CASC1 and the five most related genes was generated. We further analyzed the first 100 genes associated with CASC1 using the clusterProfiler package. GO terms with q-values less than 0.05 were considered significantly enriched. The scatter plot representing the correlation between has-miR-150 and CASC1 (for bladder cancer) was created using ggploT2, ggExtra, and ggpubr.In order to predict lncRNAs targeted by has-miR-150, we used the StarBase database (version 3), and Cytoscape (version 3.8.0) to plot the ceRNA network. For CASC1 and miR-150 expression, we divided the samples by the median expression levels calculated for bladder cancer. The clusterProfiler was used to perform the KEGG gene set enrichment analysis (GSEA).


## 3. Results

### 3.1. Mutants, Location-Related Diseases, and Tissue Specificity of CASC1

The protein topology of CASC1 presented two natural variants (Arg33Ser and Ala633Glu) (Figure1(a)). CASC1 was primarily associated with neurological diseases, respiratory diseases, tumors, and congenital diseases (Figure 1(b)). According to immunofluorescence images of CASC1 obtained from the HPA database, CASC1 is primarily distributed in the intracellular cytoplasmic matrix and vesicles (Figure 1(c)). The expression levels of CASC1 in human tissues (mRNA) are shown in Figure 1(d). High levels of expression were detected in testes, fallopian tubes, and choroid plexuses. CASC1 has also been demonstrated to interact with several proteins (MYO9A, HADHA, RUNDC1, LRIF1, BARD1, and PPP1CA) (Figure 1(e)).Figure 1
Mutants, location-related diseases, and tissue specificity of CASC1. (a) Cancer susceptibility candidate gene 1 (CASC1) protein topology revealed that it consisted of two natural variants, Arg33Ser (from arginine to serine) and Ala633Glu (from alanine to glutamate); (b) Casc1-related disease networks; (c) immunofluorescence staining images ofCASC1 in JURKAT, SuSa, and U-2 OS cells; (d) expression of CASC1 in healthy human tissues; (e) functional companion of CASC1.
(a)(b)(c)(d)(e)


### 3.2. Differential Expression and Prognostic Value ofCASC1 in Tumors

CASC1 expression in tumor and normal samples from the TCGA and GTEx databases was compared (Figure 2(a)). The results revealed that CASC1 was highly expressed in tumor tissues associated with UCEC, THYM, PAAD, OV, LGG, LAML, KIRP, GBM, DLBC, CHOL, and BRCA. In order to study the prognostic value of CASC1, 33 TCGA cancer types were analyzed using the univariate Cox proportional risk regression method (Figure 2(b)). The Log-rank test was also used to compare the survival differences for the groups with high and low levels of CASC1 expression (Figure 2(c)). ACC, CESC, GBMLGG, KIRC, MESO, SKCM, UCS, and UVM had significantly different survival rates between high and low CASC1 expression groups. In GBMLGG and UCS, the survival prognosis was better in the group with low CASC1 expression than in the group with high CASC1 expression. For ACC, CESC, KIRC, MESO, SKCM, and UVM, the high CASC1 expression group had a better survival prognosis than the low expression group. A difference in expression levels of CASC1 was also assessed at various tumor pathological stages (Figure 2(d)). There were significant differences in the expression levels of BRCA, KIRC, THCA, and LUSC at different stages of disease. In brief, CASC1 appeared to become overexpressed during the early stages of tumor development.Figure 2
Differential expression and prognostic value ofCASC1 in tumors. (a) Differential expression of CASC1 in 30 tumor tissues compared to normal/paracancer tissues (log2 (TPM+1)). These data were obtained from the TCGA and GTEx databases. (b) Forest map presents the results obtained following the univariate cox regression method for the analysis of CASC1 in 34 types of TCGA tumors. (c) OS KM-plot for high and low expression groups of CASC1 (grouped according to median value). (d) The expression of CASC1 differed with the tumor pathological stage. ns, p≥0.05; ∗p<0.05; ∗∗p<0.01; and ∗∗∗p<0.001.
(a)(b)(c)(d)


