Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression

A novel CpG island methylation panel predicts survival in lung adenocarcinomas

The lack of clinically useful biomarkers compromise the personalized management of lung adenocarcinomas (ADCs); epigenetic events and DNA methylation in particular have exhibited potential value as biomarkers. By comparing genome-wide DNA methylation data of paired lung ADCs and normal tissues from 6 public datasets, cancer-specific CpG island (CGI) methylation changes were identified with a pre-specified criterion. Correlations between DNA methylation and expression data for each gene were assessed by Pearson correlation analysis. A prognostically relevant CGI methylation signature was constructed by risk-score analysis, and was validated using a training-validation approach. Survival data were analyzed by log-rank test and Cox regression model. In total, 134 lung ADC-specific CGI CpGs were identified, among which, a panel of 9 CGI loci were selected as prognostic candidates, and were used to construct a risk-score signature. The novel CGI methylation signature was identified to classify distinct prognostic subgroups across different datasets, and was demonstrated to be a potent independent prognostic factor for overall survival time of patients with lung ADCs. In addition, it was identified that cancer-specific CGI hypomethylation of RPL39L, along with the corresponding gene expression, provided optimized prognostication of lung ADCs. In summary, cancer-specific CGI methylation aberrations are optimal candidates for novel biomarkers of lung ADCs; the 9-CpG methylation panel and hypomethylation of RPL39L exhibited particularly promising significance.

Expression profile and prognostic values of STAT family members in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is a highly malignant type of cancer with a poor 5-year survival rate. The development of prognostic biomarkers and novel drug targets are required in order to improve the survival for NSCLC patients. Signal transducer and activator of transcription (STAT) proteins are cytoplasmic transcription factors known to play key roles in many cellular biological processes. However, the roles of STAT family members in the development and progression of NSCLC have not yet been apparently determined. Our study investigated the roles of STATs in the prognosis of NSCLC using cBioPortal, Human Protein Atlas, ONCOMINE, and Kaplan-Meier Plotter databases. High mutation rate of STATs existed in both lung adenocarcinoma (ADE) patients and squamous cell carcinoma (SCC) patients. High mRNA expression of STAT2 was significantly associated with shorter overall survival (OS) in NSCLC patients, while increased STAT5 and STAT6 were associated with better OS in NSCLC patients. We further found that increased mRNA expressions of STAT2 and STAT3 predicted unfavorable overall survival (OS) while high mRNA expression of STAT5B and STAT6 related to favorable OS for lung ADE patients. However, no significant correlation was identified for lung SCC patients. In stratified survival analysis, high expression of STAT2 predicted poor prognosis in stage II NSLCC patients, surgical margins negative patients and female patients. Taken together, our results illustrated that STAT5B and STAT6 could be effective prognostic biomarkers for survivals of NSCLC patients. And STAT2 might be a promising therapeutic target for the treatment of NSCLC as well as ADE.

Expression of the Major and Pro-Oncogenic H3K9 Lysine Methyltransferase SETDB1 in Non-Small Cell Lung Cancer

SETDB1 is a key histone lysine methyltransferase involved in gene silencing. The SETDB1 gene is amplified in human lung cancer, where the protein plays a driver role. Here, we investigated the clinical significance of SETDB1 expression in the two major forms of human non-small cell lung carcinoma (NSCLC), i.e., adenocarcinoma (ADC) and squamous cell carcinoma (SCC), by combining a meta-analysis of transcriptomic datasets and a systematic review of the literature. A total of 1140 NSCLC patients and 952 controls were included in the association analyses. Our data revealed higher levels of SETDB1 mRNA in ADC (standardized mean difference, SMD: 0.88; 95% confidence interval, CI: 0.73-1.02; p < 0.001) and SCC (SMD: 0.40; 95% CI: 0.13-0.66; p = 0.003) compared to non-cancerous tissues. For clinicopathological analyses, 2533 ADC and 903 SCC patients were included. Interestingly, SETDB1 mRNA level was increased in NSCLC patients who were current smokers compared to non-smokers (SMD: 0.26; 95% CI: 0.08-0.44; p = 0.004), and when comparing former smokers and non-smokers (p = 0.009). Furthermore, the area under the curve (AUC) given by the summary receiver operator characteristic curve (sROC) was 0.774 (Q = 0.713). Together, our findings suggest a strong foundation for further research to evaluate SETDB1 as a diagnostic biomarker and/or its potential use as a therapeutic target in NSCLC.

Epigenetics of lung cancer: a translational perspective

BACKGROUND

Lung cancer remains the most common cause of cancer-related death, with a 5-year survival rate of only 18%. In recent years, the development of targeted pharmacological agents and immunotherapies has substantially increased the survival of a subset of patients. However, most patients lack such efficacious therapy and are, thus, treated with classical chemotherapy with poor clinical outcomes. Therefore, novel therapeutic strategies are urgently needed. In recent years, the development of epigenetic assays and their application to cancer research have highlighted the relevance of epigenetic regulation in the initiation, development, progression and treatment of lung cancer.

CONCLUSIONS

A variety of epigenetic modifications do occur at different steps of lung cancer development, some of which are key to tumor progression. The rise of cutting-edge technologies such as single cell epigenomics is, and will continue to be, crucial for uncovering epigenetic events at a single cell resolution, leading to a better understanding of the biology underlying lung cancer development and to the design of novel therapeutic options. This approach has already led to the development of strategies involving single agents or combined agents targeting epigenetic modifiers, currently in clinical trials. Here, we will discuss the epigenetics of every step of lung cancer development, as well as the translation of these findings into clinical applications.

Depleting PTOV1 sensitizes non-small cell lung cancer cells to chemotherapy through attenuating cancer stem cell traits

Background

Prostate tumor over expressed gene 1 (PTOV1) has been reported as an oncogene in several human cancers. However, the clinical significance and biological role of PTOV1 remain elusive in non-small cell lung cancer (NSCLC).

Methods

The Cancer Genome Atlas (TCGA) data and NCBI/GEO data mining, western blotting analysis and immunohistochemistry were employed to characterize the expression of PTOV1 in NSCLC cell lines and tissues. The clinical significance of PTOV1 in NSCLC was studied by immunohistochemistry statistical analysis and Kaplan–Meier Plotter database mining. A series of in-vivo and in-vitro assays, including colony formation, CCK-8 assays, flow cytometry, wound healing, trans-well assay, tumor sphere formation, quantitative PCR, gene set enrichment analysis (GSEA), immunostaining and xenografts tumor model, were performed to demonstrate the effects of PTOV1 on chemosensitivity of NSCLC cells and the underlying mechanisms.

Results

PTOV1 is overexpressed in NSCLC cell lines and tissues. High PTOV1 level indicates a short survival time and poor response to chemotherapy of NSCLC patients. Depleting PTOV1 increased sensitivity to chemotherapy drugs cisplatin and docetaxel by increasing cell apoptosis, inhibiting cell migration and invasion. Our study verified that depleting PTOV1 attenuated cancer stem cell traits through impairing DKK1/β-catenin signaling to enhance chemosensitivity of NSCLC cells.

Conclusion

These results suggest that PTOV1 plays an important role in the development and progression of human NSCLC and PTOV1 may serve as a therapeutic target for NSCLC patients.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1349-y) contains supplementary material, which is available to authorized users.

Transcriptional E2F1/2/5/8 as potential targets and transcriptional E2F3/6/7 as new biomarkers for the prognosis of human lung carcinoma

E2F is a group of genes that encode a family of transcription factors (TFs) in higher eukaryotes and participate in cell cycle regulation and DNA synthesis in mammalian cells. Evidence from cell lines, mouse models, and human tissues indicates that TFs are implicated in lung cancer (LC) tumorigenesis. However, the diverse expression patterns and prognostic values of eight E2Fs have yet to be elucidated. In the current study, we examined the transcriptional and survival data of E2Fs in patients with LC from ONCOMINE, GEPIA, Kaplan-Meier Plotter, and cBioPortal databases. We found that the expression levels of E2F1/2/3/5/6/7/8 were higher in lung adenocarcinoma and squamous cell lung carcinoma tissues than in lung tissues, whereas the expression level of E2F4 was lower in the former than in the latter. The expression levels of E2F2/4/5/7/8 were correlated with advanced tumor stage. Survival analysis using the Kaplan-Meier Plotter database revealed that the high transcription levels of E2F1/2/4/5/7/8 were associated with low relapse-free survival (RFS) in all of the patients with LC. Conversely, high E2F3/6 levels predicted high RFS in these patients. This study implied that E2F3/6/7 are potential targets of precision therapy for patients with LC and that E2F1/2/4/5/8 are new biomarkers for the prognosis of LC.

