Construction of DNA replication stress gene related risk model and for prognosis in bladder cancer

doi:10.21203/rs.3.rs-4825822/v1

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Construction of DNA replication stress gene related risk model and for prognosis in bladder cancer

https://doi.org/10.21203/rs.3.rs-4825822/v1

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Bladder cancer (BLCA) remains a widespread and serious cancer globally. The importance of constructing predictive risk models based on DNA replication stress-related genes (DRSGs) is gaining traction. Despite the scarcity of molecular studies, our analysis of Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets led to the identification of 17 DEDRSGs with prognostic significance, primarily involved in DNA replication and cell cycle control. Subsequently, utilizing LASSO regression, we filtered out nine key biomarkers to develop a risk model that effectively distinguishes immunological profiles and GSVA hallmark pathways across risk strata. Besides, significant differences were observed in the distribution of 191 drugs across the two risk groups. Finally, qRT-PCR was performed to confirmed that BUB1B was significantly highly expressed in 5637 cells line, while the expression level of RAD9A was higher in SV-HUC-1 cell line. In conclusion, the risk model, based on the nine biomarkers, offers a theoretical framework for exploring underlying molecular mechanisms in BLCA.

Biological sciences/Cancer/Cancer genetics

Biological sciences/Cancer/Cancer microenvironment

Biological sciences/Cancer/Cancer models

Biological sciences/Cancer/Cancer of unknown primary

Biological sciences/Cancer/Cancer screening

Biological sciences/Cancer/Cancer therapy

Biological sciences/Cancer/Mesothelioma

Bladder Cancer

DNA replication stress related genes

Biomarkers

Risk model

Immune cell

Bladder cancer (BLCA) is a prevalent malignant tumor of the urinary tract and ranks among the top ten most common types of cancer worldwide. It accounts for approximately 550,000 new cases each year, representing about 3% of all new cancer cases^{[1, 2]}. The incidence of BLCA varies by gender, with men experiencing a significantly higher rate than women, at about 3–4 times higher^[3]. According to the ability of cancer cells to infiltrate the bladder muscle layer, BLCA is categorized into two subtypes: non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC)^[4]. Most patients with BLCA may remain asymptomatic in the initial stage and only receive a diagnosis later when they experience symptoms such as painful urination, blood in the urine, and increased frequency of urination^{[5, 6]}. Therefore, early detection is crucial for the effective treatment of BLCA. BLCA intricately creates a highly immunosuppressive microenvironment that evades immune response through multiple mechanisms^[6]. The microenvironment of bladder tumors is characterized by a highly immunosuppressive environment, which includes the accumulation of various immune cells with immunosuppressive phenotypes, including regulatory T cells, tolerant dendritic cells, and tumor-associated macrophages. Additionally, there is an increased expression of programmed death ligand 1 (PD-L1)^[7–9]. Immune checkpoint inhibitors (ICI) represent a novel class of immunotherapy drugs that have been shown to decrease the occurrence and fatality rates of BLCA^[10].

Disruption of DNA replication can result in DNA replication stress (DRS), which in turn leads to genomic instability and tumorigenesis^[11]. DRS is characterized by an increase in replication fork stasis, the accumulation of collapsed replication forks, and the early activation of DNA damage response (DDR) during cancer development^[12]. The persistence of DRS is mainly observed in precancerous and cancer cells, and is rarely found in normal cells even when they are rapidly proliferating. This suggests the potential for therapeutic selectivity in targeting cancer cells^[11]. DNA replication stress has been studied in a range of cancer diseases, such as colorectal cancer^[13], pancreatic cancer^[14], and Ewing sarcoma^[15]. Studies have revealed the identification of four markers associated with DNA replication stress in esophageal squamous cell carcinoma, effectively predicting the prognosis of patients^[16]. Therefore, the utilization of DRS is a feasible approach for cancer treatment and presents new therapeutic possibilities^[8]. In the context of BLCA, the role of DRS remains unclear. Therefore, it is particularly important to conduct a comprehensive assessment of the degree of replication stress in BLCA patients.

