Identification and validation of novel biomarkers affecting bladder cancer immunotherapy via machine learning and its association with M2 macrophages

Application progress of artificial intelligence in tumor diagnosis and treatment

The rapid advancement of artificial intelligence (AI) has introduced transformative opportunities in oncology, enhancing the precision and efficiency of tumor diagnosis and treatment. This review examines recent advancements in AI applications across tumor imaging diagnostics, pathological analysis, and treatment optimization, with a particular focus on breast cancer, lung cancer, and liver cancer. By synthesizing findings from peer-reviewed studies published over the past decade, this paper analyzes the role of AI in enhancing diagnostic accuracy, streamlining therapeutic decision-making, and personalizing treatment strategies. Additionally, this paper addresses challenges related to AI integration into clinical workflows and regulatory compliance. As AI continues to evolve, its applications in oncology promise further improvements in patient outcomes, though additional research is needed to address its limitations and ensure ethical and effective deployment.

Integrated analysis of bulk and single-cell RNA-seq data reveals cell differentiation-related subtypes and a scoring system in bladder cancer

Bladder cancer (BLCA) exhibits notable molecular heterogeneity, influencing diverse clinical outcomes. However, the molecular subtypes associated with cell differentiation-related genes (CDR) and their prognostic implications remain unexplored. Analysing two GEO single-cell datasets, we identified genes linked to cell differentiation. Utilizing these genes, we explored distinct molecular subtypes. WGCNA analysis further identified CDR-associated genes, and the CDR score system, constructed using Lasso and Cox regression, was developed. Clinical prognosis and variations in immune-related factors among patient groups were assessed. Core genes were selected and confirmed through in vitro experiments. Two BLCA subtypes related to cell differentiation were identified: Subtype B demonstrated a favourable prognosis, while Subtype A exhibited significant immune cell infiltration. The CDR score system of nine genes revealed a positive correlation between higher scores and a poorer prognosis. The comprehensive analysis uncovered a positive association between CDR genes and M2 macrophages and unresponsiveness to immune therapy. Functional experiments validated that ANXA5 downregulation influences tumour cell migration without affecting proliferation. Our study reveals distinct cell differentiation-related molecular subtypes and introduces the CDR scoring system in BLCA. ANXA5 emerges as a potential therapeutic target, offering promising avenues for personalized treatment strategies.

Risk factors for ventricular arrhythmias after emergency percutaneous coronary intervention in elderly patients with acute myocardial infarction

OBJECTIVE

To explore the risk factors for ventricular arrhythmia after percutaneous coronary intervention (PCI) in elderly patients with acute myocardial infarction (AMI).

METHODS

A retrospective cohort of 201 elderly AMI patients who underwent PCI in the emergency department of No. 215 Hospital of Shaanxi Nuclear Industry from April 2020 to January 2023 was analyzed. The patients were randomly divided into a training set (n=134) for model development and a test set (n=67) for model validation. The training set was divided into a ventricular arrhythmia group (n=51) and a non-ventricular arrhythmia group (n=83), based on the occurrence of ventricular arrhythmia post-PCI. The factors affecting ventricular arrhythmias were analyzed by logistic regression and Lasso regression models.

RESULTS

Lasso regression screened 12 characteristic factors at λ=0.1 se. In the training set, the area under the ROC curve (AUC) of the Lasso model for predicting ventricular arrhythmia was 0.954, which was significantly higher than 0.826 for the Logistic model (P < 0.001). In the test set, the AUC of the Lasso model was 0.962, which was also significantly higher than 0.825 for the Logistic model (P=0.003).

CONCLUSION

Compared to the logistic regression model, the Lasso regression model can more accurately predict the occurrence of ventricular arrhythmia after PCI in elderly AMI patients. The Lasso regression model constructed in this study can provide a reference for the clinical identification of high-risk elderly AMI patients and the development of targeted monitoring and treatment.

Independent prognostic value of CLDN6 in bladder cancer based on M2 macrophages related signature

M2 macrophages are associated with the prognosis of bladder cancer. CLDN6 has been linked to immune infiltration and is crucial for predicting the prognosis in multi-tumor. The effect of CLDN6 on M2 macrophages in bladder cancer remains elusive. Here, we compared a total of 40 machine learning algorithms, then selected optimal algorithm to develop M2 macrophages-related signature (MMRS) based on the identified M2 macrophages related module. MMRS predicted the prognosis better than other models and associated to immunotherapy response. CLDN6, as an important variable in MMRS, was an independent factor for poor prognosis. We found that CLDN6 was highly expressed and affected immune infiltration, immunotherapy response, and M2 macrophages polarization. Meanwhile, CLDN6 promoted the growth of bladder cancer and enhanced the carcinogenic effect by inducing polarization of M2 macrophages. In total, CLDN6 is an independent risk factor in MMRS to predict the prognosis of bladder cancer.

Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review

PURPOSE OF REVIEW

The aim of the systematic review is to assess AI's capabilities in the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for such analysis as well as to assess its prospects in daily practice.

RECENT FINDINGS

In total, our analysis included 27 articles: 10 articles have reported on PCa and 17 on BCa, respectively. The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy. The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. These factors will ultimately lead to personalized and precision medicine thereby improving patient outcomes.

