In silico analysis and validation the cancer- associated fibroblasts related gene CAMK4 promotes bladder cancer progression

doi:10.21203/rs.3.rs-4438820/v1

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In silico analysis and validation the cancer- associated fibroblasts related gene CAMK4 promotes bladder cancer progression

https://doi.org/10.21203/rs.3.rs-4438820/v1

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Background:Cancer-associated fibroblasts (CAFs) are crucial in the regulation of cancer cell biological properties through complex and dynamic communication networks. However, the mechanism of action of CAFs in bladder cancer (BCa) remains elusive.

Results:This study integrated transcriptome data from multiple datasets and constructed an ensemble of genes associated with CAFs through a series of algorithms. It further categorized BCa into two molecular subtypes, distinguished by their immune cell infiltration and immune-related characteristics. CAMK4 was subsequently selected for further validation, and it was found that CAMK4 promoted the tumor-promoting ability of BCa specifically in terms of proliferative, migratory, and invasive capacities and also facilitated the development of epithelial-mesenchymal transition (EMT).

Conclusions: To sum up, our signature and its derived subtype facilitates a more accurate identification of potential candidates for immunotherapy among BCa patients. In addition, CAMK4 may be a promising target for BCa therapy.

Biological sciences/Genetics/Cancer genomics

Biological sciences/Cancer/Tumour biomarkers

CAFs

bladder cancer

CAMK4

EMT

Bladder cancer (BCa) is a highly prevalent cancer globally, with 57,3278 new diagnoses and 21,2526 fatalities in 2020. Among these cases, males constitute 74.7% of the overall burden [1]. At the time of diagnosis, 70% of individuals were diagnosed with non-muscle-invasive BCa (NMIBCa), whereas 25% presented with muscle-invasive BCa (MIBCa), and 5% exhibited distant metastases, each with different molecular drivers[2, 3]. Although little advancements have been made in the clinical treatment of BCa in the preceding three decades, there has been a substantial shift in this trajectory in recent years[4]. The utilization of sequencing and gene expression investigations has led to the identification of DNA, RNA, and protein biomarkers for numerous BCa. This offers an opportunity for a structural shift in the approach to BCa diagnosis and the detection of recurrences. Despite the development of novel therapies, such as small molecule drugs targeting fibroblast growth factor receptors, anti-PD- (L) 1 antibodies, and drug-antibody conjugates in recent years, first-line treatments have not changed.

In the last decade, substantial attention has been directed toward comprehending the significance of the tumor microenvironment (TME) in cancer development. Basic histological examination has revealed that papillary low-grade tumors exhibit a lower proportion of stromal cells, a proportion that escalates with advancing stage and grade. The TME encompasses components like the extracellular matrix (ECM), adipocytes, carcinoma-associated fibroblasts (CAFs), blood vessels, smooth muscle, nerves, and immune cells. BCa transcriptome analysis showed that the abundance ratio of immune and stromal cells was directly associated with reduced survival times in the Cancer Genome Atlas (TCGA) cohort[5, 6].

CAFs represents a heterogeneous group of cells with an origin that remains a subject of debate. They can potentially emerge from resident tissue cells or circulating precursor cells derived from the bone marrow. These precursor cells undergo differentiation into CAF upon reaggregation at the tumor site[7]. Furthermore, CAF-like phenotypes can arise through transdifferentiation of pericytes, endothelial cells, and epithelial cells[8, 9]. CAFs is closely associated with tumorigenesis by secreting various factors, including collagen, matrix metalloproteinases (MMPs), and chemokines. In spite of the wide relevance of CAFs in the field of cancer biology, research on their role in BCa remains limited.

In this study, a robust correlation between CAFs and BCa stage was initially discerned through an analysis of BCa transcriptome data derived from the publicly available Gene Expression Overview (GEO) and TCGA databases. Subsequently, by applying the WGCNA algorithm, the HUB gene exhibiting the closest relationship with CAFs-matrix score was identified. Significantly, the key gene, CAMK4, was identified through predictive analysis. The expression of CAMK4 in BCa cells was subsequently regulated, and related cellular experiments were validated, along with the preliminary exploration of mechanistic experiments. The outcomes of the current research highlight that CAMK4 could potentially be a novel target for predicting BCa progression and efficacy.

