Monitoring methylation-driven genes as prognostic biomarkers in patients with lung squamous cell cancer.
ABSTRACT: Aberrant DNA methylations have been reported to be significantly associated with lung squamous cell cancer (LUSC). The aim of this study was to investigate the DNA methylation-driven genes in LUSC by integrative bioinformatics analysis. In the present study, methylation-driven genes in LUSC were screened out, and survival analysis related to these genes was performed to confirm their value in prognostic assessment. Gene expression and methylation data were downloaded from The Cancer Genome Atlas (TCGA), and the MethylMix algorithm was used to identify methylation-driven genes. ConsensusPathDB was used to perform Gene Ontology and pathway enrichment analysis of methylation-driven genes. Survival analysis was performed to investigate the correlation with prognosis. In total, 52 differentially expressed methylation-driven genes were identified in LUSC and adjacent tissues. Survival analysis showed that DQX1, GPR75, STX12, and TRIM61 could serve as independent prognostic biomarkers. In addition, the combined methylation and gene expression survival analysis revealed that the combined expression level of the genes ALG1L, DQX1, and ZNF418 alone can be used as a prognostic marker or drug target. Methylation of four sites of gene ZNF418, four sites of ZNF701, two sites of DQX1, and four sites of DCAF4L2 was significantly associated with survival. The present study provides an important bioinformatic and relevant theoretical basis for subsequent early diagnosis and prognostic assessment of LUSC.
Project description:<h4>Background</h4>Due to tumor heterogeneity, the diagnosis, treatment, and prognosis of patients with lung squamous cell carcinoma (LUSC) are difficult. DNA methylation is an important regulator of gene expression, which may help the diagnosis and therapy of patients with LUSC.<h4>Methods</h4>In this study, we collected the clinical information of LUSC patients in the Cancer Genome Atlas (TCGA) database and the relevant methylated sequences of the University of California Santa Cruz (UCSC) database to construct methylated subtypes and performed prognostic analysis.<h4>Results</h4>Nine hundred sixty-five potential independent prognosis methylation sites were finally identified and the genes were identified. Based on consensus clustering analysis, seven subtypes were identified by using 965 CpG sites and corresponding survival curves were plotted. The prognostic analysis model was constructed according to the methylation sites' information of the subtype with the best prognosis. Internal and external verifications were used to evaluate the prediction model.<h4>Conclusions</h4>Models based on differences in DNA methylation levels may help to classify the molecular subtypes of LUSC patients, and provide more individualized treatment recommendations and prognostic assessments for different clinical subtypes. GNAS, FZD2, FZD10 are the core three genes that may be related to the prognosis of LUSC patients.
Project description:BackgroundLung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.MethodsThe genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.ResultsSeveral statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients’ overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies.ConclusionsWe constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.
Project description:Different subtypes of non-small cell lung cancer (NSCLC) have distinct sites of origin, histologies, genetic and epigenetic changes. In this study, we explored the mechanisms of ECT2 dysregulation and compared its prognostic value in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). In addition, we also investigated the enrichment of ECT2 co-expressed genes in KEGG pathways in LUAD and LUSC. Bioinformatic analysis was performed based on data from the Cancer Genome Atlas (TCGA)-LUAD and TCGA-LUSC. Results showed that ECT2 expression was significantly upregulated in both LUAD and LUSC compared with normal lung tissues. ECT2 expression was considerably higher in LUSC than in LUAD. The level of ECT2 DNA methylation was significantly lower in LUSC than in LUAD. ECT2 mutation was observed in 5% of LUAD and in 51% of LUSC cases. Amplification was the predominant alteration. LUAD patients with ECT2 amplification had significantly worse disease-free survival (p = 0.022). High ECT2 expression was associated with unfavorable overall survival (OS) (p<0.0001) and recurrence-free survival (RFS) (p = 0.001) in LUAD patients. Nevertheless, these associations were not observed in patients with LUSC. The following univariate and multivariate analysis showed that the high ECT2 expression was an independent prognostic factor for poor OS (HR: 2.039, 95%CI: 1.457-2.852, p<0.001) and RFS (HR: 1.715, 95%CI: 1.210-2.432, p = 0.002) in LUAD patients, but not in LUSC patients. Among 518 genes co-expressed with ECT2 in LUAD and 386 genes co-expressed with ECT2 in LUSC, there were only 98 genes in the overlapping cluster. Some of the genes related KEGG pathways in LUAD were not observed in LUSC. These differences might help to explain the different prognostic value of ECT2 in LUAD and LUSC, which are also worthy of further studies.
