Diagnosis of pulmonary nodules by DNA methylation analysis in bronchoalveolar lavage fluids

Evaluation of a three-gene methylation model for correlating lymph node metastasis in postoperative early gastric cancer adjacent samples

Background

Lymph node metastasis (LNM) has a profound impact on the treatment and prognosis of early gastric cancer (EGC), yet the existing evaluation methods lack accuracy. Recent research has underscored the role of precancerous lesions in tumor progression and metastasis. The objective of this study was to utilize the previously developed EGC LNM prediction model to further validate and extend the analysis in paired adjacent tissue samples.

Methods

We evaluated the model in a monocentric study using Methylight, a methylation-specific PCR technique, on postoperative fresh-frozen EGC samples (n = 129) and paired adjacent tissue samples (n = 129).

Results

The three-gene methylation model demonstrated remarkable efficacy in both EGC and adjacent tissues. The model demonstrated excellent performance, with areas under the curve (AUC) of 0.85 and 0.82, specificities of 85.1% and 80.5%, sensitivities of 83.3% and 73.8%, and accuracies of 84.5% and 78.3%, respectively. It is noteworthy that the model demonstrated superior performance compared to computed tomography (CT) imaging in the adjacent tissue group, with an area under the curve (AUC) of 0.86 compared to 0.64 (p < 0.001). Furthermore, the model demonstrated superior diagnostic capability in these adjacent tissues (AUC = 0.82) compared to traditional clinicopathological features, including ulceration (AUC = 0.65), invasional depth (AUC = 0.66), and lymphovascular invasion (AUC = 0.69). Additionally, it surpassed traditional models based on these features (AUC = 0.77).

Conclusion

The three-gene methylation prediction model for EGC LNM is highly effective in both cancerous and adjacent tissue samples in a postoperative setting, providing reliable diagnostic information. This extends its clinical utility, particularly when tumor samples are scarce, making it a valuable tool for evaluating LNM status and assisting in treatment planning.

Prediction and verification of benignancy and malignancy of pulmonary nodules based on inflammatory related biological markers

Objective

Inflammation plays an important role in the transformation of pulmonary nodules (PNs) from benign to malignant. Prediction of benignancy and malignancy of PNs is still lacking efficacy methods. Although Mayo or Brock model have been widely applied in clinical practices, their application conditions are limited. This study aims to construct a diagnostic model of PNs by machine learning using inflammation-related biological markers (IRBMs).

Methods

Inflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result.

Results

4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718.

Conclusion

SVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.

Discrimination of Lung Cancer and Benign Lung Diseases Using BALF Exosome DNA Methylation Profile

Benign lung diseases are common and often do not require specific treatment, but they pose challenges in the distinguishing of them from lung cancer during low-dose computed tomography (LDCT). This study presents a comprehensive methylation analysis using real-time PCR for minimally invasive diagnoses of lung cancer via employing BALF exosome DNA. A panel of seven epigenetic biomarkers was identified, exhibiting specific methylation patterns in lung cancer BALF exosome DNA. This panel achieved an area under the curve (AUC) of 0.97, with sensitivity and specificity rates of 88.24% and 97.14%, respectively. Each biomarker showed significantly higher mean methylation levels (MMLs) in both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) compared to non-cancer groups, with fold changes from 1.7 to 13.36. The MMLs of the biomarkers were found to be moderately elevated with increasing patient age and smoking history, regardless of sex. A strong correlation was found between the MMLs and NSCLC stage progression, with detection sensitivities of 79% for early stages and 92% for advanced stages. In the validation cohort, the model demonstrated an AUC of 0.95, with 94% sensitivity and specificity. Sensitivity for early-stage NSCLC detection improved from 88.00% to 92.00% when smoking history was included as an additional risk factor.