Pyrroline-5-Carboxylate Reductase 1 Accelerates the Migration and Invasion of Nonsmall Cell Lung Cancer In Vitro

Background: Pyrroline-5-carboxylate reductase 1 (PYCR1) is involved in tumor progression, for instance, breast cancer and prostate cancer. However, its role in tumor metastasis, especially in nonsmall cell lung cancer (NSCLC), is still elusive. Materials and Methods: The messenger RNA (mRNA) expression of PYCR1 between NSCLC and normal lung specimens was compared using Oncomine database. The endogenous PYCR1 expressions in NSCLC cell lines 95C and H1299 were knocked down by lentiviral-mediated delivery of short hairpin RNA (shRNA). Then the effects of PYCR1 on the migration and invasion of NSCLC cells were studied by wound healing assay and transwell assay. Results: PYCR1 mRNA expression was significantly higher in NSCLC specimens than that in normal lung tissues. Depletion of PYCR1 in NSCLC cell significantly repressed the cell migration and invasion. Moreover, depletion of PYCR1 influenced the expression of epithelial-mesenchymal transition molecules E-cadherin, Vimentin, N-cadherin, and Snail1. Conclusions: Our data suggested that PYCR1 plays a positive role in NSCLC metastasis in vitro and might be a promising target for treating NSCLC.

A Statistical Test for Differential Network Analysis Based on Inference of Gaussian Graphical Model

Differential network analysis investigates how the network of connected genes changes from one condition to another and has become a prevalent tool to provide a deeper and more comprehensive understanding of the molecular etiology of complex diseases. Based on the asymptotically normal estimation of large Gaussian graphical model (GGM) in the high-dimensional setting, we developed a computationally efficient test for differential network analysis through testing the equality of two precision matrices, which summarize the conditional dependence network structures of the genes. Additionally, we applied a multiple testing procedure to infer the differential network structure with false discovery rate (FDR) control. Through extensive simulation studies with different combinations of parameters including sample size, number of vertices, level of heterogeneity and graph structure, we demonstrated that our method performed much better than the current available methods in terms of accuracy and computational time. In real data analysis on lung adenocarcinoma, we revealed a differential network with 3503 nodes and 2550 edges, which consisted of 50 clusters with an FDR threshold at 0.05. Many of the top gene pairs in the differential network have been reported relevant to human cancers. Our method represents a powerful tool of network analysis for high-dimensional biological data.

TFAP2C increases cell proliferation by downregulating GADD45B and PMAIP1 in non-small cell lung cancer cells

Background

Non-small cell lung cancer (NSCLC) is one of the leading causes of death in the world. NSCLC diagnosed at an early stage can be highly curable with a positive prognosis, but biomarker limitations make it difficult to diagnose lung cancer at an early stage. To identify biomarkers for lung cancer development, we previously focused on the oncogenic roles of transcription factor TFAP2C in lung cancers and revealed the molecular mechanism of several oncogenes in lung tumorigenesis based on TFAP2C-related microarray analysis.

Results

In this study, we analyzed microarray data to identify tumor suppressor genes and nine genes downregulated by TFAP2C were screened. Among the nine genes, we focused on growth arrest and DNA-damage-inducible beta (GADD45B) and phorbol-12-myristate-13-acetate-induced protein 1 (PMAIP1) as representative TFAP2C-regulated tumor suppressor genes. It was observed that overexpressed TFAP2C resulted in inhibition of GADD45B and PMAIP1 expressions at both the mRNA and protein levels in NSCLC cells. In addition, downregulation of GADD45B and PMAIP1 by TFAP2C promoted cell proliferation and cell motility, which are closely associated with NSCLC tumorigenesis.

Conclusion

This study indicates that GADD45B and PMAIP1 could be promising tumor suppressors for NSCLC and might be useful as prognostic markers for use in NSCLC therapy.

Electronic supplementary material

The online version of this article (10.1186/s40659-019-0244-5) contains supplementary material, which is available to authorized users.

Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and the most lethal malignant disease worldwide. However, the molecular mechanisms underlying LUAD are not fully understood.

Methods

Four datasets (GSE118370, GSE85841, GSE43458 and GSE32863) were obtained from the gene expression omnibus (GEO). Identification of differentially expressed genes (DEGs) and functional enrichment analysis were performed using the limma and clusterProfiler packages, respectively. A protein–protein interaction (PPI) network was constructed via Search Tool for the Retrieval of Interacting Genes (STRING) database, and the module analysis was performed by Cytoscape. Then, overall survival analysis was performed using the Kaplan–Meier curve, and prognostic candidate biomarkers were further analyzed using the Oncomine database.

Results

Totally, 349 DEGs were identified, including 275 downregulated and 74 upregulated genes which were significantly enriched in the biological process of extracellular structure organization, leukocyte migration and response to peptide. The mainly enriched pathways were complement and coagulation cascades, malaria and prion diseases. By extracting key modules from the PPI network, 11 hub genes were screened out. Survival analysis showed that except VSIG4, other hub genes may be involved in the development of LUAD, in which MYH10, METTL7A, FCER1G and TMOD1 have not been reported previously to correlated with LUAD. Briefly, novel hub genes identified in this study will help to deepen our understanding of the molecular mechanisms of LUAD carcinogenesis and progression, and to discover candidate targets for early detection and treatment of LUAD.

ENKUR acts as a tumor suppressor in lung adenocarcinoma cells through PI3K/Akt and MAPK/ERK signaling pathways

Lung adenocarcinoma (LAD) is the most common type of lung cancer with a high possibility of tumor relapse and metastasis. ENKUR (Enkurin) was originally identified as a potential regulator or effector of TRPC channels that directly binds to several TRPC proteins and the p85 subunit of PI3K. However, the role of ENKUR in cancer development has remained unclear. In this study we analyzed the expression profile of ENKUR mRNA in clinical LAD samples and examined ENKUR mRNA expression and ENKUR protein level in LAD cells. Significant down-regulated ENKUR expression was observed in clinical tumor tissues of LAD as well as in human LAD cells. To evaluate the effects of aberrant ENKUR expression on cellular biology of LAD cells in vitro and tumor growth in vivo, ENKUR-overexpressed and -silenced LAD cell lines were constructed using lentiviral vectors. Our results showed that overexpression of ENKUR in LAD cells inhibited cell proliferation, migration and invasion, while silencing of ENKUR led to the opposite effects. Silencing of ENKUR in LAD cells also promoted tumorigenesis in nude mice model and caused epithelial to mesenchymal transition (EMT). Furthermore, using western blot and co-immunoprecipitation analyses, we demonstrated that ENKUR interacts with PI3K directly and is possibly involved in the PI3K/Akt and MAPK/ERK signaling pathways.

Quantile regression for survival data in modern cancer research: expanding statistical tools for precision medicine

Quantile regression links the whole distribution of an outcome to the covariates of interest and has become an important alternative to commonly used regression models. However, the presence of censored data such as survival time, often the main endpoint in cancer studies, has hampered the use of quantile regression techniques because of the incompleteness of data. With the advent of the precision medicine era and availability of high throughput data, quantile regression with high-dimensional predictors has attracted much attention and provided added insight compared to traditional regression approaches. This paper provides a practical guide for using quantile regression for right censored outcome data with covariates of low- or high-dimensionality. We frame our discussion using a dataset from the Boston Lung Cancer Survivor Cohort, a hospital-based prospective cohort study, with the goals of broadening the scope of cancer research, maximizing the utility of collected data, and offering useful statistical alternatives. We use quantile regression to identify clinical and molecular predictors, for example CpG methylation sites, associated with high-risk lung cancer patients, for example those with short survival.

A tropomyosin receptor kinase family protein, NTRK2 is a potential predictive biomarker for lung adenocarcinoma

Neurotrophic receptor tyrosine kinase 2 (NTRK2) is a member of the tropomyosin receptor kinase family associated with the tumor development. However, the detailed function of NTRK2 in lung cancer, especially in lung adenocarcinoma (LUAD), is still not fully understood. Here, we investigated the effects of NTRK2 on LUAD biology. Through analyzing bioinformatics data derived from several databases, such as Oncomine, Gene Expression Profiling Interactive Analysis and UALCAN, we found that NTRK2 expression was significantly decreased in LUAD tissues. Clinical data acquired from Wanderer database, which is linked to The Cancer Genome Atlas database, demonstrated that the expression and methylation site of NTRK2 were significantly related to the clinical characteristics and prognosis of LUAD. Furthermore, NTRK2 expression was increased remarkably after treatment with the protein kinase B (AKT) inhibitor MK2206 and the anticancer agent actinomycin D. Functional enrichment analysis of NTRK2-associated coexpression genes was further conducted. Together, our results suggested that downregulated NTRK2 might be used in the diagnostic and prognostic evaluation of LUAD patients, or as a potential therapeutic target for the treatment of LUAD.

Expression of CENPE and its Prognostic Role in Non-small Cell Lung Cancer

Background

Non-small cell lung cancer (NSCLC) is one of the most important causes of death worldwide. Most patients are diagnosed in the advanced stage and have a poor prognosis. This study was to investigate the expression and significance of CENPE in NSCLC.

