DNA methylation‐based diagnostic and prognostic biomarkers of nonsmoking lung adenocarcinoma patients

An integrated bioinformatics approach to early diagnosis, prognosis and therapeutics of non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is one of the most deadly tumors characterized by poor survival rates. Advances in therapeutics and precise identification of biomarkers can potentially reduce the mortality rate. Thus, this study aimed to identify a set of common and stable gene biomarkers through integrated bioinformatics approaches that might be effective for NSCLC early diagnosis, prognosis, and therapies. Four gene expression profiles (GSE19804, GSE19188, GSE10072, and GSE32863) downloaded from the Gene Expression Omnibus database to identify common differential expressed genes (DEGs). A total of 213 overlapping DEGs (oDEGs) between NSCLC and healthy samples were identified by using statistical LIMMA method. Then 6 common top-ranked key genes (KGs) (CENPF, CAV1, ASPM, CCNB2, PRC1, and KIAA0101) were selected by using four network-measurer methods in the protein- protein interaction network. The GO functional and KEGG pathway enrichment analysis were performed to reveal some significant functions and pathways associated with NSCLC progression. Transcriptional and post-transcriptional factors of KGs were identified through the regulatory interaction network. The prognostic power and expression level of KGs were validated by using the independent data through the Kaplan-Meier and Box plots, respectively. Finally, 4 KGs-guided repositioning candidate drugs (ZSTK474, GSK2126458, Masitinib, and Trametinib) were proposed. The stability of three top-ranked drug-target interactions (CAV1 vs. ZSTK474, CAV1 vs. GSK2126458, and ASPM vs. Trametinib) were investigated by computing their binding free energies for 140 ns MD-simulation based on MM-PBSA approach. Therefore, the findings of this computational study may be useful for early prognosis, diagnosis and therapies of NSCLC.

Dissecting the alternation landscape of mitochondrial metabolism-related genes in lung adenocarcinoma and their latent mechanisms

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer with high incidence and unsatisfactory prognosis. The majority of LUAD patients eventually succumb to local and/or distinct metastatic recurrence. Genomic research of LUAD has broadened our understanding of this disease's biology and improved target therapies. However, the alternation landscape and characteristics of mitochondrial metabolism-related genes (MMRGs) in LUAD progression remain poorly understood. We performed a comprehensive analysis to identify the function and mechanism of MMRGs in LUAD based on the TCGA and GEO databases, which might offer therapeutic values for clinical researchers. Then, we figured out three hub prognosis-associated MMRGs (also termed as PMMRGs: ACOT11, ALDH2, and TXNRD1) that were engaged in the evolution of LUAD. To investigate the correlation between clinicopathological characteristics and MMRGs, we divided LUAD samples into two clusters (C1 and C2) based on key MMRGs. In addition, important pathways and the immune infiltration landscape affected by LUAD clusters were also delineated. Further, we nominated potential regulatory mechanisms underlying the MMRGs in LUAD development and progression. In conclusion, our integrative analysis enables a more comprehensive understanding of the mutation landscape of MMRGs in LUAD and provides an opportunity for more precise treatment.

DNA methylation analysis of tumor suppressor genes in liquid biopsy components of early stage NSCLC: a promising tool for early detection

Purpose

Circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) analysis represents a liquid biopsy approach for real-time monitoring of tumor evolution. DNA methylation is considered to be an early event in the process of cancer development and progression. The aim of the present study was to evaluate whether detection of DNA methylation of selected tumor suppressor genes in CTC and matched ctDNA provides prognostic information in early stage NSCLC.

Experimental design

The methylation status of five selected gene promoters (APC, RASSFIA1, FOXA1, SLFN11, SHOX2) was examined by highly specific and sensitive real-time methylation specific PCR assays in: (a) a training group of 35 primary tumors and their corresponding adjacent non-cancerous tissues of early stage NSCLC patients, (b) a validation group of 22 primary tumor tissues (FFPEs) and 42 peripheral blood samples of early stage NSCLC patients. gDNA was isolated from FFPEs, CTCs (size-based enriched by Parsortix; Angle and plasma, and (c) a control group of healthy blood donors (n = 12).

