Genome-wide DNA methylation analyses in lung adenocarcinomas: Association with EGFR, KRAS and TP53 mutation status, gene expression and prognosis

Multi-omics analysis reveals epigenetically regulated processes and patient classification in lung adenocarcinoma

Aberrant DNA methylation is a hallmark of many cancer types. Despite our knowledge of epigenetic and transcriptomic alterations in lung adenocarcinoma (LUAD), we lack robust multi-modal molecular classifications for patient stratification. This is partly because the impact of epigenetic alterations on lung cancer development and progression is still not fully understood. To that end, we identified disease-associated processes under epigenetic regulation in LUAD. We performed a genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis by integrating DNA methylation and gene expression data from 453 patients in the TCGA cohort. Using a community detection algorithm, we identified distinct communities of CpG-gene associations with diverse biological processes. Interestingly, we identified a community linked to hormone response and lipid metabolism; the identified CpGs in this community were enriched in enhancer regions and binding regions of transcription factors such as FOXA1/2, GRHL2, HNF1B, AR, and ESR1. Furthermore, the CpGs were connected to their associated genes through chromatin interaction loops. These findings suggest that the expression of genes involved in hormone response and lipid metabolism in LUAD is epigenetically regulated through DNA methylation and enhancer-promoter interactions. By applying consensus clustering on the integrated expression-methylation pattern of the emQTL-genes and CpGs linked to hormone response and lipid metabolism, we further identified subclasses of patients with distinct prognoses. This novel patient stratification was validated in an independent patient cohort of 135 patients and showed increased prognostic significance compared to previously defined molecular subtypes.

Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions

We consider a Bayesian functional data analysis for observations measured as extremely long sequences. Splitting the sequence into several small windows with manageable lengths, the windows may not be independent especially when they are neighboring each other. We propose to utilize Bayesian smoothing splines to estimate individual functional patterns within each window and to establish transition models for parameters involved in each window to address the dependence structure between windows. The functional difference of groups of individuals at each window can be evaluated by the Bayes factor based on Markov Chain Monte Carlo samples in the analysis. In this paper, we examine the proposed method through simulation studies and apply it to identify differentially methylated genetic regions in TCGA lung adenocarcinoma data.

Smoking, blood DNA methylation sites and lung cancer risk

Altered DNA methylation (DNAm) might be a biological intermediary in the pathway from smoking to lung cancer. In this study, we investigated the contribution of differential blood DNAm to explain the association between smoking and lung cancer incidence. Blood DNAm was measured in 2321 Strong Heart Study (SHS) participants. Incident lung cancer was assessed as time to event diagnoses. We conducted mediation analysis, including validation with DNAm and paired gene expression data from the Framingham Heart Study (FHS). In the SHS, current versus never smoking and pack-years single-mediator models showed, respectively, 29 and 21 differentially methylated positions (DMPs) for lung cancer with statistically significant mediated effects (14 of 20 available, and five of 14 available, positions, replicated, respectively, in FHS). In FHS, replicated DMPs showed gene expression downregulation largely in trans, and were related to biological pathways in cancer. The multimediator model identified that DMPs annotated to the genes AHRR and IER3 jointly explained a substantial proportion of lung cancer. Thus, the association of smoking with lung cancer was partly explained by differences in baseline blood DNAm at few relevant sites. Experimental studies are needed to confirm the biological role of identified eQTMs and to evaluate potential implications for early detection and control of lung cancer.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies

BACKGROUND

DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.

METHODS

We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively.

RESULTS

Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques.

CONCLUSIONS

There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

CCRL2 Expression by Specialized Lung Capillary Endothelial Cells Controls NK-cell Homing in Lung Cancer

Patterns of receptors for chemotactic factors regulate the homing of leukocytes to tissues. Here we report that the CCRL2/chemerin/CMKLR1 axis represents a selective pathway for the homing of natural killer (NK) cells to the lung. C-C motif chemokine receptor-like 2 (CCRL2) is a nonsignaling seven-transmembrane domain receptor able to control lung tumor growth. CCRL2 constitutive or conditional endothelial cell targeted ablation, or deletion of its ligand chemerin, were found to promote tumor progression in a Kras/p53Flox lung cancer cell model. This phenotype was dependent on the reduced recruitment of CD27- CD11b+ mature NK cells. Other chemotactic receptors identified in lung-infiltrating NK cells by single-cell RNA sequencing (scRNA-seq), such as Cxcr3, Cx3cr1, and S1pr5, were found to be dispensable in the regulation of NK-cell infiltration of the lung and lung tumor growth. scRNA-seq identified CCRL2 as the hallmark of general alveolar lung capillary endothelial cells. CCRL2 expression was epigenetically regulated in lung endothelium and it was upregulated by the demethylating agent 5-aza-2'-deoxycytidine (5-Aza). In vivo administration of low doses of 5-Aza induced CCRL2 upregulation, increased recruitment of NK cells, and reduced lung tumor growth. These results identify CCRL2 as an NK-cell lung homing molecule that has the potential to be exploited to promote NK cell-mediated lung immune surveillance.

A Systematic Review of Progress toward Unlocking the Power of Epigenetics in NSCLC: Latest Updates and Perspectives

Epigenetic research has the potential to improve our understanding of the pathogenesis of cancer, specifically non-small-cell lung cancer, and support our efforts to personalize the management of the disease. Epigenetic alterations are expected to have relevance for early detection, diagnosis, outcome prediction, and tumor response to therapy. Additionally, epi-drugs as therapeutic modalities may lead to the recovery of genes delaying tumor growth, thus increasing survival rates, and may be effective against tumors without druggable mutations. Epigenetic changes involve DNA methylation, histone modifications, and the activity of non-coding RNAs, causing gene expression changes and their mutual interactions. This systematic review, based on 110 studies, gives a comprehensive overview of new perspectives on diagnostic (28 studies) and prognostic (25 studies) epigenetic biomarkers, as well as epigenetic treatment options (57 studies) for non-small-cell lung cancer. This paper outlines the crosstalk between epigenetic and genetic factors as well as elucidates clinical contexts including epigenetic treatments, such as dietary supplements and food additives, which serve as anti-carcinogenic compounds and regulators of cellular epigenetics and which are used to reduce toxicity. Furthermore, a future-oriented exploration of epigenetic studies in NSCLC is presented. The findings suggest that additional studies are necessary to comprehend the mechanisms of epigenetic changes and investigate biomarkers, response rates, and tailored combinations of treatments. In the future, epigenetics could have the potential to become an integral part of diagnostics, prognostics, and personalized treatment in NSCLC.

