Novel blood-based hypomethylation of SH3BP5 is associated with very early-stage lung adenocarcinoma

The association between blood-based HYAL2 methylation and early-stage lung cancer: a case-control study

BACKGROUND

Blood-based DNA methylation biomarkers have great potential for the early detection of lung cancer (LC). Here, we investigated the association between HYAL2 methylation in peripheral blood and LC.

METHODS

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry was performed to measure the methylation levels of 4 CpG sites in HYAL2 gene in two independent case-control studies (168 LC cases and 167 controls in Study I, 677 LC cases and 833 controls in Study II). Logistic regression adjusted for covariates was conducted for odds ratios (ORs) and 95% confidence intervals (CIs). Non-parametric tests were applied for the comparisons of stratified groups.

RESULTS

Hypomethylation of all 4 CpG sites in HYAL2 was associated with early-stage LC in the two studies (ORs range from 1.91 to 3.07 in Study I, ORs range from 1.39 to 1.86 in Study II, p < 0.05 for all). The associations were still significant for the very early-stage LC patients (stage I). Subgroup analysis indicated that the associations could be enhanced by male gender and older age. Moreover, decreased HYAL2 methylation was correlated with increased tumor size, tumor length and stage.

CONCLUSIONS

Our results suggested blood-based HYAL2 hypomethylation as a potential biomarker for LC early detection.

B-cell signatures characterize the immune landscape and predict LUAD prognosis via the integration of scRNA-seq and bulk RNA-seq

Lung adenocarcinoma (LUAD) is the most common type of lung cancer, accounting for approximately 35-40% of lung cancers, and the overall survival time of patients with LUAD is still very poor. B cells are important effector cells of adaptive immunity, and B-cell infiltration increases in various tumors. The role of B cells in LUAD is still largely unknown. Therefore, it is particularly important to clarify the role of B cells in LUAD. GSE164983, GSE50081, GSE37745 and GSE30219 were obtained from the GEO database. The TCGA-LUAD dataset was obtained from the TCGA database. UMAP was used to perform clustering descending and subgroup identification on single-cell RNA-sequencing (scRNA-seq) data to obtain B-cell markers. The TCGA cohort was used to obtain differentially expressed genes (DEGs). B-cell-related differentially expressed genes (BRGs) were identified through the intersection of B-cell markers and DEGs. The LASSO method was used to identify characteristic genes of BRGs and construct a prognostic risk model. LUAD patients were divided into high-risk and low-risk groups based on risk scores, and the immune landscape of the two groups was evaluated. We also analyzed the differences in clinical characteristics, mutations, immunotherapy, and drug sensitivity between the two groups. Thirty BRGs were obtained, and 6 characteristic genes were identified. Based on the characteristic genes, a prognostic risk model was constructed. According to the prognostic risk model, LUAD patients were divided into two groups: high-risk group and low-risk group. Patients in the high-risk group had worse outcomes and shorter survival times. Low-risk patients had better survival, while patients with high TNM stage accounted for a greater proportion of patients in the high-risk group. In addition, high-risk patients had a greater probability of mutation and worse immunotherapy response. Finally, we found different susceptibility profiles between the high-risk and low-risk groups. The prognostic risk model built based on the BRGs had good predictive performance, providing a new perspective on the prognosis and immunotherapy of LUAD patients and a new reference for LUAD research.

Association between the methylations of RUNX3 in peripheral blood and lung cancer: a case-control study

BACKGROUND

RUNX3 is hypermethylated in multiple cancers. TIMP2 also functions as a regulator of tumors. However, there are only very few reports on the association of methylation of RUNX3 and TIMP2 with lung cancer (LC) in peripheral blood.

METHODS

426 LC patients and 428 age- and sex-matched healthy controls were recruited. DNA methylation in blood was semi-quantitively assessed by mass spectrometry. For the association analysis, binary logistic regression analysis adjusted covariant was applied, and ORs were presented as per +10% methylation.

