Machine learning-based screening of the diagnostic genes and their relationship with immune-cell infiltration in patients with lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the most common type of lung cancer, and has a dismal mortality rate of 80%, mainly due to diagnosis at an advanced stage. Biomarkers with high specificity and sensitivity for the early diagnosis of LUAD are sparse. This study aimed to identify markers for the early diagnosis of LUAD.MethodsThe {"type":"entrez-geo","attrs":{"text":"GSE32863","term_id":"32863"}}GSE32863 and {"type":"entrez-geo","attrs":{"text":"GSE75037","term_id":"75037"}}GSE75037 data sets were standardized and merged to screen for differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. The intersected DEGs from the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) regression analyses were considered the hub genes. Then the diagnostic ability and expression of hub genes was tested in {"type":"entrez-geo","attrs":{"text":"GSE63459","term_id":"63459"}}GSE63459 data set, Finally, CIBERSORT was used to analyze the correlation between the immune-infiltrating cells and hub genes.ResultsThe following 7 DEGs were intersected by the LASSO and SVM regression analyses: Locus 401286 (LOC401286), flavin-containing monooxygenase 2 (FMO2), XLKD1, Ras homolog family member J (RHOJ), scavenger receptor Class A member 5 (SCARA5), heat shock protein beta-2 (HSPB2), and serine incorporator 2 (SERINC2). The area under the receiver operating characteristic curve (AUC) of LOC401286, FMO2, XLKD1, RHOJ, SCARA5, HSPB2, and SERINC2 was 0.99, 1.00, 0.99, 1.00, 0.99, 0.99, and 0.98, respectively in the training groups. The AUC of LOC401286, FMO2, XLKD1, RHOJ, SCARA5, HSPB2, and SERINC2 was 0.97, 0.96, 0.94, 0.88, 0.85, 0.94 and 0.89, respectively in the validation group. The immune-cell infiltrations of naive B cells, memory B cells, plasma cells, naive cluster of differentiation (CD) 4 T cells, T follicular helper cells, regulatory T cells, gamma delta T cells, monocytes, M0 macrophages, M1 macrophages, resting mast cells, activated mast cells, and neutrophils were different between the normal and tumor tissues. Notably, these immune cells were correlated with the above-mentioned 7 diagnostic genes.ConclusionsWe identified 7 DEGs in LUAD tissue that can be considered diagnostic genes based on 2 machine-learning regression methods, which could be very helpful for the early diagnosis of LUAD in clinical practice.     

A bioinformatics-based immune-related prognostic index for lung adenocarcinoma that predicts patient response to immunotherapy and common treatments

BackgroundThere is increasing evidence of the effectiveness of immune checkpoint blockade (ICB) therapy for the treatment of lung adenocarcinoma (LUAD). However, the benefits of ICB therapy vary among LUAD patients. Due to the research dimension, existing biomarkers, such as programmed death-ligand 1 (PD-L1) expression and tumor mutation burden (TMB), could not reflect the complex tumor environment, and had low prediction accuracy of ICB. Therefore, we aimed to uncover a prognostic biomarker that could also predict whether a patient would benefit from ICB therapy and other common treatments from multiple dimensions, so as to improve the prediction accuracy of pre-treatment patients.MethodsBased on the LUAD dataset retrieved from The Cancer Genome Atlas (TCGA) database, 50 immune-related hub genes were identified using weighted gene co-expression network analysis and univariate Cox regression analyses. An immune-related gene prognostic index (IRGPI) was constructed using a Cox proportional-hazards model based on 15 genes and validated using {"type":"entrez-geo","attrs":{"text":"GSE72094","term_id":"72094"}}GSE72094 dataset. We tested its prognostic accuracy by Kaplan-Meier (K-M) survival curves of the two datasets and assessed its predictive power by comparing area under curve (AUC) of IRGPI with existing biomarkers. Subsequently, we analyzed the molecular and immune characteristics, and evaluated the benefits of ICB by PD-L1 expression and Tumor Immune Dysfunction and Exclusion (TIDE) analysis, predicted the inhibitory concentration 50 of common treatments drugs for two IRGPI score-related subgroups.ResultsPatients in the IRGPI-high subgroup had lower overall survival (OS) than patients in the IRGPI-low subgroup in K-M survival curve in two cohorts. And IRGPI has AUC values of 0.715, 0.724, and 0.743 in 1, 2, and 3 years, respectively. A higher tumor mutation burden and PD-L1 expression and the tumor microenvironment (TME) landscape demonstrated that IRGPI-high subgroup patients may respond better to ICB therapy. Genomics of Drug Sensitivity in Cancer (GDSC) analysis indicated that the IRGPI-high subgroup showed greater sensitivity to chemotherapy.ConclusionsIRGPI is a prospective biomarker for evaluating whether a patient will benefit from ICB therapy and other treatments, and distinguishing patients with different molecular and immune characteristics.     