Biological and Genetic Mechanisms of COPD, Its Diagnosis, Treatment, and Relationship with Lung Cancer

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent chronic adult diseases, with significant worldwide morbidity and mortality. Although long-term tobacco smoking is a critical risk factor for this global health problem, its molecular mechanisms remain unclear. Several phenomena are thought to be involved in the evolution of emphysema, including airway inflammation, proteinase/anti-proteinase imbalance, oxidative stress, and genetic/epigenetic modifications. Furthermore, COPD is one main risk for lung cancer (LC), the deadliest form of human tumor; formation and chronic inflammation accompanying COPD can be a potential driver of malignancy maturation (0.8-1.7% of COPD cases develop cancer/per year). Recently, the development of more research based on COPD and lung cancer molecular analysis has provided new light for understanding their pathogenesis, improving the diagnosis and treatments, and elucidating many connections between these diseases. Our review emphasizes the biological factors involved in COPD and lung cancer, the advances in their molecular mechanisms' research, and the state of the art of diagnosis and treatments. This work combines many biological and genetic elements into a single whole and strongly links COPD with lung tumor features.

Placental transcriptomic signatures of prenatal exposure to Hydroxy-Polycyclic aromatic hydrocarbons

BACKGROUND

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants originating from petrogenic and pyrogenic sources. PAH compounds can cross the placenta, and prenatal PAH exposure is linked to adverse infant and childhood health outcomes.

OBJECTIVE

In this first human transcriptomic assessment of PAHs in the placenta, we examined associations between prenatal PAH exposure and placental gene expression to gain insight into mechanisms by which PAHs may disrupt placental function.

METHODS

The ECHO PATHWAYS Consortium quantified prenatal PAH exposure and the placental transcriptome from 629 pregnant participants enrolled in the CANDLE study. Concentrations of 12 monohydroxy-PAH (OH-PAH) metabolites were measured in mid-pregnancy urine using high performance liquid chromatography tandem mass spectrometry. Placental transcriptomic data were obtained using paired-end RNA sequencing. Linear models were fitted to estimate covariate-adjusted associations between maternal urinary OH-PAHs and placental gene expression. We performed sex-stratified analyses to evaluate whether associations varied by fetal sex. Selected PAH/gene expression analyses were validated by treating HTR-8/SVneo cells with phenanthrene, and quantifying expression via qPCR.

RESULTS

Urinary concentrations of 6 OH-PAHs were associated with placental expression of 8 genes. Three biological pathways were associated with 4 OH-PAHs. Placental expression of SGF29 and TRIP13 as well as the vitamin digestion and absorption pathway were positively associated with multiple metabolites. HTR-8/SVneo cells treated with phenanthrene also exhibited 23 % increased TRIP13 expression compared to vehicle controls (p = 0.04). Fetal sex may modify the relationship between prenatal OH-PAHs and placental gene expression, as more associations were identified in females than males (45 vs 28 associations).

DISCUSSION

Our study highlights novel genes whose placental expression may be disrupted by OH-PAHs. Increased expression of DNA damage repair gene TRIP13 may represent a response to double-stranded DNA breaks. Increased expression of genes involved in vitamin digestion and metabolism may reflect dietary exposures or represent a compensatory mechanism to combat damage related to OH-PAH toxicity. Further work is needed to study the role of these genes in placental function and their links to perinatal outcomes and lifelong health.

Liquid Biopsy for Lung Cancer: Up-to-Date and Perspectives for Screening Programs

Lung cancer is the deadliest cancer worldwide. Tissue biopsy is currently employed for the diagnosis and molecular stratification of lung cancer. Liquid biopsy is a minimally invasive approach to determine biomarkers from body fluids, such as blood, urine, sputum, and saliva. Tumor cells release cfDNA, ctDNA, exosomes, miRNAs, circRNAs, CTCs, and DNA methylated fragments, among others, which can be successfully used as biomarkers for diagnosis, prognosis, and prediction of treatment response. Predictive biomarkers are well-established for managing lung cancer, and liquid biopsy options have emerged in the last few years. Currently, detecting EGFR p.(Tyr790Met) mutation in plasma samples from lung cancer patients has been used for predicting response and monitoring tyrosine kinase inhibitors (TKi)-treated patients with lung cancer. In addition, many efforts continue to bring more sensitive technologies to improve the detection of clinically relevant biomarkers for lung cancer. Moreover, liquid biopsy can dramatically decrease the turnaround time for laboratory reports, accelerating the beginning of treatment and improving the overall survival of lung cancer patients. Herein, we summarized all available and emerging approaches of liquid biopsy-techniques, molecules, and sample type-for lung cancer.

A trans-omics assessment of gene-gene interaction in early-stage NSCLC

Epigenome-wide gene-gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with P_Bonferroni  ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (β_interaction  = 0.018, P = 1.87 × 10^-12 ), which mapped to RELA × HLA-G (β_interaction  = 0.218, P = 8.82 × 10^-11 ) and cg08872738 × cg27077312 (β_interaction  = -0.010, P = 1.16 × 10^-11 ), which mapped to TUBA1B × TOMM40 (β_interaction =-0.250, P = 3.83 × 10^-10 ). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.

Neighborhood disadvantage is associated with KRAS-mutated non-small cell lung cancer risk

Purpose

It remains unclear why individuals living in disadvantaged neighborhoods have shorter non-small cell lung cancer (NSCLC) survival. It is possible that living in these deprived areas is linked with increased risk of developing aggressive NSCLC biology. Here, we explored the association of somatic KRAS mutations, which are associated with shorter survival in NSCLC patients, and 11 definitions of neighborhood disadvantage spanning socioeconomic and structural environmental elements.

Methods

We analyzed data from 429 NSCLC patients treated at a Comprehensive Cancer Center from 2015 to 2018. Data were abstracted from medical records and each patient’s home address was used to assign publicly available indices of neighborhood disadvantage. Prevalence Ratios (PRs) for the presence of somatic KRAS mutations were estimated using modified Poisson regression models adjusted for age, sex, smoking status, race/ethnicity, educational attainment, cancer stage, and histology.

Results

In the NSCLC cohort, 29% had KRAS mutation-positive tumors. We found that five deprivation indices of socioeconomic disadvantage were associated with KRAS mutation. A one decile increase in several of these socioeconomic disadvantage indices was associated with a 1.06 to 1.14 increased risk of KRAS mutation. Measures of built structural environment were not associated with KRAS mutation status.

Conclusion

Socioeconomic disadvantage at the neighborhood level is associated with higher risk of KRAS mutation while disadvantage related to built environmental structural measures was inversely associated. Our results indicate not only that neighborhood disadvantage may contribute to aggressive NSCLC biology, but the pathways linking biology to disadvantage are likely operating through socioeconomic-related stress.

Integrative metabolomics and transcriptomics analysis reveals novel therapeutic vulnerabilities in lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) comprises the majority (~85%) of all lung tumors, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being the most frequently diagnosed histological subtypes. Multi-modal omics profiling has been carried out in NSCLC, but no studies have yet reported a unique metabolite-related gene signature and altered metabolic pathways associated with LUAD and LUSC.

METHODS

We integrated transcriptomics and metabolomics to analyze 30 human lung tumors and adjacent noncancerous tissues. Differential co-expression was used to identify modules of metabolites that were altered between normal and tumor.

RESULTS

We identified unique metabolite-related gene signatures specific for LUAD and LUSC and key pathways aberrantly regulated at both transcriptional and metabolic levels. Differential co-expression analysis revealed that loss of coherence between metabolites in tumors is a major characteristic in both LUAD and LUSC. We identified one metabolic onco-module gained in LUAD, characterized by nine metabolites and 57 metabolic genes. Multi-omics integrative analysis revealed a 28 metabolic gene signature associated with poor survival in LUAD, with six metabolite-related genes as individual prognostic markers.

