Construction of a new prognosis prediction model and immune infiltration analysis of bladder urothelial cancer based on disulfidptosis-related immune genes

doi:10.21203/rs.3.rs-4389522/v1

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Research Article

Construction of a new prognosis prediction model and immune infiltration analysis of bladder urothelial cancer based on disulfidptosis-related immune genes

https://doi.org/10.21203/rs.3.rs-4389522/v1

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Introduction:

The intricate nature and varied forms of bladder urothelial carcinoma (BLCA) highlight the need for new signals to define tumor prognosis. Disulfidptosis, a novel cell death form, is closely linked to BLCA progression, prognosis, and treatment outcomes. Our current goal is to develop a novel disulfidptosis-related immune prognostic model to enhance BLCA treatment strategies.

Methods

RNA-seq data from TCGA included 419 patients, with clinical details and prognostic data (19 normal, 400 tumor samples). Weighted gene co-expression network analysis (WGCNA) identified disulfidptosis-related immune genes. Univariate, multivariate Cox, and LASSO regression established a disulfidptosis-related immune risk score. A nomogram combining risk score and clinical features predicted prognosis. Model performance was validated through curve analysis and independent prediction. Immune checkpoints, cell infiltration, and tumor mutation load were assessed. Differential gene enrichment analysis was conducted. Prognostic genes were validated via in vitro experiments.

Results

Eight immune genes related to disulfidptosis were identified and verified in BLCA prognosis. A prognostic model outperformed previous ones in predicting overall survival (OS) for high- and low-risk groups. Patients with low risk-scores had higher OS rates and mutation load expression compared to high risk-score patients. CD4 memory T cells, CD8 T cells, M1 macrophages, and resting NK cells were higher in the low-risk group. ICIS treatment may be more effective for the low risk-score group. High risk-score group exhibited stronger correlation with cancer malignant pathways. Knocking out TNFRSF12A inhibits BLAC cell proliferation and invasion, while overexpressing it has the opposite effect.

Conclusions

We constructed a novel risk score model combining disulfidptosis and immune genes with good prognostic prediction performance. We discovered and verified that the TNFRSF12A gene is an oncogene in BLAC, which may help provide personalized guidance for individualized treatment and immunotherapy selection for BLCA patients to a certain extent.

bladder urothelial carcinoma

disulfidptosis

immunotherapy

risk models

prognostic signals

Bladder urothelial carcinoma (BLCA) is a highly aggressive malignant tumor with poor prognosis. BLCA is estimated to cause 500,000 new cases and 200,000 deaths worldwide, with more than 80,000 new cases and 17,000 deaths in the United States each year [1]. According to the depth of tumor invasion into the bladder wall, BLCA can be divided into non-muscle-invasive BLCA (NMIBC) limited to the urothelium and lamina propria and muscle-invasive BLCA (MIBC) involving muscle invasion (T2 stage) or above. NMIBC accounts for approximately 70% of organ-limited BLCA[2]. Over the past five years, approximately 90% of patients have survived, but approximately 15–20% will progress to MIBC [3]. MIBC accounts for the remaining 30% of organ-limited BLCA and has a higher propensity to spread to lymph nodes and other organs, with a 5-year OS rate of approximately 60–70%. Therefore, careful risk stratification of BLCA is needed to enhance clinical decision-making and counseling of patients.

In BLCA, immune cells, cytokines, and rich immune gene expression patterns form a unique tumor microenvironment, making it a good candidate for immunotherapy [4]. In fact, BLCA was one of the first cancers to be treated with immunotherapy using Bacillus Calmette-Guérin (BCG), which has been used to treat BLCA for nearly 50 years [5]. It is a pathogen-associated molecular pattern (PAMP) that activates Toll-like receptors, produces inflammatory cytokines, TNF, and GM-CSF, thereby activating neutrophils and monocytes. [6]. Secondly, the BCG stimulates CD4 + T cells to activate adaptive immune responses dominated by Th1 cells. [6, 7]. In spite of BCG's effectiveness, progression and relapse rates vary from 5–50%, depending on the individual's risk stratification, along with side effects [8]. It is estimated that 30%-40% of patients with intermediate- or high-risk NMIBC fail the BCG treatment for a variety of reasons despite initial success with BCG [9]. CTLA4, PD-1, and PD-L1 have been identified as key immune checkpoint proteins, making cancer immunotherapy through immune checkpoint blockade (ICB) an effective treatment for BLCA. In contrast, only 25% of metastatic/advanced BLCA respond to anti-PD-1/PD-L1 ICB[10]. Therefore, there is an urgent need to explore the immunological characteristics of high-risk groups with BLCA, obtain key indicators for judging prognosis, and find new potential therapeutic targets.