Integrated bioinformatics analysis reveals CDK1 and PLK1 as potential therapeutic targets of lung adenocarcinoma

This study is to identify potential biomarkers and therapeutic targets for lung adenocarcinoma (LUAD).{"type":"entrez-geo","attrs":{"text":"GSE6044","term_id":"6044"}}GSE6044 and {"type":"entrez-geo","attrs":{"text":"GSE118370","term_id":"118370"}}GSE118370 raw data from the Gene Expression Omnibus database were normalized with Robust Multichip Average. After merging these two datasets, the combat function of sva packages was used to eliminate batch effects. Then, limma packages were used to filtrate differentially expressed genes. We constructed protein–protein interaction relationships using STRING database and hub genes were identified based on connectivity degrees. The cBioportal database was used to explore the alterations of the hub genes. The promoter methylation of cyclin dependent kinase 1 (CDK1) and polo-like Kinase 1 (PLK1) and their association with tumor immune infiltration in patients with LUAD were investigated using DiseaseMeth version 2.0 and TIMER databases. The Cancer Genome Atlas-LUAD dataset was used to perform gene set enrichment analysis.We identified 10 hub genes, which were upregulated in LUAD, among which 8 were successfully verified in the Cancer Genome Atlas and Oncomine databases. Kaplan–Meier analysis indicated that the expressions of CDK1 and PLK1 in LUAD patients were associated with overall survival and disease-free survival. The methylation levels in the promoter regions of these 2 genes in LUAD patients were lower than those in normal lung tissues. Their expressions in LUAD were associated with tumor stages and relative abundance of tumor infiltrating immune cells, such as B cells, CD4+ T cells, and macrophages. Moreover, cell cycle, DNA replication, homologous recombination, mismatch repair, P53 signaling pathway, and small cell lung cancer signaling were significantly enriched in CDK1 and PLK1 high expression phenotype.CDK1 and PLK1 may be used as potential biomarkers and therapeutic targets for LUAD.     

Identification of key immune-related genes and immune infiltration in atrial fibrillation with valvular heart disease based on bioinformatics analysis

BackgroundAtrial fibrillation (AF) is the most common persistent arrhythmia. Valvular heart disease (VHD) and AF frequently coexist. In our study, from performing bioinformatics analysis, we sought to identify immune-related genes (IRGs) and explore the role of immune cell infiltration in AF-VHD in depth, aiming at investigating the potential molecular mechanism and developing new therapeutic targets for AF, including AF-VHD.MethodsThe gene expression of the {"type":"entrez-geo","attrs":{"text":"GSE41177","term_id":"41177"}}GSE41177 and {"type":"entrez-geo","attrs":{"text":"GSE79768","term_id":"79768"}}GSE79768 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were analyzed via the limma package in Bioconductor with R software. Differentially expressed immune-related genes (DEIRGs) were selected via combination ImmPort database with DEGs, and the enrichment function and pathway analysis were explored. A protein-protein interaction (PPI) network was built with a Search Tool for the Retrieval of Interacting Genes/Proteins plugin in Cytoscape. The CIBERSORT algorithm was used to evaluate immune infiltration in the left atrial (LA) tissues between AF-VHD and sinus rhythm (SR) patients. Finally, a correlation analysis between key DEIRGs and infiltrating immune cells was performed.ResultsA total of 130 DEIRGs were detected. Enrichment function of DEIRGs demonstrated that they are significant in immune and inflammatory responses. The key DEIRGs assessed by the PPI network and involved in both the immune and inflammatory responses were the C-X-C motif chemokine ligand (CXCL) 1, pro-platelet basic protein (PPBP), CXCL12, and C-C motif chemokine ligand 4 (CCL4). The immune infiltration findings indicated that, compared with the LA tissues from SR patients, the tissues from AF-VHD patients contained a higher proportion of gamma delta T cells, but a lower proportion of CD8 and regulatory T cells. The results of correlation analysis demonstrated that CXCL1 was positively correlated with activated mast cells and significantly negatively correlated with resting mast cells. PPBP, CXCL12, and CCL4 were positively correlated with the infiltration of various immune cells, such as neutrophils, plasma cells, and resting dendritic cells.ConclusionsThe key immune-related genes and the differences in immune infiltration in LA tissues play an essential role in the occurrence and progression of AF-VHD.     