CONCLUSIONS

We demonstrated the clinical utility of this integrated metabolic gene signature in LUAD by using it to guide repurposing of AZD-6482, a PI3Kβ inhibitor which significantly inhibited three genes from the 28-gene signature. Overall, we have integrated metabolomics and transcriptomics analyses to show that LUAD and LUSC have distinct profiles, inferred gene signatures with prognostic value for patient survival, and identified therapeutic targets and repurposed drugs for potential use in NSCLC treatment.

Prognostic Significance of the Loss of Heterozygosity of KRAS in Early-Stage Lung Adenocarcinoma

Lung cancer is a common disease with a poor prognosis. Genomic alterations involving the KRAS gene are common in lung carcinomas, although much is unknown about how different mutations, deletions, and expressions influence the disease course. The first approval of a KRAS-directed inhibitor was recently approved by the FDA. Mutations in the KRAS gene have been associated with poor prognosis for lung adenocarcinomas, but implications of the loss of heterozygosity (LOH) of KRAS have not been investigated. In this study, we have assessed the LOH of KRAS in early-stage lung adenocarcinoma by analyzing DNA copy number profiles and have investigated the effect on patient outcome in association with mRNA expression and somatic hotspot mutations. KRAS mutation was present in 36% of cases and was associated with elevated mRNA expression. LOH in KRAS was associated with a favorable prognosis, more prominently in KRAS mutated than in wild-type patients. The presence of both LOH and mutation in KRAS conferred a better prognosis than KRAS mutation alone. For wild-type tumors, no difference in prognosis was observed between patients with and without LOH in KRAS. Our study indicates that LOH in KRAS is an independent prognostic factor that may refine the existing prognostic groups of lung adenocarcinomas.

DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma

Aims

Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis.

Methods

In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation.

Results

Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis.

Conclusion

The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01924-0.

Hyper-Methylated Hub Genes of T-Cell Receptor Signaling Predict a Poor Clinical Outcome in Lung Adenocarcinoma

Background

Immune checkpoint inhibitors (ICIs) emerge as the first-line treatment of lung adenocarcinoma (LUAD); selection of subpopulations acquiring clinical benefit is required. Associations between epigenetic modulation of tumor microenvironment (TME) and clinical outcome are far from clear. We focused on immune-related genes closely regulated by DNA methylation to identify the potential clinical outcome indicators.

Methods

We systematically calculated immunophenotype score (IMpS) and classified immunophenotypes based on seven TME features in three independent cohorts. The overlapping of differential expressed genes and methylated probes targeted genes was regarded as genes closely regulated by DNA methylation. Then, probe/gene pairs which highly correlated with each other and IMpS were identified and named as immune-related probe/gene pairs (mIMg). Prognostic mIMg were selected and verified in seven independent validation cohorts.

Results

Three immune phenotypes were clustered, and similar results were obtained in the three independent training cohorts. C2 displayed as an immunologically hot phenotype, whereas C3 corresponded with immunologically cold phenotype. Average methylation level was decreased from C2 to C3 (C2 > C1 > C3). Similarly, ICIs nonresponders showed global hypo-methylation compared with responders. Genes in mIMg were mainly enriched, especially in T-cell receptor activation, and repressed in noninflamed TME by hyper-methylation. Among mIMg, low expression and hyper-methylation of CD247, LCK, and PSTPIP1 were risk factors of overall survival (OS). ICIs nonresponders were more likely to be hyper-methylated in the three genes. By integrating with the oncogenes status, we demonstrated that EGFR wt and SRGN overexpressed patients were associated with chronic inflammation and immune evasion, showing an immunologically hot phenotype, which might lead to the short OS but derive clinical benefit from ICIs.

Conclusions

This study identifies hyper-methylation and concurrent repression of CD247, LCK, PSTPIP1 as immune negative indicators and risk factors for prognosis in LUAD. Moreover, EGFR/SRGN axis may participate in immune modification to influence ICIs response and clinical outcome in LUAD.

DNA Methylation in Lung Cancer: Mechanisms and Associations with Histological Subtypes, Molecular Alterations, and Major Epidemiological Factors

Lung cancer is the major leading cause of cancer-related mortality worldwide. Multiple epigenetic factors-in particular, DNA methylation-have been associated with the development of lung cancer. In this review, we summarize the current knowledge on DNA methylation alterations in lung tumorigenesis, as well as their associations with different histological subtypes, common cancer driver gene mutations (e.g., KRAS, EGFR, and TP53), and major epidemiological risk factors (e.g., sex, smoking status, race/ethnicity). Understanding the mechanisms of DNA methylation regulation and their associations with various risk factors can provide further insights into carcinogenesis, and create future avenues for prevention and personalized treatments. In addition, we also highlight outstanding questions regarding DNA methylation in lung cancer to be elucidated in future studies.

Epigenome-wide three-way interaction study identifies a complex pattern between TRIM27, KIAA0226, and smoking associated with overall survival of early-stage NSCLC

The interaction between DNA methylation of tripartite motif containing 27 (cg05293407_TRIM27 ) and smoking has previously been identified to reveal histologically heterogeneous effects of TRIM27 DNA methylation on early-stage non-small-cell lung cancer (NSCLC) survival. However, to understand the complex mechanisms underlying NSCLC progression, we searched three-way interactions. A two-phase study was adopted to identify three-way interactions in the form of pack-year of smoking (number of cigarettes smoked per day × number of years smoked) × cg05293407_TRIM27  × epigenome-wide DNA methylation CpG probe. Two CpG probes were identified with FDR-q ≤ 0.05 in the discovery phase and P ≤ 0.05 in the validation phase: cg00060500_KIAA0226 and cg17479956_EXT2 . Compared to a prediction model with only clinical information, the model added 42 significant three-way interactions using a looser criterion (discovery: FDR-q ≤ 0.10, validation: P ≤ 0.05) had substantially improved the area under the receiver operating characteristic curve (AUC) of the prognostic prediction model for both 3-year and 5-year survival. Our research identified the complex interaction effects among multiple environment and epigenetic factors, and provided therapeutic target for NSCLC patients.

Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma

Intertumoral immune heterogeneity is a critical reason for distinct clinical benefits of immunotherapy in lung adenocarcinoma (LUAD). Tumor immunophenotype (immune 'Hot' or 'Cold') suggests immunological individual differences and potential clinical treatment guidelines. However, employing epigenome signatures to determine tumor immunophenotypes and responsive treatment is not well understood. To delineate the tumor immunophenotype and immune heterogeneity, we first distinguished the immune 'Hot' and 'Cold' tumors of LUAD based on five immune expression signatures. In terms of clinical presentation, the immune 'Hot' tumors usually had higher immunoactivity, lower disease stages and better survival outcomes than 'Cold' tumors. At the epigenome levels, we observed that distinct DNA methylation patterns between immunophenotypes were closely associated with LUAD development. Hence, we identified a set of five CpG sites as the immunophenotype-related methylation signature (iPMS) for tumor immunophenotyping and further confirmed its efficiency based on a machine learning framework. Furthermore, we found iPMS and immunophenotype-related immune checkpoints (IPCPs) could contribute to the risk of tumor progression, implying IPCP has the potential to be a novel immunotherapy blockade target. After further parsing of the role of iPMS-predicted immunophenotypes, we found immune 'Hot' was a protective factor leading to better survival outcomes when patients received the anti-PD-1/PD-L1 immunotherapy. And iPMS was also a well-performed signature (AUC = 0.752) for predicting the durable/nondurable clinical benefits. In summary, our study explored the role of epigenome signature in clinical tumor immunophenotyping. Utilizing iPMS to characterize tumor immunophenotypes will facilitate developing personalized epigenetic anticancer approaches.

Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.

Genome-wide analysis of methylation CpG sites in gene promoters identified four pairs of CpGs-mRNAs associated with lung adenocarcinoma prognosis

BACKGROUND

Activation of oncogenes through promoter hypomethylation and silencing of tumor suppressor genes induced by promoter hypermethylation played essential roles in the progression of lung adenocarcinoma (LUAD). This study aimed to identify the LUAD prognostic CpG sites and the regulated genes which contributed to LUAD progression.

METHODS

Methylation profiles from TCGA and GSE60645 were used to screen the differentially methylated CpGs. Then, the Log-rank test was adopted to identify LUAD prognosis-associated CpGs. Differential gene expression and survival analyses were further performed to suggest the roles of methylation-driven genes in LUAD prognosis. Finally, models and nomograms were constructed to predict the prognosis of LUAD.

RESULTS

A total of 1891 CpGs at gene promoters were differentially methylated. Among them, 54 CpGs were significantly associated with LUAD prognosis. Nine of them showed significant correlations with the expression of four genes (CCDC181, CFTR, PPP1R16B, MYEOV). CCDC181, CFTR and PPP1R16B were aberrantly down-regulated in LUAD, while MYEOV was up-regulated. All of them were significantly associated with LUAD prognosis. The LASSO regression analysis indicated that tumor stages, cg09181792, cg16998150, cg22779330 and PPP1R16B were promising prognostic factors. The AUC (area under the curve) of the model containing the clinical predictors was 0.643. The combination of CpGs and PPP1R16B with clinical variables significantly improved the predictive efficiency with an AUC of 0.714 (P = 0.036).

CONCLUSION

This study identified four pairs of promoter CpGs and genes that were significantly associated with LUAD prognosis. The integration of CpGs methylation and gene expression showed better predictive ability for LUAD prognosis.

Whole genome copy number analyses reveal a highly aberrant genome in TP53 mutant lung adenocarcinoma tumors

BACKGROUND

Genetic alterations are common in non-small cell lung cancer (NSCLC), and DNA mutations and translocations are targets for therapy. Copy number aberrations occur frequently in NSCLC tumors and may influence gene expression and further alter signaling pathways. In this study we aimed to characterize the genomic architecture of NSCLC tumors and to identify genomic differences between tumors stratified by histology and mutation status. Furthermore, we sought to integrate DNA copy number data with mRNA expression to find genes with expression putatively regulated by copy number aberrations and the oncogenic pathways associated with these affected genes.

METHODS

Copy number data were obtained from 190 resected early-stage NSCLC tumors and gene expression data were available from 113 of the adenocarcinomas. Clinical and histopathological data were known, and EGFR-, KRAS- and TP53 mutation status was determined. Allele-specific copy number profiles were calculated using ASCAT, and regional copy number aberration were subsequently obtained and analyzed jointly with the gene expression data.

RESULTS

The NSCLC tumors tissue displayed overall complex DNA copy number profiles with numerous recurrent aberrations. Despite histological differences, tissue samples from squamous cell carcinomas and adenocarcinomas had remarkably similar copy number patterns. The TP53-mutated lung adenocarcinomas displayed a highly aberrant genome, with significantly altered copy number profiles including gains, losses and focal complex events. The EGFR-mutant lung adenocarcinomas had specific arm-wise aberrations particularly at chromosome7p and 9q. A large number of genes displayed correlation between copy number and expression level, and the PI(3)K-mTOR pathway was highly enriched for such genes.

CONCLUSIONS

The genomic architecture in NSCLC tumors is complex, and particularly TP53-mutated lung adenocarcinomas displayed highly aberrant copy number profiles. We suggest to always include TP53-mutation status when studying copy number aberrations in NSCLC tumors. Copy number may further impact gene expression and alter cellular signaling pathways.

The Absence of Mutations in the Exon 2 KRAS Gene in Several Ethnic Groups in North Sumatra May Not the Main Factor for Lung Cancer

Background:

Rat Sarcoma (RAS) protein encoded Guanosine Triphosphate (GTP-ase) activity, known as a switch of cell proliferation. The mutation of this protein alters the early stage of carcinogenesis and along with the interaction with other oncogene drivers and environmental factors affect the clinical characteristics and prognosis in cancer patients, particularly lung cancer.

Objective:

This study aims to determine the Kristen Rat Sarcoma (KRAS) mutation in lung cancer patients in North Sumatera and evaluate factors that might contribute in the development of lung cancer in the absence of KRAS mutation.

Methods:

This was a retrospective cohort study enrolled 44 subjects age > 18 year with the diagnosis of lung cancer. Histopathology preparation was obtained from surgery, bronchoscopy, and percutaneus needle biopsy then formed as paraffin-block. KRAS mutation was analyzed using Polymerase Chain Reaction (PCR) method with specific primer of exon 2 for evaluating the expression of RAS protein then continued with Sanger Sequencing Method at 12th and 13th codon.

Results:

The majority of subjects were male, age > 40 years old, bataknese, heavy smoker, with Adenocarcinoma. Almost all the subjects showed the expression of exon 2 of RAS protein in PCR examinations. However, Sequencing analysis using Bioedit Software, BLASTs and Finch T showed GGT GGC as protein base 219-224 which represented 12th and 13th Codon 12 and 13. The results interpreted there was no mutations of exon 2 of KRAS in North Sumatera Population.

Conclusion:

The absence of KRAS mutation in exon 2 in several ethnics in North Sumatera populations was not the main factors of lung cancer.

