Association between the CpG island methylator phenotype and its prognostic significance in primary pulmonary adenocarcinoma

Identification of diagnostic markers for moyamoya disease by combining bulk RNA-sequencing analysis and machine learning

Moyamoya disease (MMD) remains a chronic progressive cerebrovascular disease with unknown etiology. A growing number of reports describe the development of MMD relevant to infection or autoimmune diseases. Identifying biomarkers of MMD is to understand the pathogenesis and development of novel targeted therapy and may be the key to improving the patient's outcome. Here, we analyzed gene expression from two GEO databases. To identify the MMD biomarkers, the weighted gene co-expression network analysis (WGCNA) and the differential expression analyses were conducted to identify 266 key genes. The KEGG and GO analyses were then performed to construct the protein interaction (PPI) network. The three machine-learning algorithms of support vector machine-recursive feature elimination (SVM-RFE), random forest and least absolute shrinkage and selection operator (LASSO) were used to analyze the key genes and take intersection to construct MMD diagnosis based on the four core genes found (ACAN, FREM1, TOP2A and UCHL1), with highly accurate AUCs of 0.805, 0.903, 0.815, 0.826. Gene enrichment analysis illustrated that the MMD samples revealed quite a few differences in pathways like one carbon pool by folate, aminoacyl-tRNA biosynthesis, fat digestion and absorption and fructose and mannose metabolism. In addition, the immune infiltration profile demonstrated that ACAN expression was associated with mast cells resting, FREM1 expression was associated with T cells CD4 naive, TOP2A expression was associated with B cells memory, UCHL1 expression was associated with mast cells activated. Ultimately, the four key genes were verified by qPCR. Taken together, our study analyzed the diagnostic biomarkers and immune infiltration characteristics of MMD, which may shed light on the potential intervention targets of moyamoya disease patients.

Systems biology approach to identify biomarkers and therapeutic targets for colorectal cancer

Background

Colorectal cancer (CRC), is the third most prevalent cancer across the globe, and is often detected at advanced stage. Late diagnosis of CRC, leave the chemotherapy and radiotherapy as the main options for the possible treatment of the disease which are associated with severe side effects. In the present study, we seek to explore CRC gene expression data using a systems biology framework to identify potential biomarkers and therapeutic targets for earlier diagnosis and treatment of the disease.

Methods

The expression data was retrieved from the gene expression omnibus (GEO). Differential gene expression analysis was conducted using R/Bioconductor package. The PPI network was reconstructed by the STRING. Cystoscope and Gephi software packages were used for visualization and centrality analysis of the PPI network. Clustering analysis of the PPI network was carried out using k-mean algorithm. Gene-set enrichment based on Gene Ontology (GO) and KEGG pathway databases was carried out to identify the biological functions and pathways associated with gene groups. Prognostic value of the selected identified hub genes was examined by survival analysis, using GEPIA.

Results

A total of 848 differentially expressed genes were identified. Centrality analysis of the PPI network resulted in identification of 99 hubs genes. Clustering analysis dissected the PPI network into seven interactive modules. While several DEGs and the central genes in each module have already reported to contribute to CRC progression, survival analysis confirmed high expression of central genes, CCNA2, CD44, and ACAN contribute to poor prognosis of CRC patients. In addition, high expression of TUBA8, AMPD3, TRPC1, ARHGAP6, JPH3, DYRK1A and ACTA1 was found to associate with decreased survival rate.

Conclusion

Our results identified several genes with high centrality in PPI network that contribute to progression of CRC. The fact that several of the identified genes have already been reported to be relevant to diagnosis and treatment of CRC, other highlighted genes with limited literature information may hold potential to be explored in the context of CRC biomarker and drug target discovery.

Microbiome dysbiosis and epigenetic modulations in lung cancer: From pathogenesis to therapy

The lung microbiome plays an essential role in maintaining healthy lung function, including host immune homeostasis. Lung microbial dysbiosis or disruption of the gut-lung axis can contribute to lung carcinogenesis by causing DNA damage, inducing genomic instability, or altering the host's susceptibility to carcinogenic insults. Thus far, most studies have reported the association of microbial composition in lung cancer. Mechanistic studies describing host-microbe interactions in promoting lung carcinogenesis are limited. Considering cancer as a multifaceted disease where epigenetic dysregulation plays a critical role, epigenetic modifying potentials of microbial metabolites and toxins and their roles in lung tumorigenesis are not well studied. The current review explains microbial dysbiosis and epigenetic aberrations in lung cancer and potential therapeutic opportunities.