Diagnosis,clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction—a bioinformatics analysis

BackgroundAccurate myocardial infarction (AMI) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) modification plays an important role in the development of cardiac remodeling and the cardiomyocyte contractile function. The aim of this study is to analyze the m6A-related molecular biological mechanisms of AMI in terms of accurate diagnosis and prognosis.MethodsThe platform data and probe data of the {"type":"entrez-geo","attrs":{"text":"GSE66360","term_id":"66360"}}GSE66360 data set were downloaded. The differential analysis was conducted by combining the m6A-related gene expression. Thereafter, a diagnostic model was established using the random-forest method. The diagnostic accuracy of the diagnostic models was assessed by using the area under the receiver operating characteristic (ROC) curve (AUC). Next, the patients with AMI were clustered by unsupervised machine learning using the R software. Finally, an immune cell clustering analysis for each cluster was conducted to determine the correlations between m6A-related gene expression and the infiltration amount of the immune cells. The case and control groups were not matched in terms of demographics.ResultsThe {"type":"entrez-geo","attrs":{"text":"GSE6636","term_id":"6636"}}GSE6636 data set comprised 99 participants (49 patients with AMI and 50 without in control group). The differential analysis identified 10 m6A-related genes: 5 writers [Methyltransferase-like 3 (METTL3), Methyltransferase-like 14 (METTL14), Wilms tumor 1-associated protein (WTAP), Zinc Finger CCCH-Type Containing 13 (ZC3H13), and Casitas B-lineage proto-oncogene like 1 (CBLL1)], 4 readers [YT521-B homology domain-containing family 3 (YTHDF3), Fragile X mental retardation type 1 (FMR1), YT521-B homology-domain-containing protein 1 (YTHDC1), and insulin-like growth factor binding protein 3 (IGFBP3)] and 1 eraser [fat mass and obesity associated (FTO) gene]. The Mean Decrease Gini (MDG) values of these 10 genes were greater than 2. The FTO, WTAP, YTHDC1, IGFBP3, and CBLL1 were included in the model with a C index of 0.842. METTL3, ZC3H13, WTAP, and CBLL1 were highly expressed in Type A, and YTHDF3 was highly expressed in Type B.ConclusionsA diagnostic model of AMI was established based on the genes of FTO, WTAP, YTHDC1, IGFBP3, and CBLL1. Additionally, 2 molecular subtypes were successfully identified from the above-mentioned gene. Our findings could provide a novel method for the accurate diagnosis of AMI.     

Screening of sphingolipid metabolism-related genes associated with immune cells in myocardial infarction: a bioinformatics analysis

BackgroundInflammation and immune cell infiltration in infarcted myocardial tissue are critical to myocardial infarction (MI) prognosis, and alterations in sphingolipid metabolism (SM) have been shown to potentially influence the inflammatory response and induce cardioprotection, but the underlying mechanisms are unclear. We therefore performed bioinformatics analysis to screen for key genes of SM in MI immune cells.MethodsThree matrix files including {"type":"entrez-geo","attrs":{"text":"GSE61145","term_id":"61145"}}GSE61145, {"type":"entrez-geo","attrs":{"text":"GSE23294","term_id":"23294"}}GSE23294, and {"type":"entrez-geo","attrs":{"text":"GSE71906","term_id":"71906"}}GSE71906 were downloaded from the Gene Expression Omnibus (GEO) database. {"type":"entrez-geo","attrs":{"text":"GSE61145","term_id":"61145"}}GSE61145 was a human peripheral blood database, and {"type":"entrez-geo","attrs":{"text":"GSE23294","term_id":"23294"}}GSE23294 and {"type":"entrez-geo","attrs":{"text":"GSE71906","term_id":"71906"}}GSE71906 were 2 mouse myocardial tissue databases. R and annotation packages were used to screen for differentially-expressed genes (DEGs). Datasets of human and mouse cardiac tissues were downloaded from the GEO database for subsequent validation. The downloaded platform and matrix files were processed using R language and annotation packages. Key targets and enrichment pathways were identified using Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The Wilcoxon test was performed on the genes involved in SM pathways in neutrophils.ResultsA total of 261 DEGs were obtained from human peripheral blood datasets, among which 101 were immune-related. GO analysis revealed that neutrophil activation, T cell activation, and T cell differentiation were significantly enriched in the immune-related DEGs. Three types of immune cells were identified in infarcted myocardial tissues. In addition, 194 DEGs were obtained from mouse myocardial tissue data, among which 6 SM-related genes (Asah1, Degs1, Neu1, Sptlc2, Sphk1, and Gba2) were significantly associated with MI. Evaluation of the relationships between these DEGs and neutrophils showed that the expression of the Sptlc2 gene was significantly upregulated in neutrophils of the MI group, while the expression levels of the Asah1 and Degs1 genes were downregulated.ConclusionsWe identified 3 SM-related genes that were highly associated with neutrophils in MI, which may advance our understanding of SM in immune cells after MI.     