2.1 Acquisition and Processing of Raw Data

Data was retrieved from the Cancer Genome Atlas (TCGA) database, comprising the Fragments Per Kilobase of Transcript Per Million Mapped Reads (FPKM) format RNA-seq data for 424 patients with BCa and their associated clinical features (TCGA-BCa)[10]. In cases where multiple Ensembl IDs were mapped to the same gene, the data were averaged. GEO database downloads data obtained normalized expression data and clinical features in GSE13507 versus GSE31684 datasets. All sequencing data acquired from the GEO database underwent processing involving log-quadratic transformation, background adjustment, and normalization. Subsequently, following the exclusion of individuals lacking survival information, a total of 407, 166, and 93 patients with BCa were retained for further analysis in the TCGA database, GSE13507 and GSE31684, respectively.

2.2 CAF Infiltration and Immune Score Calculation

CAF abundance scores were calculated from Estimate the Proportion of Immune and Cancer cells (EPIC) and MCPcounter algorithms[11]. The determination of immune scores and immune cell scores was performed with the "gene signature enrichment–based xCell algorithm (xCELL)"[12], all implemented through the R package IOBR (v0.99.9).

2.3 WCGNA Analysis

Weighted gene co-expression network analysis (WGCNA), also referred to as Weighted correlation network analysis, is a systems biology technique employed to characterize the patterns of gene associations across various samples. This method facilitates the identification of highly synergistically changing gene sets for mining co-expressed coding genes and co-expression modules [13]. Initially, the expression profiles of protein-coding genes were isolated from the expression data available in the TCGA-BCa and GSE13507 databases. Subsequently, adjacency matrices were clustered utilizing topological overlap measure (TOM) and dissimilarity (1-TOM) between genes. Additionally, the Pearson correlation coefficient was employed to compute the Pearson correlation between modules and CAF infiltration and immune scores. Modules with the highest correlation between the two were selected, and the genes within these modules from both TCGA-BCa and GSE13507 were intersected to obtain hub genes.

2.4 Molecular Subtype Identification Based on CAFs-immune Related Genes

Consistency clustering is a resampling-based method used to identify individual members, assign them to respective subgroup numbers, and validate the resulting clusters. To discover molecular subtypes based on CAFs-immune-related important genes, the ConensusClusterPlus package in R was used [14].

2.5 Immunocorrelation Analysis of Molecular Subtypes

The expression data of TCGA-BCa and GSE13507 datasets were analyzed using ssGSEA and ESTIMATE with the "GSVA" and "ESTIMATE" packages. Differences in the expression of different subtypes in related immune cells, immune function, and immune checkpoints were then compared.

2.6 Screening Prognostic Related Baseline Factors

Genes significant for survival were selected through one-way COX survival analysis using the ‘'Limma'' package.

2.7 RT-qPCR

Total RNA extraction from tissues and cells involved the use of TRIzol reagent (R0016, Beyotime, China), followed by reverse transcription into cDNA utilizing the Hifair® II 1st Strand cDNA Synthesis Kit (gDNA digester plus) (11121ES60, YEASEN, China). The RNA was diluted tenfold, and 2 µL of the cDNA product served as the template for PCR amplification, employing Hieff® qPCR SYBR Green Master Mix (No Rox) (11201ES03, YEASEN, China). Gene quantification was normalized to GAPDH utilizing the 2-ΔΔCt approach. The primers (as stated below) were retrieved from the PrimerBank database: human CAMK4, forward (F) 5′- AGTTCTTCTTCGCCTCTCACA − 3′; reverse (R): 5′- CATCTCGCTCACTGTAATATCCC − 3′; human GAPDH, forward (F) 5′- CTGGGCTACACTGAGCACC − 3′; reverse (R): 5′- AAGTGGTCGTTGAGGGCAATG − 3′; human CDH1, forward (F) 5′- CGAGAGCTACACGTTCACGG − 3′; reverse (R): 5′- GGGTGTCGAGGGAAAAATAGG − 3′; human CDH2, forward (F) 5′- AGCCAACCTTAACTGAGGAGT − 3′; reverse (R): 5′- GGCAAGTTGATTGGAGGGATG-3′; human VIM, forward (F) 5′- GACGCCATCAACACCGAGTT − 3′; reverse (R): 5′- CTTTGTCGTTGGTTAGCTGGT-3′.

