Abstract
Bladder cancer (BC) is a malignant tumor that occurs in bladder mucosa. However, relationship between myosin light chain kinase (MYLK) and CALD1 and BC remains unclear. The BC datasets GSE65635 and GSE100926 were downloaded from gene expression omnibus by GPL14951 and GPL14550. Multiple datasets were merged and batched. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis was performed. gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome analysis, gene set enrichment analysis, immune infiltration analysis, survival analysis and Comparative Toxicogenomics Database were performed. TargetScan screened miRNAs that regulated central DEGs. 1026 DEGs were identified. According to GO analysis, DEGs were mainly enriched in cancer pathway, cGMP-PKG signaling pathway, Apelin signaling pathway and proteoglycans in cancer. The enrichment items are similar to GO and Kyoto Encyclopedia of Gene and Genome enrichment projects for DEGs, which were mainly enriched in cancer pathways and leukocyte trans-endothelial cell migration. Among enrichment projects of metascape, GO has regulation of the enzyme-linked receptor protein signaling pathway and silk-based process, as well as an enrichment network stained by enrichment terms and P values. Nine core genes (ACTA2, MYLK, MYH11, MYL9, ACTG2, TPM1, TPM2, TAGLN and CALD1) were obtained, which were highly expressed in tumor tissue samples and lowly expressed in normal tissue samples. Nine genes were associated with necrosis, inflammation, tumor, edema, and ureteral obstruction. MYLK and CALD1 are highly expressed in the BC. The higher expression of MYLK and CALD1, the worse prognosis.
Keywords: Bioinformatics, bladder cancer, CALD, MYLK
1. Introduction
Bladder cancer (BC) refers to the malignant transformation of a layer of mucosal cells covering the surface of the bladder.[1] Incidence of BC has gradually increased, rising in many countries, particularly in Europe, where an estimated 118,000 new cases and 52,000 deaths were reported in 2012.[2] It accounts for more than 70,000 new cases and 16,000 cancer-related deaths each year in United States.[3] The majority of BC is composed of urothelial cancer.[4] Smoking is the most established risk factor for BC.[5,6] The initial clinical manifestation of more than 90% of BC patients is hematuria. Bladder irritation symptoms, such as urgency, pain and dysuria, can occur first in up to 10% of patients with BC, while patients have no obvious gross hematuria.[7] The mortality rate of BC is still relatively high, with 5-year survival rate of more than 80% in the early stage, about 50% in the middle stage, and <20% in the late stage.[8] Because of the complexity of its pathogenic mechanism, the cause of BC is not clear.
As an important part of the development of life science, bioinformatics has been at the forefront of life science and technology research. In recent years, China biotechnology has developed by leaps and bounds, and bioinformation resources have also grown explosively. Bioinformatics reveals the biological significance represented by big data, which is a bridge between data and clinic. Represented by the analysis and reporting of gene detection data, bioinformatics plays a role in tumor treatment.[9,10]
This paper intends to use the bioinformatics technology to explore and analyze core genes between BC and normal tissues. The public dataset was used to verify significant role of myosin light chain kinase (MYLK) and CALD1 in BC.
2. Method
2.1. BC data set
The BC dataset GSE65635 and GSE100926 were downloaded from gene expression omnibus by GPL14951 and GPL14550. GSE65635 includes 8 BC and 4 normal tissue samples, GSE100926 includes 3 BC and 3 normal tissue samples.[11]
2.2. The de-batch processing
Merge and batch multiple data sets, GSE65635 and GSE100926 were merged using R software package. R software package in silicon merge was used to merge the data set, and the merge matrix was obtained. The R software package limma removes batch effect. The matrix batch effect after removal was obtained.
2.3. Screening of differentially expressed genes (DEGs)
Probe aggregation and background correction of merge matrix of GSE65635 and GSE100926 using R package “limma.” P value were adjusted using Benjamini-Hochberg method. The fold change is calculated using false discovery rate. The cutoff value of DEG is P <.05, fold change is >1.5 and false discovery rate < 0.05. And make a visual representation of the volcano.
2.4. Weighted gene coexpression network analysis (WGCNA)
Use de-batch and post-merge matrix of GSE65635 and GSE100926 to calculate median absolute deviation of each gene. Outlier genes and samples were removed by good sample gene method of WGCNA in R package. We calculated characteristic gene differences of modules, and selected tangent line for module tree view, incorporated part of modules.
