A ten-genes-based diagnostic signature for atherosclerosis

Zhu, Feng; Zuo, Lili; Hu, Rui; Wang, Jin; Yang, Zhihua; Qi, Xin; Lin, Feng

doi:10.1186/s12872-021-02323-9

Cited by 11 publications

(7 citation statements)

References 43 publications

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“…LR is a generalised linear classification algorithm, it uses the sigmod function for non-linear mapping of all data to limit the prediction value to [0,1] and reduces the prediction range to classify samples. LR is a common machine learning method ( Wei et al, 2020 ; Li and Wang, 2021 ; Zhu et al, 2021 ). SVM is another linear classification algorithm that is one of the most popular algorithms in computational biology ( Chen et al, 2016 ; He et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites

Zhang

Wang²,

Xie³

et al. 2023

Front. Genet.

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Background: Pseudouridine (Ψ) is one of the most abundant RNA modifications found in a variety of RNA types, and it plays a significant role in many biological processes. The key to studying the various biochemical functions and mechanisms of Ψ is to identify the Ψ sites. However, identifying Ψ sites using experimental methods is time-consuming and expensive. Therefore, it is necessary to develop computational methods that can accurately predict Ψ sites based on RNA sequence information.Methods: In this study, we proposed a new model called PseU-ST to identify Ψ sites in Homo sapiens (H. sapiens), Saccharomyces cerevisiae (S. cerevisiae), and Mus musculus (M. musculus). We selected the best six encoding schemes and four machine learning algorithms based on a comprehensive test of almost all of the RNA sequence encoding schemes available in the iLearnPlus software package, and selected the optimal features for each encoding scheme using chi-square and incremental feature selection algorithms. Then, we selected the optimal feature combination and the best base-classifier combination for each species through an extensive performance comparison and employed a stacking strategy to build the predictive model.Results: The results demonstrated that PseU-ST achieved better prediction performance compared with other existing models. The PseU-ST accuracy scores were 93.64%, 87.74%, and 89.64% on H_990, S_628, and M_944, respectively, representing increments of 13.94%, 6.05%, and 0.26%, respectively, higher than the best existing methods on the same benchmark training datasets.Conclusion: The data indicate that PseU-ST is a very competitive prediction model for identifying RNA Ψ sites in H. sapiens, M. musculus, and S. cerevisiae. In addition, we found that the Position-specific trinucleotide propensity based on single strand (PSTNPss) and Position-specific of three nucleotides (PS3) features play an important role in Ψ site identification. The source code for PseU-ST and the data are obtainable in our GitHub repository (https://github.com/jluzhangxinrubio/PseU-ST).

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Section: Methodsmentioning

confidence: 99%

PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites

Zhang

Wang²,

Xie³

et al. 2023

Front. Genet.

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“…[7][8][9][10] Atrophic gastritis can cause hyperhomocysteinemia, an independent risk factor for atherosclerosis. [8] The 2 conditions include overlapping inflammatory environmental factors, including IL1B, [11,12] IL1RN, [13,14] PTGS2, [15,16] IL1A, [17,18] MUC1, [19][20][21] IL10, [22][23][24] S100A8, [25,26] and GSTM1. [27,28] In addition, multiple signaling pathways are involved in both diseases.…”

Section: Introductionmentioning

confidence: 99%

Exploring the pathogenesis of chronic atrophic gastritis with atherosclerosis via microarray data analysis