2.8 Cell culture and transfection

For this study, healthy bladder epithelial cell lines (SV-HUC-1) and BCa cell lines (UMUC3, T24, 5637) were chosen. These cell lines were procured from Procell Life Science&Technology Co.,Ltd. (Wuhan, China). Cells were grown in RPMI-1640 medium (Gibco, Carlsbad, CA) comprising 10% FBS, 10 µg/mL streptomycin, and 100 U/mL penicillin and maintained at 37°C in a 5% CO2 atmosphere.

Transfection of the cells was executed utilizing Lipofectamine 2000 reagent (Invitrogen, Carlsbad, CA) once they reached a confluence of 75%. Transfection efficiency was subsequently verified by RT-qPCR 48 hours after transfection. The CAMK4 overexpression plasmid and siRNA were procured from GenePharma (Shanghai, China) at a 50 ng/mL concentration.

2.9 Western blot analysis

Cells were lysed utilizing an enhanced RIPA lysis buffer that contained protease inhibitors. Protein concentrations were subsequently assessed utilizing the BCA Kit (Beyotime, China). Proteins were separated through electrophoresis on 10% sodium dodecyl sulfate-polyacrylamide gels and subsequently electrotransferred onto polyvinylidene fluoride membranes. Membranes were blocked using 5% BSA and diluted CAMK4 (Rb, 4032, 1:1000, CST), E-cadherin (Rb, 24E10, 1:1000, CST), N-cadherin (Rb, D4R1H, 1:1000, CST), vimentin (Rb, 5741, 1:1000, CST), and GAPDH (Rb, 5174, 1:3000, CST). The following day, membranes were incubated once more, this time with HRP-labeled goat anti-mouse secondary antibody (ab205719; 1:1000; CST) or goat anti-rabbit secondary antibody (ab205718, 1:2000, CST) for one hour at room temperature. Immune complexes on the membrane were visualized using an ECL reagent (P0018s, Beyotime, China).

2.10 EdU assay

Cells to be detected were placed in 24-well plates. After incubation with 100µL of staining solution (C0071S, Beyotime, China) for 30 min, the nuclei were visualized using DAPI and filmed with a fluorescence microscope.

2.11 Wound healing assay

Cells to be examined were cultured in a 6-well plate. When the cell density reached 90%, a wound of uniform thickness was created between them. Subsequently, images were acquired using an inverted microscope. The cells were then cultured with 1% FBS medium for 48 hours, followed by additional photographs to observe the wound healing process.

2.12 Transwell assay

Matrigel was applied to coat the bottom membrane surface of the transwell upper chamber. A medium comprising 10% FBS was introduced into the lower chamber. A 100µL of serum-free cell suspension was introduced to the upper chamber for 24 hours at 37 ℃, enabling the removal of non-invading cells from the Matrigel membrane surface. Cells underwent fixation with 4% paraformaldehyde and were subjected to staining with 1% purple crystals. The cells were then observed and quantified under an inverted light microscope.

2.13 Ethical statement

The Ethics Committee of Jingzhou Hospital Affiliated to Yangtze University reviewed and approved the study in strict accordance with the Declaration of Helsinki. Prior to sample collection, informed consent was obtained from all participants or their respective families.

2.14 Clinical sample collection

Cancer tissue and adjacent healthy tissue were surgically obtained from 12 individuals with BCa at Jingzhou Hospital Affiliated to Yangtze University from April 2023 to October 2023. This group comprised nine males and three females, with ages ranging from 42 to 79 years and a mean age of (62.54 ± 11.20) years. Four pairs of BCa tissue and adjacent healthy tissue samples were chosen for mRNA and protein analysis. The selection of these samples was verified through postoperative pathological experiments. It is important to note that none of the patients had undergone antineoplastic therapy before their surgical procedures.

