Using bioinformatics and genome analysis for new therapeutic interventions

Identification and validation of novel prognostic biomarkers and therapeutic targets for non-small cell lung cancer

Background: Despite the significant survival benefits of anti-PD-1/PD-L1 immunotherapy, non-small cell lung cancer (NSCLC) remains one of the most common tumors and major causes of cancer-related deaths worldwide. Thus, there is an urgent need to identify new therapeutic targets for this refractory disease.

Methods: In this study, microarray datasets GSE27262, GSE75037, GSE102287, and GSE21933 were integrated by Venn diagram. We performed functional clustering and pathway enrichment analyses using R. Through the STRING database and Cytoscape, we conducted protein-protein interaction (PPI) network analysis and identified the key genes, which were verified by the GEPIA2 and UALCAN portal. Validation of actin-binding protein anillin (ANLN) was performed by quantitative real-time polymerase chain reaction and Western blotting. Additionally, Kaplan-Meier methods were used to compute the survival analyses.

Results: In total, 126 differentially expressed genes were identified, which were enriched in mitotic nuclear division, mitotic cell cycle G2/M transition, vasculogenesis, spindle, and peroxisome proliferator-activated receptor signaling pathway. 12 central node genes were identified in the PPI network complex. The survival analysis revealed that high transcriptional levels were associated with inferior survival in NSCLC patients. The clinical implication of ANLN was further explored; its protein expression showed a gradually increasing trend from grade I to III.

Conclusion: These Key genes may be involved in the carcinogenesis and progression of NSCLC, which may serve as useful targets for NSCLC diagnosis and treatment.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

BACKGROUND AND OBJECTIVES

This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.

METHODS

We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.

RESULTS

It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.

CONCLUSIONS

Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

Cinnamaldehyde and eugenol change the expression folds of AKT1 and DKC1 genes and decrease the telomere length of human adipose-derived stem cells (hASCs): An experimental and in silico study

Objective(s):

To investigate the effect of cinnamaldehyde and eugenol on the telomere-dependent senescence of stem cells. In addition, to search the probable targets of mentioned phytochemicals between human telomere interacting proteins (TIPs) using in silico studies.

Materials and Methods:

Human adipose derived stem cells (hASCs) were studied under treatments with 2.5 µM/ml cinnamaldehyde, 0.1 µg/ml eugenol, 0.01% DMSO or any additive. The expression of TERT, AKT1 and DKC1 genes and the telomere length were assessed over 48-hr treatment. In addition, docking study was conducted to show probable ways through which phytochemicals interact with TIPs.

Results:

Treated and untreated hASCs had undetectable TERT expression, but they had different AKT1 and DKC1 expression levels (CI=0.95; P<0.05). The telomere lengths were reduced in phytochemicals treated with hASCs when compared with the untreated cells (P<0.05). Docking results showed that the TIPs might be the proper targets for cinnamaldehyde and eugenol. Data mining showed there are many targets for cinnamaldehyde and eugenol in the intracellular environment.

Conclusion:

The general effect of cinnamaldehyde and eugenol is their induction of stem cell senescence. Therefore, they could be applicable as chemo-preventive or antineoplastic agents.

KRAS AND THE REALITY OF PERSONALIZED MEDICINE IN NON-SMALL CELL LUNG CANCER

Lung cancer is the leading cause of mortality among all cancer types, worldwide. Latest available global statistics of World Health Organization report 1.59 million casualities in 2012 alone. Worldwide, 1 in 5 cancer deaths are caused by lung cancer. In 2016, in USA alone, estimated new cases of lung cancer are 224,390, of which 158,080 are expected to result in death as reported by National Cancer Institute. Non-small cell lung cancer (NSCLC), a histological subtype, comprises of about 85% of all cases, which is nearly 9 out of 10 lung cancer patients. Efforts are underway to develop and improve targeted therapy strategies. Certain mutations are being clinically targeted such as those in EGFR and ALK genes. However, one of the most frequently mutated genes in NSCLC is the KRAS oncogene, which is currently untargetable. Approximately 25% of all types of NSCLC tumors contain KRAS mutations, which remain as an undruggable challenge. These mutations are indicative of poor prognosis and confer negative response to standard chemotherapy. Furthermore, tumors harboring KRAS mutations are unlikely to respond to currently available targeted treatments such as Tyrosine Kinase Inhibitors. Therefore, there is a definitive, urgent need to generate new targeted therapy approaches for KRAS mutations. Current strategies have major limitations and evolve around targeting molecules upstream and downstream of KRAS. Direct targeting is not available in the clinic. Combination therapies of multiple agents are being sought. Concentrated efforts are needed to accelerate basic research and consecutive clinical trials to achieve effective targeting of KRAS.

