A Diagnostic Panel of DNA Methylation Biomarkers for Lung Adenocarcinoma

Cell-free DNA methylation in the clinical management of lung cancer

The clinical use of cell-free DNA (cfDNA) methylation in managing lung cancer depends on its ability to differentiate between malignant and healthy cells, assign methylation changes to specific tissue sources, and elucidate opportunities for targeted therapy. From a technical standpoint, cfDNA methylation analysis is primed as a potential clinical tool for lung cancer screening, early diagnosis, prognostication, and treatment, pending the outcome of elaborate validation studies. Here, we discuss the current state of the art in cfDNA methylation analysis, examine the unique features and limitations of these new methods in a clinical context, propose two models for applying cfDNA methylation data for lung cancer screening, and discuss future research directions.

Use of Multiple Machine Learning Approaches for Selecting Urothelial Cancer-Specific DNA Methylation Biomarkers in Urine

Diagnosing urothelial cancer (UCa) via invasive cystoscopy is painful, specifically in men, and can cause infection and bleeding. Because the UCa risk is higher for male patients, urinary non-invasive UCa biomarkers are highly desired to stratify men for invasive cystoscopy. We previously identified multiple DNA methylation sites in urine samples that detect UCa with a high sensitivity and specificity in men. Here, we identified the most relevant markers by employing multiple statistical approaches and machine learning (random forest, boosted trees, LASSO) using a dataset of 251 male UCa patients and 111 controls. Three CpG sites located in ALOX5, TRPS1 and an intergenic region on chromosome 16 have been concordantly selected by all approaches, and their combination in a single decision matrix for clinical use was tested based on their respective thresholds of the individual CpGs. The combination of ALOX5 and TRPS1 yielded the best overall sensitivity (61%) at a pre-set specificity of 95%. This combination exceeded both the diagnostic performance of the most sensitive bioinformatic approach and that of the best single CpG. In summary, we showed that overlap analysis of multiple statistical approaches identifies the most reliable biomarkers for UCa in a male collective. The results may assist in stratifying men for cystoscopy.

AI/ML advances in non-small cell lung cancer biomarker discovery

Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.

Whole-genome DNA methylation and DNA methylation-based biomarkers in lung squamous cell carcinoma

Exploring early detection methods through comprehensive evaluation of DNA methylation for lung squamous cell carcinoma (LUSC) patients is of great significance. By using different machine learning algorithms for feature selection and model construction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, five methylation biomarkers in LUSC (along with mapped genes) were identified including cg14823851 (TBX4), cg02772121 (TRIM15), cg10424681 (C6orf201), cg12910906 (ARHGEF4), and cg20181079 (OR4D11), achieving extremely high sensitivity and specificity in distinguishing LUSC from normal samples in independent cohorts. Pyrosequencing assay verified DNA methylation levels, meanwhile qRT-PCR and immunohistochemistry results presented their accordant methylation-related gene expression statuses in paired LUSC and normal lung tissues. The five methylation-based biomarkers proposed in this study have great potential for the diagnosis of LUSC and could guide studies in methylation-regulated tumor development and progression.

HOXA9 gene promotor methylation and copy number variation of SOX2 and HV2 genes in cell free DNA: A potential diagnostic panel for non-small cell lung cancer

BACKGROUND

Most cases of lung cancer are diagnosed at advanced stage. Detection of genetic and epigenetic markers in cell-free DNA (cfDNA) is a promising tool for the diagnosis of lung cancer at an early stage. The aim of this study was to identify non-invasive diagnostic markers in cell free DNA (cfDNA) for non-small cell lung cancer (NSCLC) as it is the most common type of lung cancer.

METHODS

We investigated the cfDNA HOXA9 gene promotor methylation by pyrosequencing. Copy number variation of SOX2 and HV2 genes were detected by real-time PCR in cfDNA extracted from plasma samples of 25 newly diagnosed NSCLC patients and 25 age and sex matched controls.

RESULTS

Methylation level of HOXA9 was significantly higher in NSCLC patients than controls (p > 0.001). SOX2 showed significantly higher CNV and HV2 showed lower CNV in patients than controls (p > 0.001, p = 0.001 respectively). Receiver Operating Characteristic (ROC) curve analysis for HOXA9 methylation, SOX2 CNV and HV2 CNV showed a discrimination power of 79.4%, 80% and 77.5% respectively and the area under the curve for the combined analysis of the three genes was 0.958 with 88% sensitivity and 100% specificity.

CONCLUSIONS

In this study, we suggest a potentially diagnostic panel that may help in detection of lung cancer with high sensitivity and specificity using cell free DNA. This Panel included HOXA9 gene methylation and the CNV of SOX2 and HV2 genes.