In this study, we utilized the TCGA and GEO databases to employ a series of bioinformatics technologies for the identification of 9 key genes as potential biomarkers. Subsequently, we developed a BLCA risk model capable of effectively distinguishing immune characteristics and GSVA marker pathways across different risk layers. The study enhances our understanding of the intricate relationship between DNA replication stress genes and BLCA. It also shows promise for the development of new therapeutic interventions for this type of cancer.

2.1 Data Collection

The clinical and RNA-seq data of 412 Bladder Cancer (BLCA) cases and 19 controls were extracted from TCGA database, which was utilized as a training set^[17]. The GSE13507 dataset was downloaded as a validation set, including 165 BLCA cases and 68 controls^{[18, 19]}. Then, 1653 DNA replication stress genes (DRSGs) were derived from earlier research^[20].

2.2 Identification of DERSGs

The DEGs between BLCA cases and controls were screened, with a threshold of |log₂FC| >1 and P < 0.05^[21]. The DEGs were visualized using volcano plots and heatmaps. Moreover, the intersection DEDRSGs were obtained with the DEGs and the DRSGs using Venn diagram. Then, univariate cox analysis (p < 0.05) was used to screened the genes substantially associated with prognosis in conjunction with the clinical data of BLCA samples^[22]. Subsequently, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were then analyzed using ClusterProfiler, with a pvalue cutoff of 0.05^[23].

2.3 Screening of biomarkers and construction and validation of risk models

The LASSO analysis was implemented by glmnet package. And the prognostic biomarkers were acquired. The correlation of biomarkers was performed using psych package^[24]. Using data from the TCGA database, the risk model was constructed. The predict package was used to estimate the risk score for each patient, and subsequently, the patients were categorized into high and low-risk groups. Furthermore, the K-M survival analysis was utilized to examine the connections between the Overall Survival (OS) of two risk groups. The risk curves of the model were drawn, and the heatmap of biomarkers were plotted using pheatmap package. Using the pROC package, the ROC curve was shown for 1-, 3-, 5-years^[25]. The GSE13507 dataset served as an external validation set to confirm the model's applicability.

2.4 Construction of a nomogram

The uni- and mul- cox analyses were used to examine the connection between clinical features in the TCGA database and risk models^[25]. A nomogram was constructed based on independent prognostic indicators using survival package and rms package in R. The calibration curves for 1-, 2-, 3-years were utilized to observe differences between the expected and actual survival rates^[26]. To assess the accuracy of this nomogram, a prognostic ROC analysis was conducted at the same time.

2.5 Immune cell infiltration landscape

For every sample in the TCGA dataset, the infiltration score of immune cells was determined using CIBERSOFT method^[27]. The estimate package was utilized to assess the significance of immune score, stromal score, and ESTIMATE score between two risk groups. Afterwards, the correlations between immune, stromal, ESTIMATE score and risk score were calculated^[28]. Finally, the expression of 48 immune checkpoint was detected in different risk groups.

2.6 GSVA in two risk group

We used GSVA to investigate the variations in signaling pathways between the two risk groups. Hallmark annotation was accomplished using the GSVA package, and the limma package measured the disparity in enrichment results between the risk groups^[29].

2.7 Chemotherapy sensitivity prediction

Chemotherapy sensitivity was assessed using the Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerRxgene.org) across different risk groups^[30]. By using a pRRophetic package between the two risk groups, the 50% inhibitory concentration (IC50) was utilized to assess the chemotherapeutic sensitivity of the drugs.

2.8 Validation the expression level of biomarkers

The real-time quantitative polymerase chain reaction (qRT-PCR) was employed to validate the expression levels of biomarkers. BCa cell lines (T24, 5637) and normal urothelial cell lines (SV-HUC-1) were purchased from the Shanghai Cell Bank. The Trizol reagent was used to extract the total RNA. A reverse transcriptional reaction is subsequently carried out. The qRT-PCR reaction conditions were conducted following the protocol established by Rao X et al, furthermore, the relative mRNA expression was determined using the 2^{− △△Ct} method^[31].

