Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer

ADME gene-driven prognostic model for bladder cancer: a breakthrough in predicting survival and personalized treatment

BACKGROUND

Genes that participate in the absorption, distribution, metabolism, excretion (ADME) processes occupy a central role in pharmacokinetics. Meanwhile, variability in clinical outcomes and responses to treatment is notable in bladder cancer (BLCA).

METHODS

Our study utilized expansive datasets from TCGA and the GEO to explore prognostic factors in bladder cancer. Utilizing both univariate Cox regression and the lasso regression techniques, we identified ADME genes critical for patient outcomes. Utilizing genes identified in our study, a model for assessing risk was constructed. The evaluation of this model's predictive precision was conducted using Kaplan-Meier survival curves and assessments based on ROC curves. Furthermore, we devised a predictive nomogram, offering a straightforward visualization of crucial prognostic indicators. To explore the potential factors mediating the differences in outcomes between high and low risk groups, we performed comprehensive analyses including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)-based enrichment analyses, immune infiltration variations, somatic mutation landscapes, and pharmacological sensitivity response assessment etc. Immediately following this, we selected core genes based on the PPI network and explored the prognostic potential of the core genes as well as immune modulation, and pathway activation. And the differential expression was verified by immunohistochemistry and qRT-PCR. Finally we explored the potential of the core genes as pan-cancer biomarkers.

RESULTS

Our efforts culminated in the establishment of a validated 17-gene ADME-centered risk prediction model, displaying remarkable predictive accuracy for BLCA prognosis. Through separate cox regression analyses, the importance of the model's risk score in forecasting BLCA outcomes was substantiated. Furthermore, a novel nomogram incorporating clinical variables alongside the risk score was introduced. Comprehensive studies established a strong correlation between the risk score and several key indicators: patterns of immune cell infiltration, reactions to immunotherapy, landscape of somatic mutation and profiles of drug sensitivity. We screened the core prognostic gene CYP2C8, explored its role in tumor bioregulation and validated its upregulated expression in bladder cancer. Furthermore, we found that it can serve as a reliable biomarker for pan-cancer.

CONCLUSION

The risk assessment model formulated in our research stands as a formidable instrument for forecasting BLCA prognosis, while also providing insights into the disease's progression mechanisms and guiding clinical decision-making strategies.

Identification of bladder cancer subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes

Immunotherapy based on immune checkpoint genes (ICGs) has recently made significant progress in the treatment of bladder cancer patients, but many patients still cannot benefit from it. In the present study, we aimed to perform a comprehensive analysis of ICGs in bladder cancer tissues with the aim of evaluating patient responsiveness to immunotherapy and prognosis. We scored ICGs in each BLCA patient from TCGA and GEO databases by using ssGSEA and selected genes that were significantly associated with ICGs scores by using the WCGNA algorithm. NMF clustering analysis was performed to identify different bladder cancer molecular subtypes based on the expression of ICGs-related genes. Based on the immune related genes differentially expressed among subgroups, we further constructed a novel stratified model containing nine genes by uni-COX regression, LASSO regression, SVM algorithm and multi-COX regression. The model and the nomogram constructed based on the model can accurately predict the prognosis of bladder cancer patients. Besides, the patients classified based on this model have large differences in sensitivity to immunotherapy and chemotherapy, which can provide a reference for individualized treatment of bladder cancer.

Generalized machine learning based on multi-omics data to profile the effect of ferroptosis pathway on prognosis and immunotherapy response in patients with bladder cancer

INTRODUCTION

Bladder cancer (BLCA) affects millions of people worldwide, with high rates of incidence and mortality. Ferroptosis proves to be a novel form of cell death process that is triggered by oxidative stress.

METHODS

We procured a total of 25 single nuclear RNA-seq (snRNA-seq) samples from GSE169379 in GEO database. We obtained different cohorts of BLCA patients from the TCGA and GEO databases for model training and validation. A total of 369 ferroptosis-related genes (FRGs) were selected from the FerrDb database. AUCell analysis was performed to assign ferroptosis scores to all the cell types. Weighted Gene Co-Expression Network Analysis (WGCNA), COX, and LASSO regression analysis were conducted to retain and finalize the genes of prognostic values. Various bioinformatic approaches were utilized to depict immune infiltration profile. We conducted a series of colony formation analysis, flow cytometry and western blot (WB) analysis to determine the role of SKAP1 in BLCA.