Promoter Methylation of Two HOXA9 and NISCH Genes in Opium Users

BACKGROUND

Opiate abuse has been critically increased in the world, especially in Iran. Owing to the association of opiate use with multiple human cancers and neurological disorders, seeking for genetic and epigenetic effects of opium can pave the way for early diagnosis of major health defects in addicted users. Accordingly, the present study aimed to determine the methylation status of the promoter of two genes, which are actively involved in neurodevelopment and cancer evolution.

METHODS

DNA was isolated from peripheral blood of 28 opium abusers and 19 healthy controls and then subjected to sonication. Sonicated DNAs undergone methylated DNA immunoprecipitation-real time polymerase chain reaction (MeDIP-Real Time PCR) using specific primer pairs designed for HOXA9 and NISCH genes. Obtained data were analyzed using SPSS software.

FINDINGS

HOXA9 and NISCH genes were found to be significantly methylated in addicted users compared to controls (P<0.001) which was significantly associated with the mean of the age regarding HOXA9 gene (P=0.002). Neither opium amount nor duration or route of using was associated with the methylation status of HOXA9 or NISCH genes.

CONCLUSION

Hypermethylation of HOXA9 and NISCH genes as tumor suppressor in opium-addicted individuals can be considered as confirmatory evidence for carcinogenesis of opium. Further studies are required to figure out the role of epigenetic alterations in cancer evolution among opium users.

Integrative analysis of DNA methylation and gene expression through machine learning identifies stomach cancer diagnostic and prognostic biomarkers

DNA methylation is an early event in tumorigenesis. Here, by integrative analysis of DNA methylation and gene expression and utilizing machine learning approaches, we introduced potential diagnostic and prognostic methylation signatures for stomach cancer. Differentially-methylated positions (DMPs) and differentially-expressed genes (DEGs) were identified using The Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) data. A total of 256 DMPs consisting of 140 and 116 hyper- and hypomethylated positions were identified between 443 tumour and 27 nontumour STAD samples. Gene expression analysis revealed a total of 2821 DEGs with 1247 upregulated and 1574 downregulated genes. By analysing the impact of cis and trans regulation of methylation on gene expression, a dominant negative correlation between methylation and expression was observed, while for trans regulation, in hypermethylated and hypomethylated genes, there was mainly a negative and positive correlation with gene expression, respectively. To find diagnostic biomarkers, we used 28 hypermethylated probes locating in the promoter of 27 downregulated genes. By implementing a feature selection approach, eight probes were selected and then used to build a support vector machine diagnostic model, which had an area under the curve of 0.99 and 0.97 in the training and validation (GSE30601 with 203 tumour and 94 nontumour samples) cohorts, respectively. Using 412 TCGA-STAD samples with both methylation and clinical data, we also identified four prognostic probes by implementing univariate and multivariate Cox regression analysis. In summary, our study introduced potential diagnostic and prognostic biomarkers for STAD, which demands further validation.

Methylation biomarkers for early cancer detection and diagnosis: Current and future perspectives

The increase in recent scientific studies on cancer biomarkers has brought great new insights into the field. Moreover, novel technological breakthroughs such as long read sequencing and microarrays have enabled high throughput profiling of many biomarkers, while advances in bioinformatic tools have made the possibility of developing highly reliable and accurate biomarkers a reality. These changes triggered renewed interest in biomarker research and provided tremendous opportunities for enhancing cancer management and improving early disease detection. DNA methylation alterations are known to accompany and contribute to carcinogenesis, making them promising biomarkers for cancer, namely due to their stability, frequency and accessibility in bodily fluids. The advent of newer minimally invasive experimental methods such as liquid biopsies provide the perfect setting for methylation-based biomarker development and application. Despite their huge potential, accurate and robust biomarkers for the conclusive diagnosis of most cancer types are still not routinely used, hence a strong need for sustained research in this field is still needed. This review provides a brief exposition of current methylation biomarkers for cancer diagnosis and early detection, including markers already in clinical use as well as various upcoming ones. It also outlines how recent big data and novel technologies will revolutionise the next generation of cancer tests in supplementing or replacing currently existing invasive techniques.

Identification of prognostic values of the transcription factor-CpG-gene triplets in lung adenocarcinoma: A narrative review

OBJECTIVE

Abnormal DNA methylation can regulate carcinogenesis in lung adenocarcinoma (LUAD), while transcription factors (TFs) mediate methylation in a site-specific manner to affect downstream transcriptional regulation and tumor progression. Therefore, this study aimed to explore the TF-methylation-gene regulatory relationships that influence LUAD prognosis.

