Identification of NETs-related biomarkers and molecular clusters in systemic lupus erythematosus

BackgroundThe global prevalence and metastasis rates of colon adenocarcinoma (COAD) are high, and therapeutic success is limited. Although previous research has primarily explored changes in gene phenotypes, the incidence rate of COAD remains unchanged. Metabolic reprogramming is a crucial aspect of cancer research and therapy. The present study aims to develop cluster and polygenic risk prediction models for COAD based on glucose metabolism pathways to assess the survival status of patients and potentially identify novel immunotherapy strategies and related therapeutic targets.MethodsCOAD‐specific data (including clinicopathological information and gene expression profiles) were sourced from The Cancer Genome Atlas (TCGA) and two Gene Expression Omnibus (GEO) datasets (GSE33113 and GSE39582). Gene sets related to glucose metabolism were obtained from the MSigDB database. The Gene Set Variation Analysis (GSVA) method was utilized to calculate pathway scores for glucose metabolism. The hclust function in R, part of the Pheatmap package, was used to establish a clustering system. The mutation characteristics of identified clusters were assessed via MOVICS software, and differentially expressed genes (DEGs) were filtered using limma software. Signature analysis was performed using the least absolute shrinkage and selection operator (LASSO) method. Survival curves, survival receiver operating characteristic (ROC) curves and multivariate Cox regression were analyzed to assess the efficacy and accuracy of the signature for prognostic prediction. The pRRophetic program was employed to predict drug sensitivity, with data sourced from the Genomics of Drug Sensitivity in Cancer (GDSC) database.ResultsFour COAD subgroups (i.e., C1, C2, C3 and C4) were identified based on glucose metabolism, with the C4 group having higher survival rates. These four clusters were bifurcated into a new Clust2 system (C1 + C2 + C3 and C4). In total, 2175 DEGs were obtained (C1 + C2 + C3 vs. C4), from which 139 prognosis‐related genes were identified. ROC curves predicting 1‐, 3‐ and 5‐year survival based on a signature containing nine genes showed an area under the curve greater than 0.7. Meanwhile, the study also found this feature to be an important predictor of prognosis in COAD and accordingly assessed the risk score, with higher risk scores being associated with a worse prognosis. The high‐risk and low‐risk groups responded differently to immunotherapy and chemotherapeutic agents, and there were differences in functional enrichment pathways.ConclusionsThis unique signature based on glucose metabolism may potentially provide a basis for predicting patient prognosis, biological characteristics and more effective immunotherapy strategies for COAD.

Section: Resultsmentioning

confidence: 99%

Glucose metabolism‐based signature predicts prognosis and immunotherapy strategies for colon adenocarcinoma

Bai,

Yan,

Nie

et al. 2023

“…Then, to identify important genes, we used the random forest (RF) algorithm and the least absolute shrinkage and selection operator (LASSO) analysis. LASSO analysis is a dimensionality reduction algorithm, and its accuracy is better than regression analysis 23,24 . The RF algorithm is a supervised learning method based on components that is frequently viewed as a decision tree extension 24,25 .…”

Section: Methodsmentioning

confidence: 99%

“…LASSO analysis is a dimensionality reduction algorithm, and its accuracy is better than regression analysis 23,24 . The RF algorithm is a supervised learning method based on components that is frequently viewed as a decision tree extension 24,25 . Finally, by merging the screening findings from the two analytical techniques, we were able to identify key PRGs.…”

Section: Methodsmentioning

confidence: 99%

Relevance of pyroptosis‐associated genes in nasopharyngeal carcinoma diagnosis and subtype classification

Wang,

Zou,

Chen

et al. 2024

BackgroundNasopharyngeal carcinoma (NPC) is a highly aggressive and metastatic malignancy originating in the nasopharyngeal tissue. Pyroptosis is a relatively newly discovered, regulated form of necrotic cell death induced by inflammatory caspases that is associated with a variety of diseases. However, the role and mechanism of pyroptosis in NPC are not fully understood.MethodsWe analyzed the differential expression of pyroptosis‐related genes (PRGs) between patients with and without NPC from the GSE53819 and GSE64634 datasets of the Gene Expression Omnibus (GEO) database. We mapped receptor operating characteristic profiles for these key PRGs to assess the accuracy of the genes for disease diagnosis and prediction of patient prognosis. In addition, we constructed a nomogram based on these key PRGs and carried out a decision curve analysis. The NPC patients were classified into different pyroptosis gene clusters by the consensus clustering method based on key PRGs, whereas the expression profiles of the key PRGs were analyzed by applying principal component analysis. We also analyzed the differences in key PRGs, immune cell infiltration and NPC‐related genes between the clusters. Finally, we performed differential expression analysis for pyroptosis clusters and obtained differentially expressed genes (DEGs) and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.ResultsWe obtained 14 differentially expressed PRGs from GEO database. Based on these 14 differentially expressed PRGs, we applied least absolute shrinkage and selection operator analysis and the random forest algorithm to obtain four key PRGs (CHMP7, IL1A, TP63 and GSDMB). We completely distinguished the NPC patients into two pyroptosis gene clusters (pyroptosis clusters A and B) based on four key PRGs. Furthermore, we determined the immune cell abundance of each NPC sample, estimated the association between the four PRGs and immune cells, and determined the difference in immune cell infiltration between the two pyroptosis gene clusters. Finally, we obtained and functional enrichment analyses 259 DEGs by differential expression analysis for both pyroptosis clusters.ConclusionsPRGs are critical in the development of NPC, and our research on the pyroptosis gene cluster may help direct future NPC therapeutic approaches.

“…Consensus clustering was employed to group the 590 CRC samples using the ConsensusClusterPlus package, 12 yielding distinct patient subgroups that were visually delineated, thereby illuminating intrinsic patterns within the data and underscoring the heterogeneity of the disease. 13,14 The pheatmap package (https://CRAN.R-project.org/ package=pheatmap) was utilized to construct a heatmap that compared the differences in clinical composition ratio between the two subgroups. PCA was conducted using the ggplot2 package to assess the ability of EMT survival-related differentially expressed genes in discriminating the two subgroups of CRC patients.…”

Section: Consensus Clustering Principal Component Analysis (Pca) and ...mentioning

confidence: 99%

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi‐omics approach based on epithelial–mesenchymal transition‐related genes

Zhang,

Fan,

2024

The progression and the metastatic potential of colorectal cancer (CRC) are intricately linked to the epithelial–mesenchymal transition (EMT) process. The present study harnesses the power of machine learning combined with multi‐omics data to develop a risk stratification model anchored on EMT‐associated genes. The aim is to facilitate personalized prognostic assessments in CRC. We utilized publicly accessible gene expression datasets to pinpoint EMT‐associated genes, employing a CoxBoost algorithm to sift through these genes for prognostic significance. The resultant model, predicated on gene expression levels, underwent rigorous independent validation across various datasets. Our model demonstrated a robust capacity to segregate CRC patients into distinct high‐ and low‐risk categories, each correlating with markedly different survival probabilities. Notably, the risk score emerged as an independent prognostic indicator for CRC. High‐risk patients were characterized by an immunosuppressive tumor milieu and a heightened responsiveness to certain chemotherapeutic agents, underlining the model's potential in steering tailored oncological therapies. Moreover, our research unearthed a putative repressive interaction between the long non‐coding RNA PVT1 and the EMT‐associated genes TIMP1 and MMP1, offering new insights into the molecular intricacies of CRC. In essence, our research introduces a sophisticated risk model, leveraging machine learning and multi‐omics insights, which accurately prognosticates outcomes for CRC patients, paving the way for more individualized and effective oncological treatment paradigms.