Results

All five gene promoters tested were highly methylated in the training group; methylation of SHOX2 promoter in primary tumors was associated with unfavorable outcome. RASSFIA and APC were found methylated in plasma-cfDNA samples at 14.3% and 11.9%, respectively, whereas in the corresponding CTCs SLFN11 and APC promoters were methylated in 7.1%. The incidence of relapses was higher in patients with a) promoter methylation of APC and SLFN11 in plasma-cfDNA (P = 0.037 and P = 0.042 respectively) and b) at least one detected methylated gene promoter in CTC or plasma-cfDNA (P = 0.015).

Conclusions

DNA methylation of these five gene promoters was significantly lower in CTCs and plasma-cfDNA than in the primary tumors. Combination of DNA methylation analysis in CTC and plasma-cfDNA was associated with worse DFI of NSCLC patients. Additional studies are required to validate our findings in a large cohort of early stage NSCLC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01283-x.

ASMdb: a comprehensive database for allele-specific DNA methylation in diverse organisms

DNA methylation is known to be the most stable epigenetic modification and has been extensively studied in relation to cell differentiation, development, X chromosome inactivation and disease. Allele-specific DNA methylation (ASM) is a well-established mechanism for genomic imprinting and regulates imprinted gene expression. Previous studies have confirmed that certain special regions with ASM are susceptible and closely related to human carcinogenesis and plant development. In addition, recent studies have proven ASM to be an effective tumour marker. However, research on the functions of ASM in diseases and development is still extremely scarce. Here, we collected 4400 BS-Seq datasets and 1598 corresponding RNA-Seq datasets from 47 species, including human and mouse, to establish a comprehensive ASM database. We obtained the data on DNA methylation level, ASM and allele-specific expressed genes (ASEGs) and further analysed the ASM/ASEG distribution patterns of these species. In-depth ASM distribution analysis and differential methylation analysis conducted in nine cancer types showed results consistent with the reported changes in ASM in key tumour genes and revealed several potential ASM tumour-related genes. Finally, integrating these results, we constructed the first well-resourced and comprehensive ASM database for 47 species (ASMdb, www.dna-asmdb.com).

The mechanism research on the anti-liver fibrosis of emodin based on network pharmacology

AIMS

This study was designated to illustrate the underlying mechanisms of emodin anti-liver fibrosis via network pharmacology and experiment.

METHODS

The TSMCP and Genecards database were applied to screen the relevant targets of emodin or liver fibrosis. The essential target was selected by using Cytoscape to analyze the topological network of potential targets. Furthermore, we constructed a preliminary molecule docking study to explore the binding site by Surflex-Dock suite SYBYL X 2.0. The DAVID database was selected for gene functional annotations and KEGG enrichment analysis. Moreover, we demonstrated the ameliorating effect of emodin on carbon tetrachloride (CCl4 )-induced liver injury in mice. We also verified the network predictions in vitro via various techniques.

RESULTS

The collected results showed that 35 targets were related to emodin, and 6,198 targets were associated with liver fibrosis. The Venn analysis revealed that 17 intersection targets were correlated with emodin anti-liver fibrosis. The topological network analysis suggested that the p53 was the remarkable crucial target. Besides, the molecule docking results showed that emodin could directly interact with p53 by binding the active site residues ASN345, GLN331, and TYR347. Finally, KEGG pathway enrichment results indicated that essential genes were mainly enriched in mitogen-activated protein kinase (MAPK) signaling pathways. Moreover, our study confirmed that emodin alleviated CCl4 -induced liver injury in mice, inducing hepatic stellate cells (HSCs) apoptosis via regulating the p53/ERK/p38 axis.

CONCLUSIONS

This study partially verified the network pharmacological prediction of emodin inducing HSCs cell apoptosis through the p53/ERK/p38 axis.

6-lncRNA Assessment Model for Monitoring and Prognosis of HER2-Positive Breast Cancer: Based on Transcriptome Data