The signature of cancer methylation markers in maternal plasma: Factors influencing the development and application of cancer liquid biopsy assay

BACKGROUND

DNA methylation is highly correlated with cancer and embryo development, and plasma-based methylation markers have been widely used for cancer early detection. However, whether the commonly used cancer methylation markers cause "false positives" in the plasma of pregnant women has not been comprehensively evaluated.

METHODS

We conducted a case-control study from February 2021 to March 2023, which included 138 pregnant women and 44 control women. Plasma cell-free DNA (cfDNA) was isolated and bisulfite-converted, and then the methylation levels of eight methylated markers related to gastrointestinal cancer (SEPT9, SDC2, C9orf50, KCNQ5, CLIP4, TFPI2, ELMO1 and ZNF582) and three markers related to lung cancer (SHOX2, RASSF1A and PTGER4) were analyzed.

RESULTS

When comparing the plasma of pregnant women to that of control women, SEPT9, CLIP4, ZNF582, SHOX2, RASSF1A and PTGER showed significantly higher levels of methylation (p < 0.05). These positive signals originate from the placenta/fetus rather than the mother. We found no discernible difference in DNA methylation levels between fetal cfDNA fractions of < 10 % and ≥ 10 % in pregnant women (p > 0.05), while CLIP4 and PTGER4 showed high methylation levels in the assisted fertilization group compared to the natural fertilization group (p < 0.05).

CONCLUSION

Our study shows that cancer and fetus/placenta exhibit similar DNA methylation patterns, and some gastrointestinal cancer and lung cancer-related methylation markers also show positives in maternal plasma. This is an important consideration in the design and application of plasma-based cancer liquid biopsy assays.

Blood FOLR3 methylation dysregulations and heterogeneity in non-small lung cancer highlight its strong associations with lung squamous carcinoma

BACKGROUND

Non-small cell lung cancer (NSCLC) accounts for the vast majority of lung cancers. Early detection is crucial to reduce lung cancer-related mortality. Aberrant DNA methylation occurs early during carcinogenesis and can be detected in blood. It is essential to investigate the dysregulated blood methylation markers for early diagnosis of NSCLC.

METHODS

NSCLC-associated methylation gene folate receptor gamma (FOLR3) was selected from an Illumina 850K array analysis of peripheral blood samples. Mass spectrometry was used for validation in two independent case-control studies (validation I: n = 2548; validation II: n = 3866). Patients with lung squamous carcinoma (LUSC) or lung adenocarcinoma (LUAD), normal controls (NCs) and benign pulmonary nodule (BPN) cases were included. FOLR3 methylations were compared among different populations. Their associations with NSCLC clinical features were investigated. Receiver operating characteristic analyses, Kruskal-Wallis test, Wilcoxon test, logistics regression analysis and nomogram analysis were performed.

RESULTS

Two CpG sites (CpG_1 and CpG_2) of FOLR3 was significantly lower methylated in NSCLC patients than NCs in the discovery round. In the two validations, both LUSC and LUAD patients presented significant FOLR3 hypomethylations. LUSC patients were highlighted to have significantly lower methylation levels of CpG_1 and CpG_2 than BPN cases and LUAD patients. Both in the two validations, CpG_1 methylation and CpG_2 methylation could discriminate LUSC from NCs well, with areas under the curve (AUCs) of 0.818 and 0.832 in validation I, and 0.789 and 0.780 in validation II. They could also differentiate LUAD from NCs, but with lower efficiency. CpG_1 and CpG_2 methylations could also discriminate LUSC from BPNs well individually in the two validations. With the combined dataset of two validations, the independent associations of age, gender, and FOLR3 methylation with LUSC and LUAD risk were shown and the age-gender-CpG_1 signature could discriminate LUSC and LUAD from NCs and BPNs, with higher efficiency for LUSC.

CONCLUSIONS

Blood-based FOLR3 hypomethylation was shown in LUSC and LUAD. FOLR3 methylation heterogeneity between LUSC and LUAD highlighted its stronger associations with LUSC. FOLR3 methylation and the age-gender-CpG_1 signature might be novel diagnostic markers for the early detection of NSCLC, especially for LUSC.