Solid predominant subtype in lung adenocarcinoma is related to poor prognosis after surgical resection: A systematic review and meta-analysis

BACKGROUND

Recent studies have indicated that solid predominant (SP) subtype of lung adenocarcinoma (LADC) may be associated with early recurrence and worse prognosis. Hence, a systematic review and meta-analysis were performed to evaluate the association between LADC subtype and survival.

METHODS

The MEDLINE, SCOPUS, Web of Science and Cochrane Libraries were reviewed for eligible studies in December 2017. Studies were included if they compared outcomes of patients with and without SP subtype in resection specimens of LADC patients after surgical treatment by using multivariate Cox regression analysis. A meta-analysis for overall survival (OS) and disease-free survival (DFS) was performed. The hazard ratios (HR) or odds ratios with 95% confidence intervals (CIs) from each study were used to calculate pooled HRs. Statistical analyses were performed using Review Manager 5.3.

RESULTS

In total, 14 eligible studies including 12,137 LADC patients were identified, which assessed the impact of SP subtype on OS and DFS in patients treated with pulmonary resection. SP subtype was reported in 1246 (10.2%) patients and was associated with significantly worse OS (pooled HR, 1.51; 1.29-1.75) and DFS (pooled HR, 1.26; 1.14-1.40).

CONCLUSIONS

SP subtype is associated with significantly worse OS and DFS in patients with LADC after pulmonary resection. These data provide evidence for the integration of the distinct histological LADC subtyping into prognostic tools and guidelines for adjuvant treatment after complete surgical resection.

Epigenome-wide association study of smoking and DNA methylation in non-small cell lung neoplasms

Tobacco smoke is a well-established lung cancer carcinogen. We hypothesize that epigenetic processes underlie carcinogenesis. The objective of this study is to examine the effects of smoke exposure on DNA methylation to search for novel susceptibility loci. We obtained epigenome-wide DNA methylation data from lung adenocarcinoma (LUAD) and lung squamous cell (LUSC) tissues in The Cancer Genome Atlas (TCGA). We performed a two-stage discovery (n = 326) and validation (n = 185) analysis to investigate the association of epigenetic DNA methylation level with cigarette smoking pack-years. We also externally validated our findings in an independent dataset. Linear model with least square estimator and spline regression were performed to examine the association between DNA methylation and smoking. We identified five CpG sites highly associated with pack-years of cigarette smoking. Smoking was negatively associated with methylation levels in cg25771041 (WWTR1, p = 3.6 × 10⁻⁹), cg16200496 (NFIX, p = 3.4 × 10⁻¹²), cg22515201 (PLA2G6, p = 1.0 × 10⁻⁹) and cg24823993 (NHP2L1, p = 5.1 × 10⁻⁸) and positively associated with the methylation level in cg11875268 (SMUG1, p = 4.3 × 10⁻⁸). The CpG-smoking association was stronger in LUSC than LUAD. Of the five loci, smoking explained the most variation in cg16200496 (R² = 0.098 [both types] and 0.144 [LUSC]). We identified 5 novel CpG candidates that demonstrate differential methylation patterns associated with smoke exposure in lung neoplasms.

Identification and validation of colorectal neoplasia-specific methylation biomarkers based on CTCF-binding sites

To date, the sensitivity of currently available biomarkers based on the methylation of gene promoters is suboptimal for detecting adenomas and early-stage colorectal cancer (CRC). We aimed to develop biomarkers with methylated DNA binding sites of the multifunctional transcriptional factor CTCF for early detection of CRC. Using combined analyses of genome-wide occupation and the methylation profile of CTCF-binding sites, we identified candidate CTCF-binding sites. Then, we applied methylation-sensitive high-resolution melting (MS-HRM) and mass spectrometry analysis to screen and validate these candidate sites in diverse sample sets. We identified a set of colorectal neoplasia-specific biomarkers with robust performance. The top five biomarkers were selected and recommended for early detection of colorectal neoplasia. All of the five novel biomarkers exhibited a more robust discriminatory performance than that by BMP3 and NDRG4, two currently acknowledged robust methylation biomarkers. When the five new biomarkers were considered as a marker panel and tumor-positive was defined as having two or more (of the five) positive biomarkers, the marker panel could achieve a sensitivity of 91.67% for adenomas, 97.44% for Stage I CRC, 94.06% for Stage II CRC, 93.62% for Stage III CRC, and 93.54% for total colorectal tumors with a specificity of 94.05%. To our knowledge, this is the first study for colorectal neoplasia-specific methylation biomarkers based on CTCF-binding sites. Using a similar strategy, CTCF-binding sites could be potentially developed into biomarkers for other tumors. In summary, this study opens a new area in developing biomarkers for tumor prevention and treatment.