Project description:Of the different types of lung cancer, lung squamous cell cancer (LUSC) has the second highest rates of morbidity and mortality, which have been increasing in recent years. Epigenetic abnormalities may serve as potential biomarkers and diagnostic and/or therapeutic targets, which may help to monitor and improve the prognosis of patients with cancer. In the present study, data were obtained from The Cancer Genome Atlas database and survival and joint survival analyses were conducted using the R MethylMix package. Peptidase, mitochondrial processing a subunit pseudogene 1 (PMPCAP1), sosondowah ankyrin repeat domain family member C (SOWAHC) and zinc finger protein (ZNF) 454 were identified as independent prognosis?related hub methylation?driven genes (MDGs). Of these three genes, PMPCAP1 and SOWAHC, characterized by hypomethylation and high expression levels, were associated with poor prognosis in patients with LUSC, whilst ZNF454 was associated with an improved prognosis. In addition, pathway enrichment analysis suggested that PMPCAP1, SOWAHC and ZNF454 were primarily involved in gene expression or transcription pathways. Furthermore, 5, 1 and 10 key methylation sites of PMPCAP1, SOWAHC and ZNF454, respectively, were confirmed to be significantly relevant to gene expression, establishing a basis for further investigation into the mechanisms and more precise targets of these 3 genes. In conclusion, the MDGs PMPCAP1, SOWAHC and ZNF454 may be potential prognostic biomarkers of LUSC for guiding diagnosis and therapy options, as well as providing a theoretical basis for further investigation.
Project description:Purpose: Alternative splicing (AS) is a post-transcriptional process that plays a significant role in enhancing the diversity of transcription and protein. Accumulating evidences have demonstrated that dysregulation of AS is associated with oncogenic processes. However, AS signature specifically in lung squamous cell carcinoma (LUSC) remains unknown. This study aimed to evaluate the prognostic values of AS events in LUSC patients. Methods: The RNA-seq data, AS events data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to identify survival-related AS events and survival-related parent genes were subjected to Gene Ontology enrichment analysis and gene network analysis. The least absolute shrinkage and selection operator (LASSO) method and multivariate Cox regression analysis were used to construct prognostic prediction models, and their predictive values were assessed by Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. Then a nomogram was established to predict the survival of LUSC patients. And the interaction network of splicing factors (SFs) and survival-related AS events was constructed by Spearman correlation analysis and visualized by Cytoscape. Results: Totally, 467 LUSC patients were included in this study and 1,991 survival-related AS events within 1,433 genes were identified. SMAD4, FOS, POLR2L, and RNPS1 were the hub genes in the gene interaction network. Eight prognostic prediction models (seven types of AS and all AS) were constructed and all exhibited high efficiency in distinguishing good or poor survival of LUSC patients. The final integrated prediction model including all types of AS events exhibited the best prognostic power with the maximum AUC values of 0.778, 0.816, 0.814 in 1, 3, 5 years ROC curves, respectively. Meanwhile, the nomogram performed well in predicting the 1-, 3-, and 5-year survival of LUSC patients. In addition, the SF-AS regulatory network uncovered a significant correlation between SFs and survival-related AS events. Conclusion: This is the first comprehensive study to analyze the role of AS events in LUSC specifically, which improves our understanding of the prognostic value of survival-related AS events for LUSC. And these survival-related AS events might serve as novel prognostic biomarkers and drug therapeutic targets for LUSC.