Background: In view of the high malignancy and poor prognosis of human epidermal growth factor receptor 2 (HER2)-positive breast cancer, we analyzed the RNA expression profiles of HER2-positive breast cancer samples to identify the new prognostic biomarkers. Methods: The linear fitting method was used to identify the differentially expressed RNAs from the HER2-positive breast cancer RNA expression profiles in the Cancer Genome Atlas (TCGA). Then, a series of methods including univariate Cox, Kaplan-Meier, and random forests, were used to identify the core long non-coding RNAs (lncRNAs) with stable prognostic value for HER2-positive breast cancer. A clinical feature analysis was performed, and a competing endogenous RNA network was constructed to explore the role of these core lncRNAs in HER2-positive breast cancer. In addition, a functional analysis of differentially expressed messenger RNAs in HER-2 positive breast cancer also provided us with some enlightening insights. Results: The high expression of four core lncRNAs (AC010595.1, AC046168.1, AC069277.1, and AP000904.1) was associated with worse overall survival, while the low expression of LINC00528 and MIR762HG was associated with worse overall survival. The 6-lncRNA model has an especially good predictive power for overall survival (p < 0.0001) and 3-year survival (the area under the curve = 0.980) in HER2-positive breast cancer patients. Conclusion: This study provides a new efficient prognostic model and biomarkers of HER2-positive breast cancer. Meanwhile, it also provides a new perspective for elucidating the molecular mechanisms underlying HER2-positive breast cancer.

circRNA circ_102049 Implicates in Pancreatic Ductal Adenocarcinoma Progression through Activating CD80 by Targeting miR-455-3p

Emerging evidence has shown that circular RNAs (circRNAs) and DNA methylation play important roles in the causation and progression of cancers. However, the roles of circRNAs and abnormal methylation genes in the tumorigenesis of pancreatic ductal adenocarcinoma (PDAC) are still largely unknown. Expression profiles of circRNA, gene methylation, and mRNA were downloaded from the GEO database, and differentially expressed genes were obtained via GEO2R, and a ceRNA network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Inflammation-associated genes were collected from the GeneCards database. Then, functional enrichment analysis and protein-protein interaction (PPI) networks of inflammation-associated methylated expressed genes were investigated using Metascape and STRING databases, respectively, and visualized in Cytoscape. Hub genes of PPI networks were identified using the NetworkAnalyzer plugin. Also, we analyzed the methylation, protein expression levels, and prognostic value of hub genes in PDAC patients through the UALCAN, Human Protein Atlas (HPA), and Kaplan-Meier plotter databases, respectively. The circRNA_102049/miR-455-3p/CD80 axis was identified by the ceRNA network and hub genes. In vitro and in vivo experiments were performed to evaluate the functions of circRNA_102049. The regulatory mechanisms of circRNA_102049 and miR-455-3p were explored by RT-PCR, western blot, and dual-luciferase assays. In the present study, twelve hub genes (STAT1, CCND1, KRAS, CD80, ICAM1, ESR1, RAF1, RPS6KA2, KDM6B, TNRC6A, FOSB, and DNM1) were determined from the PPI networks. Additionally, the circRNA_102049 was upregulated in PDAC cell lines. Functionally, the knockdown of circRNA_102049 by siRNAs inhibited cell growth, inflammatory factors, and migratory and invasive potential and promoted cell apoptosis. Mechanistically, circRNA_102049 functioned as a sponge of miR-455-3p and partially reversed the effect of miR-455-3p and consequently upregulated CD80 expression. Our findings showed that circRNA_102049 and methylated hub genes play an important role in the proliferation, apoptosis, migration, invasion, and inflammatory response of PDAC, which might be selected as a promising prognostic marker and therapeutic target for PDAC.

Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis

Purpose

The conventional interventions of anaplastic thyroid carcinoma (ATC) patients are mainly through surgery, chemotherapy, and radiotherapy; however, it is hardly to improve survival rate. We aimed to investigate the differential expressed genes (DEGs) between ATC and normal thyroid gland through bioinformatics analysis of the microarray datasets and find new potential therapeutic targets for ATC.

Methods

Microarray datasets GSE9115, GSE29265, GSE33630, GSE53072, and GSE65144 were downloaded from Gene Expression Omnibus (GEO) database. Compared with the normal tissue, GEO2R was conducted to screen the DEGs in each chip under the condition of |log FC| > l, adjusted P‐values (adj. P) < 0.05. The Retrieval of Interacting Genes (STRING) database was used to calculate PPI networks of DEGs with a combined score >0.4 as the cut-off criteria. The hub genes in the PPI network were visualized and selected according to screening conditions in Cytoscape software. In addition, the novel genes in ATC were screened for survival analysis using Kaplan–Meier plotter from those hub genes and validated by RT-qPCR.