METHODS

Differential analyses of methylation sites and genes were generated by integrating transcriptome and methylome profiles from public databases. Through target gene identification, motif enrichment in the promoter region, and TF prediction, TF-methylation and methylation-gene relation pairs were obtained. Then, the prognostic TF-methylation-gene network was constructed using univariate Cox regression analysis. Prognostic models were constructed based on the key regulatory axes. Finally, Kaplan-Meier curves were created to evaluate the model efficacy and the relationship between candidate genes and prognosis.

RESULTS

A total of 1878 differential expressed genes and 1233 differential methylation sites were screened between LUAD and normal samples. Then 10 TFs were predicted to bind 144 enriched motifs. After integrating TF-methylation and methylation-gene relations, a prognostic TF-methylation-gene network containing 4 TFs, 111 methylation sites, and 177 genes was constructed. In this network, ERG-cg27071152-MTURN and FOXM1-cg19212949-PTPR regulatory axes were selected to construct the prognostic models, which showed robust abilities in predicting 1-, 3-, and 5-year survival probabilities. Finally, ERG and MTURN were downregulated in LUAD samples, whereas FOXM1 and PTPR were upregulated. Their expression levels were related to LUAD prognosis.

CONCLUSION

ERG-cg27071152-MTURN and FOXM1-cg19212949-PTPR regulatory axes were proposed as potential biomarkers for predicting the prognosis of LUAD.

Design and experimental validation of OPERA_MET-A panel for deep methylation analysis by next generation sequencing

DNA methylation is the most recognized epigenetic mark that leads to a massive distortion in cancer cells. It has been observed that a large number of DNA aberrant methylation events occur simultaneously in a group of genes, thus providing a growth advantage to the cell in promoting cell differentiation and neoplastic transformation. Due to this reason, methylation profiles have been suggested as promising cancer biomarkers. Here, we designed and performed a first step of validation of a novel targeted next generation sequencing (NGS) panel for methylation analysis, which can simultaneously evaluate the methylation levels at CpG sites of multiple cancer-related genes. The OPERA_MET-A methylation panel was designed using the Ion AmpliSeq™ technology to amplify 155 regions with 125-175 bp mean length and covers a total of 1107 CpGs of 18 cancer-related genes. The performance of the panel was assessed by running commercially available fully methylated and unmethylated control human genomic DNA (gDNA) samples and a variable mixture of them. The libraries were run on Ion Torrent platform and the sequencing output was analyzed using the “methylation_analysis” plugin. DNA methylation calls on both Watson (W) and Crick (C) strands and methylated:unmethylated ratio for each CpG site were obtained. Cell lines, fresh frozen and formalin-fixed paraffin-embedded (FFPE) lung cancer tissues were tested. The OPERA_MET-A panel allows to run a minimum of 6 samples/530 chip to reach an observed mean target depth ≥2,500X (W and C strands) and an average number of mapped reads >750,000/sample. The conversion efficiency, determined by spiking-in unmethylated Lambda DNA into each sample before the bisulfite conversion process, was >97% for all samples. The observed percentage of global methylation for all CpGs was >95% and <5% for fully methylated and unmethylated gDNA samples, respectively, and the observed results for the variable mixtures were in agreement with what was expected. Methylation-specific NGS analysis represents a feasible method for a fast and multiplexed screening of cancer patients by a high-throughput approach. Moreover, it offers the opportunity to construct a more robust algorithm for disease prediction in cancer patients having a low quantity of biological material available.

Epigenetic Alterations of DNA Methylation and miRNA Contribution to Lung Adenocarcinoma

This study focused on the epigenetic alterations of DNA methylation and miRNAs for lung adenocarcinoma (LUAD) diagnosis and treatment using bioinformatics analyses. DNA methylation data and mRNA and miRNA expression microarray data were obtained from The Cancer Genome Atlas (TCGA) database. The differentially methylated genes (DMGs), differentially expressed genes (DEGs), and differentially expressed miRNAs were analyzed by using the limma package. The DAVID database performed GO and KEGG pathway enrichment analyses. Using STRING and Cytoscape, we constructed the protein-protein interaction (PPI) network and achieved visualization. The online analysis tool CMap was used to identify potential small-molecule drugs for LUAD. In LUAD, 607 high miRNA-targeting downregulated genes and 925 low miRNA-targeting upregulated genes, as well as 284 hypermethylated low-expression genes and 315 hypomethylated high-expression genes, were obtained. They were mainly enriched in terms of pathways in cancer, neuroactive ligand-receptor interaction, cAMP signaling pathway, and cytosolic DNA-sensing pathway. In addition, 40 upregulated and 84 downregulated genes were regulated by both aberrant alternations of DNA methylation and miRNAs. Five small-molecule drugs were identified as a potential treatment for LUAD, and five hub genes (SLC2A1, PAX6, LEP, KLF4, and FGF10) were found in PPI, and two of them (SLC2A1 and KLF4) may be related to the prognosis of LUAD. In summary, our study identified a series of differentially expressed genes associated with epigenetic alterations of DNA methylation and miRNA in LUAD. Five small-molecule drugs and five hub genes may be promising drugs and targets for LUAD treatment.

Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis

Differentiation between the various non-small-cell lung cancer subtypes is crucial for providing an effective treatment to the patient. For this purpose, machine learning techniques have been used in recent years over the available biological data from patients. However, in most cases this problem has been treated using a single-modality approach, not exploring the potential of the multi-scale and multi-omic nature of cancer data for the classification. In this work, we study the fusion of five multi-scale and multi-omic modalities (RNA-Seq, miRNA-Seq, whole-slide imaging, copy number variation, and DNA methylation) by using a late fusion strategy and machine learning techniques. We train an independent machine learning model for each modality and we explore the interactions and gains that can be obtained by fusing their outputs in an increasing manner, by using a novel optimization approach to compute the parameters of the late fusion. The final classification model, using all modalities, obtains an F1 score of 96.81±1.07, an AUC of 0.993±0.004, and an AUPRC of 0.980±0.016, improving those results that each independent model obtains and those presented in the literature for this problem. These obtained results show that leveraging the multi-scale and multi-omic nature of cancer data can enhance the performance of single-modality clinical decision support systems in personalized medicine, consequently improving the diagnosis of the patient.

Impact of KMN network genes on progression and prognosis of non-small cell lung cancer

The Knl1-Mis12-Ndc80 (KMN) network genes (including KNL, MIS12 and NDC80 complexes) encode a highly conserved network of protein complexes that act in cell mitosis. In recent years, multiple studies revealed that KMN network genes also play a vital role in tumor appearance and growth. However, the role of the KMN gene network in non-small cell lung cancer (NSCLC) remains unknown. In this study, we analyzed the effects of KMN genes expression and clinical phenotype in patients with lung adenocarcinoma (LUAD). The expression of KMN network genes and related clinical information was extracted from The Cancer Genome Atlas. The samples were classified into cluster I and II by consistent clustering. We analyzed the gene distribution by principal component analysis, and the potential risk characteristics were analyzed using the least absolute shrinkage and selection operator Cox regression algorithm. Univariate and multivariate Cox regression analyses were used to analyze the clinical information. The Database for Annotation, Visualization, and Integrated Discovery, Gene MANIA and gene set enrichment analysis were used to analyze function and correlation among genes of the KMN network. The expression levels of nine out of ten KMN genes were significantly up-regulated in LUAD and were associated with poor overall survival (OS). Higher expression of NDC80 and KNL1 was related to low OS in both univariate and multivariate analyses. According to two independent prognostic KMN network genes (KNL1 and NDC80), a risk signature was established to predict the prognosis of patients with LUAD. Additionally, the genes NDC80 and KNL1 were considerably enriched in pathways associated with signaling pathways, biological processes, and the cell cycle. The results indicate that KMN network genes are intimately related to lung adenocarcinoma. KMN network genes are involved in the malignant process of LUAD. Assessment of NDC80 and KNL1 might be helpful for prognostic stratification and treatment strategy development.

A functional methylation signature to predict the prognosis of Chinese lung adenocarcinoma based on TCGA

Background

Lung cancer is the leading cause of cancer morbidity and mortality worldwide, however, the individualized treatment is still unsatisfactory. DNA methylation can affect gene regulation and may be one of the most valuable biomarkers in predicting the prognosis of lung adenocarcinoma. This study was aimed to identify methylation CpG sites that may be used to predict lung adenocarcinoma prognosis.

Methods

The Cancer Genome Atlas (TCGA) database was used to detect methylation CpG sites associated with lung adenocarcinoma prognosis and construct a methylation signature model. Then, a Chinese cohort was carried out to estimate the association between methylation and lung adenocarcinoma prognosis. Biological function studies, including demethylation treatment, cell proliferative capacity, and gene expression changes in lung adenocarcinoma cell lines, were further performed.

