DNA methylation profiles of bronchoscopic biopsies for the diagnosis of lung cancer

Goldmann, Torsten; Schmitt, Bernhard; Müller, Julia; Kröger, Maren; Scheufele, Swetlana; Marwitz, Sebastian; Nitschkowski, Dörte; Schneider, Marc A.; Meister, Michael; Muley, Thomas; Thomas, Michael; Kügler, Christian; Rabe, Klaus F.; Siebert, Reiner; Reck, Martin; Ammerpohl, Ole

doi:10.1186/s13148-021-01024-6

Cited by 12 publications

(9 citation statements)

References 38 publications

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“…To test the stability and extendibility, we applied this computational framework to external data from NCBI Gene Expression Omnibus (GEO) database, under accession GSE90496 [31], GSE155207 [32], GSE158075 [33], and GSE164988. The brain tumours DNA methylation dataset (GSE90496) includes 2801 samples, meanwhile 294 of these samples (294/2801) were selected out, the disease stage of which is initially operation, the age distributed in range of 25 to 70, and the WHO grade is I, II or III.…”

Section: Resultsmentioning

confidence: 99%

“…GSE155207 [32], GSE158075 [33], and GSE164988. The brain tumours DNA methylation dataset (GSE90496) includes 2801 samples, meanwhile 294 of these samples (294/2801) were selected out, the disease stage of which is initially operation, the age distributed in range of 25 to 70, and the WHO grade is I, II or III.…”

Section: External Validation With Tissue Dna Methylation Datamentioning

confidence: 99%

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A Self-attention Graph Convolutional Network for Precision Multi-tumour Early Diagnostics with DNA Methylation Data

Jiang

Wang

et al. 2022

Preprint

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DNA methylation data-based precision tumour early diagnostics is emerging as the state of the art for molecular tumour recognition, which could capture the signals of cancer occurrence 3~5 years in advance and clinically more homogenous groups. However, the sensitive of early detection for many tumors is about 30%, which needs to be greatly improved. Nevertheless, on the basis of the whole genome bisulfite sequencing methylation data, a comprehensive characterisation of the entire molecular genetic landscape of the tumor as well as the subtle differences between different tumours could be identified. With the accumulation of methylation data, high performance deep learning models that considering and modeling more unbiased information need to be developed. According to the above analysis, we designed a pipeline to investigate genome-wide DNA methylation patterns for precision multi-tumour early diagnostics. We proposed a graph convolutional network considering the attention mechanism to dissect DNA methylation heterogeneity of different cancer types. The attention mechanism in the graph convolutional network architecture could detect the cancer signals from genome-wide methylated molecular interactions, improving the robustness and sensitivity of multi-tumor classification. Experimental results demonstrates the feasibility of precision multi-tumour early diagnostics with the DNA methylation data. The workflow presented here is very useful for tumor classification, which highly relevant for the future blood diagnosis and treatment of the tumour.

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Section: Resultsmentioning

confidence: 99%

Section: External Validation With Tissue Dna Methylation Datamentioning

confidence: 99%

A Self-attention Graph Convolutional Network for Precision Multi-tumour Early Diagnostics with DNA Methylation Data

Jiang

Wang

et al. 2022

Preprint

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“…However, considerable progress has been made with different methods to diagnose lung cancer. The diagnostic accuracy of lung cancer was 97.3% when some scholars studied the lung cancer tissue sections under weak supervision [ 47 , 48 ]. It clearly demonstrates that this four-differentially expressed MMRG-signature model has great potential for lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis of Alteration Landscape and Its Clinical Significance of Mitochondrial Energy Metabolism Pathway‐Related Genes in Lung Cancers