Method

Collecting information about CENPE in the Oncoming database, and perform a further analysis of the data in the current database to conduct a meta-analysis for its functional role in NSCLC. Patient life cycle analysis using Kaplan-Meier Plotter and GEPIA databases are used to perform patient survival analysis.

Result

A total of 12 studies involved the expression of CENPE in NSCLC cancer tissues and normal tissues, including 1195 samples. CENPE was highly expressed in NSCLC cell carcinoma compared with the control group (P < 0.05). Moreover, the expression of CENPE was correlated with the overall survival rate of CENPE. The overall survival rate of patients with high expression of CENPE was poor, and the prognosis of patients with low expression of CENPE was better (P<0.05).

Conclusion

We propose high expression of CENPE in NSLCL tissue is related to the prognosis of NSCLC, which may provide important basis for the development of tumor drugs.

Relationship of DNA methylation to mutational changes and transcriptional organization in fusion-positive and fusion-negative rhabdomyosarcoma

Our previous study of DNA methylation in the pediatric soft tissue tumor rhabdomyosarcoma (RMS) demonstrated that fusion-positive (FP) and fusion-negative (FN) RMS tumors exhibit distinct DNA methylation patterns. To further examine the significance of DNA methylation differences in RMS, we investigated genome-wide DNA methylation profiles in discovery and validation cohorts. Unsupervised analysis of DNA methylation data identified novel distinct subsets associated with the specific fusion subtype in FP RMS and with RAS mutation status in FN RMS. Furthermore, the methylation pattern in normal muscle is most similar to the FN subset with wild-type RAS mutation status. Several biologically relevant genes were identified with methylation and expression differences between the two fusion subtypes of FP RMS or between the RAS wild-type and mutant subsets of FN RMS. Genomic localization studies showed that promoter and intergenic regions were hypomethylated and the 3' untranslated regions were hypermethylated in FP compared to FN tumors. There was also a significant difference in the distribution of PAX3-FOXO1 binding sites between genes with and without differential methylation. Moreover, genes with PAX3-FOXO1 binding sites and promoter hypomethylation exhibited the highest frequency of overexpression in FP tumors. Finally, a comparison of RMS model systems revealed that patient-derived xenografts most closely recapitulate the DNA methylation patterns found in human RMS tumors compared to cell lines and cell line-derived xenografts. In conclusion, these findings highlight the interaction of epigenetic changes with mutational alterations and transcriptional organization in RMS tumors, and contribute to improved molecular categorization of these tumors.

The inherited variations of a p53-responsive enhancer in 13q12.12 confer lung cancer risk by attenuating TNFRSF19 expression

Background

Inherited factors contribute to lung cancer risk, but the mechanism is not well understood. Defining the biological consequence of GWAS hits in cancers is a promising strategy to elucidate the inherited mechanisms of cancers. The tag-SNP rs753955 (A>G) in 13q12.12 is highly associated with lung cancer risk in the Chinese population. Here, we systematically investigate the biological significance and the underlying mechanism behind 13q12.12 risk locus in vitro and in vivo.

Results

We characterize a novel p53-responsive enhancer with lung tissue cell specificity in a 49-kb high linkage disequilibrium block of rs753955. This enhancer harbors 3 highly linked common inherited variations (rs17336602, rs4770489, and rs34354770) and six p53 binding sequences either close to or located between the variations. The enhancer effectively protects normal lung cell lines against pulmonary carcinogen NNK-induced DNA damages and malignant transformation by upregulating TNFRSF19 through chromatin looping. These variations significantly weaken the enhancer activity by affecting its p53 response, especially when cells are exposed to NNK. The effect of the mutant enhancer alleles on TNFRSF19 target gene in vivo is supported by expression quantitative trait loci analysis of 117 Chinese NSCLC samples and GTEx data. Differentiated expression of TNFRSF19 and its statistical significant correlation with tumor TNM staging and patient survival indicate a suppressor role of TNFRSF19 in lung cancer.

Conclusion

This study provides evidence of how the inherited variations in 13q12.12 contribute to lung cancer risk, highlighting the protective roles of the p53-responsive enhancer-mediated TNFRSF19 activation in lung cells under carcinogen stress.

Electronic supplementary material

The online version of this article (10.1186/s13059-019-1696-1) contains supplementary material, which is available to authorized users.

Genome-Wide Analysis of Lung Adenocarcinoma Identifies Novel Prognostic Factors and a Prognostic Score

Background and Objective

Lung adenocarcinoma (LUAD) is the most common histological type of all lung cancers and is associated with genetic and epigenetic aberrations. The tumor, node, and metastasis (TNM) stage is the most authoritative indicator of the clinical outcome in LUAD patients in current clinical practice. In this study, we attempted to identify novel genetic and epigenetic modifications and integrate them as a predictor of the prognosis for LUAD, to supplement the TNM stage with additional information.

Methods

A dataset of 445 patients with LUAD was obtained from The Cancer Genome Atlas database. Both genetic and epigenetic aberrations were screened for their prognostic impact on overall survival (OS). A prognostic score (PS) integrating all the candidate prognostic factors was then developed and its prognostic value validated.

Results

A total of two micro-RNAs, two mRNAs and two DNA methylation sites were identified as prognostic factors associated with OS. The low- and high-risk patient groups, divided by their PS level, showed significantly different OS (p < 0.001) and recurrence-free survival (RFS; p = 0.005). Patients in the early stages (stages I/II) and advanced stages (stages III/IV) of LUAD could be further subdivided by PS into four subgroups. PS remained efficient in stratifying patients into different OS (p < 0.001) and RFS (p = 0.005) when the low- and high-risk subgroups were in the early stages of the disease. However, there was only a significant difference in OS (p = 0.04) but not RFS (p = 0.2), between the low-risk and high-risk subgroups when both were in advanced stages.

Conclusion

PS, in combination with the TNM stage, provides additional precision in stratifying patients with significantly different OS and RFS prognoses. Further studies are warranted to assess the efficiency of PS and to explain the effects of the genetic and epigenetic aberrations observed in LUAD.

Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection

Background

Lung adenocarcinoma is the most common type of lung cancer, with high mortality worldwide. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, DNA methylation, and miRNA quantification. However, the hub genes, which can be served as bio-markers for discriminating cancer and healthy individuals, are not well screened.

Result

Here we present a new method for extracting gene predictors, aiming to obtain the least predictors without losing the efficiency. We firstly analyzed three different expression microarrays and constructed multi-interaction network, since the individual expression dataset is not enough for describing biological behaviors dynamically and systematically. Then, we transformed the undirected interaction network to directed network by employing Granger causality test, followed by the predictors screened with the use of the stepwise character selection algorithm. Six predictors, including TOP2A, GRK5, SIRT7, MCM7, EGFR, and COL1A2, were ultimately identified. All the predictors are the cancer-related, and the number is very small fascinating diagnosis. Finally, the validation of this approach was verified by robustness analyses applied to six independent datasets; the precision is up to 95.3% ∼ 100%.

Conclusion

Although there are complicated differences between cancer and normal cells in gene functions, cancer cells could be differentiated in case that a group of special genes expresses abnormally. Here we presented a new, robust, and effective method for extracting gene predictors. We identified as low as 6 genes which can be taken as predictors for diagnosing lung adenocarcinoma.

Prognostic and Predictive Value of Three DNA Methylation Signatures in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response. The DNA methylation patterns of LUAD display a great potential as a specific biomarker that will complement invasive biopsy, thus improving early detection. Method: In this study, based on the whole-genome methylation datasets from The Cancer Genome Atlas (TCGA) and several machine learning methods, we evaluated the possibility of DNA methylation signatures for identifying lymph node metastasis of LUAD, differentiating between tumor tissue and normal tissue, and predicting the overall survival (OS) of LUAD patients. Using the regularized logistic regression, we built a classifier based on the 3616 CpG sites to identify the lymph node metastasis of LUAD. Furthermore, a classifier based on 14 CpG sites was established to differentiate between tumor and normal tissues. Using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we built a 16-CpG-based model to predict the OS of LUAD patients. Results: With the aid of 3616-CpG-based classifier, we were able to identify the lymph node metastatic status of patients directly by the methylation signature from the primary tumor tissues. The 14-CpG-based classifier could differentiate between tumor and normal tissues. The area under the receiver operating characteristic (ROC) curve (AUC) for both classifiers achieved values close to 1, demonstrating the robust classifier effect. The 16-CpG-based model showed independent prognostic value in LUAD patients. Interpretation: These findings will not only facilitate future treatment decisions based on the DNA methylation signatures but also enable additional investigations into the utilization of LUAD DNA methylation pattern by different machine learning methods.

Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a set of heterogeneous diseases with distinct genetic and transcriptomic characteristics. Since the introduction of the 2011 International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society histologic classification, increasing evidence has provided insights into genomic mutations and rearrangements among individual histologic subtypes of LUAD. However, how genotypic and phenotypic features of LUAD are interconnected is not well understood.

Methods

We obtained the genomic, transcriptomic, and clinical data sets of 488 LUAD patients from The Cancer Genome Atlas database. Advanced statistical models were used to disentangle the interactions between genetic mutations and expression profiles, and to assess the alterations and changes in expression of each histologic subtype. The prognostic impacts of genetic mutations, expression profiles, and clinicopathological features were integrated to predict the outcomes of LUAD patients.

Results

From our data, one or more genetic mutations correlate with expression levels of 6054/18175 (33.3%) genes and explain 8–40% of observed variability in LUAD. The genetic mutations and expression profiles varied remarkably among the histologic subtypes of LUAD, which helped to explain the different prognostic impact based on subtype classification. Genomic, transcriptomic, and clinical data were all shown to have utility for predicting overall and recurrence‐free survival, with the largest contribution from the transcriptome.

Conclusion

Our prediction model integrating genetic mutations, expression profiles, and clinicopathological features exhibited superior accuracy over the current tumor node metastasis staging system to prognosticate outcomes of patients with LUAD (overall survival 67% vs. 55%, recurrence‐free survival 57% vs. 49%; P < 0.01).

Comprehensive genomic and prognostic analysis of the IL‑17 family genes in lung cancer

The six members of the interleukin (IL)‑17 gene family (IL‑17A‑F) have been identified in various types of cancer. Although lung cancer is the leading cause of cancer‑related death worldwide and IL‑17A was found to play a critical role in lung cancer, there is little knowledge concerning the association between the other five members of the IL‑17 family and lung cancer. The genetic mutations and expression of IL‑17 family members were investigated using the Catalogue of Somatic Mutations in Cancer (COSMIC), Oncomine, and cBio Cancer Genomics Portal (cBioPortal) databases. Prognostic values and interaction networks of the members were assessed by the Kaplan‑Meier plotter, Search Tool for the Retrieval of Interacting Genes (STRING) database and FunRich software. The results found that, across 5,238 lung cancer patients in the cBioPortal, the results of IL‑17 family gene alteration frequencies and types showed that IL‑17A, IL‑25 and IL‑17F exhibited higher alteration frequencies (2, 2.1 and 1.9%, respectively), and gene amplification accounted for the majority of changes. IL‑17B, IL‑17C and IL‑17D exhibited lower alteration frequencies (0.8, 1.1 and 1.1%, respectively), and deep deletion accounted for the majority of changes. The rates of point mutations in IL‑17A through IL‑17F family genes in lung cancer were 0.66, 0.18, 0.13, 0.09, 0.27 and 0.44% in the COSMIC database. Within the Oncomine database, five datasets showed that IL‑17D was significantly decreased in lung cancer, while no dataset showed a significant difference in the expression of IL‑17A, IL‑17B, IL‑17C, IL‑25 or IL17‑F between lung cancer and normal controls. The frequencies of IL‑17A, IL‑17B and IL‑17C mRNA upregulation in lung squamous cell carcinoma were lower than those in lung adenocarcinoma (2.7, 1.9 and 2.1%, respectively), whereas the frequencies of IL‑17D, IL‑25 and IL‑17F mRNA upregulation were higher in lung squamous cell carcinoma than those in lung adenocarcinoma (3, 6 and 6%, respectively). IL‑17A and IL‑17B were unrelated to overall survival (p=0.11; P=0.17), whereas IL‑17C, IL‑17D, IL‑25 and IL‑17F influenced prognosis (P=0.0023, P=0.0059, P=0.039 and P=0.0017, respectively) according to the Kaplan‑Meier plotter. Moreover, the expression level of IL‑17C was the highest in lung tissues, and IL‑17 family genes mainly participate in the 'IFN‑γ pathway' according to the STRING database and Funrich software. In conclusion, we performed the first comprehensive investigation of the IL‑17 gene family in lung cancer, including gene mutation, mRNA expression levels, prognostic values and network pathways. Our results revealed that IL‑17 family gene mutation rates were in general low and that amplification and deep deletion were the main mutation type. The expression and function of IL‑17A and IL‑17B in lung cancer are still not fully elucidated and warrant research with larger sample sizes. IL‑17D was significantly decreased in lung cancer and was correlated with better OS. Studies of IL‑17C‑F in lung cancer are limited. Further experimental studies on the association between IL‑17D and lung cancer progression are needed to identify more effective therapeutic targets for lung cancer.

Identification of CAV1 and DCN as potential predictive biomarkers for lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common histological form of lung cancer that is clinically diagnosed. The aim of this study is to explore the novel genes associated with LUAD tumorigenesis. Comprehensive bioinformatics analyses of the data were obtained from several publicly available databases, such as the Gene Expression Omnibus, the Human Protein Atlas project, and the Cancer Cell Line Encyclopedia. The clinical relevance of these novel genes in LUAD was further examined by immunohistochemistry. We identified the overlapping differentially expressed genes (DEGs) in five independent microarray data sets from the Gene Expression Omnibus database ( GSE75037 , GSE85716 , GSE85841 , GSE63459 , and GSE32867 ). Using the criteria of |log (fold change)| ≥ 1 and P value <0.05, 167 genes were preliminarily validated as co-DEGs. Protein-protein interaction network analysis indicated that caveolin 1 (CAV1) and decorin (DCN) levels were significantly reduced and that these genes were the most promising predictive biomarkers for the occurrence and prognosis of LUAD. A cell proliferation assay indicated that overexpressed CAV1 and DCN could significantly inhibit the proliferation rate of A549 and H157 cells. Additionally, these two downregulated candidate genes were further verified by immunohistochemistry conducted on a LUAD tissue array and comprehensive bioinformatics analyses, including those using the Oncomine platform and the Cancer Cell Line Encyclopedia. Our study demonstrates low levels of CAV1 and DCN in LUAD. An understanding of their functional roles in LUAD biology would give us important insights that would be useful in further investigations.

Screening of important lncRNAs associated with the prognosis of lung adenocarcinoma, based on integrated bioinformatics analysis

The study aimed to elucidate the mechanisms underlying the occurrence and development of lung adenocarcinoma, and to reveal long non-coding RNA (lncRNA) prognostic factors to identify patients at high risk of disease recurrence or metastasis. Based on extensive RNA sequencing data and clinical survival prognosis information from patients with lung adenocarcinoma, obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases, a co-expression network of lncRNAs with different expression levels was built using weighted correlation network analysis and MetaDE.ES. The prognostic lncRNAs were identified using the Cox proportional hazards model and Kaplan-Meier survival curves to construct a risk scoring system. The reliability of the system was confirmed in validation datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on the genes significantly associated with the prognostic lncRNAs using gene set enrichment analysis. A total of 58 and 1,633 differentially expressed lncRNAs and mRNAs were identified, respectively. Considering the module stability, annotation, correlation between modules and clinical factors, and the differential expression levels of lncRNAs, 32 differentially expressed lncRNAs were selected from the brown, red, blue, green and yellow modules for subsequent survival analysis. A signature-based risk scoring system involving five lncRNAs [DIAPH2 antisense RNA 1, FOXN3 antisense RNA 2, long intergenic non-protein coding RNA 652, maternally expressed 3 and RHPN1 antisense RNA 1 (head to head)] was developed. The system successfully distinguished between low- and high-risk prognostic samples. System effectiveness was further verified using two independent validation datasets. Further KEGG pathway analysis indicated that the target genes of the five prognostic lncRNAs were associated with a number of cellular processes and signaling pathways, including the cell receptor-mediated signaling and cell adhesion pathways. A five-lncRNA signature predicts the prognosis of patients with lung adenocarcinoma. These prognostic lncRNAs may be potential diagnostic markers. The present results may help elucidate the pathogenesis of lung adenocarcinoma.

Predictability of human differential gene expression

The identification of genes that are differentially expressed provides a molecular foothold onto biological questions of interest. Whether some genes are more likely to be differentially expressed than others, and to what degree, has never been assessed on a global scale. Here, we reanalyze more than 600 studies and find that knowledge of a gene’s prior probability of differential expression (DE) allows for accurate prediction of DE hit lists, regardless of the biological question. This result suggests redundancy in transcriptomics experiments that both informs gene set interpretation and highlights room for growth within the field.