Results

In the TCGA set, three methylation CpG sites were selected that were associated with lung adenocarcinoma prognosis (cg14517217, cg15386964, and cg18878992). The risk of mortality was increased in lung adenocarcinoma patients with the gradual increase level of methylation signature based on three methylation sites levels (HR = 45.30, 95% CI = 26.69–66.83; p < 0.001). The C‐statistic value increased to 0.77 when age, gender, and other clinical variables were added to the signature to prediction model. A similar situation was confirmed in Chinese lung adenocarcinoma cohort. In the biological function studies, the proliferative capacity of cell lines was inhibited when the cells were demethylated with 5‐aza‐2'‐deoxycytidine (5‐aza‐2dC). The mRNA and protein expression levels of SEPT9 and HIST1H2BH (cg14517217 and cg15386964) were downregulated with different concentrations of 5‐aza‐2dC treatment, while cg18878992 showed the opposite result.

Conclusion

This study is the first to develop a three‐CpG‐based model for lung adenocarcinoma, which is a practical and useful tool for prognostic prediction that has been validated in a Chinese population.

Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study

Lung adenocarcinoma (LUAD) is a common and very lethal cancer. Accurate staging is a prerequisite for its effective diagnosis and treatment. Therefore, improving the accuracy of the stage prediction of LUAD patients is of great clinical relevance. Previous works have mainly focused on single genomic data information or a small number of different omics data types concurrently for generating predictive models. A few of them have considered multi-omics data from genome to proteome. We used a publicly available dataset to illustrate the potential of multi-omics data for stage prediction in LUAD. In particular, we investigated the roles of the specific omics data types in the prediction process. We used a self-developed method, Omics-MKL, for stage prediction that combines an existing feature ranking technique Minimum Redundancy and Maximum Relevance (mRMR), which avoids redundancy among the selected features, and multiple kernel learning (MKL), applying different kernels for different omics data types. Each of the considered omics data types individually provided useful prediction results. Moreover, using multi-omics data delivered notably better results than using single-omics data. Gene expression and methylation information seem to play vital roles in the staging of LUAD. The Omics-MKL method retained 70 features after the selection process. Of these, 21 (30%) were methylation features and 34 (48.57%) were gene expression features. Moreover, 18 (25.71%) of the selected features are known to be related to LUAD, and 29 (41.43%) to lung cancer in general. Using multi-omics data from genome to proteome for predicting the stage of LUAD seems promising because each omics data type may improve the accuracy of the predictions. Here, methylation and gene expression data may play particularly important roles.

Epigenetic approaches for cervical neoplasia screening (Review)

Human papillomavirus (HPV) infection is the leading cause of cervical cancer. The Papanicolaou cytology test is the usually employed type of screening for this infection; however, its sensibility is limited. Only a small percentage of women infected with high-risk HPV develop cervical cancer with an array of genetic and epigenetic modifications. Thus, it is necessary to develop rapid, reproducible and minimally invasive technologies for screening. DNA methylation has gained attention as an alternative method for molecular diagnosis and prognosis in HPV infection. The aim of the present review was to highlight the potential of DNA methylation in cervical neoplasia screening for clinical applications. It was observed that the methylation human and viral genes was correlated with high-grade lesions and cancer. Methylation biomarkers have shown a good capacity to discriminate between high-grade lesions with a transformative potential and cervical cancer, being able to detect these modifications at an early stage. With further research, the epigenetic profiles and subtypes of the tumors could be elaborated, which would aid in therapy selection by opening avenues in personalized precision medicine. Response to therapy could also be evaluated through such methods and the accessibility of liquid biopsies would allow a constant monitoring of the patient's status without invasive sampling techniques.

TSHZ3 functions as a tumor suppressor by DNA methylation in colorectal cancer

OBJECTIVE

Teashirt zinc finger homeobox 3 (TSHZ3) is currently reported to be aberrantly expressed in several tumors, but the detailed functions and epigenetic mechanisms of TSHZ3 in colorectal cancer (CRC) remain unclear.

MATERIALS AND METHODS

In this study, the TSHZ3 expression in 118 CRC and normal adjacent tissues (NATs) was evaluated, and the methylation status of the TSZH3 promoter region in CRC tissues and cell lines was also analyzed.

RESULTS

The results of PCR analysis showed that TSHZ3 was significantly down-regulated in CRC tissues, and patients with low TSHZ3 levels had a poorer 5-year overall survival (OS) rate. Analyzing the promoter sequence (-1000∼0) by MethPrimer, TSHZ3 promoter was found to harbor abundant of CpG islands. The methylation specific PCR (MSP) analysis presented a relatively hypermethylated status of THSZ3 promoter in CRC samples. The data of MSP and bisulfite sequencing PCR (BSP) also confirmed that CpG sites of TSHZ3 promoter were methylated in CRC cells, and the DNA methyltransferase (DNMT) inhibitor 5-aza-2'-deoxycytidine (5-Aza) could effectively restored the TSHZ3 expression in vitro. Functionally, the proliferation, apoptosis and metastasis of CRC cells were regulated by TSZH3 over-expression, and the suppressing effects of TSHZ3 in CRC were also confirmed in a xenograft mouse model.