2.5. Construction and analysis of protein-protein interaction (PPI) network
The STRING contains the predicted results using bioinformatics methods. The DEGs were input into STRING to construct PPI network and predict the core genes. PPI network was visualized, core genes are predicted by Cytoscape software. First of all, we import PPI network into the Cytoscape, and then find module with the best correlation through MCODE. MCC and MNC were used to calculate the best correlated genes. Finally, the list of core genes was obtained after visualization.
2.6. Functional enrichment analysis
Gene ontology (GO) analysis is a computational method to evaluate gene functions and biological pathways, and it is a key step to endow sequence information with practical biological significance. Kyoto Encyclopedia of Gene and Genome (KEGG) is an online database dedicated to collecting information on genomes, molecular interaction networks, enzyme catalytic pathways, and biochemical products. The genomic information and gene function were linked, and gene function was systematically analyzed. The list of differential genes screened by Wayne map was input into KEGG rest API obtained latest KEGG Pathway gene annotation. Gene set enrichment results were obtained using R package cluster Profiler.
Metascape (http://metascape.org/) can realize cognition of gene or protein function, and can be visually exported. We used Metascape database to analyze functional enrichment of the above differential gene list and derive it.
2.7. Gene set enrichment analysis (GSEA)
GSEA is based on level-specific gene probes that evaluate data from microarrays and is a way to uncover genomic expression data through fundamental knowledge. The samples were divided into BC and normal tissue. 5 is minimum gene set and 5000 is maximum gene set, 1000 resampling times.
2.8. Gene expression heat map
The R-packet heatmap was used to map the expression of core genes in PPI network in GSE65635 and GSE100926, and to visualize the expression difference of core genes in BC and normal tissue samples. We also used R software package p receiver operator characteristic curve (ROC) (version1.17.0.1) for ROC analysis.
2.9. Immune infiltration analysis
The CIBERSORT (http://CIBERSORT.stanford.edu/) is a very common method for calculating immune cell infiltration. We applied the integrated bioinformatics method, used the CIBERSORT software package to analyze the de-batch merging matrix of GSE65635 and GSE100926, and immune cell abundance was estimated by deconvoluting the expression matrix of immune cell subtypes by linear support vector regression principle.
2.10. Survival analysis
The clinical survival data and corresponding gene expression data of BC were found from the cancer genome atlas, and the ability of core genes to predict survival was verified. The R software package maxstat was used to calculate the best cutoff value of the risk score of core genes. Accordingly, the patients were divided into 2 groups. The R package survival was used to analyze the difference in prognosis between the 2 groups, and the log-rank test was used to evaluate the significant difference in prognosis between different groups. Forest plots of core genes were made using the R package forest.
2.11. Comparative Toxicogenomics Database (CTD) analysis
CTD is a powerful public database, which predict gene/protein relationships with disease, are used to identify integrated chemical diseases, chemical genes, and gene disease interactions to predict new associations and generate extended networks. We input core gene into CTD, find disease most related to core gene. Excel was used to draw radar map of differential expression of each gene.
2.12. The miRNA
TargetScan (www.targetscan.org) can predict and analyze miRNA and target genes. Screening of miRNAs regulating central DEGs was performed using TargetScan in this study.
3. Results
3.1. DEGs analysis
1026 DEGs were identified according to debatching merge matrix of GSE65635 and GSE100926 (Fig. 1A).
Figure 1.
Differential gene analysis. (A) A total of 1026 DEGs. (B) WGCNA and DEGs and take the intersection to create and analyze the protein-protein interaction network. DEGs = differentially expressed genes, WGCNA = weighted gene co-expression network analysis.
3.2. Functional enrichment analysis
3.2.1. Differentially expressed genes.
We analyzed DEGs by GO and KEGG. According to GO analysis, DEGs were mainly enriched in cancer pathway, cGMP-PKG signal pathway, Apelin signal pathway and proteoglycan in cancer (Fig. 2A, B, E, F).
Figure 2.
Functional enrichment analysis (A, B, E, F) DEGs. (C, D, G, H) GSEA. DEGs = differentially expressed genes, GSEA = gene set enrichment analysis.
3.2.2. Gene set enrichment analysis.
A genome-wide GSEA enrichment analysis was performed to search for possible enrichment items in non-DEGs. The enrichment items are similar to GOKEGG enrichment items of DEGs, mainly enriched in cancer pathway and leukocyte migration across endothelial cells (Fig. 2C, D, G, H).
3.3. Metascape enrichment analysis
GO has the regulation of enzyme-linked receptor protein signal pathway and actin silk-based process (Fig. 3A), and an enrichment network stained by enrichment term and P value (Fig. 3B and C).
