Identifying Key Genes of Liver Cancer by Networking of Multiple Data Sets

Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set

This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformation-based adaptive thresholding-based segmentation process. After the segmentation, certain features are extracted that include multi-texon based features, Improved Local Ternary Pattern (LTP-based features), and GLCM features during this phase. In the Classification phase, an improved Deep Maxout model is proposed for liver cancer detection. The adopted scheme is evaluated over other schemes based on various metrics. While the learning rate is 60%, an improved deep maxout model achieved a higher F-measure value (0.94) for classifying liver cancer; however, the previous method like Support Vector Machine (SVM), Random Forest (RF), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), K-Nearest Neighbor (KNN), Deep maxout, Convolutional Neural Network (CNN), and DL model holds less F-measure value. An improved deep maxout model achieved minimal False Positive Rate (FPR), and False Negative Rate (FNR) values with the best outcomes compared to other existing models for liver cancer classification.

The Role of LINC01564, RAMS11, CBX4 and TOP2A in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is the most common histologic type of primary liver cancers worldwide. Hepatitis C virus (HCV) infection remains a major risk factor for chronic liver disease, cirrhosis, and HCC. To understand the molecular pathogenesis of HCC in chronic HCV infection, many molecular markers are extensively studied, including long noncoding RNAs (lncRNA). Objective: To evaluate the expression levels of lncRNAs (LINC01564, RAMS11), CBX4, and TOP2A in patients with chronic HCV infection and patients with HCC on top of chronic HCV infection and correlate these levels with the clinicopathological features of HCC. Subjects and Methods: One hundred and fifty subjects were enrolled in this study and divided into three groups: group I included 50 patients with HCC on top of chronic hepatitis C (CHC), group II included 50 patients with CHC only, and group III included 50 healthy individuals as a control group. LncRNAs relative expression level was determined by RT-PCR. Results: lncRNA (LINC01564, RAMS11), CBX4, and TOP2A relative expression levels were upregulated in both patient groups compared to controls (p < 0.001*), with the highest levels in the HCC group compared with the CHC group. Additionally, these levels were significantly positively correlated with the clinicopathological features of HCC. Conclusions: The lncRNA (LINC01564, RAMS11), CBX4, and TOP2A relative expression levels were upregulated in CHC patients—in particular, patients with HCC. Thus, these circulatory lncRNAs may be able to serve as promising noninvasive diagnostic markers for HCC associated with viral C hepatitis.

DeFusion: a denoised network regularization framework for multi-omics integration

With diverse types of omics data widely available, many computational methods have been recently developed to integrate these heterogeneous data, providing a comprehensive understanding of diseases and biological mechanisms. But most of them hardly take noise effects into account. Data-specific patterns unique to data types also make it challenging to uncover the consistent patterns and learn a compact representation of multi-omics data. Here we present a multi-omics integration method considering these issues. We explicitly model the error term in data reconstruction and simultaneously consider noise effects and data-specific patterns. We utilize a denoised network regularization in which we build a fused network using a denoising procedure to suppress noise effects and data-specific patterns. The error term collaborates with the denoised network regularization to capture data-specific patterns. We solve the optimization problem via an inexact alternating minimization algorithm. A comparative simulation study shows the method's superiority at discovering common patterns among data types at three noise levels. Transcriptomics-and-epigenomics integration, in seven cancer cohorts from The Cancer Genome Atlas, demonstrates that the learned integrative representation extracted in an unsupervised manner can depict survival information. Specially in liver hepatocellular carcinoma, the learned integrative representation attains average Harrell's C-index of 0.78 in 10 times 3-fold cross-validation for survival prediction, which far exceeds competing methods, and we discover an aggressive subtype in liver hepatocellular carcinoma with this latent representation, which is validated by an external dataset GSE14520. We also show that DeFusion is applicable to the integration of other omics types.

High expression of TOP2A in hepatocellular carcinoma is associated with disease progression and poor prognosis

Hepatocellular carcinoma (HCC) is a common malignant tumor in the clinic. Although there are increasing numbers of available treatment methods, their therapeutic effects are not satisfactory. The clinical indicators commonly used to predict the prognosis of HCC include tumor size, degree of cirrhosis, degree of tumor differentiation and tumor microvascular invasion; however, there are currently no molecular indicators that can predict the prognosis of HCC. Due to the differences in the progression of liver cancer among individuals, there is a growing need for prognostic biomarkers to accurately stratify patients for appropriate risk-adaptive treatment. The DNA topoisomerase 2-α (TOP2A) gene, which is located on human chromosome 17, encodes DNA topoisomerase IIα. Previous studies have demonstrated that TOP2A indicates a poor prognosis in patients with various types of tumors, but no such studies are currently available on HCC. By analyzing the differential expression of TOP2A in 50 pairs of tumor and paracancerous tissue samples in The Cancer Genome Atlas (TCGA) database, the present study revealed that the expression of TOP2A was significantly higher in tumor tissue compared with that in paracancerous tissue (P=6.319×10-16). In the collected clinical samples, the mRNA expression levels of TOP2A were significantly upregulated in HCC tumor tissues compared with those in the paracancerous tissues (P=6.40×10-3), suggesting that TOP2A was associated with the occurrence and development of liver cancer. In addition, the associations between TOP2A expression, clinicopathological features and prognosis were analyzed using a multi-center large sample dataset from TCGA database, and the results demonstrated that high expression of TOP2A was associated with a higher T stage, poorer clinical stage and higher histological grade compared with those in patients with low TOP2A expression. High expression of TOP2A was also identified to be associated with a poor prognosis of HCC, particularly in Asian populations. These results suggested that high expression of TOP2A in HCC tissues may be closely associated with tumor progression and metastasis, which may be used as a biological indicator to predict tumor prognosis in clinical practice.

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review

Next-generation sequencing approaches and genome-wide studies have become essential for characterizing the mechanisms of human diseases. Consequently, many researchers have applied these approaches to discover the genetic/genomic causes of common complex and rare human diseases, generating multiomics big data that span the continuum of genomics, proteomics, metabolomics, and many other system science fields. Therefore, there is a significant and unmet need for biological databases and tools that enable and empower the researchers to analyze, integrate, and make sense of big data. There are currently large number of databases that offer different types of biological information. In particular, the integration of gene expression profiles and protein-protein interaction networks provides a deeper understanding of the complex multilayered molecular architecture of human diseases. Therefore, there has been a growing interest in developing methodologies that integrate and contextualize big data from molecular interaction networks to identify biomarkers of human diseases at a subnetwork resolution as well. In this expert review, we provide a comprehensive summary of most popular biomolecular databases for molecular interactions (e.g., Biological General Repository for Interaction Datasets, Kyoto Encyclopedia of Genes and Genomes and Search Tool for The Retrieval of Interacting Genes/Proteins), gene-disease associations (e.g., Online Mendelian Inheritance in Man, Disease-Gene Network, MalaCards), and population-specific databases (e.g., Human Genetic Variation Database), and describe some examples of their usage and potential applications. We also present the most recent subnetwork identification approaches and discuss their main advantages and limitations. As the field of data science continues to emerge, the present analysis offers a deeper and contextualized understanding of the available databases in molecular biomedicine.