信号识别粒子相关的LASSO回归、SVM-RFE和人工神经网络联合模型用于系统性硬化症相关肺动脉高压诊断的表6

数据集获取（Figshare）

• 源站标题：Table6_A signal recognition particle-related joint model of LASSO regression, SVM-RFE and artificial neural network for the diagnosis of systemic sclerosis-associated pulmonary hypertension.XLSX
• DOI：10.3389/fgene.2022.1078200.s007
• 发布日期：2022-11-28
• 文件数：1 个
• 文件总体积：12.98 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table6_A_signal_recognition_particle-related_joint_model_of_LASSO_regression_SVM-RFE_and_artificial_neural_network_for_the_diagnosis_of_systemic_sclerosis-associated_pulmonary_hypertension_XLSX/21627926

命令行下载

curl -L -o "Table6_A signal recognition particle-related joint model of LASSO regression, SVM-RFE and artificial neural network for the diagnosis of systemic scle.XLSX" "https://ndownloader.figshare.com/files/38341898"

文件清单

• Table6_A signal recognition particle-related joint model of LASSO regression, SVM-RFE and artificial neural network for the diagnosis of systemic scle.XLSX（12.98 KB）

文件总体积 12.98 KB（源站 API 实测）。

数据说明

Background: Systemic sclerosis-associated pulmonary hypertension (SSc-PH) is one of the most common causes of death in patients with systemic sclerosis (SSc). The complexity of SSc-PH and the heterogeneity of clinical features in SSc-PH patients contribute to the difficulty of diagnosis. Therefore, there is a pressing need to develop and optimize models for the diagnosis of SSc-PH. Signal recognition particle (SRP) deficiency has been found to promote the progression of multiple cancers, but the relationship between SRP and SSc-PH has not been explored.
Methods: First, we obtained the GSE19617 and GSE33463 datasets from the Gene Expression Omnibus (GEO) database as the training set, GSE22356 as the test set, and the SRP-related gene set from the MSigDB database. Next, we identified differentially expressed SRP-related genes (DE-SRPGs) and performed unsupervised clustering and gene enrichment analyses. Then, we used least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) to identify SRP-related diagnostic genes (SRP-DGs). We constructed an SRP scoring system and a nomogram model based on the SRP-DGs and established an artificial neural network (ANN) for diagnosis. We used receiver operating characteristic (ROC) curves to identify the SRP-related signature in the training and test sets. Finally, we analyzed immune features, signaling pathways, and drugs associated with SRP and investigated SRP-DGs’ functions using single gene batch correlation analysis-based GSEA.
Results: We obtained 30 DE-SRPGs and found that they were enriched in functions and pathways such as “protein targeting to ER,” “cytosolic ribosome,” and “coronavirus disease—COVID-19”. Subsequently, we identified seven SRP-DGs whose expression levels and diagnostic efficacy were validated in the test set. As one signature, the area under the ROC curve (AUC) values for seven SRP-DGs were 0.769 and 1.000 in the training and test sets, respectively. Predictions made using the nomogram model are likely beneficial for SSc-PH patients. The AUC values of the ANN were 0.999 and 0.860 in the training and test sets, respectively. Finall

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fgene.2022.1078200.s007（source: https://frontiersin.figshare.com/articles/dataset/Table6_A_signal_recognition_particle-related_joint_model_of_LASSO_regression_SVM-RFE_and_artificial_neural_network_for_the_diagnosis_of_systemic_sclerosis-associated_pulmonary_hypertension_XLSX/21627926）。