Comprehensive analysis of prognosis-related methylated sites in breast carcinoma

The predictive value of BTG1 for the response of newly diagnosed acute myeloid leukemia to decitabine

BACKGROUND

Decitabine has been widely used to treat acute myeloid leukemia (AML); however as AML is a heterogeneous disease, not all patients benefit from decitabine. This study aimed to identify markers for predicting the response to decitabine.

METHODS

An intersection of in vitro experiments and bioinformatics was performed using a combination of epigenetic and transcriptomic analysis. A tumor-suppressor gene associated with methylation and the response to decitabine was screened. Then the sensitivity and specificity of this marker in predicting the response to decitabine was confirmed in 54 samples from newly diagnosed AML patients treated with decitabine plus IA regimen in a clinical trial (ChiCTR2000037928).

RESULTS

In vitro experiments showed that decitabine caused hypomethylation and upregulation of BTG1, while downregulation of BTG1 attenuated the inhibitory effect of decitabine. In newly diagnosed AML patients who received decitabine plus IA regimen, the predictive value of BTG1 to predict complete remission (CR) was assigned with a sensitivity of 86.7% and a specificity of 100.0% when BTG1 expression was < 0.292 (determined using real-time quantitative PCR), with area under the curve (AUC) = 0.933, P = 0.021. The predictive value of BTG1 to predict measurable residual disease (MRD) negativity was assigned with a sensitivity of 100.0% and a specificity of 80.0% when BTG1 expression was < 0.292 (AUC = 0.892, P = 0.012). Patients were divided into low and high BTG1 expression groups according to a cutoff of 0.292, and the CR rate of the low-expression group was significantly higher than that of the high-expression group (97.5% vs. 50%, P < 0.001).

CONCLUSIONS

Low expression of BTG1 was associated with CR and MRD negativity in newly diagnosed AML patients treated with a decitabine-containing regimen, suggesting that BTG1 is a potential marker for predicting the response to decitabine in newly diagnosed AML.

CLINICAL TRIAL REGISTRATION

ChiCTR2000037928.

Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis

The progression of non-small cell lung cancer (NSCLC) is linked to epithelial-mesenchymal transition (EMT), a biologic process that enables tumor cells to acquire a migratory phenotype and resistance to chemo- and immunotherapies. Discovery of novel biomarkers in NSCLC progression is essential for improved prognosis and pharmacological interventions. In the current study, we performed an integrated bioinformatics analysis on gene expression datasets of TGF-β-induced EMT in NSCLC cells to identify novel gene biomarkers and elucidate their regulation in NSCLC progression. The gene expression datasets were extracted from the NCBI Gene Expression Omnibus repository, and differentially expressed genes (DEGs) between TGF-β-treated and untreated NSCLC cells were retrieved. A protein-protein interaction network was constructed and hub genes were identified. Functional and pathway enrichment analyses were conducted on module DEGs, and a correlation between the expression levels of module genes and survival of NSCLC patients was evaluated. Prediction of interactions of the biomarker genes with transcription factors and miRNAs was also carried out. We described four protein clusters in which DEGs were associated with ubiquitination (Module 1), regulation of cell death and cell adhesions (Module 2), oxidation-reduction reactions of aerobic respiration (Module 3) and mitochondrial translation (Module 4). From the module genes, we identified ten prognostic gene biomarkers in NSCLC. Low expression levels of KCTD6, KBTBD7, LMO7, SPSB2, RNF19A, FOXA2, DHTKD1, CDH1 and PDHB and high expression level of KLHL25 were associated with reduced overall survival of NSCLC patients. Most of these biomarker genes were involved in protein ubiquitination. The regulatory network of the gene biomarkers revealed their interaction with tumor suppressor miRNAs and transcription factors involved in the mechanisms of cancer progression. This ten-gene prognostic signature can be useful to improve risk prediction and therapeutic strategies in NSCLC. Our analysis also highlights the importance of deregulation of ubiquitination in EMT-associated NSCLC progression.

MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

Comprehensive analysis of prognosis-related methylated sites in breast carcinoma

Background

Breast carcinoma has become a nonnegligible public health problem in China with its increasing incidence and mortality in woman. As a early event regulating tumorigenesis and development, DNA methylation became one of the focuses of current carcinoma researches on potential diagnostic and therapeutic targets.

Methods

In this study, we comprehensively analyzed the gene expression data and DNA methylation data of breast carcinoma and adjacent normal tissues samples in the Gene Expression Omnibus database. Influences of tumor stage, adjuvant therapy, hormone therapy, and chemotherapy on CpG methylation level were explored by linear regression analysis. Correlations between methylation and gene expression levels were determined by spearman rank correlation analysis. Log‐rank test was applied for determining significance of associations between CpG sites methylation level and breast cancer patients' Kaplan–Meier survival.

Results

A total of 229 CpG sites were found to be significantly associated with tumor stage or treatment, and eight of which were potential markers that affect the survival of breast carcinoma and negatively correlated with their genes' expression levels.

Conclusions

We reported eight CpG sites as potential breast cancer prognosis signatures through comprehensively analyzed gene expression and DNA methylation datasets, and excluding influences of tumor stage and treatment. This should be helpful for breast cancer early diagnosis and treatment.