RESULTS

We divided the cells into high ferroptosis group and low ferroptosis group according to ferroptosis activity score, and then screened 2150 genes most associated with ferroptosis by differential expression analysis, which are related to UV-induced DNA damage, male hormone response, fatty acid metabolism and hypoxia. Subsequently, WGCNA algorithm further screened 741 ferroptosis related genes from the 2150 genes for the construction of prognostic model. Lasso-Cox regression analysis was used to construct the prognostic model, and the prognostic model consisting of 6 genes was obtained, namely JUN, SYT1, MAP3K8, GALNT14, TCIRG1, and SKAP1. Next, we constructed a nomogram model that integrated clinical factors to improving the accuracy. In addition, we performed drug sensitivity analyses in different subgroups and found that Staurosporine, Rapamycin, Gemcitabine, and BI-2536 may be candidates for the drugs treatment in high-risk populations. The ESTIMATE results showed higher stromal scores, immune scores, and ESTIMATE scores in the low-risk group, indicating a higher overall immunity level and immunogenicity of tumor microenvironment (TME) in this group, and tumor immune dysfunction and exclusion (TIDE) analysis confirmed a better response to immunotherapy in the low-risk group. Finally, we selected the oncogene SKAP1 in the prognostic gene for in vitro validation, and found that SKAP1 directly regulated BLCA cell proliferation and apoptosis.

CONCLUSION

We identified a set of six genes, JUN, SYT1, MAP3K8, GALNT14, TCIRG1, and SKAP1, that exhibited significant potential in stratification of BLCA patients with varying prognosis. In addition, we uncovered the direct regulatory effect of SKAP1 on BLCA cell proliferation and apoptosis, shedding some light on the role of FRGs in pathogenesis of BLCA.

Development and verification of a newly established cuproptosis-associated lncRNA model for predicting overall survival in uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignant tumor with high recurrence and mortality rates. This study aimed to develop and validate a prognostic model for patients with UCEC based on cuproptosis-related long non-coding RNA (lncRNA) signature.

Methods

Transcriptome and clinical UCEC data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis was conducted to screen out the cuproptosis-related lncRNAs, and univariate regression analysis was performed to determine prognostic factors associated with overall survival (OS). A cuproptosis-related lncRNA risk model was constructed through least absolute shrinkage and selection operator (LASSO) regression and cross-validation. The accuracy and reliability of the model were verified through Kaplan-Meier (KM), proportional hazards model (Cox) regression, nomogram, principal component analysis (PCA), and stage analysis. Gene Ontology (GO) enrichment, immune function, and tumor mutation burden (TMB) analyses were conducted between low-risk and high-risk groups, and antineoplastic drugs were predicted.

Results

By correlation analysis, 155 cuproptosis-related lncRNAs were acquired, and 9 lncRNAs were identified as independent prognostic factors. A 6-cuproptosis-related lncRNA model was established. The results revealed that patients in the high-risk group were more inclined to have a poor OS than those in the low-risk group. Risk score was an independent prognostic factor and had a high accuracy and predictive value. The extracellular structure and anchored components of membrane-related GO terms were significantly enriched. Immune function and TMB results were assumed to be different from each other, which might explain a better outcome in the low-risk group than that in the high-risk group. Eighteen compounds were predicted as chemotherapy drugs with high half maximal inhibitory concentration (IC50) in the high-risk group.

Conclusions

We successfully developed a cuproptosis-related lncRNA risk model for the prediction of prognosis, while simultaneously providing insights on new approaches for immunotherapy and chemotherapy for patients with UCEC.

Construction of a prediction model for prognosis of bladder cancer based on the expression of ion channel-related genes

OBJECTIVES

To construct a prediction model for the prognosis of bladder cancer patients based on the expression of ion channel-related genes (ICRGs).

METHODS

ICRGs were obtained from the existing researches. The clinical information and the expression of ICRGs mRNA in breast cancer patients were obtained from the Cancer Genome Atlas database. Cox regression analysis, minimum absolute shrinkage and selection operator regression analysis were used to screen breast cancer prognosis related genes, which were verified by immunohistochemistry and qRT-PCR. The risk scoring equation for predicting the prognosis of patients with bladder cancer was constructed, and the patients were divided into high-risk group and low-risk group according to the median risk score. Immune cell infiltration was compared between the two groups. Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve were used to evaluate the accuracy and clinical application value of the risk scoring equation. The factors related to the prognosis of bladder cancer patients were analyzed by univariate and multivariate Cox regression, and a nomogram for predicting the prognosis of bladder cancer patients was constructed.

