Construction of a lymphangiogenesis related gene score signature and prognostic model for bladder cancer

doi:10.21203/rs.3.rs-3449029/v1

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Construction of a lymphangiogenesis related gene score signature and prognostic model for bladder cancer

https://doi.org/10.21203/rs.3.rs-3449029/v1

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Background: Lymph node metastasis is an important prognostic factor for bladder cancer patients, and the critical importance of lymphangiogenesis as an elemental process of metastasis has been reported. However, the effect of lymphangiogenesis genes on the prognosis of bladder cancer is still unclear.

Method: The gene expression and clinical data of bladder cancer patients were obtained from the TCGA database, and lymphangiogenesis-related genes were screened using univariate Cox regression analysis and LASSO analysis to construct a risk assessment model. The model was validated in the bladder cancer cohort patients in the GEO database. Finally, multi-omics analysis was performed to explore the predictive value of the model, and the transcriptional regulatory mechanism of related genes.

Results：We screened 17 lymphangiogenesis-related genes and established a risk score model for predicting survival in BCa patients. Better overall survival was observed in the low-risk group of both the training and test groups. A nomogram was developed to predict the survival outcomes of postoperative patients by integrating several clinical factors, and a well-predictive effect was observed. In addition, we performed drug sensitivity analysis to learn the chemosensitivity of each sample, assessed the relationship between the model and the immune microenvironment, and explored the specific signaling pathways involved in the prognostic model through enrichment analyses.

Conclusion: We developed a risk model using 17 lymphangiogenesis-related genes, which showed good predictive efficacy among both the internal and external validation groups. The clinical predictive value of the model was also explored in a multi-omics study.

Bladder cancer

lymphangiogenesis༛immune microenvironment

prognostic model

Bladder cancer is one of the most common urologic malignancies[1], with an estimated 62420 new cases and 12160 deaths in men in 2023, ranking fourth among systemic tumors in terms of estimated new cases and eighth in terms of estimated deaths[2]. Lymph nodes are the most common metastatic sites, and approximately 25% of patients have lymph node metastases by the time of radical cystectomy[3, 4]. Several studies have shown that lymph node metastasis correlates with other clinicopathological factors including tumor stage and tumor grade[5], and lymph node involvement portends an extremely poor prognosis in BCa[6, 7]. Lymphangiogenesis is thought to be an important step in cancer lymph node metastasis. Lymphangiogenesis is regulated by multiple factors, such as tumor-associated macrophages and vascular endothelial growth factor (VEGF)[8–10]. Lymphangiogenesis can be induced by the VEGF-C/VEGFR3 signaling pathway[11, 12]. Meanwhile, tumor-associated macrophages also regulate lymphangiogenesis by secreting VEGF-C, VEGF-D, and VEGFR-3[13, 14]. It has been shown that tumor-associated macrophages in tongue squamous cell carcinoma and non-small cell lung cancer are positively associated with VEGF-C[14, 15]. Lymphangiogenesis also has an impact on the prognosis of patients with bladder cancer[9]. We intend to extract lymphangiogenesis-related genes from public databases and screen prognostic significantly influencing factors. The prognostic model of bladder cancer patients was established based on the screened genes, and the functions of the related genes were predicted.

1. Data acquisition

We obtained the original bladder cancer mRNA expression data that were processed from the TCGA database (https://portal.gdc.cancer.gov/). The website was used by various groups, comprising a normal group consisting of 19 individuals and a tumor group consisting of 412 individuals. The Series Matrix File data files of GSE13507 and GSE32894 were retrieved from the GEO repository. A total of 389 patients were selected, and their gene expression profiles and clinical information were obtained. We obtained 515 lymphangiogenesis genes through the GeneCards database. Lymphangiogenesis genes with predictive value were identified using univariate Cox regression analysis.

2. Functions analysis

The clustering analysis provided by the R package “ClusterProfiler” was employed to investigate the functional relationship among these prognostic genes. Functional analysis of genes was performed using GO and KEGG analysis methods. The p value and q value both below 0.05 were deemed to carry considerable importance.

3. Risk score modeling

The LASSO algorithm was used to identify prognostic genes associated with lymphangiogenesis. Risk assessments were determined by utilizing information about gene expression and regression coefficients. Patients were divided into groups of higher risk and lower risk according to their median risk score. Survival outcomes were evaluated for both cohorts using Kaplan-Meier curves, and a log-rank test was conducted to compare the differences in survival between the two groups. The model accuracy was assessed using the ROC curve.