Men,

Shi,

et al. 2024

Medicine

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Although several studies have reported a link between chronic atrophic gastritis (CAG) and atherosclerosis, the underlying mechanisms have not been elucidated. The present study aimed to investigate the molecular mechanisms common to both diseases from a bioinformatics perspective. Gene expression profiles were obtained from the Gene Expression Omnibus database. Data on atherosclerosis and CAG were downloaded from the GSE28829 and GSE60662 datasets, respectively. We identified the differentially expressed genes co-expressed in CAG and atherosclerosis before subsequent analyses. We constructed and identified the hub genes and performed functional annotation. Finally, the transcription factor (TF)-target genes regulatory network was constructed. In addition, we validated core genes and certain TFs. We identified 116 common differentially expressed genes after analyzing the 2 datasets (GSE60662 and GSE28829). Functional analysis highlighted the significant contribution of immune responses and the positive regulation of tumor necrosis factor production and T cells. In addition, phagosomes, leukocyte transendothelial migration, and cell adhesion molecules strongly correlated with both diseases. Furthermore, 16 essential hub genes were selected with cytoHubba, including PTPRC, TYROBP, ITGB2, LCP2, ITGAM, FCGR3A, CSF1R, IRF8, C1QB, TLR2, IL10RA, ITGAX, CYBB, LAPTM5, CD53, CCL4, and LY86. Finally, we searched for key gene-related TFs, especially SPI1. Our findings reveal a shared pathogenesis between CAG and atherosclerosis. Such joint pathways and hub genes provide new insights for further studies.

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“…For instance, a study employed machine learning to predict the prognosis of cancer survival by analyzing gene expressions [ 10 ]. While specific biomarkers were indeed identified [ 11 ], the precision of the machine learning models on AS was notably absent.…”

Section: Introductionmentioning

confidence: 99%

Online application for the diagnosis of atherosclerosis by six genes

Zhao,

Chen,

Wei

et al. 2024

PLoS ONE

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Background Atherosclerosis (AS) is a primary contributor to cardiovascular disease, leading to significant global mortality rates. Developing effective diagnostic indicators and models for AS holds the potential to substantially reduce the fatalities and disabilities associated with cardiovascular disease. Blood sample analysis has emerged as a promising avenue for facilitating diagnosis and assessing disease prognosis. Nonetheless, it lacks an accurate model or tool for AS diagnosis. Hence, the principal objective of this study is to develop a convenient, simple, and accurate model for the early detection of AS. Methods We downloaded the expression data of blood samples from GEO databases. By dividing the mean values of housekeeping genes (meanHGs) and applying the comBat function, we aimed to reduce the batch effect. After separating the datasets into training, evaluation, and testing sets, we applied differential expression analyses (DEA) between AS and control samples from the training dataset. Then, a gradient-boosting model was used to evaluate the importance of genes and identify the hub genes. Using different machine learning algorithms, we constructed a prediction model with the highest accuracy in the testing dataset. Finally, we make the machine learning models publicly accessible by shiny app construction. Results Seven datasets (GSE9874, GSE12288, GSE20129, GSE23746, GSE27034, GSE90074, and GSE202625), including 403 samples with AS and 325 healthy subjects, were obtained by comprehensive searching and filtering by specific requirements. The batch effect was successfully removed by dividing the meanHGs and applying the comBat function. 331 genes were found to be related to atherosclerosis by the DEA analysis between AS and health samples. The top 6 genes with the highest importance values from the gradient boosting model were identified. Out of the seven machine learning algorithms tested, the random forest model exhibited the most impressive performance in the testing datasets, achieving an accuracy exceeding 0.8. While the batch effect reduction analysis in our study could have contributed to the increased accuracy values, our comparison results further highlight the superiority of our model over the genes provided in published studies. This underscores the effectiveness of our approach in delivering superior predictive performance. The machine-learning models were then uploaded to the Shiny app’s server, making it easy for users to distinguish AS samples from normal samples. Conclusions A prognostic Shiny application, built upon six potential atherosclerosis-associated genes, has been developed, offering an accurate diagnosis of atherosclerosis.

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A ten-genes-based diagnostic signature for atherosclerosis

Cited by 11 publications

References 43 publications

PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites

PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites

Exploring the pathogenesis of chronic atrophic gastritis with atherosclerosis via microarray data analysis

Online application for the diagnosis of atherosclerosis by six genes

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