Figure 3.
Metascape enrichment analysis.
3.4. Weighted gene coexpression network analysis
The network topology is analyzed and the soft threshold power of WGCNA is set to 9 (Fig. 4A and B). The hierarchical clustering tree of all genes was constructed, 3 important modules were generated (Fig. 4C). Then analyze interaction between these modules (Fig. 4D). The module-phenotypic correlation heat map (Fig. 5A) and the GS-MM correlation scatter map of related hub genes (Fig. 5B–G) were generated. We also draw the Wayne diagram of differential genes screened by WGCNA and DEGs and take intersection to create and analyze PPI network (Fig. 1B).
Figure 4.
WGCNA. (A) β = 9,0.86. (B) β = 9,81.02. (C) The hierarchical clustering tree of all genes was constructed, and 3 important modules were generated. (D) The interaction between these modules. WGCNA = weighted gene co-expression network analysis.
Figure 5.
WGCNA. (A)The module-phenotypic correlation heat map. (B–G)The GS-MM correlation scatter map of related hub genes were generated. WGCNA = weighted gene co-expression network analysis.
3.5. Construction and analysis of PPI network
DEGs PPI network was constructed and analyzed by Cytoscape software (Fig. 6A). The core gene cluster (Fig. 6B) was obtained. Two different algorithms were used to identify central genes (Fig. 6C and D). Wayne graph was used to merge (Fig. 6E). Nine core genes (ACTA2, MYLK, MYH11, MYL9, ACTG2, TPM1, TPM2, TAGLN, CALD1) were obtained.
Figure 6.
Construction and analysis of protein-protein interaction (PPI) network. (A)DEGs PPI network (B) the core gene cluster (C) MCC was used to identify central genes (D) MNC was used to identify central genes (E) Wayne graph was used to merge. DEGs = differentially expressed genes.
3.6. Relationship between prognostic score and gene expression
We obtained the prognosis score relationship map and the differential heat map of core gene expression between BC and normal tissue samples. It was found that survival time and survival rate of the low-risk group were significantly higher than those of the high-risk group (Fig. 7A and B). We can also visualize heat map of expression of the core gene in the sample (Fig. 7C) and the ROC curve of the risk score (Fig. 7D). We found that the core gene (ACTA2, MYLK, MYH11, MYL9, ACTG2, TPM1, TPM2, TAGLN, CALD1) is highly expressed in tumor tissue samples and low expression in normal tissue samples (Fig. 7C).
Figure 7.
Prognostic score relationship and gene expression thermograph. (A, B)The survival time and survival rate in the low-risk group were significantly higher than those in the high-risk group. (C) Visualize the heat map of the expression of the core gene in the sample. (D) The ROC curve of the risk score.
3.7. Immune infiltration analysis
We analyzed the de-batch merging matrix of GSE65635 and GSE100926 using CIBERSORT software package, and obtained the proportion of immune cells in the whole gene expression matrix (Fig. 8A) and the heat map of immune cell expression in the data set (Fig. 8B). We also analyzed the correlation of infiltrating immune cells and obtained the co-expression pattern among immune cell components (Fig. 8C).
Figure 8.
Immune infiltration analysis. (A) The proportion of immune cells in the whole gene expression matrix. (B) The heat map of immune cell expression in the data set. (C) The co-expression pattern among immune cell components.
3.8. Survival analysis
We obtained the forest map of ACTA2, MYLK, MYH11, MYL9, ACTG2, TPM1, TPM2, TAGLN, CALD1 associated with BC (Fig. 9A) and the survival curve of 9 core genes (Fig. 9B–J). The box diagram of the core gene in BC was also obtained (Fig. 10).
Figure 9.
Survival analysis. (A) The forest map of core genes associated with bladder cancer. (B–J) The survival curve of 9 core genes.
Figure 10.
Survival analysis. The box diagram of the core gene in bladder cancer.
3.9. CTD analysis
Find diseases related to core genes by CTD. Nine genes (ACTA2, MYLK, MYH11, MYL9, ACTG2, TPM1, TPM2, TAGLN, CALD1) were found to be associated with necrosis, inflammation, tumor, edema and ureteral obstruction (Fig. 11).
Figure 11.
CTD analysis. Nine genes (ACTA2, MYLK, MYH11, MYL9, ACTG2, TPM1, TPM2, TAGLN, CALD1) were associated with necrosis, inflammation, tumor, edema and ureteral obstruction. CTD = Comparative Toxicogenomics Database, MYLK = myosin light chain kinase.