Differential expression (DE) is commonly used to explore molecular mechanisms of biological conditions. While many studies report significant results between their groups of interest, the degree to which results are specific to the question at hand is not generally assessed, potentially leading to inaccurate interpretation. This could be particularly problematic for metaanalysis where replicability across datasets is taken as strong evidence for the existence of a specific, biologically relevant signal, but which instead may arise from recurrence of generic processes. To address this, we developed an approach to predict DE based on an analysis of over 600 studies. A predictor based on empirical prior probability of DE performs very well at this task (mean area under the receiver operating characteristic curve, ∼0.8), indicating that a large fraction of DE hit lists are nonspecific. In contrast, predictors based on attributes such as gene function, mutation rates, or network features perform poorly. Genes associated with sex, the extracellular matrix, the immune system, and stress responses are prominent within the “DE prior.” In a series of control studies, we show that these patterns reflect shared biology rather than technical artifacts or ascertainment biases. Finally, we demonstrate the application of the DE prior to data interpretation in three use cases: (i) breast cancer subtyping, (ii) single-cell genomics of pancreatic islet cells, and (iii) metaanalysis of lung adenocarcinoma and renal transplant rejection transcriptomics. In all cases, we find hallmarks of generic DE, highlighting the need for nuanced interpretation of gene phenotypic associations.

AHRR (cg05575921) methylation extent of leukocyte DNA and lung cancer survival

BACKGROUND

Prior studies have shown that AHRR (cg05575921) hypomethylation may be a marker of smoking, lung cancer risk and potentially lung cancer survival (in some lung cancer subtypes). It is unknown if AHRR (cg05575921) hypomethylation is associated with reduced survival among lung cancer patients.

METHODS

In bisulfite treated leukocyte DNA from 465 lung cancer patients from the Copenhagen prospective lung cancer study, we measured AHRR (cg05575921) methylation. 380 died during max follow-up of 4.4 years. Cox proportional hazard models were used to analyze survival as a function of AHRR (cg05575921) methylation.

RESULTS

We observed the expected inverse correlation between cumulative smoking and AHRR methylation, as methylation (%) decreased (Coefficient -0.03; 95% confidence interval, -0.04- -0.02, p = 8.6x10-15) for every pack-year. Cumulative smoking > 60 pack-years was associated with reduced survival (hazard ratio and 95% confidence interval 1.48; 1.05-2.09), however, AHRR (cg05575921) methylation was not associated with survival when adjusted for sex, body mass index, smoking status, ethnicity, performance status, TNM Classification, and histology type of lung cancer.

CONCLUSION

AHRR (cg05575921) methylation is linked to smoking but does not provide independent prognostic information in lung cancer patients.

The Relevance of Gender in Tumor-Influencing Epigenetic Traits

Tumorigenesis as well as the molecular orchestration of cancer progression are very complex mechanisms that comprise numerous elements of influence and regulation. Today, many of the major concepts are well described and a basic understanding of a tumor's fine-tuning is given. Throughout the last decade epigenetics has been featured in cancer research and it is now clear that the underlying mechanisms, especially DNA and histone modifications, are important regulators of carcinogenesis and tumor progression. Another key regulator, which is well known but has been neglected in scientific approaches as well as molecular diagnostics and, consequently, treatment conceptualization for a long time, is the subtle influence patient gender has on molecular processes. Naturally, this is greatly based on hormonal differences, but from an epigenetic point of view, the diverse susceptibility to stress and environmental influences is of prime interest. In this review we present the current view on which and how epigenetic modifications, emphasizing DNA methylation, regulate various tumor diseases. It is our aim to elucidate gender and epigenetics and their interconnectedness, which will contribute to understanding of the prospect molecular orchestration of cancer in individual tumors.

MiRNA-195-5p Functions as a Tumor Suppressor and a Predictive of Poor Prognosis in Non-small Cell Lung Cancer by Directly Targeting CIAPIN1

Accumulating evidence suggests that microRNAs (miRNAs) has been proven to be a critical regulator in the tumor progression, of which miR-195-5p was reported to function as tumor suppressor in prostate cancer and oral squamous cell carcinoma. However, studies on the clinical significance and biological function of miR-195-5p in non-small cell lung cancer (NSCLC) were still unavailable. Here, we reported that the expression of miR-195-5p was decreased in NSCLC tissues and cell lines. Downregulation of miR-195-5p was significantly associated with TNM stage, tumor size and lymph node metastasis. The Kaplan-Meier survival analysis demonstrated that the survival time of NSCLC patients with high expression of miR-195-5p was longer than those with low expression during the 5-year follow up period (p = 0.0410). COX regression analysis indicated that miR-195-5p expression was an independent prognostic indicator for the survival of NSCLC patients (HR = 2.45, 95% CI: 1.53–4.63; p = 0.007). Results of functional analyses revealed that overexpression of miR-195-5p in A549 cells inhibited cell proliferation, induced cell cycle G0/G1 phase arrest and apoptosis using MTT and flow cytometry analysis. Furthermore, bioinformatics and luciferase reporter assays demonstrated that cytokine-induced apoptosis inhibitor 1 (CIAPIN1), an anti-apoptotic molecule was a direct target of miR-195-5p in NSCLC cells. Meta-analysis based on Oncomine database showed CIAPIN1 was significantly up-regulated in human lung cancer tissues. Consistently, knockdown of CIAPIN1 phenocopied the inhibitory effects of miR-195-5p overexpression in NSCLC cell function. These findings suggest that miR-195-5p could be used as a potential prognostic predictor and tumor suppressor in NSCLC.

High KRT8 Expression Independently Predicts Poor Prognosis for Lung Adenocarcinoma Patients

Keratin 8 (KRT8), a type II basic intermediate filament (IF) protein, is essential for the development and metastasis of various cancers. In this study, by analyzing RNA-seq data from the Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we have determined the expression profile of KRT8, and assessed its prognostic significance and the possible mechanism underlying the dysregulation. Our results showed that KRT8 mRNA expression was significantly up-regulated in both LUAD and LUSC tissues compared with normal lung tissues. The high KRT8 expression group for LUAD patients significantly reduced overall survival (OS) and recurrence-free survival (RFS). Univariate and multivariate analysis revealed that KRT8 expression was an independent prognostic indicator for poor OS and RFS in LUAD patients. However, KRT8 expression had no prognostic value in terms of OS and RFS for LUSC. By exploring DNA copy number alterations (CNAs) of the KRT8 gene in LUAD, we found that DNA low copy gain (+1 and +2) was associated with elevated KRT8 mRNA expression. From the above findings, we have deduced that KRT8 is aberrantly expressed in LUAD tissues and that its expression might independently predict poor OS and RFS for LUAD patients, but not for LUSC patients.

Expression and prognosis analyses of forkhead box A (FOXA) family in human lung cancer

Despite advances in early diagnosis and treatment, cancer still remains the major reason of mortality worldwide. The forkhead box A (FOXA) family is reported to participate in diverse human diseases. However, little is known about their expression and prognostic values in human lung cancer. Herein, we conducted a detailed cancer vs. normal analysis. The mRNA expression levels of FOXA family in numerous kind of cancers, including lung cancer, were analyzed using the Oncomine and GEPIA database. We observed that the mRNA expression levels of FOXA1, and FOXA3 were all increased while FOXA2 were decreased in most cancers compared with normal tissues, especially in lung cancer. Moreover, the expression levels of FOXA1, and FOXA3 are also highly expressed, while FOXA2 were decreased in almost all cancer cell lines, particularly in lung cancer cell lines, analyzing by Cancer Cell Line Encyclopedia (CCLE) and EMBL-EBI databases. Furthermore, the LinkedOmics database was used to evaluate the prognostic values, indicating that higher expression of FOXA1, FOXA3 indicated a poor overall survival (OS), while increased FOXA2 revealed a better OS in lung cancer. To conclusion, FOXA family showed significant expression differences between cancer and normal tissues, especially lung cancer, and FOXA1, FOXA3 could be promising prognostic biomarkers for lung cancer.

An integrated methylation and gene expression microarray analysis reveals significant prognostic biomarkers in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a life‑threatening disease with a poor prognosis. Although previous studies have reported that the methylation of certain genes is associated with the pathogenesis of OSCC, the methylation of genes that have relevance to OSCC progression is not clearly documented. The present study aimed to gain insights into the mechanisms underlying DNA methylation regulation associated with OSCC progression and to identify potential prognostic markers for OSCC treatment. DNA methylation dataset GSE41114 and gene expression dataset GSE74530 were downloaded from the Gene Expression Omnibus database. The global methylation status of OSCC tumor samples and normal control samples was determined, and differentially methylated genes (DMGs) in OSCC samples compared with control samples were identified. The mRNA expression data were then integrated to identify differentially expressed genes (DEGs) in OSCC samples compared with control samples. Overlapping genes between DEGs and DMGs were identified, and functional enrichment analysis was performed. In addition, survival analysis of the overlapping genes was performed to screen genes with prognostic significance in OSCC. A total of 40,115 differential methylation CpG sites spanning 3,360 DMGs were identified; CpG sites in the promoter, gene body and intergenic regions were generally highly hypermethylated or hypomethylated. Additionally, 508 DEGs in OSCC samples were identified, including 332 upregulated and 176 downregulated genes. A total of 82 overlapping genes between DEGs and DMGs were found, which were mainly involved in protein metabolism, regulation of the metabolic process and the immune system. Additionally, differential methylation or expression of several genes, including fibroblast activation protein α (FAP), interferon α inducible protein 27 (IFI27), laminin subunit γ2 (LAMC2), matrix metallopeptidase 1 (MMP1), serine peptidase inhibitor Kazal‑type 5 (SPINK5) and zinc finger protein 662 (ZNF662), was significantly associated with the survival of OSCC patients, and their differential expression in OSCC patients was further confirmed by reverse transcription‑quantitative polymerase chain reaction in OSCC and normal oral cell lines. Overall, FAP, IFI27, LAMC2, MMP1, SPINK5 and ZNF662 genes caused by epigenetic changes via DNA methylation may be associated with the development and progression of OSCC, and should be valuable OSCC therapeutic biomarkers.