2.15 Statistical analysis

Data were processed employing SPSS 21.0 software (IBM Corp., Armonk, NY). Measured data are expressed as ± standard deviation. Paired or unpaired t-test was employed to compare data between the two groups. One-way analysis of variance (ANOVA) and Tukey's posttest were employed for comparison of data between multiple groups. Data from different time points were compared utilizing Bonferroni corrected repeated measures analysis of variance. Pearson correlation was utilized to assess the correlation of outcome measures. In all statistical analyses, P < 0.05 was deemed as a statistically significant value.

3.1 Positive Correlation Between CAF Score and T Stage in BCa Patients

Initially, CAF infiltration scores were computed using EPIC and MCPcounter methods in the GSE13507, GSE31684 and TCGA databases. Subsequently, CAF scores of BCa patients with varying T stages in these three datasets were compared, revealing a direct relationship, i.e., the higher the CAF score, the higher the T stage (Fig. 1).

3.2 Analysis of the correlation between CAFs score and immune cells

Pearson correlation analysis was performed between CAFs scores and immune cell scores for TCGA-BCa, GSE13507, and GSE31684, and it was found that CAFs was positively correlated with important immune cells, particularly B cells, and CD4 + cells (Fig. 2A-2C).

3.3 WGCNA analysis and identification of hub module gene.

The expression matrix of mRNA from BCa patients was extracted from TCGA-BCa and GSE13507, and both were analyzed using the WGCNA method. A scale-free network was constructed, with the soft threshold (β) of 6 and a scale-free index of 0.9 in TCGA-BCa (Fig. 3A), and the same threshold of 6 with a scale-free index of 0.9 was applied in GSE13507 (Fig. 3B), as well as a favorable mean value. After applying the criteria for dynamic shear trees, each network yielded a minimum connectivity module of 60 genes. Once the gene modules were determined through the dynamic shearing method, signature genes for each module were computed. Subsequently, the modules were subjected to clustering, merging closer modules into new ones. The final hierarchical clustering tree in TCGA-BCA showed that 12 co-expression modules co-clustered (Fig. 3C), and the brown module exhibited positive association with CAFs score (Cor = 0.46, P = 2e-22), and ImmuneScore (Cor = 0.78, P = 9e-83) (Fig. 3E). Twenty-two co-expression modules were co-clustered in GSE13507 (Fig. 3D), and the brown module was positively correlated with CAFs score (Cor = 0.86, P = 4e-57), and ImmuneScore (Cor = 0.51, P = 9e-14) (Fig. 3F). Two brown modules yielded 1645 and 1696 genes, respectively. The gene sets in the two modules were intersected to yield 146 common genes (Supplementary table 1). Following this, subsequent analysis was conducted on these 146 genes.

3.4 HUB Gene-based Molecular Subtype and Immunocorrelation Analysis

Utilizing the 146 hub genes for cluster analysis, the most optimal clustering was achieved by segregating BCa patients into two subgroups within GSE13507 cohort. This division exhibited improved internal consistency and stability in the subgroups (Fig. 4A). Subsequently, differences in immune infiltration between the two subtypes were analyzed using different algorithms. Significant differences were identified between the two clusters in immune cells and immune-related functions or pathways (Fig. 4B-4D). The findings indicated that subtype B was associated with more immune cell infiltration, such as B cells, CD8 + T cells, dendritic cells, and macrophages. Similarly, subtype B exhibited heightened immune function, along with higher expression levels of immune checkpoints compared to subtype 1. The same result was found in the TCGA-BCa (Fig. 4E). Interestingly, it was observed that BCa patients in the B subtype were much more sensitive to immunotherapy than those in the A subtype in both cohorts (P < 0.001; Fig. 4F-4H). These findings underscore the reference significance associated with the HUB gene-based classification.