DNA hypermethylation of the ZNF132 gene participates in the clinicopathological aggressiveness of 'pan-negative'-type lung adenocarcinomas

Although some previous studies have examined epigenomic alterations in lung adenocarcinomas, correlations between epigenomic events and genomic driver mutations have not been fully elucidated. Single-CpG resolution genome-wide DNA methylation analysis with the Infinium HumanMethylation27 BeadChip was performed using 162 paired samples of adjacent normal lung tissue (N) and the corresponding tumorous tissue (T) from patients with lung adenocarcinomas. Correlations between DNA methylation data on the one hand and clinicopathological parameters and genomic driver mutations, i.e. mutations of EGFR, KRAS, BRAF and HER2 and fusions involving ALK, RET and ROS1, were examined. DNA methylation levels in 12 629 probes from N samples were significantly correlated with recurrence-free survival. Principal component analysis revealed that distinct DNA methylation profiles at the precancerous N stage tended not to induce specific genomic driver aberrations. Most of the genes showing significant DNA methylation alterations during transition from N to T were shared by two or more driver aberration groups. After small interfering RNA knockdown of ZNF132, which showed DNA hypermethylation only in the pan-negative group and was correlated with vascular invasion, the proliferation, apoptosis and migration of cancer cell lines were examined. ZNF132 knockdown led to increased cell migration ability, rather than increased cell growth or reduced apoptosis. We concluded that DNA hypermethylation of the ZNF132 gene participates in the clinicopathological aggressiveness of ‘pan-negative’ lung adenocarcinomas. In addition, DNA methylation alterations at the precancerous stage may determine tumor aggressiveness, and such alterations that accumulate after driver mutation may additionally modify clinicopathological features through alterations of gene expression.

Extreme Downregulation of Chromosome Y and Cancer Risk in Men

BACKGROUND

Understanding the biological differences between sexes in cancer is essential for personalized treatment and prevention. We hypothesized that the extreme downregulation of chromosome Y gene expression (EDY) is a signature of cancer risk in men and the functional mediator of the reported association between the mosaic loss of chromosome Y (LOY) and cancer.

METHODS

We advanced a method to measure EDY from transcriptomic data. We studied EDY across 47 nondiseased tissues from the Genotype Tissue-Expression Project (n = 371) and its association with cancer status across 12 cancer studies from The Cancer Genome Atlas (n = 1774) and seven other studies (n = 7562). Associations of EDY with cancer status and presence of loss-off function mutations in chromosome X were tested with logistic regression models, and a Fisher's test was used to assess genome-wide association of EDY with the proportion of copy number gains. All statistical tests were two-sided.

RESULTS

EDY was likely to occur in multiple nondiseased tissues (P < .001) and was statistically significantly associated with the EGFR tyrosine kinase inhibitor resistance pathway (false discovery rate = 0.028). EDY strongly associated with cancer risk in men (odds ratio [OR] = 3.66, 95% confidence interval [CI] = 1.58 to 8.46, P = .002), adjusted by LOY and age, and its variability was largely explained by several genes of the nonrecombinant region whose chromosome X homologs showed loss-of-function mutations that co-occurred with EDY during cancer (OR = 2.82, 95% CI = 1.32 to 6.01, P = .007). EDY associated with a high proportion of EGFR amplifications (OR = 5.64, 95% CI = 3.70 to 8.59, false discovery rate < 0.001) and EGFR overexpression along with SRY hypomethylation and nonrecombinant region hypermethylation, indicating alternative causes of EDY in cancer other than LOY. EDY associations were independently validated for different cancers and exposure to smoking, and its status was accurately predicted from individual methylation patterns.

CONCLUSIONS

EDY is a male-specific signature of cancer susceptibility that supports the escape from X-inactivation tumor suppressor hypothesis for genes that protect women compared with men from cancer risk.

Statistical and bioinformatic analysis of hemimethylation patterns in non-small cell lung cancer

Background

DNA methylation is an epigenetic event involving the addition of a methyl-group to a cytosine-guanine base pair (i.e., CpG site). It is associated with different cancers. Our research focuses on studying non-small cell lung cancer hemimethylation, which refers to methylation occurring on only one of the two DNA strands. Many studies often assume that methylation occurs on both DNA strands at a CpG site. However, recent publications show the existence of hemimethylation and its significant impact. Therefore, it is important to identify cancer hemimethylation patterns.

Methods

In this paper, we use the Wilcoxon signed rank test to identify hemimethylated CpG sites based on publicly available non-small cell lung cancer methylation sequencing data. We then identify two types of hemimethylated CpG clusters, regular and polarity clusters, and genes with large numbers of hemimethylated sites. Highly hemimethylated genes are then studied for their biological interactions using available bioinformatics tools.

Results

In this paper, we have conducted the first-ever investigation of hemimethylation in lung cancer. Our results show that hemimethylation does exist in lung cells either as singletons or clusters. Most clusters contain only two or three CpG sites. Polarity clusters are much shorter than regular clusters and appear less frequently. The majority of clusters found in tumor samples have no overlap with clusters found in normal samples, and vice versa. Several genes that are known to be associated with cancer are hemimethylated differently between the cancerous and normal samples. Furthermore, highly hemimethylated genes exhibit many different interactions with other genes that may be associated with cancer. Hemimethylation has diverse patterns and frequencies that are comparable between normal and tumorous cells. Therefore, hemimethylation may be related to both normal and tumor cell development.

Conclusions

Our research has identified CpG clusters and genes that are hemimethylated in normal and lung tumor samples. Due to the potential impact of hemimethylation on gene expression and cell function, these clusters and genes may be important to advance our understanding of the development and progression of non-small cell lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07990-7.

SETDB1 Overexpression Sets an Intertumoral Transcriptomic Divergence in Non-small Cell Lung Carcinoma

An increasing volume of evidence suggests that SETDB1 plays a role in the tumorigenesis of various cancers, classifying SETDB1 as an oncoprotein. However, owing to its numerous protein partners and their global-scale effects, the molecular mechanism underlying SETDB1-involved oncogenesis remains ambiguous. In this study, using public transcriptome data of lung adenocarcinoma (ADC) and squamous-cell carcinoma (SCC), we compared tumors with high-level SETDB1 (SH) and those with low-level SETDB1 (comparable with normal samples; SL). The results of principal component analysis revealed a transcriptomic distinction and divergence between the SH and SL samples in both ADCs and SCCs. The results of gene set enrichment analysis indicated that genes involved in the “epithelial–mesenchymal transition,” “innate immune response,” and “autoimmunity” collections were significantly depleted in SH tumors, whereas those involved in “RNA interference” collections were enriched. Chromatin-modifying genes were highly expressed in SH tumors, and the variance in their expression was incomparably high in SCC-SH, which suggested greater heterogeneity within SCC tumors. DNA methyltransferase genes were also overrepresented in SH samples, and most differentially methylated CpGs (SH/SL) were undermethylated in a highly biased manner in ADCs. We identified interesting molecular signatures associated with the possible roles of SETDB1 in lung cancer. We expect these SETDB1-associated molecular signatures to facilitate the development of biologically relevant targeted therapies for particular types of lung cancer.

Identification of Potential Biomarkers for CAD Using Integrated Expression and Methylation Data

DNA methylation plays an essential role in the pathogenesis of coronary artery disease (CAD) through regulating mRNA expressions. This study aimed to identify hub genes regulated by DNA methylation as biomarkers of CAD. Gene expression and methylation datasets of peripheral blood leukocytes (PBLs) of CAD were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, multiple computational approaches were performed to analyze the regulatory networks and to recognize hub genes. Finally, top hub genes were verified in a case-control study, based on their differential expressions and methylation levels between CAD cases and controls. In total, 535 differentially expressed-methylated genes (DEMGs) were identified and partitioned into 4 subgroups. TSS200 and 5′UTR were confirmed as high enrichment areas of differentially methylated CpGs sites (DMCs). The function of DEMGs is enriched in processes of histone H3-K27 methylation, regulation of post-transcription and DNA-directed RNA polymerase activity. Pathway enrichment showed DEMGs participated in the VEGF signaling pathway, adipocytokine signaling pathway, and PI3K-Akt signaling pathway. Besides, expressions of hub genes fibronectin 1 (FN1), phosphatase (PTEN), and tensin homolog and RNA polymerase III subunit A (POLR3A) were discordantly expressed between CAD patients and controls and related with DNA methylation levels. In conclusion, our study identified the potential biomarkers of PBLs for CAD, in which FN1, PTEN, and POLR3A were confirmed.