Disulfidptosis is a new type of cell death caused by disulfide stress caused by excess cystine accumulation. In contrast to ferroptosis, apoptosis, and necroptosis, it is a new method of cell death. It is different from autophagy. Glucose starvation triggers disulfidptosis in cells when the Solute Carrier Family 7 Member 11 (SLC7A11) gene is expressed at high levels [11]. This finding demonstrates the metabolic vulnerability of cancer cells with high SLC7A11 expression, providing a potential therapeutic avenue for the treatment of tumors by targeting their dependence on glucose and NADPH for survival. Now, multiple studies have established a strong link between disulfidptosis and cancer. Liu et al.'s study shows that inhibition of glucose transporters leads to robust cell death in cancer cells with high expression of SLC7A11, a process that may be mediated by the disulfidptosis mechanism[11]. According to Qi et al., gene expression levels related to disulfide proteolysis may correlate with lung adenocarcinoma progression using extensive RNA-seq data [12]. Similarly, multiple prognostic models have been established in colon cancer[13], glioma[14], melanoma[15] and breast cancer[16] to predict cancer prognosis and survival rates. However, the relationship between BLCA and disulfide mortality remains unknown.

This study aimed to identify a disulphideptosis and immune gene signature to predict prognosis in BLCA. An eight-gene signature based on disulfidptosis-related immune genes was identified and validated from extensive BLCA transcript data. Through this feature, BLCA's molecular mechanism will be better understood and guidance for personalized immunotherapy will be provided.

Collection of data set and sample information

RNA-seq data sets of 400 BLCA specimens and 19 normal specimens were obtained from the TCGA database. Download corresponding clinical characteristics data about the patient. These raw data were normalized at the fragment expression level per million bases, and TPM values were transformed using log2(TPM + 1) calculation. The TCGA-BLCA samples were randomly divided into train groups and internal test groups according to 1:1 using R software. The external test group (ID: GSE32894) and single-cell sequencing data (ID: GSE130001) were also obtained from the GEO database. Those samples with duplicate characteristics or survival data were excluded.

Establishing a risk score to predict overall survival in BLCA in the training cohort

In the training cohort, genes associated with OS were identified using Univariate Cox regression analysis filtered at < 0.05. The LASSO algorithm eliminated overfitting between genes related to prognosis, and in the case of prognosis-related genes, the penalty parameters were adjusted through 10-fold cross-validation using the R package "glmnet". Multivariable Cox regression was conducted on genes with non-zero regression coefficients derived from LASSO regression analysis. The risk score was constructed by multiplying the expression level of each gene by its corresponding regression coefficient derived from multivariable Cox regression analysis for each gene. Patients were divided into high-risk and low-risk groups based on the optimal cutoff value of the risk score. R packages "survminer", "survival ROC" and "timeROC" were used to generate Kaplan–Meier survival curves and time-dependent receiver operating characteristic curves (ROCs) to evaluate the performance of risk score in predicting survival.

Correlation and stratification analysis of risk-taking scores

In order to determine whether the risk score can be used as a prognostic indicator, we performed a multivariate and univariate Cox regression analysis incorporating the risk score and clinical characteristics. Genetic mutations were analyzed using TCGA data on genetic changes.

Nomogram construction

We used the “Regplot” software package to create a line graph to predict OS based on clinical characteristics and risk scores. Each clinically significant feature was scored using a nomogram model, and all individual scores were summed to give a total score. Comparison of the AUCs of time-dependent ROC curves for one, three, and five-year survival rates was used to evaluate the predictive accuracy of the nomogram. The nomogram was tested for its predictive accuracy using calibration curves and C-indexes.

