DNA methylation of PTGER4 in peripheral blood plasma helps to distinguish between lung cancer, benign pulmonary nodules and chronic obstructive pulmonary disease patients

A combined model of circulating tumor DNA methylated SHOX2/SCT/HOXA7 and clinical features facilitates the discrimination of malignant from benign pulmonary nodules

BACKGROUND

Despite the advancements in early lung cancer detection attributed to the widespread use of low-dose computed tomography (LDCT), this technology has also led to an increasing number of pulmonary nodules (PNs) of indeterminate significance being identified. Therefore, this study was aimed to develop a model that leverages plasma methylation biomarkers and clinical characteristics to distinguish between malignant and benign PNs.

METHODS

In a training cohort of 210 patients with PNs, we evaluated plasma circulating tumor DNA (ctDNA) for the presence of three lung cancer-specific methylation markers: SHOX2, SCT, and HOXA7. Subsequently, we constructed a combined model utilizing methylated SHOX2/SCT/HOXA7 (mSHOX2/SCT/HOXA7) ctDNA levels, the largest nodule size measured by LDCT, and age, employing the binary logistic regression algorithm. Furthermore, we compared the diagnostic performances of the combined model with the Mayo Clinic model and the single mSHOX2/SCT/HOXA7 model by analyzing the area under the receiver operating characteristic curve (AUC) for each.

RESULTS

The combined model demonstrated an impressive AUC of 0.87 and an accuracy of 0.75 in the training cohort, using pathologic diagnoses as the gold standard. This performance was significantly superior to that of the single mSHOX2/SCT/HOXA7 panel (AUC = 0.81, P < 0.0001) and the Mayo model (AUC = 0.65, P = 0.0005). Further validation in a cohort of 82 patients with PNs confirmed the diagnostic value of the combined model. Additionally, we observed that as the size of the nodule increased, the diagnostic accuracy of the combined model also improved.

CONCLUSIONS

A combined model incorporating the ctDNA-based methylation status of SHOX2/SCT/HOXA7 genes, the largest nodule size measured by LDCT, and age can serve as a supplementary approach to LDCT for lung cancer. This model enhances the precision in identifying high-risk individuals and optimizes the clinical management strategies for PNs detected by CT.

Non-invasive diagnosis of pulmonary nodules by circulating tumor DNA methylation: A prospective multicenter study

BACKGROUND

With the popularization of computed tomography, more and more pulmonary nodules (PNs) are being detected. Risk stratification of PNs is essential for detecting early-stage lung cancer while minimizing the overdiagnosis of benign nodules. This study aimed to develop a circulating tumor DNA (ctDNA) methylation-based, non-invasive model for the risk stratification of PNs.

METHODS

A blood-based assay ("LUNG-TRAC") was designed to include novel lung cancer ctDNA methylation markers identified from in-house reduced representative bisulfite sequencing data and known markers from the literature. A stratification model was trained based on 183 ctDNA samples derived from patients with benign or malignant PNs and validated in 62 patients. LUNG-TRAC was further single-blindly tested in a single- and multi-center cohort.

RESULTS

The LUNG-TRAC model achieved an area under the curve (AUC) of 0.810 (sensitivity = 74.4 % and specificity = 73.7 %) in the validation set. Two test sets were used to evaluate the performance of LUNG-TRAC, with an AUC of 0.815 in the single-center test (N = 61; sensitivity = 67.5 % and specificity = 76.2 %) and 0.761 in the multi-center test (N = 95; sensitivity = 50.7 % and specificity = 80.8 %). The clinical utility of LUNG-TRAC was further assessed by comparing it to two established risk stratification models: the Mayo Clinic and Veteran Administration models. It outperformed both in the validation and the single-center test sets.

CONCLUSION

The LUNG-TRAC model demonstrated accuracy and consistency in stratifying PNs for the risk of malignancy, suggesting its utility as a non-invasive diagnostic aid for early-stage peripheral lung cancer.

CLINICAL TRIAL REGISTRATION

www.

CLINICALTRIALS

gov (NCT03989219).

The novel DNA methylation marker FIBIN suppresses non-small cell lung cancer metastasis by negatively regulating ANXA2

OBJECTIVES

The clinical T1 stage solid lung cancer with metastasis is a serious threat to human life and health. In this study, we performed RNA sequencing on T1 advanced-stage lung cancer and adjacent tissues to identify a novel biomarker and explore its roles in lung cancer.