CONLUSIONS

Our results indicated that promoter methylation was one of the mechanisms contributing to the down-regulation of TSHZ3 in CRC, and TSZH3 might served as a potential tumor suppressor gene in the development and progression of CRC.

Prognostic Analysis of Lung Adenocarcinoma Based on DNA Methylation Regulatory Factor Clustering

There is a known link between DNA methylation and cancer immunity/immunotherapy; however, the effect of DNA methylation on immunotherapy in lung adenocarcinoma (LUAD) remains to be elucidated. In the current study, we aimed to screen key markers for prognostic analysis of LUAD based on DNA methylation regulatory factor clustering. We classified LUAD using the NMF clustering method, and as a result, we obtained 20 DNA methylation regulatory genes. These 20 regulatory genes were used to determine the pattern of DNA methylation regulation, and patients were grouped for further analysis. The risk score model was analyzed in the TCGA dataset and an external validation set, and the correlation between the risk score and DNA methylation regulatory gene expression was explored. We analyzed the correlation between the prognostic model and immune infiltration and checkpoints. Finally, we analyzed the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functions of the prognosis model and established the nomogram model and decision tree model. The survival analyses of ClusterA and ClusterB were significantly different. Survival analysis showed that patients with a high risk score had a poor prognosis. Survival models (tobacco, T, N, M, stage, sex, age, status, and risk score) were abnormally correlated with T cells and macrophages. The higher the risk score associated with smoking was and the higher the stage was, the more severe the LUAD and the more maladjusted the immune system were. Immune infiltration and abnormal expression of immune checkpoint genes in the prognostic model of LUAD were associated with the risk score. The prognostic models were mainly enriched in the cell cycle and DNA replication. Characterization of DNA methylation regulatory patterns is helpful to improve our understanding of the immune microenvironment in LUAD and to guide the development of a more personalized immunotherapy strategy in the future.

DNA methylome-based validation of induced sputum as an effective protocol to study lung immunity: construction of a classifier of pulmonary cell types

Flow cytometry is a classical approach used to define cell types in peripheral blood. While DNA methylation signatures have been extensively employed in recent years as an alternative to flow cytometry to define cell populations in peripheral blood, this approach has not been tested in lung-derived samples. Here, we compared bronchoalveolar lavage with a more cost-effective and less invasive technique based on sputum induction and developed a DNA methylome-based algorithm that can be used to deconvolute the cell types in such samples. We analysed the DNA methylome profiles of alveolar macrophages and lymphocytes cells isolated from the pulmonary compartment. The cells were isolated using two different methods, sputum induction and bronchoalveolar lavage. A strong positive correlation between the DNA methylome profiles of cells obtained with the two isolation methods was found. We observed the best correlation of the DNA methylomes when both isolation methods captured cells from the lower parts of the lungs. We also identified unique patterns of CpG methylation in DNA obtained from the two cell populations, which can be used as a signature to discriminate between the alveolar macrophages and lymphocytes by means of open-source algorithms. We validated our findings with external data and obtained results consistent with the previous findings. Our analysis opens up a new possibility to identify different cell populations from lung samples and promotes sputum induction as a tool to study immune cell populations from the lung.

A panel of DNA methylation biomarkers for detection and improving diagnostic efficiency of lung cancer

Lung cancer remains the leading cause of cancer deaths worldwide. Although low-dose spiral computed tomography (LDCT) screening is used for the detection of lung cancer in a high-risk population, false-positive results of LDCT remain a clinical problem. Here, we developed a blood test of a novel panel of three established lung cancer methylation biomarkers for lung cancer detection. Short stature homeobox 2 gene (SHOX2), ras association domain family 1A gene (RASSF1A), and prostaglandin E receptor 4 gene (PTGER4) methylation was analyzed in a training cohort of 351 individuals (197 controls, 154 cases) and validated from an independent cohort of 149 subjects (89 controls, 60 cases). The novel panel biomarkers distinguished between malignant and benign lung disease at high sensitivity and specificity: 87.0% sensitivity [95% CI 80.2–91.5%], 98.0% specificity [95% CI 94.9–99.4%]. Sensitivity in adenocarcinoma, squamous cell carcinoma, small cell lung cancer, and other lung cancer was 89.0%, 87.5%, 85.7%, and 77.8%, respectively. Notably, cancer patients in stage I and II showed high diagnostic sensitivity at 82.5% and 90.5%, respectively. Moreover, the diagnostic efficiency did not show bias toward age, gender, smoking, and the presence of other (nonlung) cancers. The performance of the panel in the validation cohort confirmed the diagnostic value. These findings clearly showed that this panel of DNA methylation biomarkers was effective in detecting lung cancer noninvasively and may provide clinical utility in stand-alone or in combination with current imaging techniques to improve the diagnosis of lung cancer.

