Accurate detection of early-stage lung cancer using a panel of circulating cell-free DNA methylation biomarkers

Biogenesis and roles of tRNA queuosine modification and its glycosylated derivatives in human health and diseases

Various types of post-transcriptional modifications contribute to physiological functions by regulating the abundance and function of RNAs. In particular, tRNAs have the widest variety and largest number of modifications, with crucial roles in protein synthesis. Queuosine (Q) is a characteristic tRNA modification with a 7-deazaguanosine core structure bearing a bulky side chain with a cyclopentene group. Q and its derivatives are found in the anticodon of specific tRNAs in both bacteria and eukaryotes. In metazoan tRNAs, Q is further glycosylated with galactose or mannose. The functions of these glycosylated Qs remained unknown for nearly half a century since their discovery. Recently, our group identified the glycosyltransferases responsible for these tRNA modifications and elucidated their biological roles. We, here, review the biochemical and physiological functions of Q and its glycosylated derivatives as well as their associations with human diseases, including cancer and inflammatory and neurological diseases.

Dissection of the cell communication interactions in lung adenocarcinoma identified a prognostic model with immunotherapy efficacy assessment and a potential therapeutic candidate gene ITGB1

Background

The tumor microenvironment (TME) in lung adenocarcinoma (LUAD) influences tumor progression and immunosuppressive phenotypes through cell communication. We aimed to decipher cellular communication and molecular patterns in LUAD.

Methods

We analyzed scRNA-seq data from LUAD patients in multiple cohorts, revealing complex cell communication networks within the TME. Using cell chat analysis and COSmap technology, we inferred LUAD's spatial organization. Employing the NMF algorithm and survival screening, we identified a cell communication interactions (CCIs) model and validated it across various datasets.

Results

We uncovered intricate cell communication interactions within the TME, identifying three LUAD patient subtypes with distinct prognosis, clinical characteristics, mutation status, expression patterns, and immune infiltration. Our CCI model exhibited robust performance in prognosis and immunotherapy response prediction. Several potential therapeutic targets and agents for high CCI score patients with immunosuppressive profiles were identified. Machine learning algorithms pinpointed the novel candidate gene ITGB1 and validated its role in LUAD tumor phenotype in vitro.

Conclusion

Our study elucidates molecular patterns and cell communication interactions in LUAD as effective biomarkers and predictors of immunotherapy response. Targeting cell communication interactions offers novel avenues for LUAD immunotherapy and prognostic evaluations, with ITGB1 emerging as a promising therapeutic target.

A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology

INTRODUCTION

Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer.

METHODS

This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models.

RESULTS

Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001).

CONCLUSIONS

In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.

Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis

Detecting aberrant cell-free DNA (cfDNA) methylation is a promising strategy for lung cancer diagnosis. In this study, our aim is to identify methylation markers to distinguish patients with lung cancer from healthy individuals. Additionally, we sought to develop a deep learning model incorporating cfDNA methylation and fragment size profiles. To achieve this, we utilized methylation data collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Then we generated methylated DNA immunoprecipitation sequencing and genome-wide Enzymatic Methyl-seq (EM-seq) form lung cancer tissue and plasma. Using these data, we selected 366 methylation markers. A targeted EM-seq panel was designed using the selected markers, and 142 lung cancer and 56 healthy samples were produced with the panel. Additionally, cfDNA samples from healthy individuals and lung cancer patients were diluted to evaluate sensitivity. Its lung cancer detection performance reached an accuracy of 81.5% and an area under the receiver operating characteristic curve of 0.87. In the serial dilution experiment, we achieved tumor fraction detection of 1% at 98% specificity and 0.1% at 80% specificity. In conclusion, we successfully developed and validated a combination of methylation panel and a deep learning model that can distinguish between patients with lung cancer and healthy individuals.

Identification and validation of PCDHGA12 and PRRX1 methylation for detecting lung cancer in bronchial washing sample