METHODS

Quantitative reversed-transcription PCR, reverse transcription PCR and Western blot, MSP and Methtarget were utilized to evaluate FIBIN expression levels at both the transcriptional and protein levels as well as its methylation status. Differential target protein was evaluated for relative and absolute quantitation by isobaric tags. Co-IP was performed to detect the interactions between target protein. Precise location and expression levels of target proteins were revealed by immunofluorescence staining and component protein extraction using specific kits, respectively.

RESULTS

We reported that FIBIN was frequently silenced due to promoter hypermethylation in lung cancer. Additionally, both in vitro and in vivo experiments confirmed the significant anti-proliferation and anti-metastasis capabilities of FIBIN. Mechanistically, FIBIN decreased the nuclear accumulation of β-catenin by reducing the binding activity of GSK3β with ANXA2 while promoting interaction between GSK3β and β-catenin.

CONCLUSION

Our findings firstly identify FIBIN is a tumor suppressor, frequently silenced due to promoter hypermethylation. FIBIN may serve as a predictive biomarker for progression or metastasis among early-stage lung cancer patients.

Combined detection of SHOX2 and PTGER4 methylation with serum marker CYFRA21-1 for improved diagnosis of malignant pleural mesothelioma

AIMS

To investigate the performance of a combined biomarker approach using the methylation status of the short stature homeobox 2 (SHOX2) and prostaglandin E2 receptor EP4 (PTGER4) genes, along with the serum levels of CYFRA21-1, for differential diganosis of malignant pleural mesothelioma (MPM) from benign reactive mesothelial hyperplasia (RMH).

METHODS

We analysed 48 MPM tissue or pleural effusion cell block specimens and 42 cases with RMH. Real-time quantitative methylation-specific PCR was used to examine the methylation status of SHOX2, PTGER4, ras association domain family 1 isoform A, septin 9 gene and homeobox gene A9 genes. Additionally, we employed electrochemiluminescence immunoassay to measure nine serum tumour markers commonly used in pan-cancer screening tests.

RESULTS

The receiver operating curve indicated that SHOX2, PTGER4 gene methylation and serum biomarker CYFRA21-1 exhibited good diagnostic performance in identifying MPM, with area under curves (AUCs) of 0.761, 0.904 and 0.847, respectively. The combination of SHOX2, PTGER4 methylation and CYFRA21-1 yielded an AUC value of 0.972. The diagnostic sensitivity and specificity of this panel in differentiating MPM from RMH were 91.3% (42/46) and 97.6% (41/42), respectively. Both tissue and cell block specimens can be used in the diagnostic process. Furthermore, elevated CYFRA21-1 levels were associated with poor prognosis (p<0.05). Hypermethylation level of PTGER4 may indicate an unfavourable prognosis of MPM, but the difference was not statistically significant.

CONCLUSIONS

The combined detection of SHOX2 and PTGER4 methylation alongside serum CYFRA21-1 level significantly enhances the diagnosis of MPM. Additionally, CYFRA21-1 can serve as a prognostic indicator for MPM.

RNA-Binding Proteins as a Molecular Link between COPD and Lung Cancer

Chronic obstructive pulmonary disease (COPD) represents an independent risk factor for lung cancer development. Accelerated cell senescence, induced by oxidative stress and inflammation, is a common pathogenic determinant of both COPD and lung cancer. The post transcriptional regulation of genes involved in these processes is finely regulated by RNA-binding proteins (RBPs), which regulate mRNA turnover, subcellular localization, splicing and translation. Multiple pro-inflammatory mediators (including cytokines, chemokines, proteins, growth factors and others), responsible of lung microenvironment alteration, are regulated by RBPs. Several mouse models have shown the implication of RBPs in multiple mechanisms that sustain chronic inflammation and neoplastic transformation. However, further studies are required to clarify the role of RBPs in the pathogenic mechanisms shared by lung cancer and COPD, in order to identify novel biomarkers and therapeutic targets. This review will therefore focus on the studies collectively indicating the role of RBPs in oxidative stress and chronic inflammation as common pathogenic mechanisms shared by lung cancer and COPD.