Tumor immune analysis

In order to examine the correlation between this model and immune infiltration status, seven algorithms were used (XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT-ABS) to calculate the immune infiltration profile of the TCGA dataset. For analyzing differences in the content of immune infiltrating cells among different risk groups, Wilcoxon signed-rank test, limma, tidyverse, scales, ggplot2 and ggtext R software packages were used. With the estimation algorithm, we compared immune and stromal cell abundance among different groups and calculated StromalScore, ImmuneScore, and ESTIMATE scores (StromalScore + immune score). Furthermore, we examined the differential expression of immune checkpoint molecules in high- and low-risk groups using the Wilcoxon test to compare inhibitory or stimulatory checkpoint molecules. P value < 0.05 was considered statistically significant.

Calculation of TMB

TMB was calculated from the change in total human exon length for each sample using a Perl script. The chromosomal location of the gene is denoted by R. The "ggpubr" package was used to examine potential differences between low- and high-risk groups in immune checkpoint blockade (ICB) responses.

Differential gene expression analysis and functional enrichment analysis

Patient samples were assigned to high-risk and low-risk groups based on the optimal cutoff value of the risk score. The differentially expressed genes (DEGs) were identified when the |Log2FC| was greater than 1 and the p value adjusted to 0.05. Using ‘‘clusterProfiler’’ package, we examined the difference among biological processes (BP), cellular components (CC), and molecular function (MF) of the target gene list based on enriched biological activities and signaling pathways. In addition, the enriched pathways were identified by KEGG enrichment analysis. A p-value and a q-value < 0.05 were taken as statistically significant.

Cell cultures

We used 2 cell lines, the human BLCA cell lines TCCSUP and 5637, both from the American ATCC cell bank. The culture medium used DMEM (Gibco) + 10% FBS (Gibco) + 1% P/S (Gibco). The freezing conditions are liquid nitrogen. During the passage process, 0.25% trypsin (Gibco) was used for digestion and digestion was carried out in a 37°C incubator for 1 min.

Cell transfection

Using RNAi technology, human BLCA cell lines TCCSUP and 5637 were transiently transfected with Lipofetamine3000 (Invitgen, USA), Opti-MEM (Life Technologies, USA) and other reagents. Cell RNA was extracted after culturing under optimal conditions, and RT-qPCR was used to determine how effectively genes were knocked out or overexpression. The constructed cells were seeded in a 96-well plate, and the fluorescence value (OD value) of the two cells at 450 nm was detected at 0, 24, 48, and 72 h using the CCK-8 kit (Solaibao, Beijing, China) to compare the proliferation ability of the two cells.

Colony formation assay

TCCSUP and 5637 cells (1000 cells per well) were incubated in 6-well plates, and after culturing the cells for 2 weeks, the resulting colonies were fixed and stained with crystal violet. Images of cell colonies were taken using a digital camera and counted. All experiments have been performed in triplicate.

Transwell assay

With culture medium without fetal bovine serum (FBS) and anti-penicillin and streptomycin antibodies (Corning, USA), Liquid Matrigel matrix gel (Corning) was diluted 1:9 with culture medium. Then, take 70ul of the diluted substrate gel, spread it in the Transwell chamber of a 24-well plate, and place it in the incubator for 1 hour to solidify the substrate gel. After the cells are digested, resuspend them in 200 ul of culture medium without fetal bovine serum according to the cell density, adjust the cell volume to 5 × 10⁴ cells, and place them in the upper chamber. The lower chamber was filled with 600 ul of complete culture medium and placed in the cell culture incubator for 48 hours. The chamber was taken out, the medium aspirated, and PBS washed twice. Wipe the cells in the Matrigel matrix gel and small chamber with a cotton swab, and add 600ul of polymethanol to the lower chamber for 30 min. Aspirate the polymethanol and add 600ul of crystal violet to the lower chamber for staining. Stain for 30 minutes. After washing twice with PBS, take pictures and record the images under a microscope. All experiments have been performed in triplicate.

Statistical analysis

Data were analyzed using R v.4.2.2. A t-test was performed on continuous variables for comparison with independent variables. Wilcoxon rank sum tests were conducted for comparisons of categorical variables for comparison with non-normally distributed variables. An analysis of Kaplan-Meier curves and log-rank tests was carried out, with a p value of less than 0.05 considered statistically significant.