Host-microbial interactions and regulation of intestinal epithelial barrier function: From physiology to pathology

The gastrointestinal tract is the largest reservoir of commensal bacteria in the human body, providing nutrients and space for the survival of microbes while concurrently operating mucosal barriers to confine the microbial population. The epithelial cells linked by tight junctions not only physically separate the microbiota from the lamina propria, but also secrete proinflammatory cytokines and reactive oxygen species in response to pathogen invasion and metabolic stress and serve as a sentinel to the underlying immune cells. Accumulating evidence indicates that commensal bacteria are involved in various physiological functions in the gut and microbial imbalances (dysbiosis) may cause pathology. Commensal bacteria are involved in the regulation of intestinal epithelial cell turnover, promotion of epithelial restitution and reorganization of tight junctions, all of which are pivotal for fortifying barrier function. Recent studies indicate that aberrant bacterial lipopolysaccharide-mediated signaling in gut mucosa may be involved in the pathogenesis of chronic inflammation and carcinogenesis. Our perception of enteric commensals has now changed from one of opportunistic pathogens to active participants in maintaining intestinal homeostasis. This review attempts to explain the dynamic interaction between the intestinal epithelium and commensal bacteria in disease and health status.

Target discovery from data mining approaches

Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced.

High-throughput siRNA screening as a method of perturbation of biological systems and identification of targeted pathways coupled with compound screening

High-throughput RNA interference (HT-RNAi) is a powerful research tool for parallel, 'genome-wide', targeted knockdown of specific gene products. Such perturbation of gene product expression allows for the systematic query of gene function. The phenotypic results can be monitored by assaying for specific alterations in molecular and cellular endpoints, such as promoter activation, cell proliferation and survival. RNAi profiling may also be coupled with drug screening to identify molecular correlates of drug response. As with other genomic-scale data, methods of data analysis are required to handle the unique aspects of data normalization and statistical processing. In addition, novel techniques or knowledge-mining strategies are required to extract useful biological information from HT-RNAi data. Knowledge-mining strategies involve the novel application of bioinformatic tools and expert curation to provide biological context to genomic-scale data such as that generated from HT-RNAi data. Pathway-based tools, whether text-mining based or manually curated, serve an essential role in knowledge mining. These tools can be applied during all steps of HT-RNAi screen experiments including pre-screen knowledge gathering, assay development and hit confirmation and validation. Most importantly, pathway tools allow the interrogation of HT-RNAi data to identify and prioritize pathway-based biological information as a result of specific loss of gene function.

Technology insight: microarrays--research and clinical applications

For microarrays, the transition from research to clinical and diagnostic applications is well underway. Microarrays use a range of specific probes that are immobilized in known locations on a support matrix; this technique can measure levels of specific DNA, RNA and proteins, as well as carbohydrates and lipids. It is anticipated that analysis of these levels will lead to identification of biomarkers for the diagnosis, treatment and prognosis of a wide range of diseases. So far, this type of analysis has been particularly useful in clinical oncology, but the technology is being actively and successfully explored for diseases such as diabetes, endocrine tumors and endocrine modulators of tumors. There are now many commercial sources of microarrays, which have robust quality-control procedures in place. Progress will be enhanced when biomarkers can be established, statistical approaches can be refined and when we better understand the interactions of genes and of particular gene loci in disease progression.