Intratumor microbiome derived glycolysis-lactate signatures depicts immune heterogeneity in lung adenocarcinoma by integration of microbiomic, transcriptomic, proteomic and single-cell data

Introduction

Microbiome plays roles in lung adenocarcinoma (LUAD) development and anti-tumor treatment efficacy. Aberrant glycolysis in tumor might promote lactate production that alter tumor microenvironment, affecting microbiome, cancer cells and immune cells. We aimed to construct intratumor microbiome score to predict prognosis of LUAD patients and thoroughly investigate glycolysis and lactate signature's association with LUAD immune cell infiltration.

Methods

The Cancer Genome Atlas-LUAD (TCGA-LUAD) microbiome data was downloaded from cBioPortal and analyzed to examine its association with overall survival to create a prognostic scoring model. Gene Set Enrichment Analysis (GSEA) was used to find each group's major mechanisms involved. Our study then investigated the glycolysis and lactate pattern in LUAD patients based on 19 genes, which were correlated with the tumor microenvironment (TME) phenotypes and immunotherapy outcomes. We developed a glycolysis-lactate risk score and signature to accurately predict TME phenotypes, prognosis, and response to immunotherapy.

Results

Using the univariate Cox regression analysis, the abundance of 38 genera were identified with prognostic values and a lung-resident microbial score (LMS) was then developed from the TCGA-LUAD-microbiome dataset. Glycolysis hallmark pathway was significantly enriched in high-LMS group and three distinct glycolysis-lactate patterns were generated. Patients in Cluster1 exhibited unfavorable outcomes and might be insensitive to immunotherapy. Glycolysis-lactate score was constructed for predicting prognosis with high accuracy and validated in external cohorts. Gene signature was developed and this signature was elevated in epithelial cells especially in tumor mass on single-cell level. Finally, we found that the glycolysis-lactate signature levels were consistent with the malignancy of histological subtypes.

Discussion

Our study demonstrated that an 18-microbe prognostic score and a 19-gene glycolysis-lactate signature for predicting prognosis of LUAD patients. Our LMS, glycolysis-lactate score and glycolysis-lactate signature have potential roles in precision therapy of LUAD patients.

Mechanisms Contributing to the Comorbidity of COPD and Lung Cancer

Lung cancer and chronic obstructive pulmonary disease (COPD) often co-occur, and individuals with COPD are at a higher risk of developing lung cancer. While the underlying mechanism for this risk is not well understood, its major contributing factors have been proposed to include genomic, immune, and microenvironment dysregulation. Here, we review the evidence and significant studies that explore the mechanisms underlying the heightened lung cancer risk in people with COPD. Genetic and epigenetic changes, as well as the aberrant expression of non-coding RNAs, predispose the lung epithelium to carcinogenesis by altering the expression of cancer- and immune-related genes. Oxidative stress generated by tobacco smoking plays a role in reducing genomic integrity, promoting epithelial-mesenchymal-transition, and generating a chronic inflammatory environment. This leads to abnormal immune responses that promote cancer development, though not all smokers develop lung cancer. Sex differences in the metabolism of tobacco smoke predispose females to developing COPD and accumulating damage from oxidative stress that poses a risk for the development of lung cancer. Dysregulation of the lung microenvironment and microbiome contributes to chronic inflammation, which is observed in COPD and known to facilitate cancer initiation in various tumor types. Further, there is a need to better characterize and identify the proportion of individuals with COPD who are at a high risk for developing lung cancer. We evaluate possible novel and individualized screening strategies, including biomarkers identified in genetic studies and exhaled breath condensate analysis. We also discuss the use of corticosteroids and statins as chemopreventive agents to prevent lung cancer. It is crucial that we optimize the current methods for the early detection and management of lung cancer and COPD in order to improve the health outcomes for a large affected population.

The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment

Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.

The Role of TSHR, PTEN and RASSF1A Promoters' Methylation Status for Non-Invasive Detection of Papillary Thyroid Carcinoma