Screening of DEGs, construction of WGCNA network, and identification of BLCA disulfidptosis-related immune genes

This study downloaded the BLCA dataset from TCGA, which identified a total of 4925 DEGs (containing sample data from 19 BLCA patients and 400 healthy individuals) based on |log2FC| > 2, corrected p < 0.05. With the WGCNA algorithm, a gene co-expression network was constructed. Gene correlation was used to construct a dendrogram of gene hierarchies, resulting in the identification of six gene modules (Fig. 1A). Subsequently, based on the correlation between module feature values and the disulfidptosis phenotype of BLCA, we finally determined that the “Turquoise” module containing 6080 genes was the most clinically valuable module in BLCA (Cor = 0.2, p = 1×e − 4) (Fig. 1B). In order to explore the role of disulfidptosis-related immune in predicting the prognosis of BLCA, we first selected the module with the highest positive correlation with disulfide death (purple module), then crossed with DEGs and immune related genes, and finally obtained 135 crossed genes (Fig. 1C).

Construction and validation of risk scoring models

Among the 135 crossed genes, a total of 22 genes with significant differences were found to be related to BLCA prognosis through single-factor Cox regression analysis, including ACKR3, APOBEC3G, CCL19, CCL21, CXCL12, DHX58, LMBR1, LMMP9, NFATC1, NR3C1, PDGFD, PLXNB1, PPP3CC, PSMB8, PSME2, PTPN6, S100A6, S1PR2, SDC2, SRC, TAPBP, TNFRSF12A (p < 0.05) (Fig. 1D). Subsequently, the Lasso-Cox regression algorithm was applied to the selected feature genes in the TCGA training cohort to reduce overfitting of the 22 genes. According to the independent variable coefficient and the best log value of lambda in the Lasso-Cox regression analysis, 8 genes related to the prognosis of BLCA were obtained, namely, ACKR3, MMP9, PDGFD, PSMB8, PTPN6, SDC2, SRC and TNFRSF12A (Fig. 2C) and its corresponding coefficient. In order to predict the prognosis of BLCA patients, we calculated a risk score based on the expression values of eight genes and divided the TCGA training cohort into high-risk and low-risk groups. The risk map shows the distribution of risk scores and their relationship with survival outcomes (Fig. 2A, B), and the heat map shows the expression levels of risk genes in the high-risk and low-risk groups (Fig. 2C). Kaplan-Meier analysis showed that the survival time of patients in the high-risk group was shorter than that of the lower-risk group in the training set and test set 1, 2, and 3 (P < 0.0001) (Fig. 2D-G). These results indicate that the risk score based on disulfidptosis-related immune genes has good predictive performance in BLCA patients. For the TCGA training cohort, 10-year time-dependent c-index analysis showed that risk score had good prognostic accuracy in predicting clinical outcomes compared with risk score, age, sex, and TNM in BLCA patients (Fig. 2H). As shown by the area under the risk model curve (AUC), the AUC values for predicting BLCA patients' survival rates at 1, 3, and 5 years based on their risk scores are 0.748, 0.777, and 0.777, respectively (Fig. 2I-K). Compared with other clinicopathological characteristics, it was shown Excellent predictive ability (Fig. 2I-K).

Figure 3 shows the OS curve, which is based on the Kaplan-Meier method and the LOG-RANK test, taking into account the following clinical parameters: age, sex, T stage, N stage, M stage, and AJCC stage. We observed that BLCA can be determined according to age (≤ 65, n = 159; >65, n = 241), gender (male, n = 296; female, n = 104), T (grade 3–4, n = 247), N (grade 0, n = 233; grade 1–3, n = 125), M (M0, n = 194) and AJCC stage (stage III-IV, n = 269 cases) to predict the survival of different clinical subgroups Results (p < 0.001). The results showed that high risk-score values were associated with poorer prognosis in BLCA patients (Fig. 3). As a result of these results, it appears that this model can be used to predict the prognosis of BLCA patients based on various clinical and pathological characteristics.

Comparison of the prognostic forecasting model with other published models

To evaluate the performance of the signatures relative to existing signatures, we included Cao (Fig. 4A), Chen (Fig. 4B), Liu (Fig. 4C), Luo (Fig. 4D), Wang (Fig. 4E), Zhu (Fig. 4F), etc. 6 published OS risk prediction models for BLCA patients. Based on the ROCs of each signature, it is evident that all models perform well in making predictions, and the AUC values of 1-year, 3-year and 5-year OS in our model are 0.748, 0.767, 0.775 respectively, which is higher than the AUC of the 6 previously published signatures. In addition, the C-index of our model is 0.702 higher than other models (Fig. 4G), while the C-index of the other 6 signatures are 0.655, 0.671, 0.564, 0.654, 0.656, and 0.63 respectively.