A methylation-based nomogram for predicting survival in patients with lung adenocarcinoma

BACKGROUND

DNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients.

METHOD

The DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves.

RESULT

We identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47-3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19-3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively.

CONCLUSIONS

The present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.

The increased expression and aberrant methylation of SHC1 in non-small cell lung cancer: Integrative analysis of clinical and bioinformatics databases

Despite the previous evidence showing that SHC adaptor protein 1 (SHC1) could encode three distinct isoforms (p46SHC, p52SHC and p66SHC) that function in different activities such as regulating life span and Ras activation, the precise underlying role of SHC1 in lung cancer also remains obscure. In this study, we firstly found that SHC1 expression was up‐regulated both in lung adenocarcinoma (LUAD) and in lung squamous cell carcinoma (LUSC) tissues. Furthermore, compared to patients with lower SHC1 expression, LUAD patients with higher expression of SHC1 had poorer overall survival (OS). Moreover, higher expression of SHC1 was also associated with worse OS in patients with stages 1 and 2 but not stage 3 lung cancer. Significantly, the analysis showed that SHC1 methylation level was associated with OS in lung cancer patients. It seemed that the methylation level at specific probes within SHC1 showed negative correlations with SHC1 expression both in LUAD and in LUSC tissues. The LUAD and LUSC patients with hypermethylated SHC1 at cg12473916 and cg19356022 probes had a longer OS. Therefore, it is reasonable to conclude that SHC1 has a potential clinical significance in LUAD and LUSC patients.

Detection and relevance of epigenetic markers on ctDNA: recent advances and future outlook

Liquid biopsy, a minimally invasive approach, is a highly powerful clinical tool for the real-time follow-up of cancer and overcomes many limitations of tissue biopsies. Epigenetic alterations have a high potential to provide a valuable source of innovative biomarkers for cancer, owing to their stability, frequency, and noninvasive accessibility in bodily fluids. Numerous DNA methylation markers are now tested in circulating tumor DNA (ctDNA) as potential biomarkers, in various types of cancer. DNA methylation in combination with liquid biopsy is very powerful in identifying circulating epigenetic biomarkers of clinical importance. Blood-based epigenetic biomarkers have a high potential for early detection of cancer since DNA methylation in plasma can be detected early during cancer pathogenesis. In this review, we summarize the latest findings on DNA methylation markers in ctDNA for early detection, prognosis, minimal residual disease, risk of relapse, treatment selection, and resistance, for breast, prostate, lung, and colorectal cancer.

circRNA circ_102049 Implicates in Pancreatic Ductal Adenocarcinoma Progression through Activating CD80 by Targeting miR-455-3p

Emerging evidence has shown that circular RNAs (circRNAs) and DNA methylation play important roles in the causation and progression of cancers. However, the roles of circRNAs and abnormal methylation genes in the tumorigenesis of pancreatic ductal adenocarcinoma (PDAC) are still largely unknown. Expression profiles of circRNA, gene methylation, and mRNA were downloaded from the GEO database, and differentially expressed genes were obtained via GEO2R, and a ceRNA network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Inflammation-associated genes were collected from the GeneCards database. Then, functional enrichment analysis and protein-protein interaction (PPI) networks of inflammation-associated methylated expressed genes were investigated using Metascape and STRING databases, respectively, and visualized in Cytoscape. Hub genes of PPI networks were identified using the NetworkAnalyzer plugin. Also, we analyzed the methylation, protein expression levels, and prognostic value of hub genes in PDAC patients through the UALCAN, Human Protein Atlas (HPA), and Kaplan-Meier plotter databases, respectively. The circRNA_102049/miR-455-3p/CD80 axis was identified by the ceRNA network and hub genes. In vitro and in vivo experiments were performed to evaluate the functions of circRNA_102049. The regulatory mechanisms of circRNA_102049 and miR-455-3p were explored by RT-PCR, western blot, and dual-luciferase assays. In the present study, twelve hub genes (STAT1, CCND1, KRAS, CD80, ICAM1, ESR1, RAF1, RPS6KA2, KDM6B, TNRC6A, FOSB, and DNM1) were determined from the PPI networks. Additionally, the circRNA_102049 was upregulated in PDAC cell lines. Functionally, the knockdown of circRNA_102049 by siRNAs inhibited cell growth, inflammatory factors, and migratory and invasive potential and promoted cell apoptosis. Mechanistically, circRNA_102049 functioned as a sponge of miR-455-3p and partially reversed the effect of miR-455-3p and consequently upregulated CD80 expression. Our findings showed that circRNA_102049 and methylated hub genes play an important role in the proliferation, apoptosis, migration, invasion, and inflammatory response of PDAC, which might be selected as a promising prognostic marker and therapeutic target for PDAC.

