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Aims: Currently, using clinicopathological risk factors only is not far from effective to evaluate the risk of disease progression in renal clear cell carcinoma (KIRC) patients. Molecular biomarkers might improve risk stratification of KIRC. DNA methylation occurs the whole process of tumor development and transcriptional disorders are also one of the important characteristics of tumor. Hence, this study aims to develop an effective and independent prognostic signature for KIRC patients by Integrating DNA methylation and gene expression., Main Methods: Difference analysis was conducted on DNA methylation sites and gene expression data. The Spearman's rank correlation and univariate Cox regression analysis were used to screen out the CpG sites that related with RNAs' expression and KIRC patients' overall survival. Then, a five-CpG-based prognostic classifier was established using LASSO Cox regression method., Key Findings: The seven-CpG-based classifier can successfully divide KIRC patients into high-risk from low-risk groups, even after adjustment for standard clinical prognostic factors, such as age, stage, gender and grade. Moreover, the seven-CpG-based signature was more effective as independent prognostic factors than the combined model of these clinical factors. Six differential mRNA genes corresponding to the seven CpG sites are all related to human cancers by functional exploration. The gene functional and pathway enrichment analysis found that genes in immune-related pathways were remarkably different in high and low-risk groups., Significance: The new seven-CpG-based signature could helpfully provide insights into the underlying mechanism of KIRC and may be a powerful independent biomarker for predicting of the survival of KIRC patients., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based GCN (AGCN), for breast cancer molecular subtype classification using messenger RNA expression, copy number variation and deoxyribonucleic acid methylation multi-omics data. In the extensive comparative studies, our AGCN models outperform state-of-the-art methods under different experimental conditions and both attention mechanisms and the graph convolution subnetwork play an important role in accurate cancer subtype classification. The layer-wise relevance propagation (LRP) algorithm is used for the interpretation of model decision, which can identify patient-specific important biomarkers that are reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of the GCN and attention mechanisms in multi-omics integrative analysis and the implement of the LRP algorithm can provide biologically reasonable insights into model decision. [ABSTRACT FROM AUTHOR]

The majority of the biomarkers were associated with the diagnosis of epilepsy and few of them can be applied to predict the response to antiseizure medications (ASMs). In this study, we identified 26 significantly up-regulated genes and 32 down-regulated genes by comparing the gene expression profiles of patients with epilepsy that responded to valproate with those without applying any ASM. The results of gene set enrichment analysis indicated that the ferroptosis pathway was significantly impacted (p = 0.0087) in patients who responded to valproate. Interestingly, the gene NCOA4 in this pathway exhibited significantly different expression levels between the two groups, indicating that NCOA4 could serve as a potential biomarker to better understand the mechanism of valproate resistance. In addition, six up-regulated genes SF3A2, HMGN2, PABPN1, SSBP3, EFTUD2, and CREB3L2 as well as six down-regulated genes ZFP36L1, ACRC, SUB1, CALM2, TLK1, and STX2 also showed significantly different expression patterns between the two groups. Moreover, based on the gene expression profiles of the patients with the treatment of valproate, carbamazepine, and phenytoin, we proposed a strategy for predicting the response to the ASMs by using the Connectivity Map scoring method. Our findings could be helpful for better understanding the mechanisms of drug resistance of ASMs and improving the clinical treatment of epilepsy. [ABSTRACT FROM AUTHOR]

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both in vivo and in vitro assays. For in vivo TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but in vitro assays, as alternatives, usually demonstrate poor correlation with real in vivo assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from in vivo assays based on gene expression levels and to further utilize in vitro data to better simulate in vivo data is needed. We proposed a strategy based on post‐modified non‐negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate in vivo data by correcting in vitro data. The similarities between real in vivo data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real in vivo data with in vitro data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using in vitro data to provide potential substitutions for in vivo TGx and to reduce the launch of live-animal tests. [ABSTRACT FROM AUTHOR]

Histone modifications play critical roles in mammalian development, regulating chromatin structure and gene expression. Dynamic histone modifications among cell types have been shown to associate with changes in mammalian development. However, how to quantitatively measure the histone modification alterations and how histone modifications vary across cell types under different genetic contexts remain largely unexplored and whether these changes are related to the primary DNA sequence remains limited. Here, we employed an entropy-based method to measure histone modification alterations in six definite genomic regions across five cell types and identified lineage-specific histone modification genes. We observed that histone modification alterations prefer to enrich in 5′-UTR exons, and also in 3′-UTR exons and its downstream. Then we built a model to predict the histone modification patterns from the primary DNA sequence. We found that the frequencies of k-mer sequence compositions are predictive of histone modification patterns, suggesting that the primary DNA sequence correlated with the histone modification alterations among cell types. Additionally, the lineage-specific histone modification genes display a higher conservation and lower GC-content. Together, we performed a systematic analysis for histone modification alterations and demonstrated how to identify genomic region-specific elements of epigenetic and genetic regulation and histone modification patterns across different cell types. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is currently still a major factor leading to death, lacking of reliable biomarkers. Therefore, deep understanding the pathogenesis for HCC is of great importance. The emergence of circular RNA (circRNA) provides a new way to study the pathogenesis of human disease. Here, we employed the prediction tool to identify circRNAs based on RNA-seq data. Then, to investigate the biological function of the circRNA, the candidate circRNAs were associated with the protein-coding genes (PCGs) by GREAT. We found significant candidate circRNAs expression alterations between normal and tumor samples. Additionally, the PCGs associated with these candidate circRNAs were also found have discriminative expression patterns between normal and tumor samples. The enrichment analysis illustrated that these PCGs were predominantly enriched for liver/cardiovascular-related diseases such as atherosclerosis, myocardial ischemia and coronary heart disease, and participated in various metabolic processes. Together, a further network analysis indicated that these PCGs play important roles in the regulatory and the PPI network. Finally, we built a classification model to distinguish normal and tumor samples by using candidate circRNAs and their associated genes, respectively. Both of them obtained satisfactory results (~ 0.99 of AUC for circRNA and PCG). Our findings suggested that the circRNA could be a critical factor in HCC, providing a useful resource to explore the pathogenesis of HCC. [ABSTRACT FROM AUTHOR]