### 3.3. Relationship betweenCASC1 Expression Levels and Tumor Immunity

CASC1 expression was correlated with tumor immune cell infiltration (including CD4+ T cells, B cells, CD8+ T cells, neutrophils, macrophages, and DCs) in only 4 out of 39 TCGA tumor types (LGG, LIHC, PRAD, and LUSC) (Figure 3(a)). For HNSC, KIRC, and SKCM, the level of immune cell infiltration strongly correlated with CASC1 expression. Furthermore, the extent of immune cell infiltration observed in the case of THYM correlated negatively with the expression levels of CASC1. Additionally, we examined the relationship between the level of expression of CASC1 and the infiltration of four immunosuppressive cells (M2-TAMS, CAFs, MDSCs, and Treg cells) (Figure 3(b)). CASC1 expression in KIRP, LIHC, OV, SKCM-Primary, THCA, and THYM was significantly correlated with the extent of tumor invasion recorded for MDSC. CASC1 expression in ESCA, HNSC, LUAD, LUSC, PAAD, SKCM, STAD, UCEC, UCS, and UVM is significantly inversely correlated with Treg level. There was a significant positive correlation between the expression levels of CASC1 in BRCA, COAD, HNSC, LGG, LIHC, LUAD, PAAD, PRAD, and THYM and the extent of tumor invasion recorded for M2. CASC1 expression was positively correlated with the extent of tumor invasion in ACC, BRCA, CESC, CHOL, COAD, ESCA, HNSC, LUAD, LUSC, and MESO. CASC1 was also analyzed according to the efficacy predictive power of its ICB subgroups (Figure 3(c)). Seven subcohorts of the ICB showed AUC values greater than 0.5. This demonstrated CASC1 as a predictive marker and its superiority over other biomarkers. Therefore, CASC1 exhibited a better predictive power in the Uppaluri2020_PD1_HNSC_post.Figure 3
Relationship betweenCASC1 expression levels and tumor immunity. (a) Correlations between CASC1 and six types of infiltrating immune cells in 39 types of tumors. (b) Correlations between CASC1 and four types of infiltrating immunosuppressive cells in 39 types of tumors. (c) Area under the subject operating characteristic curve (AUC) in the bar chart was used for predicting response status to immune checkpoint inhibitors (ICB).
(a)(b)(c)


### 3.4. Mutation Characteristics ofCASC1

Genetic variation significantly influences the initiation of cancer and immune tolerance. Based on TCGA database (10,967 samples), we used cBioPortal to detect genetic variations in CASC1 and to count the mutation frequency in CASC1. CASC1 mutations were found to exceed 8% in esophagogastric adenocarcinoma, ovarian epithelial tumor, and non-seminomatous germ cell tumors (Figure4(a)). In total, 140 mutated loci (110 missense, 23 truncating, 5 splicing, and 2 SV/Fusion) were identified (Figure 4(b)). In patients with CASC1 mutations, overall survivals (p<0.001) (Figure 4(c)), disease-free survivals (p<0.001) (Figure 4(d)), and progression-free survivals (p<0.001) (Figure 4(e)) were negatively correlated with those without CASC1 mutations. Furthermore, there were significant differences in the expression of mRNA associated with different CNA subtypes of CASC1 (Figure 4(f)). Compared to amplification, the level of expression was relatively higher in deep deletion.Figure 4
Mutation characteristics ofCASC1. (a) Characterization of the mutation of CASC1 in multiple TCGA cancer types. (b) Mutation site of CASC1. (c) Relationship between the CASC1 mutation status and overall survival (OS). (d) Association between the CASC1 mutation status and disease-free survival (DFS); (e) Association between the CASC1 mutation status and progression-free survival (PFS). (f) Differences in the expression levels recorded for different copy number types of CASC1.
(a)(b)(c)(d)(e)(f)