Bronchoscopy is a frequently used initial diagnostic procedure for patients with suspected lung cancer (LC). Cytological examinations of bronchial washing (BW) samples obtained during bronchoscopy often yield inconclusive results regarding LC diagnosis. The present study aimed to identify molecular biomarkers as a non-invasive method for LC diagnosis. Aberrant DNA methylation is used as a useful biomarker for LC. Therefore, microarray-based methylation profiling analyses on 13 patient-matched tumor tissues at stages I-III vs. non-tumor tissues were performed, and a group of highly differentially methylated genes was identified. A subsequent analysis using bisulfite-pyrosequencing with additional tissues and cell lines revealed six methylated genes [ADAM metallopeptidase with thrombospondin type 1 motif 20, forkhead box C2 (mesenchyme forkhead 1), NK2 transcription factor related, locus 5 (Drosophila), oligodendrocyte transcription factor 3, protocadherin γ subfamily A 12 (PCDHGA12) and paired related homeobox 1 (PRRX1)] associated with LC. Next, a highly sensitive and accurate detection method, linear target enrichment-quantitative methylation-specific PCR in a single closed tube, was applied for clinical validation using BW samples from patients with LC (n=68) and individuals with benign diseases (n=33). PCDHGA12 and PRRX1 methylation were identified as the best-performing biomarkers to detect LC. The two-marker combination showed a sensitivity of 82.4% and a specificity of 87.9%, with an area under the curve of 0.891. Notably, the sensitivity for small cell LC was 100%. The two-marker combination had a positive predictive value of 93.3% and a negative predictive value of 70.7%. The sensitivity was higher than that of cytology, which only had a sensitivity of 50%. The methylation status of the two-marker combination showed no association with sex, age or stage, but was associated with tumor location and histology. In conclusion, the present study showed that the regulatory regions of PCDHGA12 and PRRX1 are highly methylated in LC and can be used to detect LC in BW specimens as a diagnostic adjunct to cytology in clinical practice.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Detection of the DNA methylation of seven genes contribute to the early diagnosis of lung cancer

BACKGROUND

Low-dose Computed Tomography (CT) is used for the detection of pulmonary nodules, but the ambiguous risk evaluation causes overdiagnosis. Here, we explored the significance of the DNA methylation of 7 genes including TAC1, CDO1, HOXA9, ZFP42, SOX17, RASSF1A and SHOX2 in the blood cfDNA samples in distinguishing lung cancer from benign nodules and healthy individuals.

METHOD

A total of 149 lung cancer patients [72 mass and 77 ground-glass nodules (GGNs)], 5 benign and 48 healthy individuals were tested and analyzed in this study. The lasso-logistic regression model was built for distinguishing cancer and control/healthy individuals or IA lung cancer and non-IA lung cancer cases.

RESULTS

The positive rates of methylation of 7 genes were higher in the cancer group as compared with the healthy group. We constructed a model using age, sex and the ΔCt value of 7 gene methylation to distinguish lung cancer from benign and healthy individuals. The sensitivity, specificity and AUC (area under the curve) were 86.7%, 81.4% and 0.891, respectively. Also, we assessed the significance of 7 gene methylation together with patients' age and sex in distinguishing of GGNs type from the mass type. The sensitivity, specificity and AUC were 77.1%, 65.8% and 0.753, respectively. Furthermore, the methylation positive rates of CDO1 and SHOX2 were different between I-IV stages of lung cancer. Specifically, the positive rate of CDO1 methylation was higher in the non-IA group as compared with the IA group.

CONCLUSION

Collectively, this study reveals that the methylation of 7 genes has a big significance in the diagnosis of lung cancer with high sensitivity and specificity. Also, the 7 genes present with certain significance in distinguishing the GGN type lung cancer, as well as different stages.

DNA methylation-mediated repression of microRNA-410 promotes the growth of human glioma cells and triggers cell apoptosis through its interaction with STAT3

This study's purpose was to confirm the observed underexpression of miRNA-410 in glioma tissues and several glioma cells by Quantitative RT-PCR. Our findings suggest that epigenetic alterations occurring at the promoter region of miR-410 may be responsible for the reduced expression of miR-410 in glioma. The occurrence of DNA methylation in the miR-410 promoter was verified to be more prevalent through glioma tissues contrasted to adjacent non-tumor brain tissues through the utilization of methylation-specific PCR and CpG bisulfite sequencing sites in the miR-410 promoter region. Accordantly, miR-410 expression in glioma cell lines was observed to be significantly lesser in comparison to that of the human fetal glial cell line. In addition, it was demonstrated through gain- and loss-of-function investigations that miR-410 exerts significant regulation over cell growth, cell cycle development, and glioma cell apoptosis. The findings of the Luciferase reporter assay and western blot analysis indicate that miR-410 has a direct effect on the 3'-UTR of signal transducer and activator of transcription 3 (STAT3), thereby inhibiting its expression within glioma cells. Besides, our clinical investigation indicates a negative association between miR-410 expression and STAT3 within the glioma tissues of humans. In aggregate, the data provided in this investigation indicates that miR-410 is subjected to underexpression via DNA methylation. Furthermore, it has been observed to perform its function as a tumor suppressor in glioma cells through direct targeting of STAT3. The previously mentioned results could potentially have significant implications for the advancement of a new therapeutic approach for treating glioma.