Results

A total of 284 overlapping DEGs were obtained, including 121 upregulated and 161 downregulated DEGs. A total of 232 DEGs were selected by STRING database. The 50 hub genes in the PPI network were chosen according to three screening conditions. In addition, the Kaplan–Meier plotter database confirmed that high expressions of ANLN, CENPF, KIF2C, TPX2, and NDC80 were negatively correlated with poor overall survival of ATC patients. Finally, RT-qPCR experiments showed that KIF2C and CENPF were significantly upregulated in ARO cells and CAL-62 cells when compared to Nthy-ori 3–1 cells, TPX2 was upregulated only in CAL-62 cells, while ANLN and NDC80 were obviously decreased in ARO cells and CAL-62 cells.

Conclusion

Our study suggested that CENPF, KIF2C, and TPX2 might play a significant role in the development of ATC, which could be further explored as potential biomarkers for the treatment of ATC.

Identification of crucial genes associated with lung adenocarcinoma by bioinformatic analysis

Lung cancer is the world's most common malignancies and ranks first among all cancer-related deaths. Lung adenocarcinoma (LUAD) is the most frequent histological type in lung cancer. Its pathogenesis has not yet been fully elucidated, so it is of great significance to explore related genes for elucidating the molecular mechanism involved in occurrence and development of LUAD.To explore the crucial genes associated with LUAD development and progression, microarray datasets GSE7670, GSE10072, and GSE31547 were acquired from the Gene Expression Omnibus (GEO) database. R language Limma package was adopted to screen the differentially expressed genes (DEGs). The clusterProfiler package was used for enrichment analysis and annotation of the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways for DEGs. The Search Tool for the Retrieval of Interacting Genes database (STRING) was used to construct the protein interaction network for DEGs, while Cytoscape was adopted to visualize it. The functional module was screened with Cytoscape's MCODE (The Molecular Complex Detection) plugin. The crucial genes associated with LUAD were identified by cytoHubba plugin. Kaplan-Meier plotter online tool was used to perform survival analysis of the hub gene.Three hundred twenty-one DEGs in total were screened, of which 105 were upregulated and 216 were downregulated. It was found that some GO terms and pathways (e.g., collagen trimer, extracellular structure organization, heparin binding, complement and coagulation cascades, malaria, protein digestion and absorption, and PPAR signaling pathway) were considerably enriched in DEGs. UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, PRC1, and CDK1 were identified as crucial genes. Survival analysis showed that the overexpression of UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, and PRC1 significantly reduced the overall survival of LUAD patients. One of the crucial genes: UBE2C was validated by immunohistochemistry to be upregulated in LUAD tissues.This study screened out potential biomarkers of LUAD, providing a theoretical basis for elucidating the pathogenesis and evaluating the prognosis of LUAD.

Identification of diagnostic DNA methylation biomarkers specific for early-stage lung adenocarcinoma

BACKGROUND

DNA hypermethylation is a key event in oncogenesis and may act as a biomarker for the early detection of lung adenocarcinoma (LUAD). Here, we aimed to identify LUAD-specific methylation diagnostic biomarkers and explored potential mechanisms using data mining.

METHODS

Using The Cancer Genome Atlas (TCGA) LUAD and GSE83842 datasets, we identified overlapping common differentially methylated positions (DMPs) with negative correlations between methylation and gene expression. Methylation profiles of the TCGA LUAD samples were compared with 185 blood samples and 370 lung squamous cell carcinoma (LUSC) samples to build a logistic regression model. Diagnosis performance was evaluated using an independent dataset.

RESULTS

160 genes were aberrantly methylated in LUAD since stage I; these genes were enriched in DNA-binding transcription factor activity, multiple embryonic development processes, and cell signaling. A diagnostic prediction model based on 10 CpG could distinguish LUAD from LUSC (area under the curve: 0.943). The derived model showed higher sensitivity and specificity than the two existing models. The homeobox A1 gene exhibited significantly higher methylation levels in LUAD than in 10 other cancers, showing potential as a LUAD-specific diagnostic biomarker.

CONCLUSIONS

Our findings provided insights into DNA methylation alterations in LUAD and established LUAD-specific diagnostic biomarkers.

miR-133b Suppresses Invasion and Migration of Gastric Cancer Cells via the COL1A1/TGF-β Axis

Objective

The study aimed to explore the mechanism of miR-133b regulating the invasion and migration of gastric cancer (GC) cells via the COL1A1/TGF-β axis.