Transcription factor LHX9 (LIM Homeobox 9) enhances pyruvate kinase PKM2 activity to induce glycolytic metabolic reprogramming in cancer stem cells, promoting gastric cancer progression

BACKGROUND

Glycolytic metabolic reprogramming is a phenomenon in which cells undergo altered metabolic patterns during malignant transformation, mainly involving various aspects of glycolysis, electron transport chain, oxidative phosphorylation, and pentose phosphate pathway. This reprogramming phenomenon can be used as one of the markers of tumorigenesis and development. Pyruvate kinase is the third rate-limiting enzyme in the sugar metabolism process by specifically catalyzing the irreversible conversion of PEP to pyruvate.

PURPOSE

This study aimed to reveal the critical mediator(s) that regulate glycolytic metabolism reprogramming in gastric cancer and their underlying molecular mechanism and then explore the molecular mechanisms by which LHX9 may be involved in regulating gastric cancer (GC) progression.

METHODS

Firstly, we downloaded the GC and glycolysis-related microarray datasets from TCGA and MSigDB databases and took the intersection to screen out the transcription factor LHX9 that regulates GC glycolytic metabolic reprogramming. Software packages were used for differential analysis, single gene predictive analysis, and Venn diagram. In addition, an enrichment analysis of the glycolytic pathway was performed. Immunohistochemical staining was performed for LHX9 and PKM2 protein expression in 90 GC patients, and the association between their expressions was evaluated by Spearman's correlation coefficient method. Three human GC cell lines (AGS, NCI-N87, HGC-27) were selected for in vitro experimental validation. Flow cytometry was utilized to determine the stem cell marker CD44 expression status in GCSCs. A sphere formation assay was performed to evaluate the sphere-forming capabilities of GCSCs. In addition, RT-qPCR and Western blot experiments were employed to investigate the tumor stem cell markers OCT4 and SOX2 expression levels in GCSCs. Furthermore, a lentiviral expression vector was constructed to assess the impact of downregulating LHX9 or PKM2 on the glycolytic metabolic reprogramming of GCSCs. The proliferation, migration, and invasion of GCSCs were then detected by CCK-8, EdU, and Transwell assays. Subsequently, the mutual binding of LHX9 and PKM2 was verified using chromatin immunoprecipitation and dual luciferase reporter genes. In vivo experiments were verified by establishing a subcutaneous transplantation tumor model in nude mice, observing the size and volume of tumors in vivo in nude mice, and obtaining fresh tissues for subsequent experiments.

RESULTS

Bioinformatics analysis revealed that LHX9 might be involved in the occurrence and development of GC through regulating glycolytic metabolism. High LHX9 expression could be used as a reference marker for prognosis prediction of GC patients. Clinical tissue assays revealed that LHX9 and PKM2 were highly expressed in GC tissues. Meanwhile, GC tissues also highly expressed glycolysis-associated protein GLUT1 and tumor cell stemness marker CD44. In vitro cellular assays showed that LHX9 could enhance its activity and induce glycolytic metabolic reprogramming in GCSCs through direct binding to PKM2. In addition, the knockdown of LHX9 inhibited PKM2 activity and glycolytic metabolic reprogramming and suppressed the proliferation, migration, and invasive ability of GCSCs. In vivo animal experiments further confirmed that the knockdown of LHX9 could reduce the tumorigenic ability of GCSCs in nude mice by inhibiting PKM2 activity and glycolytic metabolic reprogramming.

CONCLUSION

The findings suggest that both LHX9 and PKM2 are highly expressed in GCs, and LHX9 may induce the reprogramming of glycolytic metabolism through transcriptional activation of PKM2, enhancing the malignant biological properties of GCSCs and ultimately promoting GC progression.