RESULTS

Hypermethylation of CpG_1, CpG_5 and CpG_8 in RUNX3 was significantly associated with LC (ORs = 1.45, 1.35 and 1.35, respectively, adjusted p < 0.05), and even stage I LC. The association between the three RUNX3 CpG sites and LC was enhanced by increased age (> 55 years, ORs ranged from 1.43 to 1.75, adjusted p < 0.05), male gender (ORs ranged from 1.47 to 1.59, adjusted p < 0.05) and tumor stage (stage II&III&IV, ORs ranged from 1.86 to 3.03, adjusted p < 0.05).

CONCLUSIONS

This study suggests a significant association between blood-based RUNX3 hypermethylation and LC, especially in elder people, in males and in LC patients with advanced stage.

METTL3 suppresses invasion of lung cancer via SH3BP5 m6A modification

The metastasis of lung cancer poses a major clinical challenge, and m6A modification has been implicated in regulating the invasive capabilities of tumor cells. However, the mechanisms underlying m6A modification in lung cancer metastasis are not well understood. This study aims to explore the biological functions and molecular mechanisms of methyltransferase-like 3 (METTL3) in lung cancer. In this study, METTL3 were found to be downregulated in lung cancer tissues. Functionally, METTL3 inhibited the migration and invasion abilities of lung cancer cells in vitro. Furthermore, SH3 domain binding protein 5 (SH3BP5) was identified as a downstream target of METTL3. Overexpression of SH3BP5 suppressed the invasive capacity of lung cancer cells, and this regulation was m6A-dependent. Finally, we discovered that YTH N6-methyladenosine RNA binding protein F1 (YTHDF1) mediated stability is responsible for maintaining the m6A modification of SH3BP5 mRNA. Overall, our study provides insights into the critical role of METTL3-mediated m6A modification and m6A-dependent regulatory mechanisms in the progression of human lung cancer. We demonstrated that METTL3 regulates the mRNA stability of SH3BP5 in a YTHDF1-dependent manner, thereby impacting the invasive capacity of lung cancer cells.

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.

Identification and analysis of methylation signature genes and association with immune infiltration in pediatric acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a common leukemia with low cure rate and poor prognosis among pediatric patients. The regulation of AML immune microenvironment and methylation remains to be explored. Pediatric and adult AML patients differ significantly in epigenetic factors, and the efficiency of treatment modalities varies between the two groups of patients.

METHODS

We collected mRNA, miRNA and DNA methylation data from pediatric AML patients across multiple databases. Differentially expression genes were identified, and a gene-miRNA regulatory network was constructed. Prognostic risk models were established by integrating LASSO and Cox regression, and a nomogram was generated. Based on this model, we investigated tumor-infiltrating immune cells and cell communication, analyzing the biological functions and pathways associated with prognostic factors. Furthermore, the relationships between all prognostic factors and gene modules were explored, and the impact of these factors on treatment modalities was determined.

RESULTS

We developed an efficient prognostic risk model and identified HOXA9, SORT1, SH3BP5, mir-224 and mir-335 as biomarkers. We validated these findings in an external dataset and observed a correlation between age and risk in pediatric patients. AML samples with lower risk scores have a better prognosis and higher expression of immune-upregulated biomarkers, and have lower immune scores. Furthermore, we detected discrepancies in immune cell infiltration and interactions between high- and low-risk group samples, which affected the efficacy of immunotherapy. We evaluated all prognostic factors and predicted the effect of immunotherapy and medicine.

CONCLUSION

This study comprehensively investigated the role of methylation signature genes in pediatric AML at the level of genomes and transcriptomes. The research aims to enhance the risk stratification, prognosis evaluation and assessment of treatment effectiveness of AML patients. This study also highlight the uniqueness of pediatric AML and foster the development of new immunotherapy and targeted therapy strategies.