3.5 Screening Critical Genes and Verifying Their Expression

To identify genes warranting further investigation, univariate Cox survival analysis was conducted on the 146 HUB genes in both the TCGA and GSE13507 cohorts. The intersection of the results revealed three common genes (ALDH1A1, CAMK4, DEGS1) characterized by comprehensive HR and P-values (Supplementary table 2). Ultimately, CAMK4 was chosen for an in-depth study. Initially, the clinical relevance of CAMK4 in human BCa was evaluated. The mRNA level of CAMK4 expression in cancer tissues (T) and their corresponding non-tumor adjacent tissues (N) from BCa patients was evaluated utilizing qPCR. The outcomes demonstrated a considerable elevation in CAMK4 expression in cancer tissues as opposed to adjacent non-cancerous tissues (Fig. 5A). Elevated CAMK4 expression was additionally corroborated at the protein level through western blot analysis (Fig. 5B). Similarly, BCa cells cultured in vitro were examined. The outcomes demonstrated considerable elevation in CAMK4 expression in BCa cells when compared to SVHUC1 human ureteral epithelial cells (Fig. 5C, 5D). In conclusion, CAMK4 exhibits abnormally high expression in BCa tissues and cells, indicating a potential association with an unfavorable prognosis in individuals with BCa.

3.6 CAMK4 overexpression promotes BCa cell proliferation, migration, and invasion

To further validate the role of CAMK4 on the advancement of BCa, CAMK4 was transfected for overexpression in UMUC3 and T24 cells, respectively. The proliferation rate of CAMK4-overexpressing BCa cells was assessed using a CCK8 assay. The findings revealed that the elevated expression of CAMK4 considerably enhanced the proliferative ability of UMUC3 and T24 cells (Fig. 6A). The elevated expression of CAMK4 also promoted proliferative ability of UMUC3 and T24 cells in colony-forming assays (Fig. 6B). Furthermore, validation was carried out using an EDU assay, and the results similarly demonstrated that elevated CAMK4 expression could enhance the proliferation of BCa cells (Fig. 6C). Collectively, these findings highlight that CAMK4 can enhance the proliferation of BCa cells in vitro.

Subsequently, the migration and invasion of BCa cells were examined. Initially, the migratory ability of BCa cells was verified using a wound healing assay, which revealed that BCa cells overexpressing CAMK4 exhibited improved wound healing after 48 hours of culture (Fig. 6D). Additionally, the results from the transwell assay indicated that BCa cells, following CAMK4 overexpression, displayed increased invasion capabilities (Fig. 6E). These pieces of evidence indicate that CAMK4 can facilitate the migration and metastasis of BCa cells in vitro, suggesting a positive association between CAMK4 and the progression of BCa.

3.7 Knockdown of CAMK4 suppressed proliferative, migratory, and invasive capacities of BCa cells

To further examine the role of CAMK in BCa cells, CAMK4 expression was knocked down using siRNA in BCa cells 5637 and T24. Similar to the previous overexpression experiments, the study assessed the migratory, invasive, and proliferative capacities of BCa cells after CAMK4 knockdown. The outcomes revealed that the proliferative capacity of BCa cells was considerably inhibited following silencing of CAMK4 (Fig. 7A-7C). Similarly, the invasive and migratory abilities of BCa cells were also considerably reduced upon knockdown of CAMK4 (Fig. 7D, 7E). These results collectively indicate that silencing of CAMK4 can considerably suppress migratory, invasive, and proliferative abilities of BCa cells in vitro.

(A) The cell proliferation of 5637and T24 cells after CAMK4 knockdown determined with CCK8 assay. (B) The cell proliferation of 5637 and T24 cells after CAMK4 knockdown determined with colony formation assay. (C) The cell proliferation of 5637 and T24 cells after CAMK4 knockdown determined with EDU assay. (D) The cell migration of 5637 and T24 cells after CAMK4 knockdown determined with wound healing assay. (E) The cell invasion of 5637 and T24 cells after CAMK4 knockdown determined with Transwell assay. *p < 0.05, **p < 0.01, ***p < 0.001.