Multiomics analysis on DNA methylation and the expression of both messenger RNA and microRNA in lung adenocarcinoma

Lung adenocarcinoma (LUAD) poses a significant threat to public health worldwide, while the genetic and epigenetic abnormalities involved in the oncogenesis of LUAD remains unknown. This study aimed to identify and validate key genes during the development and progression of LUAD by multiomics analysis. First, Empirical Analysis of Digital Gene Expression Data in R (EdgeR) was used to identify differentially regulated genes between normal samples and LUAD samples. Then significance analysis of microarrays (SAM) was used to identify differentially methylated genes and regulated microRNAs (miRNAs) between normal samples and LUAD samples. Following that, Kyoto Encyclopedia of Genes and Genomes (KEGG)-enrichment analysis was used to analyze the function that these genes enriched in. A total of 4,816 genes, 419 miRNAs, and 4,476 methylated genes that were significantly differentially expressed corresponding to the normal tissues in LUAD were obtained, and some of the pathways these genes enriched in were the same. Moreover, 255 genes differentially methylated and expressed at the same time were also found, and these 255 genes were the target genes of the miRNAs differentially expressed in LUAD. Finally, nine genes (BRCA1, COL1A1, ESR1, FGFR2, HNF4A, IGFBP3, MET, MMP3, and PAK1) network analysis, and two of which were found to be related to the survival of LUAD patients. In summary, a total of nine genes that may play important roles in the development of LUAD were identified, and two (PAK1 and FGFR2) of them can be served as prognostic biomarkers for LUAD patients. The genes found in this study played different roles in the tumor progression of LUAD, indicating these genes may be considered as potential target genes for LUAD treatment.

Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health

Background

Tobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or metabolic health, remains unclear.

Methods

We investigated smoking-associated DNA methylation and gene expression variation in adipose tissue biopsies from 542 healthy female twins. Replication, tissue specificity, and longitudinal stability of the smoking-associated effects were explored in additional adipose, blood, skin, and lung samples. We characterized the impact of adipose tissue smoking methylation and expression signals on metabolic disease risk phenotypes, including visceral fat.

Results

We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and upregulated in current smokers. CYP1A1 gene expression achieved 95% prediction performance of current smoking status. We validated and replicated a proportion of the signals in additional primary tissue samples, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. Metabolic disease risk traits such as visceral fat and android-to-gynoid ratio showed association with methylation at smoking markers with functional impacts on expression, such as CYP1A1, and at tissue-shared smoking signals, such as NOTCH1. At smoking-signals, BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation.

Conclusions

Our results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue. The findings relate to human metabolic health and give insights into understanding the widespread health consequence of smoking outside of the lung.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0558-0) contains supplementary material, which is available to authorized users.

Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms

BACKGROUND

Lung adenocarcinoma (LUAD) is a heterogeneous disease with poor survival in the advanced stage and a high incidence rate in the world. Novel drug targets are urgently required to improve patient treatment. Therefore, we aimed to identify therapeutic targets for LUAD based on protein-protein and protein-drug interaction network analysis with neural network algorithms using mRNA expression profiles.

RESULTS

A comprehensive meta-analysis of selective non-small cell lung cancer (NSCLC) mRNA expression profile datasets from Gene Expression Omnibus were used to identify potential biomarkers and the molecular mechanisms related to the prognosis of NSCLC patients. Using the Network Analyst tool, based on combined effect size (ES) methods, we recognized 6566 differentially expressed genes (DEGs), which included 3036 downregulated and 3530 upregulated genes linked to NSCLC patient survival. ClueGO, a Cytoscape plugin, was exploited to complete the function and pathway enrichment analysis, which disclosed "regulated exocytosis", "purine nucleotide binding", "pathways in cancer", and "cell cycle" between exceptionally supplemented terms. Enrichr, a web tool examination, demonstrated "early growth response protein 1 (EGR-1)", "hepatocyte nuclear factor 4α (HNF4A)", "mitogen-activated protein kinase 14 (MAP3K14)", and "cyclin-dependent kinase 1 (CDK1)" to be among the most prevalent TFs and kinases associated with NSCLC. Our meta-analysis identified that MAPK1 and aurora kinase (AURKA) are the most obvious class of hub nodes. Furthermore, protein-drug interaction network and neural network algorithms identified candidate drugs such as phosphothreonine and 4-(4-methylpiperazin-1-yl)-n-[5-(2-thienylacetyl)-1,5-dihydropyrrolo[3,4-c]pyrazol-3-yl] benzamide and for the targets MAPK1 and AURKA, respectively.

CONCLUSION

Our study has identified novel candidate biomarkers, pathways, transcription factors (TFs), and kinases associated with NSCLC prognosis, as well as drug candidates, which may assist treatment strategy for NSCLC patients.

Progression and prognostic value of ECT2 in non-small-cell lung cancer and its correlation with PCNA

Purpose

Epithelial cell transforming sequence 2 (ECT2) is a guanine nucleotide exchange factor, which is involved in cell division regulation and cell cycle modulation. Recent evidence indicates that ECT2 is overexpressed in many human cancers. However, the exact prognostic value of ECT2 in lung cancer has not been elucidated.

Patients and methods

In the current study, we performed correlation and prognosis analyses using public databases and conducted immunohistochemical staining in tissue microarrays, using samples from 204 lung cancer patients with survival data.

Results

We found that the expression of ECT2 was markedly increased in lung cancer tissues compared with normal tissues. Moreover, we demonstrated that the expression of ECT2 was related to tumor cell differentiation degree, TNM stage, lymph node metastasis, and prognosis in non-small-cell lung cancer (NSCLC). A correlation analysis indicated that ECT2 levels were significantly correlated with proliferating cell nuclear antigen (PCNA) levels in NSCLC. Furthermore, Kaplan–Meier analyses revealed that high ECT2 expression was associated with unfavorable overall survival (OS) and progression-free survival (PFS) in NSCLC patients.

Conclusion

Taken together, these results indicate that the overexpression of ECT2 contributes to tumor invasion and progression, suggesting that ECT2 is a potential prognostic marker for NSCLC patients.

Overexpression of GPR35 confers drug resistance in NSCLC cells by β-arrestin/Akt signaling

Background

Non-small-cell lung cancer (NSCLC) is the major leading cause of cancer-related death around the world. The resistance to chemotherapy limits the effects of clinical treatment. The aim of this study was to identify novel mechanisms involved in NSCLC chemoresistance.

Materials and methods

We explored the public database and commercial tissue microarray to evaluate the expression of G protein-coupled receptor 35 (GPR35). We established the chemoresistant A549 cell line to further investigate the biological function of GPR35 in vitro and in vivo. Then, we measured the altered signalings that GPR35 knocking down by Western blot assay.

Results

We demonstrated that GPR35 expression was significantly elevated in NSCLC tissues and correlated with poor prognosis. GPR35 was upregulated in our in vitro chemoresistance cell model. GPR35 depletion reduced the half maximal inhibitory concentration of chemodrugs and restored the sensitivity both in vitro and in vivo. Mechanically, we found that GPR35-mediated chemoresistance occurred partially via β-arrestin-2/Akt signaling. Furthermore, inhibition of β-arrestin-2 or Akt activation could suppress the GPR35 expression and overcome chemoresistance.

Conclusion

Our results suggested that GPR35 might serve as a novel therapeutic target to enhance the chemotherapy efficacy in NSCLC.

