STARD12/14 are diagnostic and prognostic biomarkers of lung adenocarcinoma associated with epigenetic regulation, immune infiltration and ferroptosis.

Background: Metabolic reprogramming plays an important role in tumor progression and antitumor immunity. START domain-containing proteins (STARDs) are responsible for lipid metabolism. However, the underlying functions of STARDs in lung adenocarcinoma (LUAD) have not been clarified yet. Methods: Oncomine, UALCAN, TCGA and CPTAC were used to explore the expression landscape and clinicopathological characteristics of STARDs in LUAD. Diagnostic and prognostic values were assessed by Kaplan-Meier Plotter, Cox regression analysis, and ROC curve. GeneMANIA, GO, KEGG and GSEA were applied for exploring the potential biological functions. Epigenetic process, including mutation and m6A modification were analyzed by cBioPortal and TCGA. TIMER, TISIDB and TCGA cohort provided an immune signature. The correlation between STARDs expression and ferroptosis was analyzed by TCGA. Finally, the STARDs expression were confirmed by RT-qPCR and western blot. Results: STARD5/10/14 were overexpressed in LUAD compared with normal, while STARD4/7/8/11/12/13 were relatively low. STARD5/12/14 levels were positively related to clinical and lymph node stage. Survival analysis showed high STARD12 expression was associated with favorable overall survival, disease special survival as well as disease free survival, while STARD14 showed the opposite. GSEA analysis found STARD12 and STARD14 were associated with glycolysis, oxidative phosphorylation and tumor related signaling pathways. STARD12 co-expressed genes participated in cell cycle and DNA replication, and STARD14 were enriched in ECM-receptor interaction. Both STARD12 and STARD14 were corelated with epigenetic regulation, especially TP53 mutation and m6A modification. STARD12 expression was positively correlated with TMB level. The level of STARD12 was significantly associated with the abundance of infiltrating immune cells, including B cells, CD8+T cells, macrophages, dendritic cells, and chemokine, receptor, MHC, immunostimulatory related genes. STARD14 was negatively associated with the infiltration of CD8+T cells, while positively with CCL28 and immune checkpoints, including CTLA4 as well as PD-L2. In addition, STARD12/14 could regulate the ferroptosis related genes. Conclusion: STARD12 and STARD14 were expected to be potential biomarkers for LUAD, which were associated with epigenetic regulation, immune infiltration and ferroptosis.

FOXP family DNA methylation correlates with immune infiltration and prognostic value in NSCLC.

Background: Forkhead box P (FOXP) family was introduced as a double-edged sword in tumorigenesis and influenced immunotherapy response by modulating host immunity. This study aimed to summarize the involvement of the FOXP family in non-small cell lung cancer (NSCLC). Methods: The UALCAN, Gene Expression Profiling Interactive Analysis (GEPIA), and Reverse transcription-quantitative polymerase chain reaction (RT‒qPCR) were used to analyse the expression levels of the FOXP family in NSCLC. The prognostic impact was evaluated using Kaplan-Meier Plotter. MethSurv, UALCAN, and cBioPortal were applied to analyse the DNA methylation and mutation status of the FOXP family respectively. COEXPEDIA, STRING, and GeneMANIA were used to explore the interaction mechanism. Finally, TISIDB was used to investigate all of the immune-related characteristics regulated by the FOXP family. Results: The expression levels of FOXP1/3/4 were dysregulated in NSCLC tissues than that in normal tissues. Groups with low expression levels of FOXP1/4 and high expression levels of FOXP2/3 were associated with poor prognosis in NSCLC. The transcriptional levels of FOXP2/3/4 were correlated with DNA methylation in NSCLC. FOXP1/3/4 DNA methylation were correlated with prognosis. Pathway enrichment analysis indicated the FOXP family was mainly related to immune-related pathways. After DNA methylation, the correlations between FOXP family and immune factors were opposite to that before alteration in NSCLC. Conclusion: This study elucidated FOXP family could serve as vital diagnostic and prognostic biomarkers in NSCLC. Our study highlighted novel potential functions of FOXP family DNA methylation in regulation of immune-related signatures in NSCLC.

Identification and Validation of CDKN1A and HDAC1 as Senescence-Related Hub Genes in Chronic Obstructive Pulmonary Disease.