3.8 CAMK4 Upregulates EMT-Related Genes

The evidence suggests that epithelial-mesenchymal transition (EMT) is a pivotal process in tumor metastasis. Several proteins, including E-cadherin (CDH1), N-cadherin (CDH2), and vimentin (VIM), are utilized as markers of EMT[15]. To investigate the association between CAMK4 and EMT, analysis was conducted using BCa data from the TCGA database. The findings revealed a negative association between CAMK4 and CDH1, along with a positive association with CDH2 and VIM (Fig. 8A), implying that CAMK4 may contribute to the development of EMT. Validation was subsequently conducted using in vitro BCa cells. RT-qPCR results showed that CAMK4 overexpression decreased gene expression of CDH1 while promoting gene expression of CDH2 and VIM(Fig. 8B). In contrast, the knockdown of CAMK4 increased CDH1 gene expression and suppressed CDH2 and VIM gene expression (Fig. 8C). In addition, Western blot results also showed that CAMK4 was able to increase the protein levels of N-cadherin and vimentin and inhibit E-cadherin protein expression levels (Fig. 8D). The above outcomes indicated that CAMK4 could increase the expression of EMT markers, highlighting that CAMK4 might facilitate the advancement of BCa by upregulating EMT.

Over the last three to four decades, patients with minimally progressive survival outcomes have been diagnosed with BCa. In fact, the 5-year survival rate following radical cystectomy or chemoradiotherapy is approximately 50%[16]. Therefore, a better understanding of BCa biology is urgently needed to help discover more efficacious treatments. Through comprehensive analysis of gene expression data, multiple research groups have performed many analyses on molecular typing and molecular subtype identification of BCa[17, 18]. In a recent consensus classification, six distinct subtypes have been defined: luminal papillary, luminal non-specified, luminal unstable, stroma-rich, basal/squamous, and neuroendocrine-like [19]. Although neuroendocrine-like subtypes are linked to a poorer prognosis, it is intriguing to note that stroma-rich subtypes appear to exhibit notably poor responses to neoadjuvant chemotherapy [19]. In addition, stroma-rich isoforms exhibit overexpression of gene signatures related to smooth muscle, endothelial cells, fibroblasts, and myofibroblasts [20]. Recently, the stromal cell population in the solid TME, known as CAFs, has gained attention as an important cell type affecting the survival outcome of BCa.

Within the TME, CAFs plays a pivotal role in establishing a structural framework, notably the ECM, where various other TME cells reside. These cells encompass, but are not restricted to, cancer cells, cytotoxic and regulatory immune cells, antigen-presenting cells, and vascular cells such as endothelial cells[20]. Additionally, it was observed that CAFs in multiple datasets exhibited a robust correlation with tumor stage and displayed a positive association with immune score. Simultaneously, a close association was identified between CAFs and immune cells, particularly B cells and CD4 + cells. Although tumor antigen presentation to T cells and production of antitumor immunoglobulins may visually indicate significant tumor suppression, specific B lymphocyte subsets can secrete tumor cell growth factors and immunosuppressive cytokines, thereby promoting evasion of immune surveillance and cancer progression [21]. The reticular organization of CAFs is mediated by CD8 T cells, whereas the accumulation of CAFs depends on the recruitment of B cells [22]. In recent years, it has been found that CAFs can directly connect and induce naive CD4 + T cells into regulatory T cells (Tregs) in an antigen-specific manner to regulate tumor immunity [23].

Hence, the co-expression network between CAFs and immune scores in two BCa cohorts was systematically investigated using WGCNA and various algorithms. Immune correlation analysis was subsequently conducted after typing the datasets via cluster analysis. These results indicated the instructive nature of this classification, with immunocompetent individuals predominantly clustering in subtype B. Additionally, the effectiveness of immunotherapy was confirmed in patients classified under subtype B.