### 3.5. Analysis of Related Genes and Functions ofCASC1 in Bladder Cancer

CASC1 interaction proteins and genes associated with CASC1 expression may influence important molecular mechanisms involved in cancer genesis and progression. STRING identified a total of 10 CASC1 interacting proteins (Figure 5(a)). We identified the first 100 genes co-expressed with CASC1 associated with bladder cancer using GEPIA2. In the first five genes, we found EFCAB12 (R=0.59), CFAP126 (R=0.56), RIBC1 (R=0.54), SPAG8 (R=0.54), and DNAI1 (R=0.53) (Figures 5(b)–(f)). By analyzing the first 100 genes using GO enrichment analysis, we found that 30 GO terms exhibited a significant correlation. We visualized the first 10 significant biological processes (BP) and cellular components (CC) (Figure 5(g)). The level of expression of has-miR-150 was reported to correlate negatively with CASC1 in bladder cancer (R=−0.11) (Figure 5(h)).Figure 5
Analysis of CASC1 related genes. (a) PPI network associated withCASC1 obtained by analyzing the STRING database. (b)–(f) First 5 genes associated with CASC1 for the case of bladder cancer. (g) Results obtained by conducting GO enrichment analysis for the top 100 genes related to CASC1 associated with bladder cancer. (h) Scatter plot of correlation between CASC1 and hsa-miR-150 for the case of bladder cancer.
(a)(b)(c)(d)(e)(f)(g)(h)The miRWALK database confirmed that has-miR-150 targetsCASC1. All potential targets for has-miR-150 were predicted using the StarBase database. A network of 66 targeted long noncoding RNAs (lncRNAs), including has-miR-150 and CASC1, was constructed (Figure 6(a)). The samples were divided into high and low expression groups based on their median expression levels of CASC1 and has-miR-150. KEGG analysis was also performed, and 37 significantly enriched pathways were associated with CASC1 and 67 significantly enriched pathways with hsa-miR-150. It was found that 10 KEGG pathways were associated with CASC1 and hsa-miR-150 (Figure 6(b)). In GSEA, pathways associated with CASC1 and has-miR-150 were visualized (Figures 6(c)–(d)). They were complement and coagulation cascades, drug metabolism cytochrome p450, hematopoietic cell lineage, Leishmania infection, metabolism of xenobiotics by cytochrome p450, natural killer cell-mediated cytotoxicity, proteasome, ribosome, systemic lupus erythematosus, and viral myocarditis.Figure 6
Analysis of CASC1 functions in bladder cancer. (a) ceRNA network of hsa-miR-150 associated withCASC1, where diamond nodes represent lncRNAs. (b) Venn diagram corresponding to the has-miR-150-related GSEA pathway and CASC1-related GSEA pathway. (c) Significant GSEA pathways in CASC1 enrichment analysis results. (d) Significant pathways in hsa-miR-150 enrichment analysis results.
(a)(b)(c)(d)


## 3.1. Mutants, Location-Related Diseases, and Tissue Specificity of CASC1

The protein topology of CASC1 presented two natural variants (Arg33Ser and Ala633Glu) (Figure1(a)). CASC1 was primarily associated with neurological diseases, respiratory diseases, tumors, and congenital diseases (Figure 1(b)). According to immunofluorescence images of CASC1 obtained from the HPA database, CASC1 is primarily distributed in the intracellular cytoplasmic matrix and vesicles (Figure 1(c)). The expression levels of CASC1 in human tissues (mRNA) are shown in Figure 1(d). High levels of expression were detected in testes, fallopian tubes, and choroid plexuses. CASC1 has also been demonstrated to interact with several proteins (MYO9A, HADHA, RUNDC1, LRIF1, BARD1, and PPP1CA) (Figure 1(e)).Figure 1
Mutants, location-related diseases, and tissue specificity of CASC1. (a) Cancer susceptibility candidate gene 1 (CASC1) protein topology revealed that it consisted of two natural variants, Arg33Ser (from arginine to serine) and Ala633Glu (from alanine to glutamate); (b) Casc1-related disease networks; (c) immunofluorescence staining images ofCASC1 in JURKAT, SuSa, and U-2 OS cells; (d) expression of CASC1 in healthy human tissues; (e) functional companion of CASC1.
(a)(b)(c)(d)(e)


## 3.2. Differential Expression and Prognostic Value ofCASC1 in Tumors

CASC1 expression in tumor and normal samples from the TCGA and GTEx databases was compared (Figure 2(a)). The results revealed that CASC1 was highly expressed in tumor tissues associated with UCEC, THYM, PAAD, OV, LGG, LAML, KIRP, GBM, DLBC, CHOL, and BRCA. In order to study the prognostic value of CASC1, 33 TCGA cancer types were analyzed using the univariate Cox proportional risk regression method (Figure 2(b)). The Log-rank test was also used to compare the survival differences for the groups with high and low levels of CASC1 expression (Figure 2(c)). ACC, CESC, GBMLGG, KIRC, MESO, SKCM, UCS, and UVM had significantly different survival rates between high and low CASC1 expression groups. In GBMLGG and UCS, the survival prognosis was better in the group with low CASC1 expression than in the group with high CASC1 expression. For ACC, CESC, KIRC, MESO, SKCM, and UVM, the high CASC1 expression group had a better survival prognosis than the low expression group. A difference in expression levels of CASC1 was also assessed at various tumor pathological stages (Figure 2(d)). There were significant differences in the expression levels of BRCA, KIRC, THCA, and LUSC at different stages of disease. In brief, CASC1 appeared to become overexpressed during the early stages of tumor development.Figure 2
Differential expression and prognostic value ofCASC1 in tumors. (a) Differential expression of CASC1 in 30 tumor tissues compared to normal/paracancer tissues (log2 (TPM+1)). These data were obtained from the TCGA and GTEx databases. (b) Forest map presents the results obtained following the univariate cox regression method for the analysis of CASC1 in 34 types of TCGA tumors. (c) OS KM-plot for high and low expression groups of CASC1 (grouped according to median value). (d) The expression of CASC1 differed with the tumor pathological stage. ns, p≥0.05; ∗p<0.05; ∗∗p<0.01; and ∗∗∗p<0.001.
(a)(b)(c)(d)