Targeted therapies in bladder cancer: signaling pathways, applications, and challenges

Bladder cancer (BC) is one of the most prevalent malignancies in men. Understanding molecular characteristics via studying signaling pathways has made tremendous breakthroughs in BC therapies. Thus, targeted therapies including immune checkpoint inhibitors (ICIs), antibody-drug conjugates (ADCs), and tyrosine kinase inhibitor (TKI) have markedly improved advanced BC outcomes over the last few years. However, the considerable patients still progress after a period of treatment with current therapeutic regimens. Therefore, it is crucial to guide future drug development to improve BC survival, based on the molecular characteristics of BC and clinical outcomes of existing drugs. In this perspective, we summarize the applications and benefits of these targeted drugs and highlight our understanding of mechanisms of low response rates and immune escape of ICIs, ADCs toxicity, and TKI resistance. We also discuss potential solutions to these problems. In addition, we underscore the future drug development of targeting metabolic reprogramming and cancer stem cells (CSCs) with a deep understanding of their signaling pathways features. We expect that finding biomarkers, developing novo drugs and designing clinical trials with precisely selected patients and rationalized drugs will dramatically improve the quality of life and survival of patients with advanced BC.

Identification of bladder cancer subtypes and predictive model for prognosis, immune features, and immunotherapy based on neutrophil extracellular trap-related genes

Bladder cancer is the most common malignant tumor of urinary system, and its morbidity and mortality are increasing rapidly. Although great advances have been made in medical technology in recent years, there is still a lack of effective prognostic and therapeutic methods for bladder cancer. NETs are reticulated DNA structures decorated with various protein substances released extracellularly by neutrophils stimulated by strong signals. Recently, it has been found that NETs are closely related to the growth, metastasis and drug resistance of many types of cancers. However, up to now, the research on the relationship between NETs and bladder cancer is still not enough. In this study, we aimed to conduct a comprehensive analysis of NRGs in bladder cancer tissues to evaluate the relationship between NRGs and prognosis prediction and sensitivity to therapy in patients with bladder cancer. We scored NRGs in each tissue by using ssGSEA, and selected gene sets that were significantly associated with NRGs scores by using the WCGNA algorithm. Based on the expression profiles of NRGs-related genes, NMF clustering analysis was performed to identify different BLCA molecular subtypes. For the differentially expressed genes between subtypes, we used univariate COX regression, LASSO regression and multivariate COX regression to further construct a hierarchical model of BLCA patients containing 10 genes. This model and the nomogram based on this model can accurately predict the prognosis of BLCA patients in multiple datasets. Besides, BLCA patients classified based on this model differ greatly in their sensitivity to immunotherapy and targeted therapies, which providing a reference for individualized treatment of patients with bladder cancer.

Integrative analysis of TROAP with molecular features, carcinogenesis, and related immune and pharmacogenomic characteristics in soft tissue sarcoma

Soft tissue sarcoma (STS) is an uncommon malignancy that often carries a grim prognosis. Trophinin-associated protein (TROAP) is augmented in a variety of tumors and can affect tumor proliferation. Nevertheless, the prognostic value and specific functions of TROAP in STS are still vague. Herein, we display that TROAP exhibits an augmented trend in STS, and its elevation correlates with a poor prognosis of STS. Furthermore, its reduction is related to increased immune cell infiltration, enhanced stroma, and elevation of immune activation. Meanwhile, the TROAP-derived genomic signature is validated to predict patient prognosis, immunotherapy, and drug response reliably. A nomogram constructed based on age, metastatic status, and a TROAP-derived risk score of an STS individual could be used to quantify the survival probability of STS. In addition, in vitro experiments have demonstrated that TROAP is overexpressed in STS, and the downregulation of TROAP could affect the proliferation, migration, metastasis, and cell cycle of STS cells. In summary, the TROAP expression is elevated in STS tissues and cells, which is related to the poor prognosis and malignant biological behaviors of STS. It could act as a potential prognostic biomarker for diagnosis and treatment of STS.

Development of a butyrate metabolism-related gene-based molecular subtypes and scoring system for predicting prognosis and immunotherapy response in bladder cancer

PURPOSE

In recent times, multiple molecular subtypes with varying prognoses have been identified in bladder cancer (BLCA). However, the attributes of butyrate metabolism-related (BMR) molecular subtypes and their correlation with immunotherapy response remain inadequately explored in BLCA.

METHODS

We utilized 594 samples of BLCA to investigate the molecular subtypes mediated by BMR genes and their correlation with the immunotherapy response. To quantify the BMR features of individual tumors, we developed a BMR score through the COX and LASSO regression methods. Clinical-related, tumor microenvironment, drug-sensitive and immunotherapy analyses were used to comprehensively analyze BMR scores.

RESULTS

Two distinct molecular subtypes related to butyrate metabolism were identified in BLCA, each with unique prognostic implications and immune microenvironments. BMR score was constructed based on 7 BMR genes and was used to classify the patients into two score groups. Clinical analysis revealed that the BMR score was an independent prognostic factor. The higher the score, the worse the prognosis. The BMR score can also predict tumor immunity. The results demonstrated that a low BMR score was associated with higher efficacy of immunotherapy, which was also validated by an external dataset.

CONCLUSION

Our study proposes both molecular subtypes and a BMR-based score as promising prognostic classifications in BLCA. These findings may offer new insights for the development of precise targeted cancer therapies.