Project description:Among early?stage non?small?cell lung cancer (NSCLC) patients, cg05293407TRIM27 was significantly and exclusively associated with survival of lung squamous cell carcinoma patients, who had higher smoking intensity compared to lung adenocarcinoma patients. Generally, the significant association between cg05293407TRIM27 and survival only remained in NSCLC patients having medium?to?high pack?year of smoking. The cg05293407TRIM27?smoking synergistic interaction might account for histologically heterogeneous effects of TRIM27 DNA methylation on NSCLC survival. Tripartite motif containing 27 (TRIM27) is highly expressed in lung cancer, including non?small?cell lung cancer (NSCLC). Here, we profiled DNA methylation of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tumours from 613 early?stage NSCLC patients and evaluated associations between CpG methylation of TRIM27 and overall survival. Significant CpG probes were confirmed in 617 samples from The Cancer Genome Atlas. The methylation of the CpG probe cg05293407TRIM27 was significantly associated with overall survival in patients with LUSC (HR = 1.65, 95% CI: 1.30–2.09, P = 4.52 × 10?5), but not in patients with LUAD (HR = 1.08, 95% CI: 0.87–1.33, P = 0.493). As incidence of LUSC is associated with higher smoking intensity compared to LUAD, we investigated whether smoking intensity impacted on the prognostic effect of cg05293407TRIM27 methylation in NSCLC. LUSC patients had a higher average pack?year of smoking (37.49LUAD vs 54.79LUSC, P = 1.03 × 10?19) and included a higher proportion of current smokers than LUAD patients (28.24%LUAD vs 34.09%LUSC, P = 0.037). cg05293407TRIM27 was significantly associated with overall survival only in NSCLC patients with medium–high pack?year of smoking (HR = 1.58, 95% CI: 1.26–1.96, P = 5.25 × 10?5). We conclude that cg05293407TRIM27 methylation is a potential predictor of LUSC prognosis, and smoking intensity may impact on its prognostic value across the various types of NSCLC.
Project description:Background:Aberrant DNA methylations are significantly associated with esophageal squamous cell carcinoma (ESCC). In this study, we aimed to investigate the DNA methylation-driven genes in ESCC by integrative bioinformatics analysis. Methods:Data of DNA methylation and transcriptome profiling were downloaded from TCGA database. DNA methylation-driven genes were obtained by methylmix R package. David database and ConsensusPathDB were used to perform gene ontology (GO) analysis and pathway analysis, respectively. Survival R package was used to analyze overall survival analysis of methylation-driven genes. Results:Totally 26 DNA methylation-driven genes were identified by the methylmix, which were enriched in molecular function of DNA binding and transcription factor activity. Then, ABCD1, SLC5A10, SPIN3, ZNF69, and ZNF608 were recognized as significant independent prognostic biomarkers from 26 methylation-driven genes. Additionally, a further integrative survival analysis, which combined methylation and gene expression data, was identified that ABCD1, CCDC8, FBXO17 were significantly associated with patients' survival. Also, multiple aberrant methylation sites were found to be correlated with gene expression. Conclusion:In summary, we studied the DNA methylation-driven genes in ESCC by bioinformatics analysis, offering better understand of molecular mechanisms of ESCC and providing potential biomarkers precision treatment and prognosis detection.
Project description:Lung cancer is one of the leading causes of cancer-related death. In recent years, there has been an increasing interest in the fields of tumor and immunity. This study focused on the possible prognostic value of immune genes in non-small cell lung cancer patients. We used The Cancer Genome Atlas (TCGA) to download gene expression data and clinical information of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). The immune gene list was downloaded from the Immport database. We then constructed immune gene prognostic models on the basis of Cox regression analysis. We further evaluated the clinical significance of the models via survival analysis, receiver operating characteristic (ROC) curves, and independent prognostic factor analysis. Moreover, we analyzed the associations of prognostic models with both mutation burdens and neoantigens. Using the Gene Expression Omnibus (GEO) and Kaplan-Meier plotter databases, we evaluated the validity of the prognostic models. The prognostic model of LUAD included 13 immune genes, and the prognostic model of LUSC contained 10 immune genes. High-risk patients based on prognostic models had a lower 5-year survival rate than did low-risk patients. The ROC curve analysis demonstrated the prediction accuracy of the prognostic models, as the area under the curve (AUC) was 0.742, 0.707, and 0.711 for LUAD, and 0.668, 0.703, and 0.668 for LUSC, when the predicted survival times were 1, 3, and 5 years, respectively. The mutation burden analysis showed that mutation level was associated with the risk score in patients with LUAD. The analysis based on GEO and Kaplan-Meier plotter demonstrated the prognostic validity of the models. Therefore, immune gene-related models of LUAD and LUSC can predict prognosis. Further study of these genes may enable us to better distinguish between LUAD and LUSC and lead to improvement in immunotherapy for lung cancer.