Potential impact of WTAP and YTHDF2 on tumor immunity in lung adenocarcinoma

WTAP and N6-methyladenosine (m6A) reader proteins (YTHDF2) are N6-methyladenosine (m6A) methyltransferase and m6A reading proteins, respectively. In recent years, the tumor immune environment has received more and more attention in the progress and treatment of cancer. The aim of this study was to investigate the relationship between N6-methyladenosine (m6A) methyltransferase (WTAP)/YTHDF2 and the immunological characteristics of lung adenocarcinoma (LUAD). Based on the expression of WTAP and YTHDF2 in the cancer genome atlas (TCGA) and gene expression omnibus (GEO) database, LUAD patients were divided into 2 clusters by coherently clustering method, and performed gene set enrichment analysis (GSEA) to identify the functional differences. Immunoinvasion analysis was performed using ESTIMATE, CIBERSORT, and single-sample GSEA (ssGSEA), and expression of immune checkpoint inhibitors (ICIs) targets was assessed, while tumor mutation burden (TMB) was calculated in tumor samples. Weighted gene co-expression network analysis (WGCNA) was used to identify the genes related to both WTAP/YTHDF2 expression and immunity. The immunological characteristics between the 2 clusters were externally verified based on {"type":"entrez-geo","attrs":{"text":"GSE39582","term_id":"39582"}}GSE39582. The expression of WTAP was higher in cluster 1 and YTHDF2 was lower, but it was opposite in cluster 2. Cluster 1 had stronger immune infiltration, more ICIs target expression, more TMB. In addition, WGCNA identified 22 genes associated with WTAP/YTHDF2 expression and immune score, including TIM3 (HAVCR2) and CD86. WTAP and YTHDF2 influence immune contexture and may be novel prognostic and druggable targets associated with the immune system of LUAD.     

ECM2 and GLT8D2 in human pulmonary artery hypertension: fruits from weighted gene co-expression network analysis

BackgroundPulmonary artery hypertension (PAH) is an incurable disease with a high mortality rate. Current medications ameliorate symptoms but cannot target adverse vascular remodeling. New therapeutic strategies for PAH need to be established.MethodsUsing the weighted gene coexpression network analysis (WGCNA) algorithm, we constructed a coexpression network of dataset {"type":"entrez-geo","attrs":{"text":"GSE117261","term_id":"117261"}}GSE117261 from the Gene Expression Omnibus (GEO) database. Key modules were identified by the relationship between module eigengenes and clinical traits. Hub genes were screened out based on gene significance (GS), module membership (MM), and mean pulmonary artery pressure (mPAP). External validations were conducted in {"type":"entrez-geo","attrs":{"text":"GSE48149","term_id":"48149"}}GSE48149 and {"type":"entrez-geo","attrs":{"text":"GSE113439","term_id":"113439"}}GSE113439. Functional enrichment and immune cell infiltration were analyzed using Metascape and CIBERSORTx.ResultsThe WGCNA analysis revealed 13 coexpression modules. The pink module had the highest correlation with PAH in terms of module eigengene (r=0.79; P=2e−18) and module significance (MS =0.43). Functional enrichment indicated genes in the pink module contributed to the immune system process and extracellular matrix (ECM). In the pink module, ECM2 (GS =0.65, MM =0.86, ρ=0.407, P=0.0019) and GLT8D2 (GS =0.63, MM =0.85, ρ=0.443, P=0.006) were identified as hub genes. For immune cells infiltration in PAH lung tissue, hub genes were positively correlated with M2 macrophages and resting mast cells, and were negatively correlated with monocytes, neutrophils, and CD4-naïve T cells.ConclusionsOur research identified 2 hub genes ECM2 and GLT8D2 related to PAH. The functions of these hub genes were involved in the immune process and ECM, indicating that they might serve as candidate therapeutic targets for PAH.     