4. Drug sensitivity analysis:

The chemosensitivity of each tumor sample was predicted using the R package "pRRophetic" based on the GDSC database. The approach acquired IC50 estimates for individual chemotherapeutic drug and assessed prediction accuracy through 10-fold cross-validation with the GDSC training set.

5. Analysis of the infiltration of immune cells:

The CIBERSORT algorithm was utilized to assess the infiltration of 22 distinct immune cell types. Spearman correlation analysis revealed the association between gene expression and infiltration of immune cells.

6. GSVA analysis:

The Molecular Signatures Database (version 7.0) will be used to download the gene set for the conducted study. The GSVA algorithm will be utilized to comprehensively evaluate the potential changes in the biological function of various samples by scoring each gene set.

7. Nomogram model construction

A nomogram is a graphical representation of a statistical prediction model that is typically used to predict the probability of a specific outcome. By integrating the risk score, gender, grade, and tumor stage, we devised a nomogram to accurately predict survival outcomes. Calibration curves were utilized to assess the anticipated performance of the model.

8. Regulatory network analysis of model genes

In this study, transcription factors were predicted using the R package “RcisTarget”. All calculations performed by RcisTarget are based on motifs. The normalized enrichment score (NES) of a motif depends on the total number of motifs in the database. In addition to motifs annotated by source data, we inferred further annotation files based on motif similarity and gene sequence. The first step in estimating the overexpression of each motif across a gene set is to calculate the area under the curve (AUC) for each motif-motif set pair. This was performed based on the recovery curve calculation of the gene set versus motif ordering. The NES of each motif is calculated from the AUC distribution of all motifs in the gene set. We used rcitarget.hg19.motifdb.cisbpont.500 bp for the Gene-motif rankings database.

9. GWAS analysis

The Gene Atlas database (http://geneatlas.roslin.ed.ac.uk/) is a large database documenting associations between hundreds of traits and millions of variants using the UK Biobank cohort. These associations were calculated using 452,264 UK individuals in the UK Biobank database, covering a total of 778 phenotypes and 30 million loci. The statistical analysis was conducted using R language (version 4.0), with statistical significance set at p < 0.05.

1. Explore the prognostic genes related to lymphangiogenesis in the BLCA cohort

To further identify the key genes in the lymphangiogenesis gene set, we collected the clinical information of BLCA patients, and used univariate Cox regression analysis to screen out the prognostic genes in BLCA. In addition, 43 prognostic-related genes (p < 0.005) were screened out (Fig. 1A).

2. Functional enrichment

We performed GO and KEGG enrichment analysis on 43 prognostic genes and found that these prognostic genes were significantly enriched in peptidyl-tyrosine phosphorylation, peptidyl-tyrosine modification, ERK1 and ERK2 cascade pathways in GO enrichment (Fig. 1B). In the process of KEGG enrichment, there were pathways such as Focal adhesion, Rap1 signaling pathway, and Ras signaling pathway (Fig. 1C).

3. Identification of prognosis-related genes and construction of a prognostic model

We further screened out the characteristic genes in BCa by the lasso algorithm. Finally, we obtained seventeen lymphangiogenesis genes with prognostic significance and their regression coefficients (Fig. 2A-C). We obtained the best risk score value corresponding to each sample for subsequent analysis. Better overall survival was observed in the low-risk group of both the training and test groups (Fig. 2D-E). The ROC curves of the two sets both suggested that the model had good validation performance (Fig. 2F-G).

4. Immune microenvironment

Through a thorough analysis of the correlation between risk score and tumor immune infiltration, we explored the intrinsic molecular mechanism underlying the impact of risk score on the progression of bladder cancer. We assessed the percentage of 22 different types of immune cells that had infiltrated and observed variations in immune cell composition between the low-risk and high-risk groups (Fig. 3A). The findings indicated a notable reduction in Monocytes, Dendritic cells resting, Dendritic cells activated, T cells CD8, T cells focal helper and T cells regulatory (Tregs) in the high-risk group. T cells CD4 memory resting, Macrophages M0, Macrophages M1, Macrophages M2 significantly increased (Fig. 3B). Furthermore, our investigation delved deeper into the association between the risk score and the composition of immune cells, revealing a significant positive correlation between the risk score and various immune cell types such as Macrophages M0, Macrophages M2, Neutrophils, etc., though exhibiting a substantial inverse relationship with Tregs, activated dendritic cells, and CD8 T cells (Fig. 3C). Finally, the expression differences in immune-related chemokines, immunosuppressants, immune stimulators, immune receptors and other genes between the two groups are presented (Fig. 4).