3.10. miRNA prediction and functional annotation related to hub gene
The hub genes was entered into TargetScan to search for relevant miRNA (Table 1). The related miRNA of ACTA2 is hsa-miR-27a-3p, the related miRNA of hsa-miR-27b-3p; MYLK is hsa-miR-129-5p; the related miRNA of MYH11 is hsa-miR-124-3p.1; the related miRNA of MYL9 is hsamiR-134-5p, hsa-miR-3118, miRNA is related to hsa-miR-760; ACTG2, the related miRNA of TPM1 is hsa-miR-183-5p.1. The related miRNA of TPM2 is hsa-miR-193b-3p, the related miRNA of hsa-miR-193a-3p; TAGLN is hsa-miR-223p; CALD1, and the related miRNA of hsa-miR-223p; CALD1 is hsa-miR-19a-3p and hsa-miR-19b-3p.
Table 1.
A summary of miRNAs that regulate hub genes.
| Gene | MIRNA | |||
|---|---|---|---|---|
| 1 | ACTA2 | hsa-miR-27a-3p | hsa-miR-27b-3p | |
| 2 | MYLK | hsa-miR-129-5p | ||
| 3 | MYH11 | hsa-miR-124-3p.1 | ||
| 4 | MYL9 | hsa-miR-134-5p | hsa-miR-3118 | hsa-miR-760 |
| 5 | ACTG2 | hsa-miR-670-3p | ||
| 6 | TPM1 | hsa-miR-183-5p.1 | ||
| 7 | TPM2 | hsa-miR-193b-3p | hsa-miR-193a-3p | |
| 8 | TAGLN | hsa-miR-22-3p | ||
| 9 | CALD1 | hsa-miR-19a-3p | hsa-miR-19b-3p | |
MYLK = Myosin light chain kinase.
4. Discussion
Incidence ranks of BC is the first among the malignant tumors of the urinary system.[12,13] There are many treatments for BC, such as transurethral cystectomy, transurethral laser surgery and photo dynamics. At present, the most effective treatment is transurethral cystectomy.[14] It is very important to actively study targeted drugs for the treatment and prevention of BC. The main result of this study is that MYLK and CALD1 are highly expressed in the BC. The higher the expression of MYLK and CALD1, the worse the prognosis.
MYLK is a member of immunoglobulin gene superfamily that encodes MYLK.[15] Studies have shown that MYLK is related to the proliferation of BC through the extracellular signal- regulated kinase (ERK1/2) and P38 pathway.[16] In addition, it is related to the regulation of tumor invasiveness and metastasis of BC. Altered MYLK is related to malignant transformation of normal cells and the migration and invasion of tumor cells.[17] Zhong et al screened expression profiles of circRNA and mRNA in BC by microarray analysis. The level of circRNA-MYLK is related to progress of BC staging and grading. Functionally, it was found that ectopic expression of circRNA-MYLK accelerated HUVEC cell proliferation, migration, tube formation and rearrangement of the cytoskeleton. In addition, up-regulation of circRNA-MYLK promotes epithelial-mesenchymal transformation. However, circRNA MYLK knockdown decreased cell proliferation, motility and induced apoptosis. Up-regulation of circRNA-MYLK promotes the growth, angiogenesis and metastasis of BC xenografts.[18] Similarly, Wenxin et al found that the circMYLK was highly expressed in BC tissues and cells. Down-regulation of CircMYLK inhibits migration and invasion of BC cells, and promotes cell apoptosis and cell cycle arrest, which shows the importance of CircMYLK in the pathogenesis of BC.[19] In Jiang Siqi study, 124 differentially coexpressed genes were identified. They are mainly related to movement, invasion and metastasis of BLCA cells in the process of muscle system.[20] In the study of Zhenhua et al, 3 DEARG (AURKA, ACTC1, MYLK) and 3 DEIRG (PDGFD, PDGFRA, TNC) were proved to be potential prognostic biomarkers of BLCA patients by bioinformatics methods.[21] Based on the above literature, we speculate that MYLK may play a role in cell migration and tumor metastasis of BC, which may be a new therapeutic target and prognostic marker of BLCA.