An improved Yolov5s based on transformer backbone network for detection and classification of bronchoalveolar lavage cells

Biological tissue information of the lung, such as cells and proteins, can be obtained from bronchoalveolar lavage fluid (BALF), through which it can be used as a complement to lung biopsy pathology. BALF cells can be confused with each other due to the similarity of their characteristics and differences in the way sections are handled or viewed. This poses a great challenge for cell detection. In this paper, An Improved Yolov5s Based on Transformer Backbone Network for Detection and Classification of BALF Cells is proposed, focusing on the detection of four types of cells in BALF: macrophages, lymphocytes, neutrophils and eosinophils. The network is mainly based on the Yolov5s network and uses Swin Transformer V2 technology in the backbone network to improve cell detection accuracy by obtaining global information; the C3Ghost module (a variant of the Convolutional Neural Network architecture) is used in the neck network to reduce the number of parameters during feature channel fusion and to improve feature expression performance. In addition, embedding intersection over union Loss (EIoU_Loss) was used as a bounding box regression loss function to speed up the bounding box regression rate, resulting in higher accuracy of the algorithm. The experiments showed that our model could achieve mAP of 81.29% and Recall of 80.47%. Compared to the original Yolov5s, the mAP has improved by 3.3% and Recall by 3.67%. We also compared it with Yolov7 and the newly launched Yolov8s. mAP improved by 0.02% and 2.36% over Yolov7 and Yolov8s respectively, while the FPS of our model was higher than both of them, achieving a balance of efficiency and accuracy, further demonstrating the superiority of our model.

Bronchoalveolar Lavage Fluid-Isolated Biomarkers for the Diagnostic and Prognostic Assessment of Lung Cancer

Lung cancer is considered one of the most fatal malignant neoplasms because of its late detection. Detecting molecular markers in samples from routine bronchoscopy, including many liquid-based cytology procedures, such as bronchoalveolar lavage fluid (BALF), could serve as a favorable technique to enhance the efficiency of a lung cancer diagnosis. BALF analysis is a promising approach to evaluating the tumor progression microenvironment. BALF's cellular and non-cellular components dictate the inflammatory response in a cancer-proliferating microenvironment. Furthermore, it is an essential material for detecting clinically significant predictive and prognostic biomarkers that may aid in guiding treatment choices and evaluating therapy-induced toxicities in lung cancer. In the present article, we have reviewed recent literature about the utility of BALF analysis for detecting markers in different stages of tumor cell metabolism, employing either specific biomarker assays or broader omics approaches.

A novel DNA methylation marker to identify lymph node metastasis of colorectal cancer

Lymph node metastasis (LNM) of colorectal cancer (CRC) is an important factor for both prognosis and treatment. Given the deficiencies of conventional tests, we aim to discover novel DNA methylation markers to efficiently identify LNM status of CRC. In this study, genome-wide methylation sequencing was performed in a cohort (n=30) using fresh CRC tissue to discover differentially methylated markers. These markers were subsequently validated with fluorescence quantitative PCR in a cohort (n=221), and the optimal marker was compared to conventional diagnostic methods. Meanwhile, immunohistochemistry was used to verify the effectiveness of the antibody corresponding to this marker in a cohort (n=56). LBX2 achieved an AUC of 0.87, specificity of 87.3%, sensitivity of 75.7%, and accuracy of 81.9%, which outperformed conventional methods including imaging (CT, PET-CT) with an AUC of 0.52, CA199 with an AUC of 0.58, CEA with an AUC of 0.56. LBX2 was also superior to clinicopathological indicators including the depth of tumor invasion and lymphatic invasion with an AUC of 0.61and 0.63 respectively. Moreover, the AUC of LBX2 antibody was 0.84, which was also better than these conventional methods. In conclusion, A novel methylation marker LBX2 could be used as a simple, cost-effective, and reliable diagnostic method for LNM of CRC.