## 3.3. Relationship betweenCASC1 Expression Levels and Tumor Immunity

CASC1 expression was correlated with tumor immune cell infiltration (including CD4+ T cells, B cells, CD8+ T cells, neutrophils, macrophages, and DCs) in only 4 out of 39 TCGA tumor types (LGG, LIHC, PRAD, and LUSC) (Figure 3(a)). For HNSC, KIRC, and SKCM, the level of immune cell infiltration strongly correlated with CASC1 expression. Furthermore, the extent of immune cell infiltration observed in the case of THYM correlated negatively with the expression levels of CASC1. Additionally, we examined the relationship between the level of expression of CASC1 and the infiltration of four immunosuppressive cells (M2-TAMS, CAFs, MDSCs, and Treg cells) (Figure 3(b)). CASC1 expression in KIRP, LIHC, OV, SKCM-Primary, THCA, and THYM was significantly correlated with the extent of tumor invasion recorded for MDSC. CASC1 expression in ESCA, HNSC, LUAD, LUSC, PAAD, SKCM, STAD, UCEC, UCS, and UVM is significantly inversely correlated with Treg level. There was a significant positive correlation between the expression levels of CASC1 in BRCA, COAD, HNSC, LGG, LIHC, LUAD, PAAD, PRAD, and THYM and the extent of tumor invasion recorded for M2. CASC1 expression was positively correlated with the extent of tumor invasion in ACC, BRCA, CESC, CHOL, COAD, ESCA, HNSC, LUAD, LUSC, and MESO. CASC1 was also analyzed according to the efficacy predictive power of its ICB subgroups (Figure 3(c)). Seven subcohorts of the ICB showed AUC values greater than 0.5. This demonstrated CASC1 as a predictive marker and its superiority over other biomarkers. Therefore, CASC1 exhibited a better predictive power in the Uppaluri2020_PD1_HNSC_post.Figure 3
Relationship betweenCASC1 expression levels and tumor immunity. (a) Correlations between CASC1 and six types of infiltrating immune cells in 39 types of tumors. (b) Correlations between CASC1 and four types of infiltrating immunosuppressive cells in 39 types of tumors. (c) Area under the subject operating characteristic curve (AUC) in the bar chart was used for predicting response status to immune checkpoint inhibitors (ICB).
(a)(b)(c)


## 3.4. Mutation Characteristics ofCASC1

Genetic variation significantly influences the initiation of cancer and immune tolerance. Based on TCGA database (10,967 samples), we used cBioPortal to detect genetic variations in CASC1 and to count the mutation frequency in CASC1. CASC1 mutations were found to exceed 8% in esophagogastric adenocarcinoma, ovarian epithelial tumor, and non-seminomatous germ cell tumors (Figure4(a)). In total, 140 mutated loci (110 missense, 23 truncating, 5 splicing, and 2 SV/Fusion) were identified (Figure 4(b)). In patients with CASC1 mutations, overall survivals (p<0.001) (Figure 4(c)), disease-free survivals (p<0.001) (Figure 4(d)), and progression-free survivals (p<0.001) (Figure 4(e)) were negatively correlated with those without CASC1 mutations. Furthermore, there were significant differences in the expression of mRNA associated with different CNA subtypes of CASC1 (Figure 4(f)). Compared to amplification, the level of expression was relatively higher in deep deletion.Figure 4
Mutation characteristics ofCASC1. (a) Characterization of the mutation of CASC1 in multiple TCGA cancer types. (b) Mutation site of CASC1. (c) Relationship between the CASC1 mutation status and overall survival (OS). (d) Association between the CASC1 mutation status and disease-free survival (DFS); (e) Association between the CASC1 mutation status and progression-free survival (PFS). (f) Differences in the expression levels recorded for different copy number types of CASC1.
(a)(b)(c)(d)(e)(f)