SERINC2-knockdown inhibits proliferation, migration and invasion in lung adenocarcinoma

Serine incorporator 2 (SERINC2) is a member of the SERINC family of transmembrane proteins that incorporate serine into membrane lipids during synthesis. In the present study, the biological function of SERINC2 in lung adenocarcinoma cells was investigated. The data from a previous study and the publicly available Oncomine database were analysed regarding the expression levels of SERINC2 mRNA in lung adenocarcinoma. A lentiviral-based short hairpin RNA (shRNA) was used to suppress SERINC2 expression in lung cancer cells. The effect of SERINC2 expression on lung cancer proliferation was determined using cell counting kit-8 and colony formation assays. The influence on invasion and migration was examined in vitro using Transwell and wound-healing assays, respectively. Phosphorylated protein kinase B (p-AKT) expression levels were assessed by immunoblotting. According to a previous study and Oncomine, expression levels of SERINC2 mRNA are significantly upregulated in tumour tissues compared with those in healthy tissues in patients with lung adenocarcinoma. SERINC2-knockdown by lentiviral-based shRNA inhibited the proliferation, migration and invasion of the H1650 and A549 cells. In addition, p-AKT expression levels were significantly decreased following SERINC2-knockdown. In conclusion, SERINC2-knockdown suppresses lung adenocarcinoma proliferation, migration and invasion through a mechanism that may be associated with phosphatidylinositol 3-kinase/AKT signalling. Based on these findings, SERINC2 serves an important role in the progression of lung adenocarcinoma.

Upregulation of HOXA11 during the progression of lung adenocarcinoma detected via multiple approaches

The altered expression of homeobox (HOX)A11 has been observed in various malignant tumor types, but it has remained to be determined in human lung adenocarcinoma (LUAD). In the present study, the expression of HOXA11 in LUAD and the potential associated mechanisms were assessed. Data from The Cancer Genome Atlas and Oncomine microarrays were gathered and in‑house polymerase chain reaction data were produced to investigate the altered expression of HOXA11 in LUAD and its association with various clinicopathological characteristics. Genes co‑expressed with HOXA11 were also identified by searching the cBioPortal and Multi Experiment Matrix databases, and performing a bioinformatics analysis, through which the potential molecular mechanisms of HOXA11 in LUAD were explored. The data analyses indicated that HOXA11 was overexpressed in the LUAD samples, and together with its co‑expressed genes, it was indicated to participate in various key signaling pathways, including the focal adhesion, extracellular matrix‑receptor interaction, axon guidance and small cell lung cancer signaling pathways. Furthermore, collagen type III α 1 chain (COL3A1), ephrin B2 (EFNB2), integrin subunit α 8 (ITGA8) and syndecan 2 (SDC2) were confirmed to be differentially expressed in LUAD vs. normal controls at the mRNA and protein level. Of note, LUAD patients with low expression of HOXA11 and ITGB1 had better overall survival rates. The present study indicated that HOXA11 may function as an oncogene in LUAD, and HOXA11 protein probably combines with ITGB1, COL3A1, EFNB2, ITGA8 and SDC2 to have a role in the focal adhesion pathway.

Homeobox oncogene activation by pan-cancer DNA hypermethylation

BACKGROUND

Cancers have long been recognized to be not only genetically but also epigenetically distinct from their tissues of origin. Although genetic alterations underlying oncogene upregulation have been well studied, to what extent epigenetic mechanisms, such as DNA methylation, can also induce oncogene expression remains unknown.

RESULTS

Here, through pan-cancer analysis of 4174 genome-wide profiles, including whole-genome bisulfite sequencing data from 30 normal tissues and 35 solid tumors, we discover a strong correlation between gene-body hypermethylation of DNA methylation canyons, defined as broad under-methylated regions, and overexpression of approximately 43% of homeobox genes, many of which are also oncogenes. To gain insights into the cause-and-effect relationship, we use a newly developed dCas9-SunTag-DNMT3A system to methylate genomic sites of interest. The locus-specific hypermethylation of gene-body canyon, but not promoter, of homeobox oncogene DLX1, can directly increase its gene expression.

CONCLUSIONS

Our pan-cancer analysis followed by functional validation reveals DNA hypermethylation as a novel epigenetic mechanism for homeobox oncogene upregulation.

The association of UNC5A expression with the clinicopathologic features and prognosis of radiotherapy in patients with non-small-cell lung cancer

UNC5A is widely known as a neuronal axonal guide factor and was found to have a low expression in a variety of tumors. In our study, we investigated the expression of UNC5A in non-small cell lung cancer (NSCLC) and analyzed its association with the clinical features and prognosis of NSCLC radiotheray patients.

METHODS

We collected 169 NSCLC patients' clinical and pathological data for the study. Immunohistochemical staining was evaluated to analyze the expression of UNC5A in NSCLC tissues. The expressions of UNC5A in normal and NSCLC tissues were analyzed using the Oncomine database. We investigated the overall prognostic value of UNC5A in NSCLC patients through the Kaplan-Meier plotter database.

RESULTS

The low expression rate of UNC5A was 55.0% (93/169) in NSCLC by immunohistochemical analysis. The overall survival (OS) of NSCLC radiotheray patients with a low expression of UNC5A was shorter than that in patients with a high expression (P = 0.000). The expression of UNC5A was strongly and significantly associated with the TNM stage (P = 0.013) but not associcated with other clinicopathologic features. The results of COX regression showed that the expression of UNC5A, general condition and TNM stage were independent prognositic factors of NSCLC patients. ROC analysis showed a high area under the curve for UNC5A expression in NSCLC (AUC = 0.746). At a cut-off level of > 1027, the UNC5A expression, general condition and TNM stages, could be used for the prognosis of NSCLC with high sensitivity and specificity. The Oncomine database showed that UNC5A expression was found to significantly decline in NSCLC tissues compared to normal tissues (P = 0.029). We used the Kaplan-Meier plotter database to analyze NSCLC patients with a high expression of UNC5A and found they had better OS than patients with a low expression (P = 0.0492).

CONCLUSION

The expression of UNC5A may be a potential prognostic biomarker of NSCLC.

[Expression of KIF23 and Its Prognostic Role in Non-small Cell Lung Cancer: Analysis Based on the Data-mining of Oncomine]

背景与目的

非小细胞肺癌（non-small cell lung cancer, NSCLC）是全球引起死亡的最重要原因之一。大多数患者发现时处于中晚期，预后较差。本研究拟探讨驱动蛋白超家族23（kinesin family member 23, KIF23）在NSCLC中的表达及意义。

方法

收集Oncomine数据库中关于KIF23的信息，并对目前数据库中资料进行二次分析，对其在NSCLC中的作用进行荟萃分析。利用Kaplan-Meier Plotter进行患者生存周期分析。

结果

Oncomine数据库中共收集了447项不同类型的研究结果，其中关于KIF23表达有统计学差异的研究结果有67个，KIF23表达增高的研究有64项、表达降低的研究有3项。共有16项研究涉及KIF23在NSCLC癌组织和正常组织中的表达，共包括1, 189个样本，与对照组相比，KIF23在NSCLC细胞癌中高表达（P＜0.05）。不仅如此，KIF23表达量与NSCLC总体生存率存在相关性，高表达KIF23的患者总体生存率较差，低表达KIF23的患者预后较好（P＜0.05）。进一步亚组分析发现，KIF23表达水平对肺腺癌患者预后有显著影响，而在鳞癌患者中，其表达水平对预后无显著影响。

结论

我们通过对Oncomine基因芯片数据库中肿瘤相关基因信息的深入挖掘，提出KIF23在NSLCL组织中高表达，且与NSCLC预后有关，可能为肿瘤药物的开发提供重要理论依据。

Hypermethylation of normal mucosa of esophagus-specific 1 is associated with an unfavorable prognosis in patients with non-small cell lung cancer

Lung cancer is the leading cause of cancer-associated mortality due to high incidence and poor survival rates, irrespective of global variations in its biology and treatment. Changes in DNA methylation are frequent in cancer and constitute an important mechanism in tumorigenesis. Normal mucosa of esophagus-specific 1 (NMES1) is expressed in epithelial tissue and is believed to be a tumor suppressor gene. The present study investigated the methylation status of the NMES1 promoter in 178 cases of primary non-small cell lung cancer (NSCLC) by pyrosequencing and evaluated the prognostic value of this methylation. NMES1 methylation-positive tumors above the background threshold for non-malignant tissue were found in 15 cases (8.4%) and were detected exclusively in malignant tissues. In addition, univariate and multivariate analyses showed that methylation-positive patients experienced worse overall survival rate (OSR) compared with methylation-negative patients (adjusted hazard ratio, 2.62; 95% confidence interval, 1.20-5.69; P=0.02). Notably, within the methylation-positive group, patients with strong methylation tended to experience worse OSR compared with those with weak methylation (adjusted hazard ratio, 2.45 vs. 3.05; P_trend=0.02). These findings suggest that NMES1 may serve an important role in lung cancer pathogenesis, and its methylation could be considered a prognostic marker for NSCLC. Further studies with large numbers of samples are required to confirm this conclusion.