Analyzing independent prognostic factors and developing nomograms

To screen independent prognostic factors, univariate and multivariate Cox analyzes were performed on clinical characteristics and risk scores. Univariate and multivariate Cox analysis (Fig. 5A and 5B) showed that risk score was an independent risk factor for patients with BLCA. Then, based on age, T, N, AJCC stage, and risk score, we constructed a nomogram model, quantified the scores for each variable, and estimated the survival probability using a calculation formula and a total score formula (Fig. 5). The calibration curve analysis and DCA were then used to verify the model's accuracy, stability, and clinical usefulness. Calibration curve results show that 1-, 3-, and 5-year OS are consistent with predicted values (Fig. 5D), indicating the good performance of the prognostic nomogram. A clinical decision curve analysis (DCA) revealed that the model constructed by combining five variables had a higher clinical benefit rate than any of the variables alone (Fig. 5E). The above results indicate that our prediction model can accurately and robustly predict prognoses.

Analysis of tumor microenvironment and immune cell infiltration

In tumor formation and prognosis, immune cells play a crucial role. To understand the immune cell infiltration of BLCA patients in the TCGA cohort, we evaluated the correlation between risk scores and immune cell infiltration levels using XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT analyses. The results showed that there were significant differences in immune cell types in tumor tissues of BLCA patients (Fig. 6A). The risk score was positively correlated with Cancer associated fibroblast, Stroma score, M0 Macrophage, Hematopoietic stem cell, Endothelial cell, and negatively correlated with CD8 + T cell, CD4 + T cell, Follicular helper T cell, and CD8 + T cell naive. Both prognostic genes and risk scores were significantly associated with infiltrating immune cells, according to Pearson correlations (Fig. 6B). Next, we analyzed the correlation between infiltrating immune cells and eight signature genes and risk scores. The results showed that M0 macrophages, memory resting CD4 T cells, Neutrophils, naive B cells, and active Mast cells were positively correlated with risk scores, while CD8 T cells, active CD4 memory T cells, T follicular helper cells, resting NK cells, γ-Delta T cells, and active Dendritic cells were negatively correlated with the risk score (Fig. 6C). The number of immune cells was also examined by our study in relation to eight genes (Fig. 6C). According to our research, these 8 genes affect most immune cells. A low-risk group had a greater number of activated CD4 memory T cells (Fig. 6D), activated CD8 memory T cells (Fig. 6E), M1 macrophages (Fig. 6F), and resting NK cells (Fig. 6G). These results indirectly suggest different immune landscapes in the two risk groups.

Immune checkpoint blockade therapy has been approved for the treatment of BLCA with great success. To explore the correlation between disulfidptosis and BLCA immunotherapy. An immunotherapy-related parameter, tumor mutation burden (TMB), and immunological checkpoints were studied in this study. First, we investigated the relationship between 8 signature genes and risk scores as well as common immune checkpoints (ADORA2A, BTLA, CD160, CD244, CD274, CD96, CSF1R, CTLA4, HAVCR2, IDO1, IL10, IL10RB, KDR, KIR2DL1, KIR2DL3, LAG3, LGALS9, NECTIN2, PDCD1, PDCD1LG2, TGFB1, TGFBR1, TIGIT and VTCN1) (Fig. 7A). The risk score was positively correlated with CSF1R, HAVCR2, IL10, KDR, PDCD1LG2, TGFB1, and TGFBR1 genes, and negatively correlated with CD160, CD96, IL10RB, and LGALS9. Eight signature gene levels were associated with the expression of most immune checkpoints. Using TMB correlation analysis, risk score was found to be negatively correlated with TMB (R = -0.12, p = 0.022, Fig. 7B). Furthermore, TMB was higher in the low-score group compared with the high-score group (p = 0.0067; Fig. 7C). We evaluated IPS immunotherapy response. According to the IPS (CTLA4-/PD-1-, CTLA4+/PD-1-, CTLA4-/PD-1 + and CTLA4+/PD-1+), patients in the low-risk group showed significant therapeutic benefit from ICIs treatment (Fig. 7D–G). Furthermore, analysis of the immunotherapy cohort found that risk scores were lower in the responder group. These results suggest that risk scores play an important role in predicting immunotherapy response and that lower risk scores may result in better immunotherapy responses.