## 3.5. Analysis of Related Genes and Functions ofCASC1 in Bladder Cancer

CASC1 interaction proteins and genes associated with CASC1 expression may influence important molecular mechanisms involved in cancer genesis and progression. STRING identified a total of 10 CASC1 interacting proteins (Figure 5(a)). We identified the first 100 genes co-expressed with CASC1 associated with bladder cancer using GEPIA2. In the first five genes, we found EFCAB12 (R=0.59), CFAP126 (R=0.56), RIBC1 (R=0.54), SPAG8 (R=0.54), and DNAI1 (R=0.53) (Figures 5(b)–(f)). By analyzing the first 100 genes using GO enrichment analysis, we found that 30 GO terms exhibited a significant correlation. We visualized the first 10 significant biological processes (BP) and cellular components (CC) (Figure 5(g)). The level of expression of has-miR-150 was reported to correlate negatively with CASC1 in bladder cancer (R=−0.11) (Figure 5(h)).Figure 5
Analysis of CASC1 related genes. (a) PPI network associated withCASC1 obtained by analyzing the STRING database. (b)–(f) First 5 genes associated with CASC1 for the case of bladder cancer. (g) Results obtained by conducting GO enrichment analysis for the top 100 genes related to CASC1 associated with bladder cancer. (h) Scatter plot of correlation between CASC1 and hsa-miR-150 for the case of bladder cancer.
(a)(b)(c)(d)(e)(f)(g)(h)The miRWALK database confirmed that has-miR-150 targetsCASC1. All potential targets for has-miR-150 were predicted using the StarBase database. A network of 66 targeted long noncoding RNAs (lncRNAs), including has-miR-150 and CASC1, was constructed (Figure 6(a)). The samples were divided into high and low expression groups based on their median expression levels of CASC1 and has-miR-150. KEGG analysis was also performed, and 37 significantly enriched pathways were associated with CASC1 and 67 significantly enriched pathways with hsa-miR-150. It was found that 10 KEGG pathways were associated with CASC1 and hsa-miR-150 (Figure 6(b)). In GSEA, pathways associated with CASC1 and has-miR-150 were visualized (Figures 6(c)–(d)). They were complement and coagulation cascades, drug metabolism cytochrome p450, hematopoietic cell lineage, Leishmania infection, metabolism of xenobiotics by cytochrome p450, natural killer cell-mediated cytotoxicity, proteasome, ribosome, systemic lupus erythematosus, and viral myocarditis.Figure 6
Analysis of CASC1 functions in bladder cancer. (a) ceRNA network of hsa-miR-150 associated withCASC1, where diamond nodes represent lncRNAs. (b) Venn diagram corresponding to the has-miR-150-related GSEA pathway and CASC1-related GSEA pathway. (c) Significant GSEA pathways in CASC1 enrichment analysis results. (d) Significant pathways in hsa-miR-150 enrichment analysis results.
(a)(b)(c)(d)