DNA methylation-based predictors of health: applications and statistical considerations

DNA methylation data have become a valuable source of information for biomarker development, because, unlike static genetic risk estimates, DNA methylation varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology. Reliable methods for genome-wide measurement at scale have led to the proliferation of epigenome-wide association studies and subsequently to the development of DNA methylation-based predictors across a wide range of health-related applications, from the identification of risk factors or exposures, such as age and smoking, to early detection of disease or progression in cancer, cardiovascular and neurological disease. This Review evaluates the progress of existing DNA methylation-based predictors, including the contribution of machine learning techniques, and assesses the uptake of key statistical best practices needed to ensure their reliable performance, such as data-driven feature selection, elimination of data leakage in performance estimates and use of generalizable, adequately powered training samples.

Cell Free Methylated Tumor DNA in Bronchial Lavage as an Additional Tool for Diagnosing Lung Cancer-A Systematic Review

This systematic review investigated circulating methylated tumor DNA in bronchial lavage fluid for diagnosing lung cancer. PROSPERO registration CRD42022309470. PubMed, Embase, Medline, and Web of Science were searched on 9 March 2022. Studies of adults with lung cancer or undergoing diagnostic workup for suspected lung cancer were included if they used bronchial lavage fluid, analyzed methylated circulating tumor DNA, and reported the diagnostic properties. Sensitivity, specificity, and lung cancer prevalence were summarized in forest plots. Risk of bias was assessed using the QUADAS-2 tool. A total of 25 studies were included. All were case-control studies, most studies used cell pellet for analysis by quantitative PCR. Diagnostic sensitivity ranged from 0% for a single gene to 97% for a four-gene panel. Specificity ranged from 8% for a single gene to 100%. The studies employing a gene panel decreased the specificity, and no gene panel had a perfect specificity of 100%. In conclusion, methylated circulating tumor DNA can be detected in bronchial lavage, and by employing a gene panel the sensitivity can be increased to clinically relevant levels. The available evidence regarding applicability in routine clinical practice is limited. Prospective, randomized clinical trials are needed to determine the further usefulness of this biomarker.

A novel DNA methylation signature associated with lymph node metastasis status in early gastric cancer

Background

Lymph node metastasis (LNM) is an important factor for both treatment and prognosis of early gastric cancer (EGC). Current methods are insufficient to evaluate LNM in EGC due to suboptimal accuracy. Herein, we aim to identify methylation signatures for LNM of EGC, facilitate precision diagnosis, and guide treatment modalities.

Methods

For marker discovery, genome-wide methylation sequencing was performed in a cohort (marker discovery) using 47 fresh frozen (FF) tissue samples. The identified signatures were subsequently characterized for model development using formalin-fixed paraffin-embedded (FFPE) samples by qPCR assay in a second cohort (model development cohort, n = 302, training set: n = 151, test set: n = 151). The performance of the established model was further validated using FFPE samples in a third cohorts (validation cohort, n = 130) and compared with image-based diagnostics, conventional clinicopathology-based model (conventional model), and current standard workups.

Results

Fifty LNM-specific methylation signatures were identified de novo and technically validated. A derived 3-marker methylation model for LNM diagnosis was established that achieved an AUC of 0.87 and 0.88, corresponding to the specificity of 80.9% and 85.7%, sensitivity of 80.6% and 78.1%, and accuracy of 80.8% and 83.8% in the test set of model development cohort and validation cohort, respectively. Notably, this methylation model outperformed computed tomography (CT)-based imaging with a superior AUC (0.88 vs. 0.57, p < 0.0001) and individual clinicopathological features in the validation cohort. The model integrated with clinicopathological features demonstrated further enhanced AUCs of 0.89 in the same cohort. The 3-marker methylation model and integrated model reduced 39.4% and 41.5% overtreatment as compared to standard workups, respectively.

Conclusions

A novel 3-marker methylation model was established and validated that shows diagnostic potential to identify LNM in EGC patients and thus reduce unnecessary gastrectomy in EGC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01219-x.