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

Serum Metabolomic Profiles for Distinguishing Lung Cancer From Pulmonary Tuberculosis: Identification of Rapid and Noninvasive Biomarker

BACKGROUND

Pulmonary tuberculosis (PTB) and lung cancer (LC) have similar clinical symptoms and atypical imaging findings, which are easily misdiagnosed. There is an urgent need for a noninvasive and accurate biomarker to distinguish LC from PTB.

METHODS

A total of 694 subjects were enrolled and divided into discovery set (n = 122), identification set (n = 214), and validation set (n = 358). Metabolites were identified by multivariate and univariate analyses. Receiver operating characteristic curve were used to evaluate the diagnostic efficacy of biomarkers.

RESULTS

Seven metabolites were identified and validated. Phenylalanylphenylalanine for distinguishing LC from PTB yielded an area under the curve of 0.89, sensitivity of 71%, and specificity of 92%. It also showed good diagnostic abilities in discovery set and identification set. Compared with that in healthy volunteers (median [interquartile range], 1.57 [1.01, 2.34] μg/mL), it was elevated in LC (4.76 [2.74, 7.08] μg/mL; ratio of median, [ROM] = 3.03, P < .01) and reduced in PTB (1.06 [0.51, 2.09] μg/mL; ROM = 0.68, P < .05).

CONCLUSIONS

The metabolomic profile of LC and PTB was described and a key biomarker identified. We produced a rapid and noninvasive method to supplement existing clinical diagnostic examinations for distinguishing LC from PTB.

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

DNA methylation-based precision tumor early diagnostics is emerging as state-of-the-art technology that could capture early cancer signs 3 ~ 5 years in advance, even for clinically homogenous groups. Presently, the sensitivity of early detection for many tumors is ~ 30%, which needs significant improvement. Nevertheless, based on the genome-wide DNA methylation data, one could comprehensively characterize tumors' entire molecular genetic landscape and their subtle differences. Therefore, novel high-performance methods must be modeled by considering unbiased information using excessively available DNA methylation data. To fill this gap, we have designed a computational model involving a self-attention graph convolutional network and multi-class classification support vector machine to identify the 11 most common cancers using DNA methylation data. The self-attention graph convolutional network automatically learns key methylation sites in a data-driven way. Then, multi-tumor early diagnostics is realized by training a multi-class classification support vector machine based on the selected methylation sites. We evaluated our model's performance through several data sets of experiments, and our results demonstrate the effectiveness of the selected key methylation sites, which are highly relevant for blood diagnosis. The pipeline of the self-attention graph convolutional network based computational framework.

Investigating and modeling the differential DNA methylation for early lung adenocarcinoma diagnosis

Background: Aberrant DNA methylations serve as rich sources of diagnostic biomarkers, but a further improvement in their accuracy and clinical utility is warranted. Methods: Large panel bisulfite sequencing were performed on paired normal and stage I/IV tumors from 226 lung adenocarcinoma cancer patients to characterize the differentially methylated regions (DMRs). Results: Random forest model achieved high prediction accuracy (sensitivity 96% and specificity 97.56%) to separate normal controls from both early and advanced cancer samples, which is superior to most previous prediction models tested in lung adenocarcinoma. Conclusion: Our results suggest that combining the random forest model with targeted bisulfite sequencing have great clinical potentials to accurately predict and early diagnose lung adenocarcinoma during cancer screening.

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

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∗ALDH18A1 - 0.0405∗CPT1B + 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.