Identification of immune infiltration and cuproptosis-related molecular clusters in tuberculosis

Abstract

BackgroundTuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) infection. Cuproptosis is a novel cell death mechanism correlated with various diseases. This study sought to elucidate the role of cuproptosis-related genes (CRGs) in TB.MethodsBased on the GSE83456 dataset, we analyzed the expression profiles of CRGs and immune cell infiltration in TB. Based on CRGs, the molecular clusters and related immune cell infiltration were explored using 92 TB samples. The Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was utilized to identify the co-expression modules and cluster-specific differentially expressed genes. Subsequently, the optimal machine learning model was determined by comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB). The predictive performance of the machine learning model was assessed by generating calibration curves and decision curve analysis and validated in an external dataset.Results11 CRGs were identified as differentially expressed cuproptosis genes. Significant differences in immune cells were observed in TB patients. Two cuproptosis-related molecular clusters expressed genes were identified. Distinct clusters were identified based on the differential expression of CRGs and immune cells. Besides, significant differences in biological functions and pathway activities were observed between the two clusters. A nomogram was generated to facilitate clinical implementation. Next, calibration curves were generated, and decision curve analysis was conducted to validate the accuracy of our model in predicting TB subtypes. XGB machine learning model yielded the best performance in distinguishing TB patients with different clusters. The top five genes from the XGB model were selected as predictor genes. The XGB model exhibited satisfactory performance during validation in an external dataset. Further analysis revealed that these five model-related genes were significantly associated with latent and active TB.ConclusionOur study provided hitherto undocumented evidence of the relationship between cuproptosis and TB and established an optimal machine learning model to evaluate the TB subtypes and latent and active TB patients.

Association between lactate metabolism‑related molecules and venous thromboembolism: A study based on bioinformatics and an in vitro model

“…2A ). The ‘limma’ package in R was used to screen DE genes from the dataset(GSE48000) ( 21 ). Compared with the control, four DE-LMRMs (SLC16A1, SLC16A7), SLC16A8 and SLC5A12) were identified in the VTE group.…”

“…Machine learning models have previously been used to predict prevalence of a number of diseases, with lower error rates and superior results compared with conventional logistic regression ( 27 ). Machine learning models, such as RF, SVM, GLM and XGB, demonstrate clinical relevance in disease prediction ( 21 ). In the present study, machine learning models based on the expression of the LMRMs were constructed.…”

“…Furthermore, LMRMs were obtained from the MsigDB database (version 7.0; gsea-msigdb.Org/gsea/msigdb/ ). As described by Li et al ( 21 ), the ‘limma’ R package was used to screen DE-LMRMs from the GSE48000 dataset and the ‘ggpubr’ and ‘pheatmap’ R packages were used to generate box plots and heatmaps, respectively. Finally, ‘corrplot’ R package was used for correlation analysis(Pearson's coefficient) of the DE-LMRMs.…”

“…Based on screened LMRMs, the ‘caret’ R package was used to build machine learning models, namely, the random forest (RF), support vector machine (SVM) and generalized linear model (GLM) and eXtreme Gradient Boosting (XGB) ( 21 ). The ‘DALEX’ R package was used to interpret the four models and generate the residual distribution of each model in the test set.…”

Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning

“…Previous studies using the GSE83456 dataset have focused on building diagnostic models for PTB (Zhu and Liu, 2023 ) or active TB (Li et al, 2023 ). Its researches in EPTB are mainly about bone tuberculosis's diagnosis biomarkers (Liang et al, 2021 ) and molecular mechanism (Liang et al, 2022 ).…”