Advances in artificial intelligence to predict cancer immunotherapy efficacy

Xie, Jun; Luo, Xiyuan; Deng, Xinpei; Tang, Yuhui; Tian, Wenwen; Cheng, Hui; Zhang, Junsheng; Zou, Yutian; Guo, Zhixing; Xie, Xiaoming

doi:10.3389/fimmu.2022.1076883

Cited by 35 publications

(30 citation statements)

References 70 publications

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“…They effectively coordinate autocrine and paracrine functions by transmitting mRNAs, miRNAs, lncRNAs and tsRNAs, thereby modulating the tumour microenvironment and facilitating tumour cell growth and metastasis. 29 , 30 Given the observed increase in mRNA levels of PDE4C in OS cells co‐cultured with M2 macrophages, we hypothesized that the transfer of PDE4C from M2 macrophages to OS cells is reliant on an mRNA delivery mechanism, such as exosome. In accordance with our hypothesis, the presence of PDE4C mRNA was observed in exosomes derived from M2 macrophages, while no protein was detected.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the effects of M2 macrophage‐derived PDE4C on the prognosis, metastasis and immunotherapy benefit of osteosarcoma

Pan,

et al. 2024

J Cellular Molecular Medi

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Tumour‐associated macrophages (TAMs), encompassing M1 and M2 subtypes, exert significant effects on osteosarcoma (OS) progression and immunosuppression. However, the impacts of TAM‐derived biomarkers on the progression of OS remains limited. The GSE162454 profile was subjected to single‐cell RNA (scRNA) sequencing analysis to identify crucial mediators between TAMs and OS cells. The clinical features, effects and mechanisms of these mediators on OS cells and tumour microenvironment were evaluated via biological function experiments and molecular biology experiments. Phosphodiesterase 4C (PDE4C) was identified as a pivotal mediator in the communication between M2 macrophages and OS cells. Elevated levels of PDE4C were detected in OS tissues, concomitant with M2 macrophage level, unfavourable prognosis and metastasis. The expression of PDE4C was observed to increase during the conversion process of THP‐1 cells to M2 macrophages, which transferred the PDE4C mRNA to OS cells through exosome approach. PDE4C increased OS cell proliferation and mobility via upregulating the expression of collagens. Furthermore, a positive correlation was observed between elevated levels of PDE4C and increased TIDE score, decreased response rate following immune checkpoint therapy, reduced TMB and diminished PDL1 expression. Collectively, PDE4C derived from M2 macrophages has the potential to enhance the proliferation and mobility of OS cells by augmenting collagen expression. PDE4C may serve as a valuable biomarker for prognosticating patient outcomes and response rates following immunotherapy.

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Section: Discussionmentioning

confidence: 99%

Analysis of the effects of M2 macrophage‐derived PDE4C on the prognosis, metastasis and immunotherapy benefit of osteosarcoma

Pan,

et al. 2024

J Cellular Molecular Medi

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“…Inspired by the latest advancement in artificial intelligence, we traversed 20 mainstream machine learning algorithms to fit the data and improve performance ( Wolpert and Macready, 1997 ; Xie et al, 2023 ). We found that Extra Tree had the highest predictive performance.…”

Section: Discussionmentioning

confidence: 99%

Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction

et al. 2023

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BackgroundIncreasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication.Materials and methodsThe gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue.ResultsOverall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified.ConclusionFor the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine.Graphical abstract

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“…The TME not only plays a major role in tumor progression, but also orchestrates immune components, thus affecting the therapeutic efficacy of ICB and patient prognosis (44,45). Recent research suggests that epigenetic changes, usually caused by chronic inflammation, occur in cancer cells and other TME components (46,47).…”

Section: Discussionmentioning

confidence: 99%

Deciphering comprehensive features of tumor microenvironment controlled by chromatin regulators to predict prognosis and guide therapies in uterine corpus endometrial carcinoma

Tian

Liu

et al. 2023

Front. Immunol.

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BackgroundDysregulation of chromatin regulators (CRs) can perturb the tumor immune microenvironment, but the underlying mechanism remains unclear. We focused on uterine corpus endometrial carcinoma (UCEC) and used gene expression data from TCGA-UCEC to investigate this mechanism.MethodsWe used weighted gene co-expression network analysis (WGCNA) and consensus clustering algorithm to classify UCEC patients into Cluster_L and Cluster_H. TME-associated CRs were identified using WGCNA and differential gene expression analysis. A CR risk score (CRRS) was constructed using univariate Cox and LASSO-Cox regression analyses. A nomogram was developed based on CRRS and clinicopathologic factors to predict patients' prognosis.ResultsLower CRRS was associated with lower grade, more benign molecular subtypes, and improved survival. Patients with low CRRS showed abundant immune infiltration, a higher mutation burden, fewer CNVs, and better response to immunotherapy. Moreover, low CRRS patients were more sensitive to 24 chemotherapeutic agents.ConclusionA comprehensive assessment of CRRS could identify immune activation and improve the efficacy of UCEC treatments.

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Advances in artificial intelligence to predict cancer immunotherapy efficacy

Cited by 35 publications

References 70 publications

Analysis of the effects of M2 macrophage‐derived PDE4C on the prognosis, metastasis and immunotherapy benefit of osteosarcoma

Analysis of the effects of M2 macrophage‐derived PDE4C on the prognosis, metastasis and immunotherapy benefit of osteosarcoma

Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction

Deciphering comprehensive features of tumor microenvironment controlled by chromatin regulators to predict prognosis and guide therapies in uterine corpus endometrial carcinoma

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