The Biological Pathways Analyses

An analysis of GO and KEGG enrichment between the two groups identified DEGs. In the GO analysis, biological processes were mostly associated with “the extracellular matrix organization”, “extracellular structure organization”, “external encapsulating”, “structure organization ossification” and “connective tissue development (Fig. 8A, B). Enrichment in the cellular component category was associated with “collagen-containing extracellular matrix”, “endoplasmic reticulum lumen”, “collagen trimer” and “basement membrane”. KEGG analysis showed that the DEGs were mostly enriched in pathways related to “PI3K-Akt signaling pathway”, “Focal adhesion”, “Human papillomavirus infection”, “Protein digestion and absorption” and “ECM-receptor interaction” (Fig. 6C, D). KEGG pathway enrichment analysis showed that the risk score was positively correlated with WNT, TGF-BETA, MAPK, HEDGEHOG, CALCIUM and other pathways, and negatively correlated with the RIG-I like receptor pathway. Eight disulfidptosis-ralative genes are associated with multiple tumor pathways (Fig. 6E).

TNFRSF12A promote BLCA cell proliferation and invasion

The above data indicate that the disulfidptosis-related immune gene has an impact on the prognosis of BLCA patients. Therefore, we explored whether the above genes could influence tumor progression at the cellular level. We found that TNFRSF12A was the highest expressed in tumor cells through single-cell data set (GSE130001) analysis (Fig. 9A), so we selected this gene to further explore its function. siRNA was used to knock out the TNFRSF12A gene in the TCCSUP cell line, and plasmid was used to overexpress the TNFRSF12A gene in the 5637-cell line. The results of CCK-8 (Fig. 9B) and colony formation experiments (Fig. 9C) showed that knocking out the TNFRSF12A gene reduced the proliferation activity of TCCSUP cells, and overexpressing the TNFRSF12A gene promoted the proliferation activity of 5637 cells. Transwell results showed that knocking out the TNFRSF12A gene reduced the invasive activity of TCCSUP cells, and the number of cells overexpressing TNFRSF12A that penetrated the chamber membrane was significantly increased (Fig. 9D). TNFRSF12A affects the invasive ability of BLCA at the cellular level. Blocking the TNFRSF12A gene may have potential implications for tumor treatment.

BLCA is one of the tenth most common cancers in the world [17]. Currently, treatment options for BLCA have expanded from traditional chemotherapy to immune checkpoint inhibitors. Among them, immune checkpoint inhibitors (ICI) targeting PD-1/PD-L1 and CTLA-4 have achieved encouraging results. Unfortunately, the clinical benefit of anti-PD-1/PD-L1 monotherapy was not significantly improved compared with standard first-line platinum-based chemotherapy [18, 19]. Due to this, it is critical to identify biomarkers with high prognostic values in order to better characterize the immune component of nodal BLCA, which is vital for patient stratification, immunotherapy, and prognosis. Recently, disulfidptosis has been identified as a novel mode of programmed cell death [11]. Previous studies have initially explored the role of disulfidptosis genes in BLCA, and several clinical indicators of disulfidptosis genes have been developed for prognostic assessment of BLCA. [20, 21]. In spite of similar clinical characteristics, patients' molecular profiles are very heterogeneous, leading to significant clinical outcomes. Tumor immunity is closely related to tumor occurrence, development, treatment response, and drug resistance [22]. More and more immune genes are used in the treatment and prognosis classification of BLCA [23, 24]. A combined predictor of both disulfidptosis and immunity is undoubtedly more effective in predicting prognosis. To assist in identifying BLCA patients' OS prognoses, we identified eight disulfide-related immune genes and developed a nomogram model which includes clinical characteristics to determine BLCA patients' individual treatment strategies. The validity of the signature was verified using an external verification dataset. In addition, an overexpressed gene TNFRSF12A was also discovered and its relationship with the malignant phenotype of BLCA cell lines was verified. To our knowledge, this is the first study to study the impact of combined application of disulfidptosis-related immune prognostic signals on the prognosis of BLCA.

