Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches

Wang, YaXuan; He, JiaXing; Zhao, QingYun; Bo, Ji; Zhou, Yu; Sun, HaoDong; Ding, BeiChen; Ren, MingHua

doi:10.3389/fimmu.2024.1416914

Front. Immunol.

2024

DOI: 10.3389/fimmu.2024.1416914

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Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches

YaXuan Wang,

JiaXing He,

QingYun Zhao

et al.

Abstract: BackgroundAngiogenesis, the process of forming new blood vessels from pre-existing ones, plays a crucial role in the development and advancement of cancer. Although blocking angiogenesis has shown success in treating different types of solid tumors, its relevance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated.MethodThis study utilized the WGCNA method to identify angiogenesis-related genes and assessed their diagnostic and prognostic value in patients with PRAD through cluster analysis.… Show more

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Cited by 6 publications

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Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC

Wang,

Zhou,

et al. 2024

BioFactors

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Most patients with non‐small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk of metastasis. Consequently, the timely identification of biomarkers associated with lymph node metastasis is essential for improving the clinical management of NSCLC patients. In this research, the WGCNA algorithm was utilized to pinpoint genes linked to lymph node metastasis in NSCLC. A cluster analysis was carried out to investigate how these genes correlate with the prognosis and the outcomes of immunotherapy for NSCLC patients. Following this, diagnostic and prognostic models were created and validated through various machine learning methodologies. The random forest technique highlighted the importance of ARHGAP11A, leading to an in‐depth examination of its role in NSCLC. By analyzing 78 tissue chip samples from NSCLC patients, the study confirmed the association between ARHGAP11A expression, patient prognosis, and lymph node metastasis. Finally, the influence of ARHGAP11A on NSCLC cells was assessed through cell function experiments. This research utilized the WGCNA technique to identify 25 genes that are related to lymph node metastasis, clarifying their connections with tumor invasion, growth, and the activation of stemness pathways. Cluster analysis revealed significant associations between these genes and lymph node metastasis in NSCLC, especially concerning immunotherapy and targeted treatments. A diagnostic system that combines various machine learning approaches demonstrated strong efficacy in forecasting both the diagnosis and prognosis of NSCLC. Importantly, ARHGAP11A was identified as a key prognostic gene associated with lymph node metastasis in NSCLC. Molecular docking analyses suggested that ARHGAP11A has a strong affinity for targeted therapies within NSCLC. Additionally, immunohistochemical assessments confirmed that higher levels of ARHGAP11A expression correlate with unfavorable outcomes for NSCLC patients. Experiments on cells showed that reducing ARHGAP11A expression can hinder the proliferation, metastasis, and stemness traits of NSCLC cells. This investigation reveals the novel insight that ARHGAP11A may function as a potential biomarker connected to lymph node metastasis in NSCLC. Moreover, reducing the expression of ARHGAP11A has demonstrated the ability to diminish tumor stemness characteristics, presenting a promising opportunity for improving treatment strategies for this condition.

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Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC

Wang,

Zhou,

et al. 2024

BioFactors

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Navigating the immune landscape with plasma cells: A pan‐cancer signature for precision immunotherapy

Ye,

Jiang,

Liang

et al. 2024

BioFactors

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Immunotherapy has revolutionized cancer treatment; however, predicting patient response remains a significant challenge. Our study identified a novel plasma cell signature, Plasma cell.Sig, through a pan‐cancer single‐cell RNA sequencing analysis, which predicts patient outcomes to immunotherapy with remarkable accuracy. The signature was developed using rigorous machine learning algorithms and validated across multiple cohorts, demonstrating superior predictive power with an area under the curve (AUC) exceeding 0.7. Notably, the low‐risk group, as classified by Plasma cell.Sig, exhibited enriched immune cell infiltration and heightened tumor immunogenicity, indicating an enhanced responsiveness to immunotherapy. Conversely, the high‐risk group showed reduced immune activity and potential mechanisms of immune evasion. These findings not only enhance understanding of the intrinsic and extrinsic immune landscapes within the tumor microenvironment but also pave the way for more precise, biomarker‐guided immunotherapy approaches in oncology.

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Machine learning model reveals the role of angiogenesis and EMT genes in glioma patient prognosis and immunotherapy

Feng,

Zhu,

Qin

et al. 2024

Biol Direct

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Gliomas represent a highly aggressive class of tumors located in the brain. Despite the availability of multiple treatment modalities, the prognosis for patients diagnosed with glioma remains unfavorable. Therefore, further exploration of new biomarkers is crucial to enhance the prognostic assessment of glioma and to investigate more effective treatment options. In this research, we utilized multiple machine learning techniques to assess the significance of genes related to angiogenesis and epithelial-mesenchymal transition (EMT) in the context of prognosis and treatment for glioma patients. The random forest algorithm highlighted the significance of CALU, and further analysis indicated that the effect of CALU on glioma progression may be regulated by MYC. Different machine learning approaches were employed in our investigation to uncover crucial genes associated with angiogenesis and EMT in glioma. Our findings verify the connection between these genes and the prognosis of patients with glioma, as well as the results of immunotherapeutic interventions. Notably, through experimental verification, we identified CALU as a new prognostic marker for glioma, and inhibiting the expression of CALU can impede the progression of glioma. Supplementary Information The online version contains supplementary material available at 10.1186/s13062-024-00565-z.

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Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches

Cited by 6 publications

References 55 publications

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC

Navigating the immune landscape with plasma cells: A pan‐cancer signature for precision immunotherapy

Machine learning model reveals the role of angiogenesis and EMT genes in glioma patient prognosis and immunotherapy

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