Hypermethylation of DRD5 Promoter Is a Biomarker Across 12 Cancer Types

Aberrant DNA methylation is thought to be an early event in cancer development. Thus, identification of DNA methylation-based markers may provide valuable evidence in clinical decision-making. In this study, a DNA methylation dataset from 514 normal-tumor pairs is used to explore possible shared differentially methylated regions (DMRs) across 12 cancer types. Results showed that DMR in Dopamine receptor D5 (DRD5) promoter may be serviced as a good candidate biomarker across different cancer types. We further validated the extended DMR (292bp) in DRD5 promoter using SEQUENOM MassARRAY platform. Detection of DRD5 promoter dynamic methylation will allow rapid risk assessment at diagnosis, for suspicious tumor with the tissue biopsies.

Epigenetic-smoking interaction reveals histologically heterogeneous effects of TRIM27 DNA methylation on overall survival among early-stage NSCLC patients

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 cg05293407_TRIM27 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 cg05293407_TRIM27 methylation in NSCLC. LUSC patients had a higher average pack-year of smoking (37.49_LUAD vs 54.79_LUSC, 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). cg05293407_TRIM27 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 cg05293407_TRIM27 methylation is a potential predictor of LUSC prognosis, and smoking intensity may impact on its prognostic value across the various types of NSCLC.

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases

Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.

Promoter Methylation of Selected Genes in Non-Small-Cell Lung Cancer Patients and Cell Lines

Specific gene promoter DNA methylation is becoming a powerful epigenetic biomarker in cancer diagnostics. Five genes (CDH1, CDKN2Ap16, RASSF1A, TERT, and WT1) were selected based on their frequently published potential as epigenetic markers. Diagnostic promoter methylation assays were generated based on bisulfite-converted DNA pyrosequencing. The methylation patterns of 144 non-small-cell lung cancer (NSCLC) and 7 healthy control formalin-fixed paraffin-embedded (FFPE) samples were analyzed to evaluate the applicability of the putative diagnostic markers. Statistically significant changes in methylation levels are shown for TERT and WT1. Furthermore, 12 NSCLC and two benign lung cell lines were characterized for promoter methylation. The in vitro tests involved a comparison of promoter methylation in 2D and 3D cultures, as well as therapeutic tests investigating the impact of CDH1/CDKN2Ap16/RASSF1A/TERT/WT1 promoter methylation on sensitivity to tyrosine kinase inhibitor (TKI) and DNA methyl-transferase inhibitor (DNMTI) treatments. We conclude that the selected markers have potential and putative impacts as diagnostic or even predictive marker genes, although a closer examination of the resulting protein expression and pathway regulation is needed.

Epigenome-wide gene-age interaction analysis reveals reversed effects of PRODH DNA methylation on survival between young and elderly early-stage NSCLC patients

DNA methylation changes during aging, but it remains unclear whether the effect of DNA methylation on lung cancer survival varies with age. Such an effect could decrease prediction accuracy and treatment efficacy. We performed a methylation–age interaction analysis using 1,230 early-stage lung adenocarcinoma patients from five cohorts. A Cox proportional hazards model was used to investigate lung adenocarcinoma and squamous cell carcinoma patients for methylation–age interactions, which were further confirmed in a validation phase. We identified one adenocarcinoma-specific CpG probe, cg14326354_PRODH, with effects significantly modified by age (HR_interaction = 0.989; 95% CI: 0.986–0.994; P = 9.18×10–7). The effect of low methylation was reversed for young and elderly patients categorized by the boundary of 95% CI standard (HR_young = 2.44; 95% CI: 1.26–4.72; P = 8.34×10-3; HR_elderly = 0.58; 95% CI: 0.42–0.82; P = 1.67×10-3). Moreover, there was an antagonistic interaction between low cg14326354_PRODH methylation and elderly age (HR_interaction = 0.21; 95% CI: 0.11–0.40; P = 2.20×10−6). In summary, low methylation of cg14326354PRODH might benefit survival of elderly lung adenocarcinoma patients, providing new insight to age-specific prediction and potential drug targeting.

Comprehensive analysis of gene expression and DNA methylation data identifies potential biomarkers and functional epigenetic modules for lung adenocarcinoma

Lung cancer has one of the highest mortality rates of malignant neoplasms. Lung adenocarcinoma (LUAD) is one of the most common types of lung cancer. DNA methylation is more stable than gene expression and could be used as a biomarker for early tumor diagnosis. This study is aimed to screen potential DNA methylation signatures to facilitate the diagnosis and prognosis of LUAD and integrate gene expression and DNA methylation data of LUAD to identify functional epigenetic modules. We systematically integrated gene expression and DNA methylation data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), bioinformatic models and algorithms were implemented to identify signatures and functional modules for LUAD. Three promising diagnostic and five potential prognostic signatures for LUAD were screened by rigorous filtration, and our tumor-normal classifier and prognostic model were validated in two separate data sets. Additionally, we identified functional epigenetic modules in the TCGA LUAD dataset and GEO independent validation data set. Interestingly, the MUC1 module was identified in both datasets. The potential biomarkers for the diagnosis and prognosis of LUAD are expected to be further verified in clinical practice to aid in the diagnosis and treatment of LUAD.

Integrated data analysis reveals significant associations of KEAP1 mutations with DNA methylation alterations in lung adenocarcinomas

KEAP1 regulates the cytoprotection induced by NRF2 and has been reported to be a candidate tumor suppressor. Recent evidence has shown that mutations in several driver genes cause aberrant DNA methylation patterns, a hallmark of cancer. However, the correlation between KEAP1 mutations and DNA methylation in lung cancer has still not been investigated. In this study, we systematically carried out an integrated multi-omics analysis to explore the correlation between KEAP1 mutations and DNA methylation and its effect on gene expression in lung adenocarcinoma (LUAD). We found that most of the DNA aberrations associated with KEAP1 mutations in LAUD were hypomethylation. Surprisingly, we found several NRF2-regulated genes among the genes that showed differential DNA methylation. Moreover, we identified an 8-gene signature with altered DNA methylation pattern and elevated gene expression levels in LUAD patients with mutated KEAP1, and evaluated the prognostic value of this signature in various clinical datasets. These results establish that KEAP1 mutations are associated with DNA methylation changes capable of shaping regulatory network functions. Combining both epigenomic and transcriptomic changes along with KEAP1 mutations may provide a better understanding of the molecular mechanisms associated with the progression of lung cancer and may help to provide better therapeutic approaches.

Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects

Background

DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions.

Research Question

Would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis?

Study Design and Methods

Biomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated.

Results

Twenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10^–17) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10^–18) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC_{3 year}, 0.88 [95% CI, 0.83-0.93]; and AUC_{5 year}, 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. G×G interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively.

Interpretation

The integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.

The Alteration of CTNNBIP1 in Lung Cancer

β-catenin is a major component of the Wnt/β-catenin signaling pathway, and is known to play a role in lung tumorigenesis. β-catenin-interacting protein 1 (CTNNBIP1) is a known repressor of β-catenin transactivation. However, little is known about the role of CTNNBIP1 in lung cancer. The aim of this study was to carry out a molecular analysis of CTNNBIP1 and its effect on β-catenin signaling, using samples from lung cancer patients and various lung cancer cell lines. Our results indicate a significant inverse correlation between the CTNNBIP1 mRNA expression levels and the CTNNBIP1 promoter hypermethylation, which suggests that the promoter hypermethylation is responsible for the low levels of CTNNBIP1 present in many lung cancer patient samples. The ectopic expression of CTNNBIP1 is able to reduce the β-catenin transactivation; this then brings about a decrease in the expression of β-catenin-targeted genes, such as matrix metalloproteinase 7 (MMP7). Conversely, CTNNBIP1 knockdown is able to increase β-catenin transactivation and the expression of MMP7. In agreement with these findings, a low level of CTNNBIP1 was found to be correlated with a high level of MMP7 when a publicly available microarray dataset for lung cancer was analyzed. Also, in agreement with the above, the ectopic expression of CTNNBIP1 inhibits the migration of lung cancer cells, whereas the CTNNBIP1 knockdown increases cancer cell migration. Our findings suggest that CTNNBIP1 is a suppressor of cancer migration, thus making it a potential prognostic predictor for lung cancer.

Tobacco carcinogens and the methionine metabolism in human bladder cancer

Cigarette smoking is a strong risk factor for bladder cancer. It has been shown that the duration of smoking is associated with a poor prognosis and a higher risk of recurrence. This is due to tobacco carcinogens forming adducts with DNA and proteins that participate in the DNA repair mechanisms. Additionally, polymorphisms of genes responsible for methyl group transfer in the methionine cycle and dosages of vitamins (from diet and supplements) can cause an increased risk of bladder cancer. Upregulated DNA methyltransferase 1 expression and activity results in a high level of methylated products of metabolism, as well as hypermethylation of tumor suppressor genes. The development of a market that provides new inhibitors of DNA methyltransferase or alternatives for current smokers is essential not only for patients but also for people who are under the danger of secondhand smoking and can experience its long-term exposure consequences.

Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk.

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.

SIPA1L3 methylation modifies the benefit of smoking cessation on lung adenocarcinoma survival: an epigenomic-smoking interaction analysis

Smoking cessation prolongs survival and decreases mortality of patients with non‐small‐cell lung cancer (NSCLC). In addition, epigenetic alterations of some genes are associated with survival. However, potential interactions between smoking cessation and epigenetics have not been assessed. Here, we conducted an epigenome‐wide interaction analysis between DNA methylation and smoking cessation on NSCLC survival. We used a two‐stage study design to identify DNA methylation–smoking cessation interactions that affect overall survival for early‐stage NSCLC. The discovery phase contained NSCLC patients from Harvard, Spain, Norway, and Sweden. A histology‐stratified Cox proportional hazards model adjusted for age, sex, clinical stage, and study center was used to test DNA methylation–smoking cessation interaction terms. Interactions with false discovery rate‐q ≤ 0.05 were further confirmed in a validation phase using The Cancer Genome Atlas database. Histology‐specific interactions were identified by stratification analysis in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. We identified one CpG probe (cg02268510_SIPA1L3) that significantly and exclusively modified the effect of smoking cessation on survival in LUAD patients [hazard ratio (HR)_interaction = 1.12; 95% confidence interval (CI): 1.07–1.16; P = 4.30 × 10^–7]. Further, the effect of smoking cessation on early‐stage LUAD survival varied across patients with different methylation levels of cg02268510_SIPA1L3. Smoking cessation only benefited LUAD patients with low methylation (HR = 0.53; 95% CI: 0.34–0.82; P = 4.61 × 10^–3) rather than medium or high methylation (HR = 1.21; 95% CI: 0.86–1.70; P = 0.266) of cg02268510_SIPA1L3. Moreover, there was an antagonistic interaction between elevated methylation of cg02268510_SIPA1L3 and smoking cessation (HR_interaction = 2.1835; 95% CI: 1.27–3.74; P = 4.46 × 10⁻³). In summary, smoking cessation benefited survival of LUAD patients with low methylation at cg02268510_SIPA1L3. The results have implications for not only smoking cessation after diagnosis, but also possible methylation‐specific drug targeting.

Adaptively Weighted and Robust Mathematical Programming for the Discovery of Driver Gene Sets in Cancers

High coverage and mutual exclusivity (HCME), which are considered two combinatorial properties of mutations in a collection of driver genes in cancers, have been used to develop mathematical programming models for distinguishing cancer driver gene sets. In this paper, we summarize a weak HCME pattern to justify the description of practical mutation datasets. We then present AWRMP, a method for identifying driver gene sets through the adaptive assignment of appropriate weights to gene candidates to tune the balance between coverage and mutual exclusivity. It embeds the genetic algorithm into the subsampling strategy to provide the optimization results robust against the uncertainty and noise in the data. Using biological datasets, we show that AWRMP can identify driver gene sets that satisfy the weak HCME pattern and outperform the state-of-arts methods in terms of robustness.

The immune microenvironment in non-small cell lung cancer is predictive of prognosis after surgery

The impact of the tumor immune microenvironment on overall survival in non‐small cell lung cancer (NSCLC) has been studied, but there is little information on its relevance for risk of relapse after surgery. Understanding more about the immune microenvironment in previously untreated NSCLC could help in identifying high‐risk patients and patients more likely to benefit from neoadjuvant/adjuvant immunotherapy. Here, we examined gene expression in 399 surgically derived NSCLC samples and 47 samples from normal lung, using Agilent microarray and RNA sequencing. In 335 of the tumor samples, programmed death‐ligand 1 (PD‐L1) expression was evaluated by immunohistochemistry. Gene expression was used to estimate content of immune cells and to calculate an immune score. Properties of the immune microenvironment, and its impact on prognosis, were compared in histological subgroups and gene expression subtypes. Tumors with an active immune microenvironment were found for both adenocarcinomas (AD) and squamous cell carcinomas (SCC). In AD, high immune score and high estimates of several immune cell types belonging to the adaptive immune system were associated with better progression‐free survival (PFS), while in SCC, no association between immune characteristics and PFS was found. The immune microenvironment, including PD‐L1 expression, and its impact on prognosis showed clear differences in AD and SCC gene expression subtypes. In conclusion, the NSCLC immune microenvironment is predictive of prognosis after surgery. Lung AD and SCC gene expression subtypes should be investigated as potential prognostic biomarkers in patients treated with immune checkpoint inhibitors.