Purpose: Cellular senescence participates in the occurrence and development of chronic obstructive pulmonary disease (COPD). This study aimed to identify senescence-related hub genes and explore effective diagnostic markers and therapeutic targets for COPD. Methods: The microarray data from the GSE38974 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The overlapping genes between genes from the GSE38974 dataset and CellAge database were considered differentially expressed senescence-related genes (DESRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R software. Protein-protein interaction (PPI), miRNA-mRNA network, and competitive endogenous RNA (ceRNA) network were constructed and visualized by Cytoscape software. GSE100281 and GSE103174 datasets were employed to validate the expression and diagnostic value of hub genes. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to measure the mRNA levels of hub genes in peripheral blood mononuclear cells (PBMCs) from COPD and control samples. Results: A total of 23 DESRGs were identified between COPD samples and healthy controls. Enrichment analysis revealed that DESRGs were mainly related to apoptosis and senescence. Moreover, four hub genes and two key clusters were acquired by Cytohubba and MCODE plugin, respectively. CDKN1A and HDAC1 were verified as final hub genes based on GSE100281 and GSE103174 datasets validation. The mRNA expression level of CDKN1A was negatively related to forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC), and HDAC1 expression had the opposite correlation. Finally, an HDAC1-based ceRNA network, including 6 miRNAs and 11 lncRNAs, was constructed. Conclusion: We identified two senescence-related hub genes, CDKN1A and HDAC1, which may be effective biomarkers for COPD diagnosis and treatment. An HDAC1-related ceRNA network was constructed to clarify the role of senescence in COPD pathogenesis.

Identification of Ferroptosis-Related Hub Genes and Their Association with Immune Infiltration in Chronic Obstructive Pulmonary Disease by Bioinformatics Analysis.

Purpose: Ferroptosis and immune infiltration are involved in the pathogenesis of chronic obstructive pulmonary disease (COPD). We aim to identify ferroptosis-related hub genes and analyze their association with immune infiltration in COPD through bioinformatics methods. Materials and Methods: The mRNA microarray data of GSE38974 were downloaded from Gene Expression Omnibus to obtain differentially expressed genes (DEGs). The DEGs were intersected with ferroptosis-related genes (FRGs) from FerrDb to obtain differentially expressed FRGs. GO and KEGG enrichment and protein-protein interaction (PPI) analyses of differentially expressed FRGs were conducted in R software and STRING database. The key module and hub genes were screened by Cytoscape software. MiRNAs, transcription factors and signal molecules were predicted in miRNet and NetworkAnalyst. The disease correlation in the Comparative Toxicomics Database (CTD) and the receiver operating characteristic (ROC) curves of hub genes were analyzed. Immune infiltration was evaluated by CIBERSORT algorithm. Spearman correlation analyses were conducted between hub genes and differentially infiltrated immune cells. Results: Fifteen differentially expressed FRGs were identified, which were enriched in some terms involving airway inflammatory responses and structural remodeling. Five hub genes were selected including HIF1A, IL6, PTGS2, CDKN1A and ATM. Inference scores in CTD indicated their association with COPD. Two miRNAs, five transcription factors and one signal molecule were predicted. The combination of hub genes could be a fine diagnostic indicator of COPD (AUC: 0.981, CI: 0.940-1.000). Immune infiltration evaluation showed that monocytes and M0 macrophages were upregulated in COPD lung tissues, while CD8 T cells, activated NK cells, M2 macrophages, resting dendritic cells and resting mast cells were downregulated. The hub genes were significantly associated with differentially infiltrated immune cells. Conclusion: We identified five ferroptosis-related hub genes (HIF1A, IL6, PTGS2, CDKN1A and ATM) in COPD, and found that they may influence the pathogenesis of COPD by regulating ferroptosis and thus affecting infiltrating immune cells.

Autophagy Induced by BCL2-Related ceRNA Network Participates in the Occurrence of COPD.