Aim: We investigated whether a difference exists between TSHR, PTEN and RASSF1A methylation status in plasma of subjects with papillary thyroid cancer (PTC). Methods: Peripheral blood samples were collected from 68 patients with PTC and 86 healthy controls (HC). Thyroid cancer tissue and corresponding adjacent normal tissue methylation levels were analyzed. DNA methylation level changes in TSHR, PTEN and RASSF1A genes were analyzed by quantitative methylation-sensitive polymerase chain reaction. Results: We observed that the methylation level of TSHR was significantly higher in the thyroid cancer tissue compared to adjacent normal tissue (p = 0.040). TSHR methylation levels in the PTC group plasma samples were significantly higher compared to HC (p = 0.022). After surgery, PTC plasma samples showed lower TSHR and PTEN methylation levels compared to the levels before surgery (p = 0.003, p = 0.031, respectively). The TSHR methylation level was significantly higher in PTC with larger tumor size (>2 cm) (p < 0.001), and lymph node metastases (p = 0.01), lymphovascular invasion (p = 0.02) and multifocality (p = 0.013) 0ROC analysis revealed that the TSHR methylation level provides high accuracy in distinguishing PTC from HC (p = 0.022, AUC of 0.616). Conclusion: TSHR methylation in peripheral blood samples is expected to be a sensitive and specific minimally invasive tool for the diagnosis of PTC, especially in combination with other diagnostic means.

Toxicoepigenetics and Environmental Health: Challenges and Opportunities

The rapidly growing field of toxicoepigenetics seeks to understand how toxicant exposures interact with the epigenome to influence disease risk. Toxicoepigenetics is a promising field of environmental health research, as integrating epigenetics into the field of toxicology will enable a more thorough evaluation of toxicant-induced disease mechanisms as well as the elucidation of the role of the epigenome as a biomarker of exposure and disease and possible mediator of exposure effects. Likewise, toxicoepigenetics will enhance our knowledge of how environmental exposures, lifestyle factors, and diet interact to influence health. Ultimately, an understanding of how the environment impacts the epigenome to cause disease may inform risk assessment, permit noninvasive biomonitoring, and provide potential opportunities for therapeutic intervention. However, the translation of research from this exciting field into benefits for human and animal health presents several challenges and opportunities. Here, we describe four significant areas in which we see opportunity to transform the field and improve human health by reducing the disease burden caused by environmental exposures. These include (1) research into the mechanistic role for epigenetic change in environment-induced disease, (2) understanding key factors influencing vulnerability to the adverse effects of environmental exposures, (3) identifying appropriate biomarkers of environmental exposures and their associated diseases, and (4) determining whether the adverse effects of environment on the epigenome and human health are reversible through pharmacologic, dietary, or behavioral interventions. We then highlight several initiatives currently underway to address these challenges.

Prostanoid Signaling in Cancers: Expression and Regulation Patterns of Enzymes and Receptors

Cancer-associated disturbance of prostanoid signaling provides an aberrant accumulation of prostanoids. This signaling consists of 19 target genes, encoding metabolic enzymes and G-protein-coupled receptors, and prostanoids (prostacyclin, thromboxane, and prostaglandins E2, F2α, D2, H2). The study addresses the systems biology analysis of target genes in 24 solid tumors using a data mining pipeline. We analyzed differential expression patterns of genes and proteins, promoter methylation status as well as tissue-specific master regulators and microRNAs. Tumor types were clustered into several groups according to gene expression patterns. Target genes were characterized as low mutated in tumors, with the exception of melanoma. We found at least six ubiquitin ligases and eight protein kinases that post-translationally modified the most connected proteins PTGES3 and PTGIS. Models of regulation of PTGIS and PTGIR gene expression in lung and uterine cancers were suggested. For the first time, we found associations between the patient’s overall survival rates with nine multigene transcriptomics signatures in eight tumors. Expression patterns of each of the six target genes have predictive value with respect to cytostatic therapy response. One of the consequences of the study is an assumption of prostanoid-dependent (or independent) tumor phenotypes. Thus, pharmacologic targeting the prostanoid signaling could be a probable additional anticancer strategy.

HPV-Related Promoter Methylation-Based Gene Signature Predicts Clinical Prognosis of Patients With Cervical Cancer

Persistent high-risk HPV infection drives tumorigenesis in various human malignancies, including cervical, oropharyngeal, anal, and vulvar carcinomas. Although HPV-related tumors arise in several different sites, they share many common genetic and epigenetic events. Complex and heterogeneous genomic aberrations and mutations induced by high-risk HPV contribute to the initiation and progression of cervical cancer (CC). However, the associations between high-risk HPV infection and DNA methylation have not been clearly investigated. In the present study, HPV-related gene promoter methylation signature was comprehensively analyzed using multiple interactive platforms. CC patients were successfully classified into high-risk and low-risk groups with significant differences in clinical outcomes based on the HPV-related gene promoter methylation signature. Moreover, the protein levels of ALDH1A2 and clinical prognostic value were confirmed in the CC patients cohort. In summary, our study provides compelling evidence that HPV-related gene promoter methylation signature serves as a strong prognostic signature for CC patients. Clinical investigations in large CC patient cohorts are greatly needed to pave the way to implement epigenetic biomarkers into better clinical management.