Gene Promoter Methylation in Endometrial Carcinogenesis

Up to 60% of untreated atypical hyperplastic endometrium will develop into endometrial carcinoma (EC), and for those who underwent a hysterectomy a coexisting EC is found in up to 50%. Gene promoter methylation might be related to the EC development. The aim of this study is to determine changes in gene promoter profiles in normal endometrium, atypical hyperplasia (AH) and EC in relation to K-Ras mutations. A retrospective study was conducted in patients diagnosed with endometrial hyperplasia with and without subsequent EC. Promoter methylation of APC, hMLh1, O6-MGMT, P14, P16, RASSF1, RUNX3 was analysed on pre-operative biopsies, and correlated to the final histological diagnosis, and related to the presence of K-Ras mutations. In the study cohort (n=98), differences in promoter methylation were observed for hMLH1, O6-MGMT, and P16. Promoter methylation of hMLH1 and O6-MGMT gradually increased from histologically normal endometrium to AH to EC; 27.3, 36.4% and 38.0% for hMLH1 and 8.3%, 18.2% and 31.4% for O6-MGMT, respectively. P16 promoter methylation was significantly different in AH (7.7%) compared to EC (38%). K-Ras mutations were observed in 12.1% of AH, and in 19.6% of EC cases. No association of K-Ras mutation with promoter methylation of any of the tested genes was found. In conclusion, hMLH1 and O6-MGMT promoter methylation are frequently present in AH, and thus considered to be early events in the carcinogenesis of EC, whereas P16 promoter methylation was mainly present in EC, and not in precursor lesions supporting a late event in the carcinogenesis.

EGLN2 DNA methylation and expression interact with HIF1A to affect survival of early-stage NSCLC

Hypoxia occurs frequently in human cancers and promotes stabilization and activation of hypoxia inducible factor (HIF). HIF-1α is specific for the hypoxia response, and its degradation mediated by three enzymes EGLN1, EGLN2 and EGLN3. Although EGLNs expression has been found to be related to prognosis of many cancers, few studies examined DNA methylation in EGLNs and its relationship to prognosis of early-stage non-small cell lung cancer (NSCLC). We analyzed EGLNs DNA methylation data from tumor tissue samples of 1,230 early-stage NSCLC patients, as well as gene expression data from The Cancer Genome Atlas. The sliding windows sequential forward feature selection method and weighted random forest were used to screen out the candidate CpG probes in lung adenocarcinomas (LUAD) and lung squamous cell carcinomas patients, respectively, in both discovery and validation phases. Then Cox regression was performed to evaluate the association between DNA methylation and overall survival. Among the 34 CpG probes in EGLNs, DNA methylation at cg25923056EGLN2 was identified to be significantly associated with LUAD survival (HR = 1.02, 95% CI: 1.01-1.03, P = 9.90 × 10-5), and correlated with EGLN2 expression (r = - 0.36, P = 1.52 × 10-11). Meanwhile, EGLN2 expression was negatively correlated with HIF1A expression in tumor tissues (r = - 0.30, P = 4.78 × 10-8) and significantly (P = 0.037) interacted with HIF1A expression on overall survival. Therefore, DNA methylation of EGLN2- HIF1A is a potential marker for LUAD prognosis and these genes are potential treatment targets for further development of HIF-1α inhibitors in lung cancer therapy.

EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma

Various feature selection algorithms have been proposed to identify cancer prognostic biomarkers. In recent years, however, their reproducibility is criticized. The performance of feature selection algorithms is shown to be affected by the datasets, underlying networks and evaluation metrics. One of the causes is the curse of dimensionality, which makes it hard to select the features that generalize well on independent data. Even the integration of biological networks does not mitigate this issue because the networks are large and many of their components are not relevant for the phenotype of interest. With the availability of multi-omics data, integrative approaches are being developed to build more robust predictive models. In this scenario, the higher data dimensions create greater challenges. We proposed a phenotype relevant network-based feature selection (PRNFS) framework and demonstrated its advantages in lung cancer prognosis prediction. We constructed cancer prognosis relevant networks based on epithelial mesenchymal transition (EMT) and integrated them with different types of omics data for feature selection. With less than 2.5% of the total dimensionality, we obtained EMT prognostic signatures that achieved remarkable prediction performance (average AUC values >0.8), very significant sample stratifications, and meaningful biological interpretations. In addition to finding EMT signatures from different omics data levels, we combined these single-omics signatures into multi-omics signatures, which improved sample stratifications significantly. Both single- and multi-omics EMT signatures were tested on independent multi-omics lung cancer datasets and significant sample stratifications were obtained.

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.

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.

Genome-wide DNA methylation analysis reveals a prognostic classifier for non-metastatic colorectal cancer (ProMCol classifier)

OBJECTIVE

Pathological staging used for the prediction of patient survival in colorectal cancer (CRC) provides only limited information.

DESIGN

Here, a genome-wide study of DNA methylation was conducted for two cohorts of patients with non-metastatic CRC (screening cohort (n=572) and validation cohort (n=274)). A variable screening for prognostic CpG sites was performed in the screening cohort using marginal testing based on a Cox model and subsequent adjustment of the p-values via independent hypothesis weighting using the methylation difference between 34 pairs of tumour and normal mucosa tissue as auxiliary covariate. From the 1000 CpG sites with the smallest adjusted p-value, 20 CpG sites with the smallest Brier score for overall survival (OS) were selected. Applying principal component analysis, we derived a prognostic methylation-based classifier for patients with non-metastatic CRC (ProMCol classifier).

RESULTS

This classifier was associated with OS in the screening (HR 0.51, 95% CI 0.41 to 0.63, p=6.2E-10) and the validation cohort (HR 0.61, 95% CI 0.45 to 0.82, p=0.001). The independent validation of the ProMCol classifier revealed a reduction of the prediction error for 3-year OS from 0.127, calculated only with standard clinical variables, to 0.120 combining the clinical variables with the classifier and for 4-year OS from 0.153 to 0.140. All results were confirmed for disease-specific survival.

CONCLUSION

The ProMCol classifier could improve the prognostic accuracy for patients with non-metastatic CRC.

NFIX downregulation independently predicts poor prognosis in lung adenocarcinoma, but not in squamous cell carcinoma

AIM

To study the expression profile of NFIX, its prognostic value and the mechanism of its dysregulation in lung adenocarcinoma (LUAD). Patients & materials: A retrospective study was performed by using data from the Cancer Genome Atlas and the Human Protein Atlas.

RESULTS

High NFIX RNA expression was an independent prognostic factor of favorable overall survival (HR: 0.687, 95% CI: 0.496-0.951; p = 0.024) and recurrence-free survival (HR: 0.700, 95% CI: 0.493-0.994, p = 0.046) in LUAD, but not in lung squamous cell carcinoma. NFIX DNA hypermethylation was associated with significantly decreased NFIX expression and shorter overall survival and recurrence-free survival in LUAD.

CONCLUSION

 NFIX downregulation might independently predict poor prognosis in LUAD. DNA hypermethylation might be an important cause of the downregulation.