Purpose: Chronic obstructive pulmonary disease (COPD) is a predominant cause of mortality worldwide. Autophagy, which depends on a lysosomal degradation pathway, plays an essential role in the occurrence of COPD. The aim of our study was to identify the potential function of autophagy and construct a BCL2-related competing endogenous RNA (ceRNA) network that induces autophagy in COPD. Methods: Blood sample data from GSE31568, GSE24709, and GSE61741 were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs in COPD and controls were identified via GEO2R. Transcription factors were obtained from FunRich. DIANA, miRDB, miRTarBase, and TargetScan were used to predict target genes of miRNAs. Autophagy genes were collected from the Human Autophagy Database (HADb). The GSE151052 dataset was used to identify autophagy-related differentially expressed genes in tissues. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted via Metascape and the STRING network. Spearman correlation analysis was used to analyze the relationship between autophagy-related differentially expressed genes and lung function. The BCL2-related ceRNA network was modeled by Cytoscape. Results: We obtained 41 differentially expressed miRNAs and 10 significantly different transcription factors. We identified 19 autophagy-related differentially expressed genes that were significantly different (P<0.05) in tissue samples. The most significant enrichment in Metascape was an autophagy item, which further confirmed autophagy participation in the occurrence of COPD. PPI network analysis found four genes (BCL2, BECN1, MAPK8, and ITPR1), among which BCL2 was correlated with both FEV1/FVC and FEV1 prediction. Finally, the BCL2-related ceRNA network was constructed to clarify the interaction of RNAs and occurrence of autophagy, including 18 miRNAs and 65 lncRNAs. Conclusion: We identified 19 autophagy-related differentially expressed genes that participated in COPD; among them, BCL2 was correlated with lung function, and a BCL2-related ceRNA network was constructed, which further revealed the potential mechanism of autophagy involvement in COPD.

Prognostic Value of Genomic Instability of m6A-Related lncRNAs in Lung Adenocarcinoma.

Background: Genomic instability of N6-methyladenosine (m6A)-related long noncoding RNAs (lncRNAs) plays a pivotal role in the tumorigenesis of lung adenocarcinoma (LUAD). Our study identified a signature of genomic instability of m6A-associated lncRNA signature and revealed its prognostic role in LUAD. Methods: We downloaded RNA-sequencing data and somatic mutation data for LUAD from The Cancer Genome Atlas (TCGA) and the GSE102287 dataset from the Gene Expression Omnibus (GEO) database. The "Limma" R package was used to identify a network of regulatory m6A-related lncRNAs. We used the Wilcoxon test method to identify genomic-instability-derived m6A-related lncRNAs. A competing endogenous RNA (ceRNA) network was constructed to identify the mechanism of the genomic instability of m6A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct a prognostic model for internal testing and validation of the prognostic m6A-related lncRNAs using the GEO dataset. Performance analysis was conducted to compare our prognostic model with the previously published lncRNA models. The CIBERSORT algorithm was used to explore the relationship of m6A-related lncRNAs and the immune microenvironment. Prognostic m6A-related lncRNAs in prognosis, the tumor microenvironment, stemness scores, and anticancer drug sensitivity were analyzed to explore the role of prognostic m6A-related lncRNAs in LUAD. Results: A total of 42 genomic instability-derived m6A-related lncRNAs were differentially expressed between the GS (genomic stable) and GU (genomic unstable) groups of LUAD patients. Four differentially expressed lncRNAs, 17 differentially expressed microRNAs, and 75 differentially expressed mRNAs were involved in the genomic-instability-derived m6A-related lncRNA-mediated ceRNA network. A prediction model based on 17 prognostic m6A-associated lncRNAs was constructed based on three TCGA datasets (all, training, and testing) and validated in the GSE102287 dataset. Performance comparison analysis showed that our prediction model (area under the curve [AUC] = 0.746) could better predict the survival of LUAD patients than the previously published lncRNA models (AUC = 0.577, AUC = 0.681). Prognostic m6A-related-lncRNAs have pivotal roles in the tumor microenvironment, stemness scores, and anticancer drug sensitivity of LUAD. Conclusion: A signature of genomic instability of m6A-associated lncRNAs to predict the survival of LUAD patients was validated. The prognostic, immune microenvironment and anticancer drug sensitivity analysis shed new light on the potential novel therapeutic targets in LUAD.

Comprehensive Bioinformatics Analysis of Lipopolysaccharide-Induced Altered Autophagy in Acute Lung Injury and Construction of Underlying Competing Endogenous RNA Regulatory Mechanism.