RESULTS

By comparing the expression levels of ICRGs in bladder cancer tissues and normal bladder tissues, 73 differentially expressed ICRGs were dentified, of which 11 were related to the prognosis of bladder cancer patients. Kaplan-Meier survival curve suggested that the risk score based on these 11 genes was negatively correlated with the prognosis of patients. The area under the ROC curve of the risk score for predicting the prognosis of patients at 1, 3 and 5 year was 0.634, 0.665 and 0.712, respectively. Stratified analysis showed that the ICRGs-based risk score performed well in predicting the prognosis of patients with American Joint Committee on Cancer (AJCC) stage Ⅲ-Ⅳ bladder cancer (P<0.05), while it had a poor value in predicting the prognosis of patients with AJCC stage Ⅰ-Ⅱ (P>0.05). There were significant differences in the infiltration of plasma cells, activated natural killer cells, resting mast cells and M2 macrophages between the high-risk group and the low-risk group. Cox regression analysis showed that risk score, smoking, age and AJCC stage were independently associated with the prognosis of patients with bladder cancer (P<0.05). The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1, 3 and 5 year overall survival rate of bladder cancer patients.

CONCLUSIONS

The study shows the potential value of ICRGs in the prognostic risk assessment of bladder cancer patients. The constructed prognostic nomogram based on ICRGs risk score has high accuracy in predicting the prognosis of bladder cancer patients.

A novel platelet risk score for stratifing the tumor immunophenotypes, treatment responses and prognosis in bladder carcinoma: results from real-world cohorts

Background: Although the durable efficacy of immune checkpoint inhibitors (ICIs) in BLCA has been confirmed in numerous studies, not all patients benefit from their application in the clinic. Platelets are increasingly being found to be closely associated with cancer progression and metastasis; however, their comprehensive role in BLCA remains unclear. Methods: We comprehensively explored platelet expression patterns in BLCA patients using an integrated set of 244 related genes. Correlations between these platelet patterns with tumor microenvironment (TME) subtypes, immune characteristics and immunotherapy efficacies were explored. In addition, a platelet risk score (PRS) was generated for individual prognosis and verified the ability to predict prognosis, precise TME phenotypes, and immunotherapy efficacies. Results: Genes were clustered into two patterns that represented different TME phenotypes and had the ability to predict immunotherapy efficacy. We constructed a PRS that could predict individual prognosis with satisfactory accuracy using TCGA-BLCA. The results remained consistent when PRS was validated in the GSE32894 and Xiangya cohort. Moreover, we found that our PRS was positively related to tumor-infiltrating lymphocytes (TILs) in the TCGA-BLCA and Xiangya cohort. As expected, patients with higher PRS exhibited more sensitive to immunotherapy than patients with lower PRS. Finally, we discovered that a high PRS indicated a basal subtype of BLCA, whereas a low PRS indicated a luminal subtype. Conclusion: Platelet-related genes could predict TME phenotypes in BLCA. We constructed a PRS that could predict the TME, prognosis, immunotherapy efficacy, and molecular subtypes in BLCA.

Identification of a New Prediction Model for Bladder Cancer Related to Immune Functions and Chemotherapy Using Gene Sets of Biological Processes

Background. Biological processes serve crucial functions in the initiation and development of cancer. Therefore, we constructed and validated a model for bladder cancer (BLCA) with good predictive power for immunity, prognosis, and therapy. Methods. Using the expression of the gene sets based on biological processes, BLCA patients were divided into three clusters by consensus cluster analysis. By performing LASSO regression analysis twice, key genes were selected, and the biological processes-related genes’ (BPRG) score was calculated. Differences in immune infiltration, tumor microenvironment, tumor mutation burden, immunotherapy, and sensitivity towards chemotherapy were analyzed between two groups divided by BPRG score. Results. Good accuracy was observed for the three clusters. They showed different prognoses and levels of immune cell infiltration. The selected key genes were mainly enriched in immune-related pathways. The high-BPRG score group was related to poor prognosis, higher immune cell infiltration, interstitial scores, and increased tumor mutation. Moreover, the effects of immunotherapy were good, and those of chemotherapy were poor. Conclusion. Overall, key genes may be involved in various complex immune regulation processes. Therefore, the quantification and verification of the BPRG score are expected to facilitate the understanding of the immunosuppressive microenvironment in BLCA and guide the choice of chemotherapeutic drugs and immunotherapeutic regimens and help predict the prognoses of patients with BLCA.