3.1 Identification and function enrichment analysis of DEDRSGs

2014 DEGs were recovered from BLCA cases and controls (Fig. 1A-B). Next, by overlaying the 2014 DEGs and 1653 DRSGs, 183 DEDRSGs were produced (Fig. 1C). Using univariate cox regression analysis, 17 DEDRSGs that were significantly correlated with prognosis were selected (Fig. 1D). 17 DEDRSGs were found to be enriched in processes related to DNA replication, negative regulation of the cell cycle, signaling of cell cycle checkpoints, and regulation of mitotic cell phase transition, etc.

(Fig. 1E). The enriched KEGG pathways were identified, including RNA polymerase, gap junction, cell cycle, etc (Fig. 1F).

3.2 The risk model was constructed by 9 biomarkers

A total of 9 genes (BUB1B, CHMP4C, CLSPN, MCM2, NFIA, RAD9A, TUBA1A, TUBB2B, and VCL) were screened using the LASSO algorithm, which were used as biomarkers to construct risk model (Fig. 2A-B). Significant correlations were found between CLSPN, VCL, TUBB2B, and other genes (Fig. 2C). The survival analysis was implemented, indicating that there was a substantial difference in two risk groups, and patients in the high-risk group exhibited a lower survival rate (Fig. 2D). The risk curves of the model were drawn and survival state, and the expression level of biomarkers were described by the heatmap in two risk groups (Fig. 2E). Additionally, the Area Under Curves (AUCs) of survival were all greater than 0.65, which indicated that the risk model was effective (Fig. 2F). The K-M curves, risk curves, and ROC curve were also drawn in the GSE13507 validation dataset, they were consistent with training set, which indicated that the risk model of BLAC patients was accurate (Fig. 2G-I).

3.3 Construction and validation of the gene-based nomogram

According to the uni- and multi-cox analyses, the risk score was considered an independent prognostic factor for BLCA patients (Fig. 3A-B). Furthermore, a nomogram of gender, age, stage, and risk score was created (Fig. 3C). According to the calibration findings, the risk score can reliably estimate 1-, 2-, and 3-year OS in BLCA patients (Fig. 3D). In addition, the AUC of 1-, 2-, 3-year of risk score were about 0.7, which demonstrated that the nomogram has good prediction performance (Fig. 3E-G).

3.4 Immune infiltration analysis

The two risk groups showed a statistically significant difference in 12 immune cells (Fig. 4A-B). The high risk group had higher immune, stromal, and ESTIMATE scores (P < 0.01), according to the results of the ESTIMATE analysis (Fig. 4C). The risk score and the other three scores had a significant positive correlation(Fig. 4D). In addition, there were 18 immune checkpoints were significant differences in two risk groups (Fig. 4E).

3.5 Forty-five pathways were enriched analysis by GSVA

An examination of the biological behaviors between the two groups was conducted using GSVA enrichment analysis. As shown in Fig. 5, there were 45 entries with notable differences between two risk groups, including angiogenesis, hypoxia, apoptosis, glycolysis, interferon alpha response, reactive oxygen species pathway, protein secretion, notch signaling, cholesterol homeostasis, xenobiotic metabolism, etc.

3.6 Chemotherapy sensitivity prediction

The results of using IC50 to evaluate the sensitivity of drugs to chemotherapy demonstrated that 191 chemotherapy drugs had IC50s that were noticeably different in two risk groups. The high-risk group had a higher sensitivity to Belinostat, Crizotinib, Dabrafenib, Docetaxel, Erlotinib and Pyrimethamine, while the low-risk group had a higher sensitivity to Bleomycin, Cyclopamine, Lapatinib, Pazopanib, and Sunitinib (Fig. 6).

3.7 Validation the expression level of biomarkers

qRT-PCR was employed to examine biomarker gene expression levels in two cell lines. The results indicated a significantly higher expression level of BUB1B in the 5637 cell line, while the expression level of RAD9A was found to be higher in the SV-HUC-1 cell line (P < 0.05) (Fig. 7).