BACKGROUND: Acute lung injury (ALI) is a fatal syndrome frequently induced by lipopolysaccharide (LPS) released from the bacterial cell wall. LPS could also trigger autophagy of lung bronchial epithelial cell to relieve the inflammation, while the overwhelming LPS would impair the balance of autophagy consequently inducing serious lung injury. METHODS: We observed the autophagy variation of 16HBE, human bronchial epithelial cell, under exposure to different concentrations of LPS through western blot, immunofluorescence staining, and electron microscopy. Eight strands of 16HBE were divided into two groups upon 1000 ng/ml LPS stimulation or not, which were sent to be sequenced at whole transcriptome. Subsequently, we analyzed the sequencing data in functional enrichment, pathway analysis, and candidate gene selection and constructed a hsa-miR-663b-related competing endogenous RNA (ceRNA) network. RESULTS: We set a series of concentrations of LPS to stimulate 16HBE and observed the variation of autophagy in related protein expression and autophagosome count. We found that the effective concentration of LPS was 1000 ng/ml at 12 hours of exposure and sequenced the 1000 ng/ml LPS-stimulated 16HBE. As a result, a total of 750 differentially expressed genes (DEGs), 449 differentially expressed lncRNAs (DElncRNAs), 76 differentially expressed circRNAs (DEcircRNAs), and 127 differentially expressed miRNAs (DEmiRNAs) were identified. We constructed the protein-protein interaction (PPI) network to visualize the interaction between DEGs and located 36 genes to comprehend the core discrepancy between LPS-stimulated 16HBE and the negative control group. In combined analysis of differentially expressed RNAs (DERNAs), we analyzed all the targeted relationships of ceRNA in DERNAs and figured hsa-miR-663b as a central mediator in the ceRNA network to play when LPS induced the variation of autophagy in 16HBE. CONCLUSION: Our research indicated that the hsa-miR-663b-related ceRNA network may contribute to the key regulatory mechanism in LPS-induced changes of autophagy and ALI.

Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis.

BACKGROUND: Tumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD). METHODS: "GEO2R," "limma" R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. "Survival," "survminer," "rms" R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs. RESULTS: A total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = -0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By "survival ROC" R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05. CONCLUSION: In conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.

Pan-Cancer Prognostic, Immunity, Stemness, and Anticancer Drug Sensitivity Characterization of N6-Methyladenosine RNA Modification Regulators in Human Cancers.

N6-methyladenosine RNA modification plays a significant role in the progression of multiple tumorigenesis. Our study identified the imperative role of m6A regulators in the tumor immune microenvironment, survival, stemness score, and anticancer drug sensitivity of pan-cancer. The Wilcox test was to identify the differential expression between 17 m6A regulators across 33 TCGA cancer types and their normal tissues from UCSC Xena GDC pan-cancer. Survival analysis of m6A-related regulators in 33 TCGA cancer types was identified using the "survival" and "survminer" package. The Spearman correlation test and Pearson correlation test were used to identify the correlation relationship between m6A regulators expression and tumor microenvironment, tumor stem cell score, and drug sensitivity of anticancer drugs. ConsensusPathDB was used for exploring m6A regulators functional enrichment. The 17 (METTL3, WTAP, METTL14, RBM15, RBM15B, VIRMA, HNRNPC, HNRNPA2B1, YTHDC1, ZC3H13, YTHDF1, YTHDC2, YTHDF2, IGF2BP3, IGF2BP1, FTO, and ALKBH5) m6A regulators were differentially expressed in 18 TCGA cancer types and adjacent normal tissues. Correlation analysis indicated that the relationship between the expression of 17 m6A regulators and tumor microenvironment indicated that the higher expression of m6A regulators, the higher the degree of tumor stem cells. The anticancer drug sensitivity analysis indicated that ZC3H13 expression had a positive relationship with anticancer drugs such as selumetinib, dabrafenib, cobimetinib, trametinib, and hypothemycin (p < 0.001). YTHDF2 expression was significantly negatively correlated with the anticancer drug dasatinib (p < 0.001). The pan-cancer immune subtype analysis showed that the 17 m6A regulators were significantly different in immune subtype C1 (wound healing), C3 (inflammatory), C2 (IFN-gamma dominant), C5 (immunological quiet), C4 (lymphocyte depleted), and C6 (TGF-beta dominant) (p < 0.001). Our study provides a comprehensive insight for revealing the significant role of m6A regulators in the tumor immune microenvironment, stemness score, and anticancer drug sensitivity of human cancers.