KMT2C promoter methylation in plasma-circulating tumor DNA is a prognostic biomarker in non-small cell lung cancer

MLL3 histone methyltransferase, encoded by the KMT2C gene, is a tumor suppressor that has an essential role in cell‐type‐specific gene expression. We evaluated the prognostic significance of KMT2C promoter methylation as a circulating epigenetic biomarker in plasma cell‐free DNA (cfDNA) in non‐small cell lung cancer (NSCLC). We examined the methylation status of KMT2C promoter using a novel highly specific and sensitive real‐time methylation‐specific PCR (MSP) assay in (a) operable NSCLC: 48 fresh‐frozen NSCLC tissues, their corresponding adjacent non‐neoplastic tissues, and 48 matched plasma samples; (b) metastatic NSCLC: 91 plasma samples; and (c) 60 plasma samples from healthy donors (HD). KMT2C promoter methylation in plasma cfDNA was detected in 7/48 (14.6%) patients with operable and in 18/91 (19.8%) patients with advanced NSCLC but in none (0/60, 0%) of the plasma samples from HD. In operable NSCLC, in corresponding adjacent non‐neoplastic tissue samples, KMT2C promoter methylation was detected in 3/48 (6.3%) cases. Moreover, in operable NSCLC, KMT2C promoter methylation in plasma cfDNA was related to reduced disease‐free survival (ΗR = 0.239; P = 0.001) and worse overall survival (OS; HR = 0.342, P = 0.023). In metastatic NSCLC, KMT2C promoter methylation in plasma cfDNA was related to worse progression‐free survival (PFS; HR = 0.431; P = 0.005) and worse OS (HR = 0.306; P < 0.001). Our data strongly suggest that the detection of KMT2C promoter methylation in plasma cfDNA predicts poor prognosis in patients with both operable and metastatic NSCLCs. KMT2C promoter methylation in plasma cfDNA therefore merits further evaluation and validation as a noninvasive circulating epigenetic biomarker.

DNA methylation-based lung adenocarcinoma subtypes can predict prognosis, recurrence, and immunotherapeutic implications

The marked heterogeneity of lung adenocarcinoma (LUAD) makes its diagnosis and treatment difficult. In addition, the aberrant DNA methylation profile contributes to tumor heterogeneity and alters the immune response. We used DNA methylation array data from publicly available databases to establish a predictive model for LUAD prognosis. Thirty-three methylation sites were identified as specific prognostic biomarkers, independent of patients' clinical characteristics. These methylation profiles were used to identify potential drug candidates and study the immune microenvironment of LUAD and response to immunotherapy. When compared with the high-risk group, the low-risk group had a lower recurrence rate and favorable prognosis. The tumor microenvironment differed between the two groups as reflected by the higher number of resting dendritic cells and a lower number of monocytes and resting mast cells in the low-risk group. Moreover, low-risk patients reported higher immune and stromal scores, lower tumor purity, and higher expression of HLA genes. Low-risk patients responded well to immunotherapy due to higher expression of immune checkpoint molecules and lower stemness index. Thus, our model predicted a favorable prognosis and increased overall survival for patients in the low-risk methylation group. Further, this model could provide potential drug targets to develop effective immunotherapies for LUAD.

Clinical Significance of P16 Gene Methylation in Lung Cancer

Lung cancer is the leading cause of death from cancer in China. The lack of early screening technologies makes most patients to be diagnosed at advanced stages with a poor prognosis which often miss the best treatment opportunities. Thus, identifying biomarkers for minimally invasive detection and prognosis of early stage disease is urgently needed. Genetic and epigenetic alterations that promote tumorigenesis and metastasis exist in multiple cancers. These aberrant alterations usually represent early events in cancer progression suggesting their potential applications as a biomarker for cancer prediction. Studies have shown that DNA methylation is one of the key factors in progression of lung cancer. P16 promoter methylation is one of the most common epigenetic change plays a key role in lung cancer. In this review, we highlight the p16 gene methylation and its clinical significance in lung cancer.