Following this, three genes, namely ALDH1A1, DEGS1, and CAMK4, were screened through prognostic analysis. Notably, these three genes have been the subject of previous studies. Aldehyde dehydrogenase 1A1 (ALDH1A1) assumes a pivotal role in cellular detoxification and ROS clearance [24], and it serves as a marker of cancer stem cells (CSCs)[25]. ALDH1A1 is critically involved in self-renewal, differentiation, and self-protection in various cancers, including lung, liver, ovarian, pancreatic, and breast cancers[26–28]. ALDH1A1 has also been found to be an important gene associated with CAFs in ovarian and prostate cancers[29, 30]. Dihydroceramide desaturase 1 (DEGS1) plays a role in ceramide biosynthesis at the final step of the sphingolipid pathway[31]. Knockdown of the DEGS1 gene utilizing small inhibitor RNA in neuroblastoma cells leads to the accumulation of endogenous dihydroceramide, reduced cell growth, and G1 cell cycle arrest[32]. Elevated DEGS1 expression contributes to tumor progression in the tuberous sclerosis complex[33]. In addition, embryonic fibroblast cells knocked down for DEGS1 are defective in proliferation[34]. Overexpression of CAMK4 in polycystic kidney disease facilitates mTOR-mediated cell proliferation[35]. Moreover, CAMK4 has been associated with the malignant potential of tumors in liver cancer [36].

Combining HR values with P values, CAMK4 was ultimately chosen for further cell experimental validation. The function of CAMK4 in enhancing the proliferation, migratory capacity, and invasive behavior of BCa cells was tested. Notably, it was observed that CAMK4 could promote the advancement of BCa by promoting EMT development. This is justified because it has been shown that CAFs can induce the development of EMT and enrichment of CSCs[37]. Furthermore, the expression of CAFs markers in the interstitial space exhibits a strong correlation with the expression of EMT markers in cancer cells. Notably, in vitro stimulation of fibroblasts has been shown to induce the development of EMT in cancer cells [38].

However, this study does have certain limitations. For instance, there are alternative methods for calculating CAFs and immune scores, and bolstering the findings with validation in additional datasets could enhance the persuasiveness of the results. Second, the study lacks extensive mechanistic experiments, but the strong association between CAMK4 and EMT-related proteins may serve as a valuable guide for future mechanistic exploration. Moreover, validating these findings through in vivo experiments would provide more compelling results and underscore their therapeutic significance.

In conclusion, a CAFs-derived signature was constructed using transcriptome data from multiple datasets and through a variety of bioinformatics analyses. This signature allowed the division of BCa into two distinct subtypes characterized by differences in immune cell infiltration landscapes and immune-related characteristics. Notably, it was found that SUBTYPE B immunotherapy is more effective. Hub gene CAMK4 demonstrated its oncogenic effect by promoting the migratory, invasive, and proliferative abilities of BCa cell lines. The research findings serve as a foundation for further elucidating the mechanisms by which CAFs promotes BCa progression. Moreover, they offer valuable insights into potential therapeutic targets and new clinical treatments for BCa.

Ethics approval and consent to participate:

This study were reviewed and approved by the ethics committee of Jingzhou Hospital Affiliated to Yangyze University (Number:2023-084-01). The written informed consent of all patients in this study was consistent with the Helsinki Declaration.

Consent for publication:

Not applicable.

Competing interests:

The authors declare no competing interests.

Author details:

1 Department of Urology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei.

Acknowledgments:

The authors sincerely thank all of the members of the laboratory for their valuable suggestions and appreciate TCGA and GEO for their data sharing.

Consent to Participate:

Twelve patients gave informed consent and agreed to participate in the study.

Authors’ Contributions:

Y.L. conceived and designed the experiments; X.S. and Y. G. performed the experiments; X.S. and Y. G. analyzed and interpreted the data; X.S. and Y. G. wrote the paper; All authors reviewed the manuscript.

Funding:

This work was no Funding.

Data Availability:

The datasets generated and/or analysed during the current study are available in the TCGA and GSE repository, [https://portal.gdc.cancer.gov/],[https://www.ncbi.nlm.nih.gov/geo/].

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No competing interests reported.

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In silico analysis and validation the cancer- associated fibroblasts related gene CAMK4 promotes bladder cancer progression

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