Methods

The miRNA expression profiles of GC downloaded from TCGA database were subjected to differential analysis to determine the target miRNA of interest, and the target genes of the miRNA were predicted by bioinformatics. GSEA was used for gene enrichment analysis. qRT-PCR was carried out to detect gene expression in GC cells. The effect of miR-133b on GC cells was examined by CCK-8, wound healing and Transwell assays. Western blot was conducted to assess the protein expression of EMT-related proteins. The binding relationship between genes was verified by dual-luciferase reporter gene assay.

Results

The expression of miR-133b was markedly downregulated in GC tissue, while that of COL1A1 was upregulated. Overexpression of miR-133b decreased the migration and invasion of GC cells, and the EMT process was inhibited as well, while inverse results were observed when miR-133b was silenced. COL1A1 was a target gene of miR-133b and its overexpression had a significant impact on the prognosis of patients. GSEA pathway enrichment results showed that COL1A1 was markedly enriched in the TGF-β signaling pathway. In addition, COL1A1 overexpression induced the activation of the TGF-β signaling pathway to promote proliferation and migration of GC cells, whereas miR-133b overexpression suppressed the signaling pathway. Thus, overexpression of miR-133b and COL1A1 simultaneously would reverse the inhibitory effect of miR-133b on cell invasion and migration.

Conclusion

In this study, miR-133b was found to inhibit the invasion and migration of GC cells via the COL1A1/TGF-β axis, which provides a new research direction for the diagnosis and targeted therapy of GC.

Promoter Methylation of Selected Genes in Non-Small-Cell Lung Cancer Patients and Cell Lines

Specific gene promoter DNA methylation is becoming a powerful epigenetic biomarker in cancer diagnostics. Five genes (CDH1, CDKN2Ap16, RASSF1A, TERT, and WT1) were selected based on their frequently published potential as epigenetic markers. Diagnostic promoter methylation assays were generated based on bisulfite-converted DNA pyrosequencing. The methylation patterns of 144 non-small-cell lung cancer (NSCLC) and 7 healthy control formalin-fixed paraffin-embedded (FFPE) samples were analyzed to evaluate the applicability of the putative diagnostic markers. Statistically significant changes in methylation levels are shown for TERT and WT1. Furthermore, 12 NSCLC and two benign lung cell lines were characterized for promoter methylation. The in vitro tests involved a comparison of promoter methylation in 2D and 3D cultures, as well as therapeutic tests investigating the impact of CDH1/CDKN2Ap16/RASSF1A/TERT/WT1 promoter methylation on sensitivity to tyrosine kinase inhibitor (TKI) and DNA methyl-transferase inhibitor (DNMTI) treatments. We conclude that the selected markers have potential and putative impacts as diagnostic or even predictive marker genes, although a closer examination of the resulting protein expression and pathway regulation is needed.

Identification of Aberrantly Methylated Differentially CpG Sites in Hepatocellular Carcinoma and Their Association With Patient Survival

This study aimed to identify aberrantly methylated differentially methylated CpG sites (DMCs) and investigate their prognostic value in hepatocellular carcinoma (HCC). A total of 2,404 DMCs were selected from Gene Expression Omnibus (GEO) and validated by The Cancer Genome Atlas (TCGA). The TCGA cohort was divided into a training cohort and a validating cohort. First, the prognostic model based on six DMCs, including cg08351331, cg02910574, cg09947274, cg17589341, cg24652919, and cg26545968, was constructed based on the least absolute shrinkage and selection operator (LASSO) regression Cox analysis. The area under the curve (AUC) of the DMC-based model was 0.765 in the training cohort and 0.734 in the validating cohort. The accuracy of a model combining the DMC signature and American Joint Committee on Cancer (AJCC) stage, with an AUC of 0.795, was better than that of the DMCs or AJCC stage alone. Second, further analysis revealed that the methylation rate of cg08351331 was negatively associated with the expression of its relative gene, lipopolysaccharide-binding protein (LBP). Besides, the gene expression of LBP was significantly associated with poor overall survival in patients with hepatitis B virus (HBV) infection. Finally, these findings were confirmed by GSE57956 data and our own cohort. In conclusion, we established an accurate DMC-based prognostic model that could be combined with AJCC stage to improve the accuracy of prognostic prediction in HCC. Moreover, our preliminary data indicate that LBP may be a new key factor in HBV-induced HCC initiation through the regulation of its methylation.