Bladder cancer is the most prevalent tumor of the urinary system, characterized by a high recurrence rate. The incidence and mortality rates of bladder cancer have been steadily increasing, in line with the accelerated aging of the global population^[32]. cells respond to increased replication stress by activating pathways that are responsible for inhibiting and repairing replication stress. This study has identified a set of key core genes that play a crucial role in the pathogenesis of BLCA by integrating differentially expressed genes in BLCA and DNA replication stress genes (DRSGs). Subsequent enrichment analysis has revealed the biological processes regulated by these genes, highlighting their significant role in driving the progression of BLCA. The genes analyzed in this study were BUB1B, RAD9A, MCM2, NFIA, TUBA1A, CHMP4C, CLSPN, TUBB2B and VCL. Among these genes, the first four were found to be associated with the cell cycle while the last five were related to mitosis. These genetic factors have been shown to impact the normal transport and secretion of substances within cells. It is possible that these findings may have implications for certain diseases or cellular physiological disorders.

BUB1B, a crucial mitotic checkpoint protein, plays a key role as part of the mitotic spindle checkpoint complex. The BUB1B gene is frequently found to be upregulated in various types of cancer^[33]. In this study, by qRT-PCR assay, the results showed that the expression level of BUB1B in BCa cell line 5637 was higher than that in normal uroepithelial cell line SV-HUC-1. Additionally, NFIA plays a important role as a transcriptional regulator in the genetic program of brown fat^[34]. Additionally, NFIA has emerged as a key regulator in cancer development^[35]. MiR-212-3p has been demonstrated to suppress the proliferation of BLCA cells and promote cell apoptosis by directly targeting NFIA in previous studies^[36]. The MCM protein is believed to be closely associated with tumor development^[37]. The expression of MCM2 protein has been found to hold prognostic significance in prostate cancer^[35], non-small cell lung cancer^[38], and oligodendroglioma^[39]. Previous research has found a correlation between MCM2 expression and prognosis in BLCA^[40]. Studies have indicated that RAD9A serves as a prognostic gene for telomere maintenance in BLCA, and it is found to be highly expressed in patients with BLCA^[41]. Zhang P B et al.'s study revealed that TUBA1A has been identified as a potential target for multi-drug treatment of high-risk MIBC^[42]. The immunohistochemical analysis by Gao M et al. revealed that CHMP4C was significantly elevated in BLCA tissues. Poor prognosis in BLCA patients was closely linked to this upregulation^[43]. Studies have demonstrated that CLSPN is associated with DNA replication stress and serves as a key factor in driving cisplatin resistance in BLCA cells^{[44, 45]}. TUBB2B and VCL have been relatively understudied in the context of BLCA. An improved prognosis may be achieved by identifying effective biomarkers for early diagnosis and timely intervention in BLCA.

Prognostic models are important for strategies for the treatment of bladder cancer. It developed using LASSO regression emphasize the potential use of genetic signatures in predicting patient outcomes, offering valuable clinical implications for risk assessment^[16]. The LASSO algorithm was used to screen 9 genes and use them as biomarkers to establish a risk model in this study. This model was used to divide the participants into high-risk and low-risk groups. The survival analysis results showed that there were significant differences in outcomes between these two risk groups. BLCA is a tumor that is sensitive to the immune system and is infiltrated by various types of immune cells, such as T cells, macrophages, dendritic cells, neutrophils, and mast cells^[46–48]. BLCA exhibits high heterogeneity in genetics, expression, and histology^[49]. Therefore, achieving a precise comprehension of this heterogeneity could simplify the process of molecularly classifying BLCA and implementing personalized medicine. The prognosis of BLCA is closely linked to the presence of tumor-infiltrating immune cells. The recurrence prognosis of BLCA is significantly correlated with six types of immune infiltrating cells as demonstrated by previous studies^[50]. The human bladder already has certain key immune cell populations that can recruit additional immune cells from circulation^[51]. In this study, gene data from BLCA patients were collected from the TCGA database. Significant differences in 12 immune cell types were observed between the two risk groups in BLCA according to the results.