Zhang

Kong

et al. 2021

Oxidative Medicine and Cellular Longevity

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Background. Mitochondria are the energy factories of cells. The abnormality of mitochondrial energy metabolism pathways is closely related to the occurrence and development of lung cancer. The abnormal genes in mitochondrial energy metabolism pathways might be the novel targets and biomarkers to diagnose and treat lung cancers. Method. Genes in major mitochondrial energy metabolism pathways were obtained from the KEGG database. The transcriptomic, mutation, and clinical data of lung cancers were obtained from The Cancer Genome Atlas (TCGA) database. Genes and clinical biomarkers were mined that affected lung cancer survival. Gene enrichment analysis was performed with ClusterProfiler and the gene set enrichment analysis (GSEA). STRING database and Cytoscape were used for protein-protein interaction (PPI) analysis. The diagnostic biomarker pattern of lung cancer was optimized, and its accuracy was verified with 10-fold cross-validation. The four genes screened by logistic regression model were verified by western blot in 5 pairs of lung cancer specimens collected in hospital. Results. In total, 188 mitochondrial energy metabolism pathway-related genes (MMRGs) were included in this study. GSEA analysis found that MMRGs in the lung cancer group were mainly enriched in the metabolic pathway of oxidative phosphorylation and electron respiratory transport chain compared to the control group. Age did not affect the mutation frequency of MMRGs. Comparative analysis of these 188 MMRGs identified 43 differentially expressed MMRGs (24 upregulated and 19 downregulated) in the lung cancer group compared to the control group. The survival analysis of these 43 differentially expressed MMRGs found that the survival time was better in the low-expressed GAPDHS group than that in the high-expressed GAPDHS group of lung cancers. The advanced age, high expression of GAPDHS, low expressions of ACSBG1 and CYP4A11, and ACOX3 mutation were biomarkers of poor prognosis in lung cancers. PPI analysis showed that proteins such as GAPDH and GAPDHS interacted with many proteins in mitochondrial metabolic pathways. A four-MMRG-signature model ( y = 0.0069 ∗ ACADL − 0.001 ∗ ALDH 18 A 1 − 0.0405 ∗ CPT 1 B + 0.0008 ∗ PPARG − 1.625 ) was established to diagnose lung cancer with the accuracy up to 98.74%, AUC value up to 0.992, and a missed diagnosis rate of only 0.6%. Western blotting showed that ALDH18A1 and CPT1B proteins were significantly overexpressed in the lung cancer group ( p < 0.05 ), and ACADL and PPARG proteins were slightly underexpressed in the lung cancer group ( p < 0.05 ), which were consistent with the results of their corresponding mRNA expressions. Conclusion. Mitochondrial energy metabolism pathway alterations are the important hallmarks of lung cancer. Age did not increase the risk of MMRG mutation. High expression of GAPDHS, low expression of ACSBG1, low expression of CYP4A11, mutated ACOX3, and old age predict a poor prognosis of lung cancer. Four differentially expressed MMRGs (ACADL, ALDH18A1, CPT1B, and PPARG) established a logistic regression model, which could effectively diagnose lung cancer. At the protein level, ALDH18A1 and CPT1B were significantly upregulated, and ACADL and PPARG were slightly underexpressed, in the lung cancer group compared to the control group, which were consistent with the results of their corresponding mRNA expressions.

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“…A study of breast tissue from healthy women found differences in PM20D1 methylation according to ethnic origin [97] . Additionally, in an epigenome-wide association study for COVID-19 severity, the PM20D1 gene was found to be differentially methylated [98] .…”

Section: Other Phenotypesmentioning

confidence: 99%

The many roles of the Alzheimer-associated gene PM20D1

Garro-Núñez¹,

Mora-Cubillo²,

Fonseca-Bone³

et al. 2022

J Transl Genet Genom

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PM20D1 is a little studied enzyme until recently, belonging to the mammalian M20 peptidase family, which catalyzes both the synthesis and hydrolysis of N-acyl amino acids (NAAs). NAAs are bioactive lipids biosynthesized from free fatty acids and free amino acids. These molecules have been associated with many biological functions; however, most of the biochemical mechanisms have not yet been described. The best-known biochemical mechanism is the one involved in thermogenesis, which also has implications for reactive oxygen species levels and cell preservation. In the last few years, genetic variation in PM20D1, as well as changes in its methylation and expression levels, have been reported to be associated with several disease phenotypes, including Alzheimer’s disease. In this review, we explore the current knowledge regarding the PM20D1 gene, including aspects such as its biology, potential functions, regulation of its expression, and role in different phenotypes such as Alzheimer’s disease, obesity, Parkinson’s disease, and several other disorders.

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DNA methylation profiles of bronchoscopic biopsies for the diagnosis of lung cancer

Cited by 12 publications

References 38 publications

A Self-attention Graph Convolutional Network for Precision Multi-tumour Early Diagnostics with DNA Methylation Data

A Self-attention Graph Convolutional Network for Precision Multi-tumour Early Diagnostics with DNA Methylation Data

Comprehensive Analysis of Alteration Landscape and Its Clinical Significance of Mitochondrial Energy Metabolism Pathway‐Related Genes in Lung Cancers

The many roles of the Alzheimer-associated gene PM20D1

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