Curcumin decreases Warburg effect in cancer cells by down-regulating pyruvate kinase M2 via mTOR-HIF1α inhibition

Warburg effect is an emerging hallmark of cancer cells with pyruvate kinase M2 (PKM2) as its key regulator. Curcumin is an extensively-studied anti-cancer compound, however, its role in affecting cancer metabolism remains poorly understood. Herein, we show that curcumin inhibits glucose uptake and lactate production (Warburg effect) in a variety of cancer cell lines by down-regulating PKM2 expression, via inhibition of mTOR-HIF1α axis. Stable PKM2 silencing revealed that PKM2 is required for Warburg effect and proliferation of cancer cells. PKM2 over-expression abrogated the effects of curcumin, demonstrating that inhibition of Warburg effect by curcumin is PKM2-mediated. High PKM2 expression correlated strongly with poor overall survival in cancer, suggesting the requirement of PKM2 in cancer progression. The study unravels novel PKM2-mediated inhibitory effect of curcumin on metabolic capacities of cancer cells. To the best of our knowledge, this is the first study linking curcumin with PKM2-driven cancer glycolysis, thus, providing new perspectives into the mechanism of its anticancer activity.

Amyloid precursor like protein-1 promotes JNK-mediated cell migration in Drosophila

The amyloid precursor like protein-1 (APLP1) is a member of the amyloid precursor protein (APP) family in mammals. While many studies have been focused on the pathologic role of APP in Alzheimer's disease, the physiological functions of APLP1 have remained largely elusive. Here we report that ectopic expression of APLP1 in Drosophila induces cell migration, which is suppressed by the loss of JNK signaling and enhanced by the gain of JNK signaling. APLP1 activates JNK signaling through phosphorylation of JNK, which up-regulates the expression of matrix metalloproteinase MMP1 required for basement membranes degradation and promotes actin remodeling essential for cell migration. Our data thus provide the first in vivo evidence for a cell-autonomous role of APLP1 protein in migration.

DACH1 antagonizes CXCL8 to repress tumorigenesis of lung adenocarcinoma and improve prognosis

BACKGROUND

C-X-C motif ligand 8 (CXCL8), known as a proinflammatory chemokine, exerts multiple effects on the proliferation, invasion, and migration of tumor cells via the autocrine or paracrine manner. Conversely, the human Dachshund homologue 1 (DACH1) is recognized as a tumor suppressor which retards the progression of various cancers. In prostate cancer, it has been demonstrated that DACH1 was negatively correlated with the expression of CXCL8 and able to antagonize the effects of CXCL8 on cellular migration. Herein, we explored the mechanisms by which DACH1 regulated the CXCL8 in non-small cell lung cancer (NSCLC).

METHODS

Public microarray and Kaplan-Meier plotter datasets were analyzed. Blood serum samples from lung adenocarcinoma (ADC) patients were collected for enzyme-linked immunosorbent assay (ELISA) analysis. Immunohistochemical staining was conducted on tissue microarray. Cell lines with stable expression of DACH1 were established, and relative gene expression was measured by Western blot, ELISA, real-time PCR, and human cytokine array. Correspondingly, cell lines transfected with shDACH1 were established, and relative gene expression was measured by real-time PCR and immunofluorescence array. Functional studies were performed by transwell and xenograft mice models. Luciferase reporter gene assay was applied to measure the regulation of DACH1 on CXCL8.

RESULTS

Our study indicated that CXCL8 both at the mRNA and protein level was associated with the high tumor burden of ADC. Correlational analyses in ADC cell lines and ADC tissues showed that DACH1 was inversely correlated with CXCL8. Meanwhile, patients with high DACH1 expression and low CXCL8 expression had prolonged time to death and recurrence. Moreover, we verified the inhibitory effects of DACH1 on CXCL8 both in vitro and in vivo. Mechanism studies proved that DACH1 transcriptionally repressed CXCL8 promoter activity through activator protein-1 (AP-1) and nuclear transcription factor-kappa B (NF-κB) sites.

CONCLUSIONS

Our study proved that CXCL8 acted as an unfavorable factor promoting to tumor progression and poor prognosis of ADC, while DACH1 antagonized CXCL8 to provide a favorable survival of ADC patients. Double detection of DACH1 and CXCL8 may provide a precise information for further evaluating the prognosis of ADC patients.

Association between fetal exposure to phthalate endocrine disruptor and genome-wide DNA methylation at birth

BACKGROUND

Phthalic acid esters are ubiquitous and antiandrogenic, and may cause systemic effects in humans, particularly with in utero exposure. Epigenetic modification, such as DNA methylation, has been hypothesized to be an important mechanism that mediates certain biological processes and pathogenic effects of in utero phthalate exposure.

OBJECTIVE

The aim of this study was to examine the association between genome-wide DNA methylation at birth and prenatal exposure to phthalate.

METHODS

We studied 64 infant-mother pairs included in TMICS (Taiwan Maternal and Infant Cohort Study), a long-term follow-up birth cohort from the general population. DNA methylation levels at more than 450,000 CpG sites were measured in cord blood samples using Illumina Infinium HumanMethylation450 BeadChips. The concentrations of three metabolites of di-(2-ethylhexyl) phthalate (DEHP) were measured using liquid chromatography tandem-mass spectrometry (LC-MS/MS) in urine samples collected from the pregnant women during 28-36 weeks gestation.

RESULTS

We identified 25 CpG sites whose methylation levels in cord blood were significantly correlated with prenatal DEHP exposure using a false discovery rate (FDR) of 5% (q-value < 0.05). Via gene-set enrichment analysis (GSEA), we also found that there was significant enrichment of genes involved in the androgen response, estrogen response, and spermatogenesis within those genes showing DNA methylation changes in response to exposure. Specifically, PA2G4, HMGCR, and XRCC6 genes were involved in genes in response to androgen.

CONCLUSIONS

Phthalate exposure in utero may cause significant alterations in the DNA methylation in cord blood. These changes in DNA methylation might serve as biomarkers of maternal exposure to phthalate in infancy and potential candidates for studying mechanisms via which phthalate may impact on health in later life. Future investigations are warranted.

A Decade of GWAS Results in Lung Cancer

Genome-wide association studies (GWAS) were successful to identify genetic factors robustly associated with lung cancer. This review aims to synthesize the literature in this field and accelerate the translation of GWAS discoveries into results that are closer to clinical applications. A chronologic presentation of published GWAS on lung cancer susceptibility, survival, and response to treatment is presented. The most important results are tabulated to provide a concise overview in one read. GWAS have reported 45 lung cancer susceptibility loci with varying strength of evidence and highlighted suspected causal genes at each locus. Some genetic risk loci have been refined to more homogeneous subgroups of lung cancer patients in terms of histologic subtypes, smoking status, gender, and ethnicity. Overall, these discoveries are an important step for future development of new therapeutic targets and biomarkers to personalize and improve the quality of care for patients. GWAS results are on the edge of offering new tools for targeted screening in high-risk individuals, but more research is needed if GWAS are to pay off the investment. Complementary genomic datasets and functional studies are needed to refine the underlying molecular mechanisms of lung cancer preliminarily revealed by GWAS and reach results that are medically actionable. Cancer Epidemiol Biomarkers Prev; 27(4); 363-79. ©2018 AACRSee all articles in this CEBP Focus section, "Genome-Wide Association Studies in Cancer."

A novel strategy of integrated microarray analysis identifies CENPA, CDK1 and CDC20 as a cluster of diagnostic biomarkers in lung adenocarcinoma

Lung adenocarcinoma (LAC) is the most lethal cancer and the leading cause of cancer-related death worldwide. The identification of meaningful clusters of co-expressed genes or representative biomarkers may help improve the accuracy of LAC diagnoses. Public databases, such as the Gene Expression Omnibus (GEO), provide rich resources of valuable information for clinics, however, the integration of multiple microarray datasets from various platforms and institutes remained a challenge. To determine potential indicators of LAC, we performed genome-wide relative significance (GWRS), genome-wide global significance (GWGS) and support vector machine (SVM) analyses progressively to identify robust gene biomarker signatures from 5 different microarray datasets that included 330 samples. The top 200 genes with robust signatures were selected for integrative analysis according to "guilt-by-association" methods, including protein-protein interaction (PPI) analysis and gene co-expression analysis. Of these 200 genes, only 10 genes showed both intensive PPI network and high gene co-expression correlation (r > 0.8). IPA analysis of this regulatory networks suggested that the cell cycle process is a crucial determinant of LAC. CENPA, as well as two linked hub genes CDK1 and CDC20, are determined to be potential indicators of LAC. Immunohistochemical staining showed that CENPA, CDK1 and CDC20 were highly expressed in LAC cancer tissue with co-expression patterns. A Cox regression model indicated that LAC patients with CENPA⁺/CDK1⁺ and CENPA⁺/CDC20⁺ were high-risk groups in terms of overall survival. In conclusion, our integrated microarray analysis demonstrated that CENPA, CDK1 and CDC20 might serve as novel cluster of prognostic biomarkers for LAC, and the cooperative unit of three genes provides a technically simple approach for identification of LAC patients.