Bioinformatics analysis identifies potential biomarkers involved in the metastasis of locoregionally advanced nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is one of the malignant epithelial tumors with a high metastasis rate. This study aimed to screen potential novel biomarkers involved in NPC metastasis. Microarray data of locoregionally advanced NPC (LA-NPC; {"type":"entrez-geo","attrs":{"text":"GSE103611","term_id":"103611"}}GSE103611) were obtained from the database of Gene Expression Omnibus. The differentially expressed genes (DEGs) between LA-NPC tissues with and without distant metastasis after radical treatment were screened. Functional analysis was performed and the protein–protein interaction and submodule were analyzed. The univariate Cox regression analysis was performed to identify prognostic genes in NPC in the validation microarray dataset {"type":"entrez-geo","attrs":{"text":"GSE102349","term_id":"102349"}}GSE102349. The drug–gene interactions and key genes were identified. Totally, 107 DEGs were identified. The upregulated DEGs and the key nodes in the protein–protein interaction network were associated with pathways or biological processes related to the cell cycle. Four genes including CD44, B2M, PTPN11, and TRIM74 were associated with disease-free survival in NPC. The drug–gene interaction analysis revealed that upregulated genes CXCL10, CD44, B2M, XRCC5, and RPL11 might be potential druggable genes for patients with LA-NPC metastasis by regulating cell cycle, autophagy, and drug resistance. Upregulated CXCL10, CD44, B2M, XRCC5, and RPL11 might play important roles in LA-NPC metastasis by regulating cell cycle-related pathways.     

Molecular clustering based on gene set expression and its relationship with prognosis in patients with lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is a subtype of lung cancer with high morbidity and mortality. While genotyping is an important determinant for the prognosis of LUAD patients, there is a paucity of studies on gene set-based expression (GSE) typing for LUAD. This current study used GSE methodology to perform gene typing of LUAD patients.MethodsClinical and genomic information of the LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients with LUAD were clustered into different molecular subtypes depending on the clinical and gene set expression characteristics. The survival rate and silhouette widths were compared between each molecular subtype. Differences in survival rate between gene sets were analyzed using Kaplan-Meier survival curves. Cox regression and Lasso regression were used to establish the prognostic gene set model based on the TCGA database, and the results were validated using the GEO dataset.ResultsA total of 10 hub genes were finally identified and clustered into 3 subtypes with a mean contour width of 0.96. There were significant differences in survival rates among the 3 subtypes (P<0.05). Gene Ontology (GO) analysis indicated that the related biological processes (BP) were mainly involved in regulation of cell cycle, mitotic cell cycle phase transition, and proteasome-mediated ubiquitin-dependent protein catabolic process. The cellular components (CC) were related to the spindle, chromosomal region, and midbody. Molecular function (MF) mainly focused on ubiquitin-like protein ligase binding, translation regulator activity, and oxidation activity. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the main pathways included the Epstein Barr virus infection pathway of neurogeneration, the p53 signaling pathway, and the proteome pathways. In addition, the protein-protein interaction network was analyzed using the STRING and Cytospace software, and the top 9 hub genes identified were KIF2C, DLGAP5, KIF20A, PSMC1, PSMD1, PSMB7, SNAI2, FGF13, and BMP2.ConclusionsPatients with LUAD can be clustered into three subtypes based on the expression of gene sets. These findings contribute to understanding the pathogenesis and molecular mechanisms in LUAD, and may lead to potential individualized pharmacogenetic therapy for patients with LUAD.     