DNA methylation in lung cancer patients: Opening a "window of life" under precision medicine

DNA methylation is a work of adding a methyl group to the 5th carbon atom of cytosine in DNA sequence under the catalysis of DNA methyltransferase (DNMT) to produce 5-methyl cytosine. Some current studies have elucidated the mechanism of lung cancer occurrence and causes of lung cancer progression and metastasis from the perspective of DNA methylation. Moreover, many studies have shown that smoking can change the methylation status of some gene loci, leading to the occurrence of lung cancer, especially central lung cancer. This review mainly introduces the role of DNA methylation in the pathogenesis, early diagnosis and screening, progression and metastasis, treatment, and prognosis of lung cancer, as well as the latest progress. We point out that methylation markers, sample tests, and methylation detection limit the clinical application of DNA methylation. If the liquid biopsy is to become the main force in lung cancer diagnosis, it must make efficient use of limited samples and improve the sensitivity and specificity of the tests. In addition, we also put forward our views on the future development direction of DNA methylation.

Diagnostic value of conventional tumor markers in young patients with pulmonary nodules

Background

Lung cancer is one of the most common malignancies, and there is a trend of increasing incidence in young patients. The preoperative diagnosis of pulmonary nodules is mainly based on the combination of imaging and tumor markers. There is no relevant report on the diagnostic value of tumor markers in young pulmonary nodules. Our study was designed to explore the value of five tumor markers in young patients with pulmonary nodules.

Methods

We reviewed the medical records of 390 young patients (age ≤45 years) with pulmonary nodules treated at two separate centers from January 1, 2015, to January 1, 2021. Malignant pulmonary nodules were confirmed in 318 patients, and the other 72 patients were diagnosed with benign pulmonary nodules. The gold standard for diagnosis of pulmonary nodules was surgical biopsy. The conventional serum biomarkers included cytokeratin 19 (CYFRA21‐1), pro‐gastrin‐releasing‐peptide (ProGRP), carcinoembryonic antigen (CEA), neuron‐specific enolase (NSE), and squamous cell carcinoma‐associated antigen (SCCA). The diagnostic values of five tumor markers were analyzed by receiver operating characteristic (ROC) curves.

Results

There were no significant differences in the expression of five tumor markers between the groups (p > 0.05). Single tumor marker (CYFRA21‐1, ProGRP, CEA, NSE, and SCCA) showed a limited value in the diagnosis of malignant pulmonary nodules, with the AUC of 0.506, 0.503 0.532, 0.548, and 0.562, respectively. The AUC of the combined examination was only 0.502~0.596, which did not improve the diagnostic value.

Conclusions

Five conventional tumor markers had a limited diagnostic value in young patients with pulmonary nodules.

Identification of Prognostic Risk Model Based on DNA Methylation-Driven Genes in Esophageal Adenocarcinoma

Background

DNA methylation is an important part of epigenetic modification, and its abnormality is closely related to esophageal adenocarcinoma (EAC). This study was aimed at using bioinformatics analysis to identify methylation-driven genes (MDGs) in EAC patients and establish a risk model as a biological indicator of EAC prognosis.

Method

Downloaded EAC DNA methylation, transcriptome, and related clinical data from TCGA database. MethylMix was used to identify MDGs. R package clusterProfiler and the ConsensusPathDB online database were used to analyze the rich functions and pathways of these MDGs. The prognostic risk model was established by univariate Cox regression, Lasso regression, and multivariate Cox regression analysis. Finally each MDG in the model were carried out through the survival R package.

Results

A total of 273 MDGs were identified, which were enriched in transcriptional regulation and embryonic organ morphogenesis. Cox regression analysis established a risk model consisting of GPBAR1, OLFM4, FOXI2, and CASP10. In addition, further survival analysis revealed that OLFM4 and its two related sites were significantly related to the EAC patients' survival.

Conclusion

In summary, this study used bioinformatics methods to identify EAC MDGs and established a reliable risk prognosis model. It provided potential biomarkers for the early treatment and prognosis evaluation of EAC.