Dataset Information

Cell-free DNA methylation profile potential in the diagnosis of lung squamous cell carcinoma.

ABSTRACT:

Background

Aberrant methylation plays an essential role in early cancer development. In this study, we investigated methylation patterns in lung squamous cell carcinoma (LUSC) and matched non-tumor tissue and plasma samples to evaluate the potential of these patterns in the diagnosis of LUSC.

Methods

The study group included 49 patients with stage I-III LUSC. We collected resected tumor tissue, paired peritumoral tissue, distant normal tissue, and corresponding plasma samples. A bespoke lung cancer bisulfite sequencing panel was used to profile the methylation level. Another 48 healthy volunteers provided control plasma samples.

Results

Peritumoral and distant normal tissues presented similar methylation signatures, distinct from those in tumor tissue samples. A comparison of methylation profiles led to the identification of 871 tumor-specific differentially methylated blocks, including 847 hypermethylated and 24 hypomethylated blocks (adjusted P value <0.05). All top-ranked blocks were tumor-related. Tissue samples were analyzed for field cancerization to identify progressively aggravating aberrant methylations during tumor initiation and development. The analysis revealed that 221 blocks presented a stepwise increase in methylation levels, while seven blocks presented a stepwise decrease in methylation pattern as the sampling drew nearer to the tumor. The malignant contaminated ratio (MCR) confirmed the presence of distinct methylation patterns between tumor and peritumoral tissue samples. We then constructed a diagnostic panel using a combined diagnostic score of cell-free DNA (cfDNA) that showed high sensitivity and specificity. The healthy controls had a significantly lower combined diagnostic score (cd-score) than LUSC patients. Additionally, based on the methylation profiles, LUSC could be classified into two subgroups, C1 and C2. The methylation profile of the C2 group was not distinct from the healthy controls, which had a significantly lower cd-score than did the C1 group.

Conclusions

LUSC-specific methylation patterns could potentially discriminate between peritumoral tissue, distant normal tumor tissue, and tumor tissues. This preliminary study also supported the potential utility of cfDNA methylation analysis in diagnosing LUSC.

SUBMITTER: Du C

PROVIDER: S-EPMC10894382 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Cell-free DNA methylation profile potential in the diagnosis of lung squamous cell carcinoma.

Du Chengli C Cai Jin J Tang Jie J Chen Yunhao Y Díaz-Peña Roberto R Tomita Yusuke Y Jassem Jacek J Zhao Jiangang J Zheng Difang D Tu Zhengliang Z

Journal of thoracic disease 20240129 1

<h4>Background</h4>Aberrant methylation plays an essential role in early cancer development. In this study, we investigated methylation patterns in lung squamous cell carcinoma (LUSC) and matched non-tumor tissue and plasma samples to evaluate the potential of these patterns in the diagnosis of LUSC.<h4>Methods</h4>The study group included 49 patients with stage I-III LUSC. We collected resected tumor tissue, paired peritumoral tissue, distant normal tissue, and corresponding plasma samples. A b ...[more]

PMID: 38410586

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Project description:BackgroundAberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC).MethodsDifferentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples.ResultsBased on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939-0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758-0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905-0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml-1) from high risk population (AUC=0.93; 95% CI 0.892-0.969).ConclusionsWe have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort.Trial registrationThe study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(# NCT04383353 ).

Project description:BackgroundLung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.MethodsThe genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.ResultsSeveral statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients' overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies.ConclusionsWe constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.

Project description:BackgroundDeoxyribose nucleic acid (DNA) methylation is an important epigenetic modification that plays an important role in the occurrence and development of tumors. Identifying key methylation-driven genes that affect the prognosis of lung squamous cell carcinoma (LUSC) can provide direction for targeted therapy research.Methods and resultsMethylation and RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA). The MethylMix package was used to integrate and analyze the methylation and gene expression data from TCGA, and the LUSC dataset (GSE37745) was downloaded from GEO for validation. Forty-five DNA-methylation-driven genes (MDGs) were obtained, and 3 genes (TRIM61, SMIM22, and ALDH7A1) were significantly associated with survival by using univariate and multivariate Cox regression. A risk model was constructed. KM analysis showed that patients with high-risk scores had poor survival. A nomination plot for prognosis prediction of LUSC patients was constructed, which showed a good predictive efficiency for tumor prognosis. The high expression of ALDH7A1 was an independent risk factor for poor prognosis in LUSC. The expression of ALDH7A1 in LUSC was negatively correlated with its methylation status (COR = -0.655). GSEA analysis showed that high expression of ALDH7A1 could activate multiple signaling pathways (JAK-STAT signaling pathway and mTOR signaling pathway). In vitro cell experiments confirmed that in LUSC, silencing ALDH7A1 could inhibit tumor progression, while overexpression of ALDH7A1 could promote tumor progression.ConclusionOur results indicated that ALDH7A1, a newly discovered MDG in LUSC, could act as an independent prognostic factor for OS in LUSC, with the potential to become a potential target for LUSC diagnosis and treatment. High expression of ALDH7A1 in LUSC could promote the occurrence and development of tumors. Signaling pathways, such as JAK-STAT and mTOR signaling pathways, might regulate the high expression of ALDH7A1.

Dataset Information

Cell-free DNA methylation profile potential in the diagnosis of lung squamous cell carcinoma.

Background

Methods

Results

Conclusions

Publications

Cell-free DNA methylation profile potential in the diagnosis of lung squamous cell carcinoma.

Similar Datasets

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