Additionally, Immune checkpoints are also an effective treatment. Immune checkpoint inhibitors (ICI) are pharmaceuticals that block immune checkpoints. The development of ICI has shown potential in improving the long-term survival rates of patients with various types of cancer, including bladder cancer and renal cell carcinoma[51]. Endogenous control mechanisms, such as immune checkpoints and immunosuppressive cells, are employed to attenuate the anti-tumor immune response during tumorigenesis[52]. ICI have demonstrated significant potential in the treatment of bladder cancer. Therefore, the current study conducted an analysis of the expression of 48 immune checkpoints in different risk groups. The results reveal significant variations in 18 immune checkpoints among the two risk groups. immune checkpoint proteins are vital in the management of inflammatory responses and cancer immunotherapy. Therefore, by identifying differentially expressed immune checkpoint proteins, it is anticipated that regulating the immune response through targeting these proteins could enhance immunotherapy for bladder cancer.

Despite the importance of DNA replication stress in the development of BLCA, the mode and mechanism of action are not well understood. We screened 9 biomarkers connected to DNA replication stress using bioinformatics methods based on data from TCGA and GEO public databases. The risk model constructed based on this screening provides a theoretical framework for exploring the molecular mechanism of BLCA. Multiple new therapies targeting DNA replication stress are currently under development, and these novel molecules and biomarkers have the potential to significantly advance the field and positively impact the treatment of cancer. While our study utilized statistical methods to screen and identify nine genes that may be associated with BLCA progression and prognosis, it is significant to note that large queue validation was lacking. Therefore, further molecular experiments on the 9 prognostic genes are necessary to confirm their association with BLCA. By identifying biomarkers linked to DNA replication stress, our study can aid in identifying prognostic risk stratification for bladder cancer and guide treatment decisions.

Our study explores the diagnostic value of DNA replication stress genes in bladder cancer, thus providing new insights for the development of therapeutic interventions for this disease.

Competing interests:

The authors declare no conflict of interest.

Funding

This study was funded by the Taishan Scholar Program of Shandong Province (no. Tsqn202103198)

Author Contribution

XH, GJH, and YYF contributed to the design of the experiments in this study. LN, LY, and THY contributed to the data analysis and interpretation. LXX, YJS, CJZ performed the experiments and statistical analysis. LCH provides funding acquisition. DCM Provides guidance on ideas. All authors edited and approved the final manuscript and agreed to be responsible for all aspects of the manuscript.

Acknowledgements:

The information of this study is obtained by the Cancer Genome Atlas database (TCGA), Gene Expression Omnibus (GEO), cBioportal. We are grateful to them for the source of data used in our study.

Data Availability

The datasets analyzed in this study were collected from TCGA database (TCGA-BLCA dataset, https://portal.gdc.cancer.gov) and GEO database (GSE13507 dataset, https://www.ncbi.nlm.nih.gov/geo/).

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Construction of DNA replication stress gene related risk model and for prognosis in bladder cancer

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Version 1

Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Data Collection

2.2 Identification of DERSGs

2.3 Screening of biomarkers and construction and validation of risk models

2.4 Construction of a nomogram

2.5 Immune cell infiltration landscape

2.6 GSVA in two risk group

2.7 Chemotherapy sensitivity prediction

2.8 Validation the expression level of biomarkers

3. Results

3.1 Identification and function enrichment analysis of DEDRSGs

3.2 The risk model was constructed by 9 biomarkers

3.3 Construction and validation of the gene-based nomogram

3.4 Immune infiltration analysis

3.5 Forty-five pathways were enriched analysis by GSVA

3.6 Chemotherapy sensitivity prediction

3.7 Validation the expression level of biomarkers

4. Discussion

5.Conclusion

Declarations

Competing interests:

Funding

Author Contribution

Acknowledgements:

Data Availability

References

Additional Declarations

Supplementary Files

Status:

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