In this study, we first developed a risk assessment model containing eight disulfidptosis-related immune genes (ACKR3, MMP9, PDGFD, PSMB8, PTPN6, SDC2, SRC, and TNFRSF12A) using Cox regression and Lasso regression. Based on the median risk score, patients were categorized as high-risk or low-risk. ACKR3 (atypical chemokine receptor 3, also known as CXCR7) is an atypical chemokine receptor with seven transmembrane regions. The protein plays a vital role in the proliferation and migration of tumor cells in a variety of tumor types. Regulation of tumor angiogenesis or drug resistance, thereby promoting tumor progression and metastasis[25]. MMP9 has been confirmed to be related to the degree of migration and invasion of BLCA [26]. Platelet-derived growth factor-D (PDGF-D) has been shown to be associated with the migration, invasion and proliferation capabilities of rectal cancer [27]. In BLCA, the sequencing results of Wen et al. showed that the expression of PDGFD was significantly down-regulated in BLCA [28]. Jin, K et al. developed a prognostic prediction model including PDGFD [29]. In BLCA, PSMB8 was identified as a CD8 T cell-related gene that promotes CD8 T cell infiltration and was enriched during MHC class I tumor antigen presentation [30]. Multiple studies use PSMB8 to predict BLCA prognosis [31, 32]. PTPN6 (protein tyrosine phosphatase nonreceptor type 6) is a tyrosine phosphatase, and TCGA-based research shows that it may be a new prognostic biomarker for BLAC [33]. Syndecan-2 (SDC2) is a heparan sulfate proteoglycan whose altered expression is associated with poor prognosis in multiple cancers [34, 35]. Aberrant activation of SRC signaling leads to enhanced BLAC migration and invasion capabilities [36]. The TNFRSF12A gene is related to aging and contributes to hypoxia-induced inflammatory responses. It also contributes to thyroid cancer [37].

The 1, 3, and 5-year AUC model has good sensitivity and specificity in predicting the prognosis of BLCA. An analysis of survival showed that the prognostic characteristics of low- and high-risk BLCA patients could be accurately separated. Patients in the high-risk group have shorter survival times and higher mortality. Many BLCA prognostic models based on gene features have been developed, such as the 6 immune-related gene signal prognostic models constructed by Cao, Chen, Liu, Luo, Wang, Zhu et al. Compared with these models, our prognostic model has better prognostic accuracy for BLCA. Correlation analysis between risk score and clinicopathological characteristics shows that risk score is closely related to age, gender, T stage, N stage, tumor stage and poor prognosis. In order to improve the accuracy of prognosis prediction, we analyzed various factors (including double Sulfur mortality risk score, age, gender, TNM stage and pathological stage), a nomogram was developed and validated. Independent prognostic analysis results showed that age, gender, TNM stage and disulfidptosis risk score were significantly related to the prognosis of BLCA.

As an important part of immunotherapy, analysis of the tumor immune microenvironment (TIME) helps predict the response to immunotherapy. In order to examine the significance of immune cell infiltration in BLCA risk groups, we analyzed lymphocyte proportions using XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT-ABS. The results showed that immune infiltration and immune checkpoint results in low-risk groups showed a large number of highly infiltrated activated CD4 memory T cells (Fig. 6D), CD8 T cells (Fig. 6E), M1 macrophages (Fig. 6F), resting NK cells, these cells have strong tumor immune function. Therefore, low-risk groups may be more responsive to immunotherapy. It is possible that our disulfidptosis-related immune score can indicate immune cell infiltration and the prognostic significance of different types of immune cells. Myoepithelial cells, fibroblasts, myofibroblasts, endothelial cells, inflammation cells, and bone marrow-derived cells (BMDCs) make up the stroma of tumors, which are widely believed to cooperate with cancer cells and other host cells to create a milieu that allows tumor growth, Microenvironment for progression, angiogenesis, invasion, and metastasis[38, 39]. Through analysis of 8 disulfidptosis genes in the single cell data set (GSE130001), we found that TNFRSF12A has the highest expression level in tumor cells. We further explored the function of this gene through in vitro experiments. Knocking down the TNFRSF12A gene reduced the proliferation and invasion ability of BLCA cells, while overexpressing the TNFRSF12A gene had the opposite effect. This suggests that TNFRSF12A may become a target for immunotherapy in BLCA patients. Tumor mutation burden (TMB) is an indicator of the average number of mutations in tumor cell DNA compared to healthy cells and serves as a predictive biomarker of general immunogenicity and tumor propensity to respond to ICIs[40]. In our study, a higher TMB level was observed in the low-risk group. This shows that a higher tumor mutation load may indicate poor efficacy of tumor immunotherapy.