CALD1 has 2 main subtypes.[22] Caldesmon can bind to calmodulin and actin to regulate smooth muscle contraction.[23] It may play a role in the tumor metastasis and proliferation. Tumor-specific splicing alterations of CALD1 have also been identified in colon and prostate cancers, which may represent a series of cancer-related splicing events.[24] Zheng et al reported the differential expression of splicing variants of CALD1 in glioma neovascularization and normal cerebral microvascular system. The activated isoform of CALD1 acts synergistically on cellular contractility of vascular components and enhances microvascular permeability, thereby promoting tumor cell extravasation and migration.[25] CALD1 is involved in regulation of muscle-and non-muscle-induced contraction and has been reported to regulate cell migration, invasion and proliferation as well as formation of stress fibers. In the study of Cheng et al, CALD1 and PD-L1 were found to be highly expressed in BC. In addition, CALD1 may promote expression of PD-L1 by the activating JAK/STAT pathway, thus affecting the progression and prognosis of BC.[26] Mingxin et al found that the overexpression of CALD1 in primary NMIBC is significantly related to the tumor progression, and the possible mechanism of LCAD activity is related to the increased cell movement and invasiveness of BC cells.[27] Yun et al confirmed that CALD1, CNN1 and TAGLN are potential prognostic molecular markers of BC, and CALD1 is significantly related to the prognosis of patients.[28] We speculate that CALD1 may play a role in migration, invasion and proliferation of BC cells and may be a new therapeutic target and prognostic marker for BC.
The overexpression of MYLK and Caldesmon 1 (CALD1) in BC may have implications for the understanding and treatment of the disease. Investigating the functions of MYLK and CALD1 genes can contribute to a deeper understanding of the biological characteristics of BC. The elevated expression of MYLK and CALD1 can serve as potential biomarkers for BC, and their detection may aid in identifying the presence and severity of the disease. It may be associated with the clinical condition and prognosis of BC, used to assess prognosis and disease progression risk. This overexpression may provide new targets for treatment, as therapeutic strategies can target these genes or their products to reduce cancer cell proliferation and invasion. Researchers can explore the development of gene therapy approaches related to MYLK and CALD1 to reduce their overexpression or restore normal gene regulation.
4.1. The limitations of the study
Although this paper has carried out rigorous bioinformatics analysis, there are still some shortcomings. Animal experiments with overexpression or knockdown of the gene were not performed in this study to further verify the function.
5. Conclusions
MYLK and CALD1 are highly expressed in the patients with BC, may play a significant role in development of BC through cell migration, invasion and proliferation. MYLK and CALD1 may be used as molecular targets of BC, and provide a basis for the study of the mechanism of BC.
Author contributions
Conceptualization: Hui Jin.
Data curation: Bin Liu, Xin Guo, Xi Qiao, Liman Yang, Xiaosen Song, Tingting Jin.
Formal analysis: Hui Jin, Bin Liu, Xi Qiao, Wenpeng Jiao, Liman Yang, Xiaosen Song, Yueyue Wei, Tingting Jin.
Methodology: Hui Jin, Xin Guo, Wenpeng Jiao, Xiaosen Song, Yueyue Wei.
Project administration: Hui Jin.
Software: Bin Liu, Wenpeng Jiao, Liman Yang.
Writing – original draft: Xiaosen Song, Tingting Jin.
Writing – review & editing: Hui Jin.
Abbreviations:
- BC
- bladder cancer
- CTD
- Comparative Toxicogenomics Database
- DEGs
- differentially expressed genes
- GO
- gene ontology
- GSEA
- gene set enrichment analysis
- KEGG
- Kyoto Encyclopedia of Gene and Genome
- MYLK
- myosin light chain kinase
- PPI
- protein-protein interaction
- ROC
- receiver operator characteristic curve
- WGCNA
- weighted gene co-expression network analysis.
JH and LB contributed equally to this work.
This study was approved by the Ethics Committee of The Fourth Hospital of Hebei Medical University.
The authors have no funding and conflicts of interest to disclose.The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Jin H, Liu B, Guo X, Qiao X, Jiao W, Yang L, Song X, Wei Y, Jin T. MYLK and CALD1 as molecular targets in bladder cancer. Medicine 2023;102:47(e36302).
Contributor Information
Hui Jin, Email: jinting1632023@163.com.
Bin Liu, Email: liubinsy123@163.com.
Xin Guo, Email: tianqi11211216@163.com.
Xi Qiao, Email: jayjoe123@sina.com.
Wenpeng Jiao, Email: jiaowenpeng163@163.com.
Liman Yang, Email: yangliman2310@163.com.
Xiaosen Song, Email: songxiaosen@qq.com.
Yueyue Wei, Email: 990142520@qq.com.
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