The application of supervised algorithms in clinical practice has been limited by the lack of information on pure cell types. Several supervised algorithms have been proposed to estimate the gene expression patterns of specific cell types from heterogeneous samples. Post‐modified non‐negative matrix factorization (NMF), the unsupervised algorithm we proposed here, is capable of estimating the gene expression profiles and contents of the major cell types in cancer samples without any prior reference knowledge. Post‐modified NMF was first evaluated using simulation data sets and then applied to deconvolution of the gene expression profiles of cancer samples. It exhibited satisfactory performance with both the validation and application data. For application in 3 types of cancer, the differentially expressed genes (DEGs) identified from the deconvoluted gene expression profiles of tumor cells were highly associated with the cancer‐related gene sets. Moreover, the estimated proportions of tumor cells showed significant difference between the 2 compared patient groups in clinical endpoints. Our results indicated that the post‐modified NMF can efficiently extract the gene expression patterns of specific cell types from heterogeneous samples for subsequent analysis and prediction, which will greatly benefit clinical prognosis. The post‐modified non‐negative matrix factorization (NMF) proposed here is an unsupervised algorithm, which can extract the estimated expression and proportion of each cell type in mixed samples, without any prior reference knowledge. This post‐modified NMF exhibited satisfactory performance in deconvoluting the gene expression profiles of the heterogeneous clinical samples with both simulation and clinical data. The post‐modified NMF provides a benefit for further biological analysis and clinical cancer prognosis prediction. [ABSTRACT FROM AUTHOR]

Prostate cancer is dangerous due to its metastatic property in the advanced stage. Considerable advance has been made in unraveling the tumor-related molecular basis. Traditionally, differentially expressed genes are focused on for biomarker identification. Here, we argued that subtle gene alterations could be amplified through the biological network, those omissive non-significantly disrupted genes might be also cancer-related. To verify our hypothesis, we constructed a co-regulatory network comprised of miRNA, TF and mRNA. Then an essential score for a gene was computed by summing its neighbor expression weighted by the Jaccard similarity coefficient. The genes with the most essential score alterations (ESA) were selected for the following analyses. Although most of these genes exhibit only subtle alterations at expression levels, they are indeed informative in discriminating between tumor and normal samples. Moreover, the enrichment analysis revealed they are cancer-associated. We, therefore, debated that they could be used as cancer-related factors. Finally, feed-forward loops (FFLs) were constructed based on these genes for further investigating their potential regulation patterns associated with the prostate cancer. We found most nodes in the FFLs related to cancer. Our results suggested the subtle alterations could impact the biological system by their interaction with other elements. This observation would shed light on deep understanding the mechanism of the prostate cancer. • We have proposed a strategy to identify the essential genes in the miRNA-TF-mRNA co-regulatory network by integrating the neighbor expression and the gene association. • Although most of the selected essential alteration genes were non-significantly differentially expressed, they are cancer-related and informative in discriminating tumor/normal samples. • These ESA mRNAs or miRNAs can interact with other nodes of differentially expressed to participate in cancer-related biological processes. [ABSTRACT FROM AUTHOR]

Dysregulated and reprogrammed metabolism are one of the most important characteristics of cancer, and exploiting cancer cell metabolism can aid in understanding the diverse clinical outcomes for patients. To investigate the differences in metabolic pathways among patients with acute myeloid leukemia (AML) and differential survival outcomes, we systematically conducted microarray data analysis of the metabolic gene expression profiles from 384 patients available from the Gene Expression Omnibus and Cancer Genome Atlas databases. Pathway enrichment analysis of differentially expressed genes (DEGs) showed that the metabolic differences between low-risk and high-risk patients mainly existed in two pathways: biosynthesis of unsaturated fatty acids and oxidative phosphorylation. Using the gene-pathway bipartite network, 62 metabolic genes were identified from 272 DEGs involved in 88 metabolic pathways. Based on the expression patterns of the 62 genes, patients with shorter overall survival (OS) durations in the training set (hazard ratio (HR) = 1.58, p = 0.038) and in two test sets (HR = 1.69 and 1.56 and p = 0.089 and 0.029, respectively) were well discriminated by hierarchical clustering analysis. Notably, the expression profiles of ALAS2 , BCAT1 , BLVRB , and HK3 showed distinct differences between the low-risk and high-risk patients. In addition, models for predicting the OS outcome of AML from the 62 gene signatures achieved improved performance compared with previous studies. In conclusion, our findings reveal significant differences in metabolic processes of patients with AML with diverse survival durations and provide valuable information for clinical translation. [ABSTRACT FROM AUTHOR]