5. Clinical predictive value of the model

We utilized the GDSC database to predict the responsiveness of each tumor sample to chemotherapy, and further explored the relationship between risk score and the sensitivity of common chemotherapy drugs. The study findings indicated a strong correlation between risk score and patients' sensitivity to ATRA, ABT.888, MS.275, and Roscovitine (Fig. 5A). Furthermore, we explored the signaling pathways involved in the high-low risk correlation model to explore the underlying mechanisms by which the risk score affects tumor progression. The GSVA results indicated that the differential pathways of the two groups of patients were mainly enriched in signaling pathways such as EPITHELIAL MESENCHYMAL TRANSITION, APICAL JUNCTION, and HYPOXIA (Fig. 5B). The GSEA results indicated that the involved pathways were ECM-receptor interaction, IL-17 signaling pathway, and TNF signaling pathway (Fig. 5C). The network of molecular interactions within each specific pathway is displayed (Fig. 5D).

6. Validation of the prognostic model with external datasets

Afterwards, we analyzed the difference in survival outcomes between the two groups in the GEO database, and significant survival differences were also found (Fig. 6A-B). The ROC curve was used to assess the performance of the risk score in external datasets, thereby determining its accuracy. The area under the curve (AUC) for predicting the 5-year survival of two gene expression datasets, GSE13507 and GSE32894, was calculated to be 0.62 and 0.79, respectively (Fig. 6C-D).

7. Incidence risk and independent prognosis analysis

We established a nomogram to accurately predict the prognosis of patients diagnosed with BCa (Fig. 6E). The outcome of the logistic regression analysis indicated that the risk score value played a substantial role in the scoring procedure of the nomogram predictive model across all of our collected samples. Calibration plots demonstrated accurate clinical predictions (Fig. 6F).

8. Relationship between the risk score and clinical indicators

For an exploration of the relationship between risk score and clinical indicators, we presented the results of each clinical index grouping in the form of a box plot. After applying the rank sum test (kruskal.test), it was determined that there was a significant difference in the distribution of risk score values among groups for various clinical indicators including age, fustat, grade, Stage, N, and T stage (Fig. 7). The reverse prediction of 17 model genes was conducted through the Mircode database, and 86 miRNAs and 764 pairs of mRNA-miRNA relationships were obtained, and visualized using Cytoscape (Fig. 8A).

9. Model gene-associated transcriptional regulation analysis

We used model genes for the gene set analysis, and we discovered that these genes are regulated by several transcription factors. Therefore, cumulative recovery curves were utilized to conduct enrichment analysis on these transcription factors. The analysis results indicated that the MOTIF annotation was cisbp__M2273 (Fig. 8B). In this motif, 10 prognostic genes were found to be enriched, including ADAM17, ANGPT1, CNTN1, ECM1, FN1, IGF1, MYO5A, NES, NTRK2 and STAT2. The NES was 4.5.

10. GWAS analysis

Next, we analyzed the GWAS data of BCa to identify the causative regions of 17 model genes in BCa. The Q-Q plot revealed significant single nucleotide polymorphism (SNP) loci associated with BCa identified by GWAS data (Fig. 8C). Through the precise location of the GWAS data, the key SNP sites distributed in the enrichment area were described. The SNP pathogenic regions corresponding to these 17 model genes are displayed (Fig. 8C). The significant SNP sites corresponding to the 17 model genes are presented in the table (GWAS data.xls).

The presence of lymph node metastasis predicts a poor prognosis[7, 16]. The formation of new lymphatic vessels intratumor and peritumor promotes the process of lymph node metastasis. Several studies have also aimed to explore the mechanisms of lymphangiogenesis and have shown that lncRNAs and circRNAs play an important role in this process. BLACAT2, circEHBP1, and circNFIB1 all promote lymphangiogenesis in bladder cancer[17–19].

MAPK10 has been proven to be associated with epithelial-mesenchymal transformation (EMT) and immune cell infiltration through the WNT pathway, and is an independent prognostic factor of BCa patients[20]. It was also found that ECM1 was highly expressed in BCa patients with significantly elevated protein levels. Knockdown of ECM1 impaired not only cell proliferation and migration but also cell invasion and apoptosis potential[21]. Ananda et al found that HDAC5 is usually downregulated in urothelial carcinoma, and overexpression generally reduces cell proliferation, suggesting a possible tumor inhibitory effect. Interestingly, EMT was induced in VM-Cub-1 cell lines, which is consistent with our results[22].