## 4. Discussion

CASC1 was detected through RNA-seq analysis to be critical for bladder cancer progression. And a negative correlation was observed between CASC1 and has-miR-150 expression in bladder cancer. CASC1 targeting with has-mir-150 was associated with bladder cancer progression.We analyzed the relationship betweenCASC1 and the extent of immune invasion and found a strong correlation between the extent of immune cell invasion of various tumors and CASC1. The infiltration of immune cells (CD8+ T cells, B cells, neutrophils, CD4+ T cells, DC, and macrophages) associated with LGG, LIHC, PRAD, and LUSC correlated positively with the expression of CASC1. In addition, tumor infiltration corresponding to the four immunosuppressive cells that promote the process of T cell rejection (MDSCs, M2-TAMs, CAFs, and Treg cells) was also significantly positively correlated with the CASC1 expression level. The overall survival, progression-free survival, and disease-free survival for patients with the CASC1 mutation were poorer than those without CASC1 mutation. The results agreed well with previously reported results. It has been previously reported that CASC1 is associated with the mitosis and microtubule polymerization of tumor cells. CASC1 and KRAS co-amplify in lung tumors. This is crucial to realize microtubule polymerization and for meeting spindle assembly checkpoints [6]. CASC1-deficient pulmonary tumor mice are sensitive to chemotherapy [27]. The Ras gene is a common gene that is associated with bladder cancer [28]. Inhibition of the expression of CASC1 is associated with increased survival of tumor patients with Ras mutation. The genetic link between CASC1 and KRAS may mitigate mitotic damage in the context of KRAS by increasing the expression level of microtubule-regulatory proteins associated with tumor genes. KRAS influences the survival prognosis of cancer patients [29]. The extent of KRAS mutation can be controlled by the cis-acting elements present in the Pas1 locus that is associated with CASC1 [30]. The KRAS mutation-specific T cells can kill KRAS mutated tumor cells, thereby inhibiting tumor growth [31]. Thus, we can speculate that a high level of CASC1 may result in the generation of an abnormal immune microenvironment.CASC1 is expressed at higher levels in many types of tumors, including bladder cancer, especially at the early stages of the disease. CASC1, a new regulator of mitotic spindle assembly in tumor cells, is necessary for tumor growth in vivo [6]. In the case of bladder cancer, CASC1-related KEGG pathways include complement and coagulation cascades, drug metabolism cytochrome p450, hematopoietic cell lineage, Leishmania infection, metabolism of xenobiotics by cytochrome p450, natural killer cell-mediated cytotoxicity, proteasome, ribosome, systemic lupus erythematosus, and viral myocarditis. Compliment has a significant effect on the working mechanism of numerous anticancer antibodies [32]. As reported by Chen et al., CYP450 regulates proliferation, cell cycle, and apoptosis [33]. Previous studies have shown that CYP450 is highly expressed in cancer tissues [34, 35]. CYP24A1 dysfunction of the CYP450 members was found to be associated with prostate cancer progression by Khan et al. [36]. There is a positive correlation between the degree of NK cell infiltration in tumors and prognosis [37]. Therefore, we hypothesized that complement and coagulation cascades, drug metabolism cytochrome p450, and natural killer cell-mediated cytotoxicity were the potential pathways through which CASC1 influenced the progression of bladder cancer.LncRNAs and microRNAs have been reported to be predictive biomarkers for invasion, progression, and metastasis prognosis in advanced bladder cancer [8]. Wang et al. constructed a ceRNA regulatory network containing 5 muscle-invasive bladder cancer-specific lncRNAs, 9 miRNAs, and 32 mRNAs [38]. The new lncRNAs were identified as potential prognostic biomarkers for muscle-invasive bladder cancer [38]. They are more susceptible to miRNA regulation because they are mainly found in cytoplasmic vesicles and intracellular matrix [39, 40]. We are the first to report that CASC1 can potentially be regulated by miR-150. It has been suggested that miR-150 may play a role in various types of tumor development. It is also associated with the regulation of ovarian cancer and the poor prognosis of NSCLC patients [41, 42]. In the case of breast cancer, miR-150 is regulated by lncRNA MAFG-AS1. This influences the proliferation and migration of breast cancer cells [43]. There was a significant negative correlation between CASC1 and the expression level of has-miR-150. The miRWALK database was analyzed to identify CASC1 that is has-miR-150 targeted. In our study, we hypothesized that the ceRNA regulatory network formed by miR-150 targeting CASC1 may lead to the progression of bladder cancer.Using bioinformatics tools, we found that miR-150 substantially influenced the development and progression of bladder cancer by targetingCASC1. CASC1 expression was found to be high in bladder cancer patients, and has-miR-150 expression level was negatively correlated with the expression of CASC1. CASC1 is also associated with immune and immunosuppressive cell infiltration into tumors. Cancer patients with CASC1 mutations had a worse survival prognosis than cancer patients without CASC1 mutations. This can be potentially attributed to the involvement of CASC1 in the tumor-regulation mechanism. In order to determine the role of the CASC1-related ceRNA regulatory network in bladder cancer progression, we explored the pathogenesis of bladder cancer further. The results can help develop methods that can be used to prevent the progression of bladder cancer and improve the prognosis of patients.Due to the fact that it is a retrospective study based on information available in different databases, its application is limited. Experimental confirmation may be lacking, and potential bias may occur. It is necessary to conduct in vitro and in vivo experiments, as well as analyze clinical data to confirm the inhibition and treatment of bladder cancer by has-miR-150 andCASC1 in the future.


## 5. Conclusion

There were high levels ofCASC1 expression in various tumor tissues, which were associated with immune cell infiltration and immunosuppressive cells, which also affected patient prognosis. Exosomes containing miR-150-targeted CASC1 may affect the progression of bladder cancer. Based on these findings, a molecular pathway can be developed to explain bladder cancer progression.



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*Source: 8100325-2022-07-05.xml*