Screening of Methylation Gene Sites as Prognostic Signature in Lung Adenocarcinoma

DNA methylation analysis in plasma for early diagnosis in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) represents the most prevalent type of lung cancer. SHOX2 and RASSF1A methylation have been identified as important biomarkers for diagnosis and prognosis of lung cancer. Bronchoalveolar lavage fluid (BALF) exhibits good specificity and sensitivity in diagnosing pulmonary diseases, but its acquisition is challenging and may cause discomfort to patients. In clinical, plasma samples are more convenient to obtain than BALF; however, there is little research on the concurrent detection of SHOX2 and RASSF1A methylation in plasma. This study aims to assess the diagnostic value of a combined promoter methylation assay for SHOX2 and RASSF1A in early-stage LUAD using plasma samples.

METHODS

BALF and blood samples were obtained from 36 early-stage LUAD patients, with a control group of nineteen non-tumor individuals. The promoter methylation levels of SHOX2 and RASSF1A in all subjects were assessed using the human SHOX2 and RASSF1A gene methylation kit.

RESULTS

The methylation detection rate of SHOX2 and RASSF1A in plasma was 61.11%, slightly lower than that in BALF (66.7%). The Chi-square test revealed no significant difference in the methylation rate between BALF and plasma (P > 0.05). The area under the receiver operating characteristic (ROC) curve analysis for blood was 0.806 (95% CI, 0.677 to 0.900), while for BALF it was 0.781 (95% CI, 0.649 to 0.881). Additionally, we conducted an analysis on the correlation between SHOX2 and RASSF1A methylation levels in plasma with gender, age, tumor differentiation, pathologic classification, and other clinicopathological variables; however, no significant correlations were observed.

CONCLUSIONS

Measurement of SHOX2 and RASSF1A methylation for early diagnosis of LUAD can be achieved with high sensitivity and specificity by using plasma as a substitute for BALF samples.

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.

A gene signature driven by abnormally methylated DEGs was developed for TP53 wild-type ovarian cancer samples by integrative omics analysis of DNA methylation and gene expression data

Background

Integrated omics analysis based on transcriptome and DNA methylation data combined with machine learning methods is very promising for the diagnosis, prognosis, and classification of cancer. In this study, the DNA methylation and gene expression data of ovarian cancer (OC) were analyzed to identify abnormally methylated differentially expressed genes (DEGs), screen potential therapeutic agents for OC, and construct a risk model based on the abnormally methylated DEGs to predict patient prognosis.

Methods

The gene expression and DNA methylation data of primary OC samples with tumor protein 53 (TP53) wild-type and normal samples were obtained from The Cancer Genome Atlas (TCGA) database. DEGs with aberrant methylation were analyzed by screening the intersection between DEGs and differentially methylated genes (DMGs). We attempted to search for potential drugs targeting DEGs with aberrant methylation by employing a network medicine framework. A gene signature based on the DEGs with aberrant methylation was constructed by regularized least absolute shrinkage and selection operator (LASSO) regression analysis.

Results

A total of 440 aberrant methylated DEGs were screened. Based on their gene expression profiles and methylation data from different regions, the results of both discriminative pattern recognition analysis and principal component analysis (PCA) showed a significant separation between tumor tissue and healthy ovarian tissue. In total, 126 potential therapeutic drugs were identified for OC by network-based proximity analysis. Five genes were identified in 440 aberrant methylated DEGs, which formed an aberrant methylated DEGs-driven gene signature. This signature could significantly distinguish the different overall survivals (OS) of OC patients and showed better predictive performance in both the training and validation sets.

Conclusions

In this study, the DNA methylation and gene expression data of OC were analyzed to identify abnormally methylated DEGs and potential therapeutic drugs, and a gene signature based on five aberrant methylation DEGs was constructed, which could better predict the risk of death in patients.

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.

Identification and Validation of a Prognostic Signature Based on Methylation Profiles and Methylation-Driven Gene DAB2 as a Prognostic Biomarker in Differentiated Thyroid Carcinoma

Recurrence is the major death cause of differentiated thyroid carcinoma (DTC), and a better understanding of recurrence risk at early stage may lead to make the optimal medical decision to improve patients’ prognosis. The 2015 American Thyroid Association (ATA) risk stratification system primary based on clinic-pathologic features is the most commonly used to describe the initial risk of persistent/recurrent disease. Besides, multiple prognostics models based on multigenes expression profiles have been developed to predict the recurrence risk of DTC patients. Recent evidences indicated that aberrant DNA methylation is involved in the initiation and progression of DTC and can be useful biomarkers for clinical diagnosis and prognosis prediction of DTC. Therefore, there is a need for integrating gene methylation feature to assess the recurrence risk of DTC. Gene methylation profile from The Cancer Genome Atlas (TCGA) was used to construct a recurrence risk model of DTC by successively performed univariate Cox regression, LASSO regression, and multivariate Cox regression. Two Gene Expression Omnibus (GEO) methylation cohorts of DTC were utilized to validate the predictive value of the methylation profiles model as external cohort by receiver operating characteristic (ROC) curve and survival analysis. Besides, CCK-8, colony-formation assay, transwell, and scratch-wound assay were used to investigate the biological significance of critical gene in the model. In our study, we constructed and validated a prognostic signature based on methylation profiles of SPTA1, APCS, and DAB2 and constructed a nomogram based on the methylation-related model, age, and AJCC_T stage that could provide evidence for the long-term treatment and management of DTC patients. Besides, in vitro experiments showed that DAB2 inhibited proliferation, colony-formation, and migration of BCPAP cells and the gene set enrichment analysis and immune infiltration analysis showed that DAB2 may promote antitumor immunity in DTC. In conclusion, promoter hypermethylation and loss expression of DAB2 in DTC may be a biomarker of unfavorable prognosis and poor response to immune therapy.