Project description:Purpose:There is plenty of evidence showing that autophagy plays an important role in the biological process of cancer. The purpose of this study was to establish a novel autophagy-related prognostic marker for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Methods:The mRNA microarray and clinical data in The Cancer Genome Atlas (TCGA) were analyzed by using a univariate Cox proportional regression model to select candidate autophagy-related prognostic genes. Bioinformatics analysis of gene function using the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) platforms was performed. A multivariate Cox proportional regression model helped to develop a prognostic signature from the pool of candidate genes. On the basis of this prognostic signature, we could divide LUAD and LUSC patients into high-risk and low-risk groups. Further survival analysis demonstrated that high-risk patients had significantly shorter disease-free survival (DFS) than low-risk patients. The signature which contains six autophagy-related genes (EIF4EBP1, TP63, BNIP3, ATIC, ERO1A and FADD) showed good performance for predicting the survival of LUAD and LUSC patients by having a better Area Under Curves (AUC) than other clinical parameters. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database. Conclusion:Collectively, the prognostic signature we proposed is a promising biomarker for monitoring the outcomes of LUAD and LUSC.
Project description:Background:Lung squamous cell carcinoma (LUSC) is the second most common histological subtype of lung cancer (LC), and the prognoses of most LUSC patients are so far still very poor. The present study aimed at integrating lncRNA, miRNA and mRNA expression data to identify lncRNA signature in competitive endogenous RNA (ceRNA) network as a potentially prognostic biomarker for LUSC patients. Methods:Gene expression data and clinical characteristics of LUSC patients were retrieved from The Cancer Genome Atlas (TCGA) database, and were integratedly analyzed using bioinformatics methods including Differentially Expressed Gene Analysis (DEGA), Weighted Gene Co-expression Network Analysis (WGCNA), Protein and Protein Interaction (PPI) network analysis and ceRNA network construction. Subsequently, univariate and multivariate Cox regression analyses of differentially expressed lncRNAs (DElncRNAs) in ceRNA network were performed to predict the overall survival (OS) in LUSC patients. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. Gene expression profiling interactive analysis (GEPIA) was used to validate key genes. Results:WGCNA showed that turquoise module including 1,694 DElncRNAs, 2,654 DEmRNAs as well as 113 DEmiRNAs was identified as the most significant modules (cor=0.99, P<1e-200), and differentially expressed RNAs in the module were used to subsequently analyze. PPI network analysis identified FPR2, GNG11 and ADCY4 as critical genes in LUSC, and survival analysis revealed that low mRNA expression of FPR2 and GNG11 resulted in a higher OS rate of LUSC patients. A lncRNA-miRNA-mRNA ceRNA network including 121 DElncRNAs, 18 DEmiRNAs and 3 DEmRNAs was established, and univariate and multivariate Cox regression analysis of those 121 DElncRNAs showed a group of 3 DElncRNAs (TTTY16, POU6F2-AS2 and CACNA2D3-AS1) had significantly prognostic value in OS of LUSC patients. ROC analysis showed that the area under the curve (AUC) of the 3-lncRNA signature associated with 3-year survival was 0.629. Conclusions:The current study provides novel insights into the lncRNA-related regulatory mechanisms underlying LUSC, and identifying 3-lncRNA signature may serve as a potentially prognostic biomarker in predicting the OS of LUSC patients.