NMFNA: A Non-negative Matrix Factorization Network Analysis Method for Identifying Modules and Characteristic Genes of Pancreatic Cancer

Pancreatic cancer (PC) is a highly fatal disease, yet its causes remain unclear. Comprehensive analysis of different types of PC genetic data plays a crucial role in understanding its pathogenic mechanisms. Currently, non-negative matrix factorization (NMF)-based methods are widely used for genetic data analysis. Nevertheless, it is a challenge for them to integrate and decompose different types of genetic data simultaneously. In this paper, a non-NMF network analysis method, NMFNA, is proposed, which introduces a graph-regularized constraint to the NMF, for identifying modules and characteristic genes from two-type PC data of methylation (ME) and copy number variation (CNV). Firstly, three PC networks, i.e., ME network, CNV network, and ME–CNV network, are constructed using the Pearson correlation coefficient (PCC). Then, modules are detected from these three PC networks effectively due to the introduced graph-regularized constraint, which is the highlight of the NMFNA. Finally, both gene ontology (GO) and pathway enrichment analyses are performed, and characteristic genes are detected by the multimeasure score, to deeply understand biological functions of PC core modules. Experimental results demonstrated that the NMFNA facilitates the integration and decomposition of two types of PC data simultaneously and can further serve as an alternative method for detecting modules and characteristic genes from multiple genetic data of complex diseases.

A novel TUBG1 mutation with neurodevelopmental disorder caused by malformations of cortical development

Neurodevelopmental disorder caused by malformations of cortical development is a rare neurological disease. Heterozygous missense variants in the TUBG1 gene lead to malformations of human cortical development, which further result in intellectual disability, developmental retardation, and epilepsy. To the best of our knowledge, only thirteen patients and a total of nine pathogenic TUBG1 variants have been described in the published literature. This study reports the case details and genetic data analysis of a girl (aged 8 years, 9 months) with developmental delay, psychomotor regression, epilepsy, and left external ear deformity. A novel TUBG1 mutation was identified by whole-exome sequencing and Sanger sequencing, confirming that this mutation may be the cause of the neurodevelopmental disorders. This case report characterizes the phenotypic spectrum, molecular genetic findings, and functional consequences of novel pathogenic TUBG1 variants in neurodevelopmental disorders caused by cortical development malformations.

Genetic Data Analysis

Genome-wide association studies (GWAS) or genetic data analysis is used to discover common genetic factors which influence the health of human beings and become a part of a disease. The concept of using genomics has increased in recent years, especially in e-healthcare. Today there is huge improvement required in this field or genomics. Note that the terms genomics and genetics are not similar terms here. Basically, the human genome is made up of DNA, which consists of four different chemical building blocks (called bases and abbreviated A, T, C, and G). Based on this, we differentiate each and every human being living on earth. The term ‘genetics' originated from the Greek word ‘genetikos'. It means ‘origin'. In simple terms, genetics can be defined as a branch of biology, which deals with the study of the functionalities and composition of a single gene in an organism. There are mainly three branches of genetics, which include classical genetics, molecular genetics, and population genetics.

The Role of Genetic Data Analysis for Precision Therapy in Cancer

Cancer has been known as a devastating disease that takes thousands of lives every year. And since this is a heterogenous disease, standard treatments, like chemotherapy, radiation, and chemo-radio therapy, are effective in specific patient population subset only. Genetic differences play a very crucial role in defining cancer susceptibility and also in determining the drug's efficacy by affecting regulation, expression, and activity of drug metabolizing enzymes, drug transporters, and drug receptors. This genetic variability of the disease lends itself to the emerging field of precision or personalized medicine. There are some specific ways of acquiring data for precision or personalized medicine approach like genome wide association scan (GWAS). This is basically identification and scanning of biomarkers throughout the complete DNA/genome of several individuals to study any type of genetic variations which are linked with any form of cancer.