NUF2 Expression Promotes Lung Adenocarcinoma Progression and Is Associated With Poor Prognosis

Aberrant expression of the gene encoding the Ndc80 kinetochore complex component (NUF2) reportedly contributes to the progression of several human cancers. However, the functional roles of NUF2 and their underlying mechanisms in lung adenocarcinoma (LUAD) are largely unknown. The current study aimed to investigate the role of NUF2 in LUAD tumorigenesis. Here, TCGA, ONCOMINE, the Human Protein Atlas, UALCAN, and the results of our cohort were used to analyze the expression of NUF2 in LUAD. A Kaplan–Meier analysis and univariate and multivariate Cox regression analyses were performed to estimate the prognostic values of NUF2 expression in the Cancer Genome Atlas cohort. We studied the effects of NUF2 expression on proliferation, migration, invasion, and tumor growth using LUAD cell lines. Gene set enrichment analysis (GSEA) was used to analyze the pathways and biological function enrichment of NUF2 in LUAD. The ssGSEA database was used to analyze the relationship between NUF2 expression and immune cell infiltration in LUAD. Results revealed elevated expression of NUF2 in LUAD specimens. Patients overexpressing NUF2 had poor prognoses relative to those with low NUF2 expression. Knockdown of NUF2 suppressed the proliferation, migration, invasion, epithelial-mesenchymal transition, and colony formation of LUAD cells. Moreover, NUF2 knockdown induced cell cycle arrest at the G0/G1 phase. Gene Ontology and GSEA analyses suggested that NUF2 may be involved in immunity, proliferation, and apoptosis-related pathways. NUF2 overexpression was positively correlated with differential immune cell infiltration. In conclusion, NUF2 expression was associated with the clinical phenotype of LUAD and hence has potential implications in LUAD treatment.

Current Adenosinergic Therapies: What Do Cancer Cells Stand to Gain and Lose?

A key objective in immuno-oncology is to reactivate the dormant immune system and increase tumour immunogenicity. Adenosine is an omnipresent purine that is formed in response to stress stimuli in order to restore physiological balance, mainly via anti-inflammatory, tissue-protective, and anti-nociceptive mechanisms. Adenosine overproduction occurs in all stages of tumorigenesis, from the initial inflammation/local tissue damage to the precancerous niche and the developed tumour, making the adenosinergic pathway an attractive but challenging therapeutic target. Many current efforts in immuno-oncology are focused on restoring immunosurveillance, largely by blocking adenosine-producing enzymes in the tumour microenvironment (TME) and adenosine receptors on immune cells either alone or combined with chemotherapy and/or immunotherapy. However, the effects of adenosinergic immunotherapy are not restricted to immune cells; other cells in the TME including cancer and stromal cells are also affected. Here we summarise recent advancements in the understanding of the tumour adenosinergic system and highlight the impact of current and prospective immunomodulatory therapies on other cell types within the TME, focusing on adenosine receptors in tumour cells. In addition, we evaluate the structure- and context-related limitations of targeting this pathway and highlight avenues that could possibly be exploited in future adenosinergic therapies.

Hsa_circ_0001361 facilitates the progress of lung adenocarcinoma cells via targeting miR-525-5p/VMA21 axis

Background

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with high recurrence rate and fatality. Circ_0001361 has been recognized as key regulators in various malignancies, but its roles in LUAD remain ambiguous.

Methods

Circ_0001361, miR-525-5p, and VMA21 levels were assessed by RT-qPCR. The growth and metastasis of LUAD cells were detected by MTT, colony formation, wound scratch, and transwell assays, respectively. The interaction between circ_0001361/VMA21 and miR-525-5p was detected by dual luciferase, RNA immunoprecipitation, and RNA pull-down assays. VMA21 protein level was detected by Western blotting. Nude mouse xenograft model was established to determine the role of circ_0001361 in tumor growth in vivo.

Results

Circ_0001361 was up-regulated, while miR-525-5p was down-regulated in LUAD tissues and cells. Functional experiments demonstrated that circ_0001361 drove LUAD cell growth and metastasis. Mechanistically, circ_0001361 functioned as a sponge of miR-525-5p to up-regulate downstream target VMA21 level. MiR-525-5p/VMA21 axis was involved in circ_0001361-mediated malignant phenotypes of LUAD cells. Finally, inhibition of circ_0001361 restrained in vivo xenograft tumor growth via regulating miR-525-5p/VMA21 axis.

Conclusion

Our findings elucidate that circ_0001361 facilitates the tumorigenesis and development of LUAD through miR-525-5p/VMA21 axis, providing evidence for circ_0001361 as a potential prognosis biomarker and therapeutic target for clinical treatment of LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03045-4.