PRMT5 was highly expressed in BCa cell lines, which was negatively correlated with overall survival (OS) and recurrence-free survival (RFS), and enhanced the proliferation and migration of BCa cells[23]. Some studies have found that PRMT5 can enhance the activation of NF-κB by targeting anti-apoptotic genes such as BCLXL and c-IAP1, and that the low expression of PRMT5 inhibits c-Myc, thus inhibiting the proliferation and invasion of tumor cells through NF-κB pathway[24]. Moreover, PRMT5 can promote EMT and lymphatic metastasis through HSF1-PRMT5-WDR5 axis. Its inhibitor FKA has been shown to block the symmetric arginine demethylation of histone H2A and H4 to inhibit the behavior of BCa[25, 26].

SEMA4D has been shown to be a poor prognostic factor for a variety of tumors and has been associated with the promotion of tumor lymphatic metastasis[27, 28]. SEMAD4 was able to enhance the invasiveness of pancreatic ductal adenocarcinoma cells and promote lymph node metastasis and distant metastasis[29]. The anti-Sema4D antibody Pepinemab has been shown to have potential antitumor activity, and can be combined with immune checkpoint inhibitors[30]. In bladder cancer, SEMA4D could promote bladder cancer metastasis by activating the PI3K/AKT pathway[27]. SEMA4D was found to promote BCa cell proliferation and metastasis in vitro and in vivo. High CNTN1 expression is also associated with poor prognosis in bladder cancer patients[31]. However, there are no reports on the function and molecular mechanism of CNTN1 in BCa, which needs to be further studied.

We found that the risk score was positively correlated with macrophage infiltration, and that tumor-associated macrophages play an important role in regulating the process of tumor lymphangiogenesis. Some studies have demonstrated that lymphangiogenesis is regulated by the immune cell-mediated VEGFC-VEGFR3 signaling pathway in the tumor microenvironment[12, 14]. It has been shown that tumor-associated neutrophil infiltration is increased in lymph node metastasis-positive bladder cancer tissues and is associated with poor prognosis[32]. Neutrophils may act synergistically with macrophages to regulate lymphatic vessel neogenesis, and studies suggest that both macrophages and neutrophils are involved in inflammatory lymphangiogenesis in zebrafish through the expression of VEGF-A, VEGF-C, and VEGF-D[33]. In melanoma, the chemokine CXCL5 may increase lymphangiogenesis by increasing the number of neutrophils. The role of tumor-associated neutrophils in lymphangiogenesis and the prognosis of tumor patients is unclear and needs to be further explored.

ADAM17 is a TNFα lyase, and its functions in EGF-R-, interleukin-6 (IL-6)- and TNFα- biology have been proven to play a decisive role in the regulation of the immune system and the development of cancer[34]. Recently, it has been reported that ADAM17 is the key driver of tumor cell metastasis through vascular endothelium. TNF-induced necroptosis depends on the activity of ADAM17 in endothelial cells. Inhibition of ADAM17 can significantly prevent tumor metastasis and progression[35]. IGF1 is generally considered to play a central role in cell growth and differentiation. Although most clinical and epidemiological studies thus far have not found a causal relationship between growth hormone therapy and increased cancer risk, the latest research links the endocrine axis of GH-IGF1 with cancer development, because cellular oncogenes need a complete signal transduction pathway of IGF1 to trigger transformation[36]. Therefore, further long-term follow-up studies are needed to determine the safety of growth hormone therapy.

There are some limitations in our research. First, the number of patients with complete data is small. Second, the biological function of selected genes has not been deeply investigated, and the interaction between different genes is not determined. Therefore, we need larger-scale research to verify our conclusions, biology, basic medicine and other studies to improve the molecular mechanism and signaling pathway of gene interactions.

We screened lymphangiogenesis genes to construct a BCa prognosis model. At the same time, we also explored its potential molecular mechanism and showed the SNP-induced regions of 17 selected genes.

Ethic approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The datasets presented in this study can be found in online repositories, including TCGA database (https://portal.gdc.cancer.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and GeneCards database (https://www.genecards.org/).

Competing interests

The authors declare that they have no conflicts of financial interests, affiliations, or personal interests or beliefs, that could be perceived to affect the objectivity or neutrality of the manuscript.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Authors’ contributions

HC conceived and designed research, HC conducted bioinformatic analysis and performed statistical analysis. HC and LKM drafted the manuscript. All authors approved the submitted manuscript.

Acknowledgements

Not applicable

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No competing interests reported.

GWASdata.xls

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Construction of a lymphangiogenesis related gene score signature and prognostic model for bladder cancer

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