In our study, DEGs related to high and low risk groups were analyzed using KEGG. The results revealed several major enriched pathways, including “PI3K-Akt signaling pathway”, “Focal adhesion”, “Human papillomavirus infection”, “Protein digestion and absorption” and “ECM-receptor interaction”. The phosphoinositide 3-kinase (PI3K)-protein kinase B (AKT) pathway has been shown to regulate multiple cellular functions, including proliferation, metabolism, and transcription.[41, 42]. Studies by López-Knowles, E et al. demonstrate that mutations in PIK3CA are common in BLCA, supporting the development of papillary and muscle-invasive tumors through different molecular pathways. [43]. The research results of Dickstein, R et al. show that the AKT inhibitor AZ7328 inhibits the PI3K/AKT/mTOR pathway in vitro and exerts a cytostatic effect on BLCA [44]. These studies revealed the key role of PI3K/Akt and other signaling pathways in the BLCA process, suggesting that disulfidptosis may regulate the occurrence and development of BLCA through the PI3K/Akt pathway. To determine the role of PI3K/Akt in disulfide ptosis, further studies are needed.

Finally, the 8 disulfidptosis genes we discovered were verified through in vitro experiments. The role of TNFRSF12A in bladder cancer is still unclear. Our study showed that silencing of TNFRSF12A gene significantly inhibited BLAC cell proliferation and invasion, while overexpression of TNFRSF12A gene did the opposite. This indicates that TNFRSF12A plays an important role in the malignant phenotype of BLAC and may be a potential BLAC tumor marker and a new target for tumor treatment.

This study has certain limitations and shortcomings. First, our study is entirely based on the TCGA data set, and the predictive ability of the study results needs to be confirmed by independent prospective clinical studies. Second, there has not been any in vitro or in vivo validation of genes other than TNFRSF12A, and it is possible to gain new insights into immunotherapy for BLCA by understanding these disulfidptosis-related immune genes more in-depth.

In summary, we developed a new prognostic assessment model involving 8 disulfidptosis-related immune genes that can accurately predict the prognosis, TMB, and immunotherapy of BLCA, which will provide new insights into the development of new treatments for BLCA. TNFRSF12A plays an oncogene role in the occurrence and development of BLCA by promoting the proliferation and invasion of BLCA cells.

Acknowledgments

None.

Authors’ contributions

KC: Conceptualization, Data curation, Formal analysis, Validation, Writing – original draft, Writing – review & editing. JYZ: Methodology, Project administration, Validation, Visualization, Writing – original draft. GJL: Methodology, Project administration, Writing – original draft, Data curation, Formal analysis. HYZ: Methodology, Writing – review & editing, Data curation, Resources. YYG: Methodology, Writing – review & editing, Software.

HWZ: Writing – review & editing, Conceptualization. All authors read and approved the fnal manuscript.

Funding

None.

Availability of data and materials

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Ethics approval and consent to participate

This study was approved by the ethics committee of Wenzhou Central Hospital and was performed in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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No competing interests reported.

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Construction of a new prognosis prediction model and immune infiltration analysis of bladder urothelial cancer based on disulfidptosis-related immune genes

Status:

Version 1

Abstract

Introduction:

Methods

Results

Conclusions

Figures

Introduction

Materials and methods

Collection of data set and sample information

Establishing a risk score to predict overall survival in BLCA in the training cohort

Correlation and stratification analysis of risk-taking scores

Nomogram construction

Tumor immune analysis

Calculation of TMB

Differential gene expression analysis and functional enrichment analysis

Cell cultures

Cell transfection

Colony formation assay

Transwell assay

Statistical analysis

Results

Screening of DEGs, construction of WGCNA network, and identification of BLCA disulfidptosis-related immune genes

Construction and validation of risk scoring models

Comparison of the prognostic forecasting model with other published models

Analyzing independent prognostic factors and developing nomograms

Analysis of tumor microenvironment and immune cell infiltration

The Biological Pathways Analyses

TNFRSF12A promote BLCA cell proliferation and invasion

DISCUSSION

Conclusion

Declarations

References

Additional Declarations

Status:

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