Cigarette smoke extract induces pyroptosis in human bronchial epithelial cells through the ROS/NLRP3/caspase-1 pathway.

AIMS: Pyroptosis and inflammation are involved in the development of chronic obstructive pulmonary disease (COPD). However, the cigarette smoke-mediated mechanism of COPD remains unclear. In this study, we aimed to investigate the role of nucleotide-binding domain-like receptor protein-3 (NLRP3) inflammasome-mediated pyroptosis in the death of human bronchial epithelial (HBE) cells after cigarette smoke extract (CSE) exposure. MAIN METHODS: The protein level of NLRP3 in lung tissue was measured after cigarette smoke exposure in vivo. In vitro, HBE cells were treated with CSE. Subsequently, the activity of caspase-1, lactate dehydrogenase (LDH) release, release of interleukin (IL)-1ß and NLRP3 expression levels were measured. The involvement of reactive oxygen species (ROS) was also explored. KEY FINDINGS: After exposure to CSE, increased release of LDH, the transcriptional and translational upregulation of NLRP3, the caspase-1 activity levels, and enhanced IL-1ß and IL-18 release were observed in 16HBE cells. In addition, NLRP3 was required to activate the caspase-1. Our results suggested that pre-stimulated of 16HBE with a caspase-1 inhibitor, or using NLRP3 siRNA to silence NLRP3 expression, also caused the decrease of IL-1ß release and pyroptosis. SIGNIFICANCES: CSE induced inflammation and contributed to pyroptosis through the ROS/NLRP3/caspase-1 pathway in 16HBE cells. The NLRP3 inflammasome participates in CSE-induced HBE cell damage and pyroptosis, which could provide new insights into COPD.

Spirometric Reference Equations for Elderly Chinese in Jinan Aged 60-84 Years.

BACKGROUND: The interpretation of spirometry varies on different reference values. Older people are usually underrepresented in published predictive values. This study aimed at developing spirometric reference equations for elderly Chinese in Jinan aged 60-84 years and to compare them to previous equations. METHODS: The project covered all of Jinan city, and the recruitment period lasted 9 months from January 1, 2017 to September 30, 2017, 434 healthy people aged 60-84 years who had never smoked (226 females and 208 males) were recruited to undergo spirometry. Vital capacity (VC), forced VC (FVC), forced expiratory volume in 1 s (FEV1), FEV1/FVC, FEV1/VC, FEV6, peak expiratory flow, and forced expiratory flow at 25%, 50%, 75%, and 25-75% of FVC exhaled (FEF25%, FEF50%, FEF75%, and FEF25-75%) were analyzed. Reference equations for mean and the lower limit of normal (LLN) were derived using the lambda-mu-sigma method. Comparisons between new and previous equations were performed by paired t-test. RESULTS: New reference equations were developed from the sample. The LLN of FEV1/FVC, FEF25-75%computed using the 2012-Global Lung Function Initiative (GLI) and 2006-Hong Kong equations were both lower than the new equations. The biggest degree of difference for FEV1/FVC was 19% (70.46% vs. 59.29%, t = 33.954, P < 0.01) and for maximal midexpiratory flow (MMEF, equals to FEF25-75%) was 22% (0.82 vs. 0.67, t = 21.303, P < 0.01). The 1990-North China and 2009-North China equations predicted higher mean values of FEV1/FVC and FEF25-75%than the present model. The biggest degrees of difference were -4% (78.31% vs. 81.27%, t = -85.359, P < 0.01) and -60% (2.11 vs. 4.68, t = -170.287, P < 0.01), respectively. CONCLUSIONS: The newly developed spirometric reference equations are applicable to elderly Chinese in Jinan. The 2012-GLI and 2006-Hong Kong equations may lead to missed diagnoses of obstructive ventilatory defects and the small airway dysfunction, while traditional linear equations for all ages may lead to overdiagnosis.