Bioinformatics analysis identified immune infiltration,risk and drug prediction models of copper-induced death genes involved in salivary glands damage of primary Sjögren’s syndrome

This study aimed to identify copper-induced death genes in primary Sjögren’s syndrome (pSS) and explore immune infiltration, risk and drug prediction models for salivary glands (SGs) damage. The 3 datasets, including {"type":"entrez-geo","attrs":{"text":"GSE40611","term_id":"40611"}}GSE40611, {"type":"entrez-geo","attrs":{"text":"GSE23117","term_id":"23117"}}GSE23117, and {"type":"entrez-geo","attrs":{"text":"GSE7451","term_id":"7451"}}GSE7451 from the Gene Expression Omnibus database were downloaded. The datasets were processed using the affy in R (version 4.0.3). In immune cells, copper-induced death genes were strongly expressed in “activated” dendritic cells (aDCs), macrophages and regulatory T cells (Treg). In immune functions, copper-induced death genes were strongly expressed in major histocompatibility complex (MHC) class I, human leukocyte antigen (HLA) and type I interferon (IFN) response. Correlation analysis showed that 5 genes including SLC31A1, PDHA1, DLD, ATP7B, and ATP7A were significantly correlated with immune infiltration. The nomogram suggested that the low expression of PDHA1 was significant for predicting the risk of pSS and the area under curve was 0.678. Drug model suggested that “Bathocuproine disulfonate CTD 00001350,” “Vitinoin CTD 00007069,” and “Resveratrol CTD 00002483” were the drugs most strongly associated with copper-induced death genes. In summary, copper-induced death genes are associated with SGs injury in pSS, which is worthy of clinicians’ attention.     

Construction and validation of a robust ferroptosis-associated gene signature predictive of prognosis in lung adenocarcinoma

To construct and validate a ferroptosis-associated signature predictive of prognosis in lung adenocarcinoma (LUAD), and systematically evaluate the underlying molecular connections in cancer biology.We retrieved mRNAs sequencing profiles of LUAD from the cancer genome atlas (TCGA) data portal and clinical information from the cBio Cancer Genomics Portal. The differentially expressed ferroptosis-associated genes (DEFAGs) were screened between normal samples and LUAD by packages “limma” in R. Then the total TCGA cohort was randomly divided into training set and testing set. Based on the training set, a DEFAG signature was built and further validated in the test set, the total TCGA cohort and other independent cohorts from the gene expression omnibus data portal. A nomogram was constructed and validated, and the correlation between high-risk group and cancer biology was further evaluated.We initially identified 68 DEFAGs from TCGA cohort. A 6 DEFAG signature was built and further validated in the test set, the total TCGA cohort and other 2 independent cohorts including {"type":"entrez-geo","attrs":{"text":"GSE31210","term_id":"31210"}}GSE31210 and {"type":"entrez-geo","attrs":{"text":"GSE72094","term_id":"72094"}}GSE72094 from gene expression omnibus data portal. Further exploration indicated that high-risk group combined with TP53 mutation harbored the most unfavorable prognosis while low-risk group with TP53 wild-type status had the most favorable survival advantage over other groups. Moreover, high-risk group was associated with higher cancer stemness, tumor mutation burden, and CD274 (programmed cell death 1 ligand 1) expression.We constructed a robust ferroptosis-associated gene signature and a nomogram predictive of prognosis in LUAD, and provided a new perspective on associations between ferroptosis and cancer.     

A novel seven-gene risk profile in BALF to identify high-risk patients with idiopathic pulmonary fibrosis

BackgroundIdiopathic pulmonary fibrosis (IPF) is a fatal heterogeneous disease with a varied clinical course that is difficult to predict. Accurate predictive models are urgently needed to identify individuals with poor survival for the optimal timing of referral for transplantation and provide some clues for mechanistic research on disease progression.MethodsWe obtained the gene expression profiles of bronchoalveolar lavage fluid (BALF) from the Gene Expression Omnibus. Individuals from the {"type":"entrez-geo","attrs":{"text":"GPL14550","term_id":"14550"}}GPL14550 platform were assigned to the derivation cohort (n=112) and individuals from the {"type":"entrez-geo","attrs":{"text":"GPL17077","term_id":"17077"}}GPL17077 platform to the validation cohort (n=64). Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were applied to select candidate genes for overall survival. A nomogram model was constructed based on Cox hazard regression analysis. The model was assessed by C-statistic, calibration curve, and decision curve analysis (DCA) and was externally validated.ResultsA nomogram model comprising seven genes was constructed. Excellent discrimination and calibration were observed in the derivation (C-index 0.815) and validation (C-index 0.812) cohorts. The AUCs for predicting 1-, 2- and 3-year survival were 0.857, 0.918, 0.930 in the derivation cohort and 0.850, 0.880, 0.925 in the validation cohort, respectively. DCA confirmed the clinical applicability of the model. A risk score based on the model was an independent prognostic predictor and could divide patients into high- and low-risk groups. The Kaplan-Meier analysis displayed that high-risk patients exhibited significantly poorer survival compared with low-risk patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk patients were primarily enriched in inflammatory hallmarks, and single sample GSEA (ssGSEA) indicated that the high-risk group is closely correlated with the immune process. These lead to increased insight into mechanisms associated with IPF progression that inflammation mediated by immune response might be involved in the disease progression.ConclusionsThe novel BALF seven-gene model performed well in risk stratification and individualized survival prediction for patients with IPF, facilitating personalized management of IPF patients. It deepened the understanding of the role of inflammation in IPF progression, which needs to be further studied.     