Post-translational modifications of histone play a key role in controlling gene expression, as they are involved in gene transcription regulation through controlling DNA accessibility or recruiting specific transcription factors. Developing computational methods to predict gene expression levels from histone modification data may facilitate understanding their function in gene regulation and contribute to develop 'epigenetic drugs' for disease treatment. Several studies have reported that transcription termination plays a crucial role in gene regulation as it is able to premature termination and determine the cellular fate of transcripts. However, previous work mainly focused on the histone characteristics flanking transcription start site (TSS) of genes, while ignoring the histone modification features around transcription termination site (TTS). Thus, we introduced a hybrid convolutional and bi-directional long short-term memory network with attention mechanism to predict gene expression from histone modification signals in both regions. Our model achieved higher results than the state-of-the-art model in both classification and regression tasks to infer gene expression level. The predictive results of model demonstrated that histone signals of TTS can provide additional information to improve the model performance. In addition, attention weights and transcription factor binding data indicated that histone modifications in TSS might regulate transcription by recruiting TFs, but the regulation mechanisms of it in TTS need to be further explored. • A new hybrid deep learning model was proposed for predicting gene expression levels and achieved better performance compared to baselines. • The model integrated histone features from both TSS and TTS region and validated the function role of histone modification of TTS in transcription regulation. • We explored the possible regulation mechanism of histone modifications in TSS and TTS. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays DNA microarray technology is widely used in clinical researches for generating gene expression profiles from the biological samples. Based on the gene expression data, identifying differentially expressed genes (DEGs) from two groups of phenotypes or distinct biological conditions is one of the crucial steps in the procedure of discovering disease biomarkers. However, the clinical samples usually contain multiple cell types. This heterogeneous cell population significantly affects the gene expression patterns and will mask the biological difference between two groups of compared samples. Using mixed gene expression profile of multiple cell types instead of that of interested cell type for the identification of DEGs will seriously decrease the sensitivity of discovering the disease-related genes. Therefore, we proposed nonnegative matrix factorization (NMF), an unsupervised learning method that has been successfully applied in bioinformatics researches, for extracting the actual gene expression profile of interested cell type from the mixed profile of heterogeneous cell population. In our study, we firstly evaluated the performance of NMF algorithm in the deconvolution of gene expression data by using a well-controlled data set comprising the gene expression profiles from three tissues and eleven different mixtures with known proportions. Then, NMF was applied to the human whole-blood gene expression data generated from 24 kidney transplant recipients for estimating the pure gene expression profiles of five major blood cells, which were subsequently used to identify the genes related to the acute rejection of kidney transplant. The results showed that the number of DEGs (probe sets), which were identified from each of the gene expression profiles of five blood cells between stable post-transplant kidney transplant recipients and those experiencing acute transplant rejections, was greater than that from whole-blood samples. Finally, the DEGs were uploaded to the Gene Set Enrichment Analysis (GSEA) for the enrichment of signaling pathways and gene ontology terms. We found that several enriched pathways and gene ontology terms were significantly associated with renal transplantation rejection when the uploaded DEGs were identified from the two high content blood cells, while none of pathways and gene ontology terms was enriched when the uploaded DEGs were identified from whole-blood samples. Our results indicated that using the gene expression profile of specific cell type deconvoluted by NMF can efficiently increase the sensitivity of discovering potentially disease-related genes. In addition, this unsupervised method can evaluate the pure gene expression profile of specific cell type from the mixtures with no prior knowledge of cell proportions. [Copyright &y& Elsevier]

Abstract: MicroRNA (miRNA) is the negative regulator of gene expression, also known as guide strand of transient miRNA:miRNA* duplex. It is critical in maintaining the normal physiological processes such as development, differentiation, and apoptosis in many organisms. With increasing miRNA data, it is desirable to design methods to identify guide strand based on machine learning algorithms. In this study, the random forest models based on local sequence–structure features were proposed to identify miRNA in four species. The accuracies achieved were 86.51% for Homo sapiens, 81.66% for Ornithorhynchus anatinus, 82.33% for Mus musculus and 85.71% for Schmidtea mediterranea, respectively. Furthermore, the important analysis of feature elements was carried out by using the conditional feature importance strategy. The analysis results revealed that most of the significant elements were related to guanine–cytosine (GC) base pair. We believed that our method could be beneficial to annotate the function of miRNA and help the further understanding of the RNA interference mechanism. [Copyright &y& Elsevier]