Identification of key genes and microRNAs for multiple sclerosis using bioinformatics analysis

To better understand the molecular mechanism underlying the pathogenesis of multiple sclerosis (MS), we aimed to identify the key genes and microRNAs (miRNA) associated with MS and analyze their interactions. Differentially expressed genes (DEGs) and miRNAs (DEMs) based on the gene miRNA dataset {"type":"entrez-geo","attrs":{"text":"GSE17846","term_id":"17846"}}GSE17846 and mRNA dataset {"type":"entrez-geo","attrs":{"text":"GSE21942","term_id":"21942"}}GSE21942 were determined using R software. Next, we performed functional enrichment analysis and constructed a protein–protein interaction network. Data validation was performed to ensure the reliability of hub genes. The miRNA-mRNA regulatory network was constructed. In total, 47 DEMs and 843 DEGs were identified. Protein–protein interaction network analysis identified several hub genes, including JUN, FPR2, AKT1, POLR2L, LYZ, CXCL8, HBB, CST3, CTSZ, and MMP9, especially LYZ and CXCL8. We constructed an miRNA-mRNA regulatory network and found that hsa-miR-142-3p, hsa-miR-107, hsa-miR-140-5p, and hsa-miR-613 were the most important miRNAs. This study reveals some key genes and miRNAs that may be involved in the pathogenesis of MS, providing potential targets for the diagnosis and treatment of MS.     

Identification of potential core genes in esophageal carcinoma using bioinformatics analysis

Background:Esophageal squamous cell carcinoma (ESCC) is a common human malignancy worldwide. The tumorigenesis mechanism in ESCC is unclear.Materials and methods:To explore potential therapeutic targets for ESCC, we analyzed 3 microarray datasets ({"type":"entrez-geo","attrs":{"text":"GSE20347","term_id":"20347"}}GSE20347, {"type":"entrez-geo","attrs":{"text":"GSE38129","term_id":"38129"}}GSE38129, and {"type":"entrez-geo","attrs":{"text":"GSE67269","term_id":"67269"}}GSE67269) derived from the gene expression omnibus (GEO) database. Then, the GEO2R tool was used to screen out differently expressed genes (DEGs) between ESCC and normal tissue. Gene ontology function and kyoto encyclopedia of genes and genomes pathway enrichment analysis were performed using the database for annotation, visualization and integrated discovery to identify the pathways and functional annotation of DEGs. Protein–protein interaction of these DEGs was analyzed based on the search tool for the retrieval of interacting genes database and visualized by Cytoscape software. In addition, we used encyclopedia of RNA interactomes (ENCORI), gene expression profiling interactive analysis (GEPIA), and the human protein atlas to confirm the expression of hub genes in ESCC. Finally, GEPIA was used to evaluate the prognostic value of hub genes expression in ESCC patients and we estimated the associations between hub genes expression and immune cell populations (B Cell, CD8⁺ T Cell, CD4⁺ T Cell, Macrophage, Neutrophil, and Dendritic Cell) in esophageal carcinoma (ESCA) using tumor immune estimation resource (TIMER).Results:In this study, 707 DEGs (including 385 upregulated genes and 322 downregulated genes) and 6 hub genes (cyclin B1 [CCNB1], cyclin dependent kinase 1 [CDK1], aurora kinase A [AURKA], ubiquitin conjugating enzyme E2C [UBE2C], cyclin A2 [CCNA2], and cell division cycle 20 [CDC20]) were identified. All of the 6 hub genes were highly expressed in ESCC tissues. Among of them, only CCNB1 and CDC20 were associated with stage of ESCC and all of them were not associated with survival time of patients.Conclusion:DEGs and hub genes were confirmed in our study, providing a thorough, scientific and comprehensive research goals for the pathogenesis of ESCC.