Weighted Gene Co-expression Network Analysis Reveals ALDH1B1 As a Prognositc Biomarker Involved in Gastric Cancer Development

BackgroundGastric cancer (GC) is one of the leading cancers associated with high mortality and poor prognosis mainly due to its relatively late diagnosis and the limited therapeutic options. Consequently, screening for prognostic GC biomarkers and novel molecular therapeutic targets is necessary to promote patient outcomes. Methods Weighted gene co-expression network analysis (WGCNA), a systems biology approach, was applied to analyze the mRNA sequencing data and clinical information of GC patients obtained from The Cancer Genome Atlas (TCGA). Gene modules and clinical traits were constructed according to the Pearson correlation analysis, and the gene ontology (GO) and functional enrichment analysis of meaningful modules were carried out. Hub genes from meaningful modules were screened out by two approaches: the intra-modular and protein-protein interaction (PPI) analysis methods. Next, through upstream regulatory analysis, hub genes with high connectivity degree were further validated with differential expression analysis, Kaplan-Meier survival analysis, and the Cox regression model. ResultsWe found that seven modules were associated with the following clinical traits: anatomical location of gastric adenocarcinoma, histological type, histological grade, and pathological stage. The hub gene ALDH1B1 was found to have potential as a biomarker for gastric cancer cells, the relationship between this hub gene and gastric cancer drug treatment is also worthy of attention.Conclusion These findings may contribute to understanding the GC tumourigenic mechanisms, as well as provide new potential prognostic factors and molecular therapeutic targets for GC. The ALDH1B1 hub gene also provides a new vantage point for further clinical experiments and large-scale cohort studies to validate its association with GC patient survival, and provide a new direction for the research of gastric cancer drug treatment.

N-myc Downstream-Regulated Gene 2 (NDRG2) Function as a Positive Regulator of Apoptosis: A New Insight into NDRG2 as a Tumor Suppressor

N-myc downstream-regulated gene 2 (NDRG2) is a tumor suppressor gene that increases tumor sensitivity to anticancer drugs, slows tumor progression, and inhibits metastasis. NDRG2 is suppressed in various aggressive tumor positions, whereas NDRG2 expression is associated with patient prognosis, such as an improved survival rate. In this review, we summarize the tumor suppressor mechanism of NDRG2 and provide information on the function of NDRG2 concerning the susceptibility of cells to apoptosis. NDRG2 increases the susceptibility to apoptosis in various physiological environments of cells, such as development, hypoxia, nutrient deprivation, and cancer drug treatment. Although the molecular and cell biological mechanisms of NDRG2 have not been fully elucidated, we provide information on the mechanisms of NDRG2 in relation to apoptosis in various environments. This review can assist the design of research regarding NDRG2 function and suggests the potential of NDRG2 as a molecular target for cancer patients.

Pharmacogenomics in Pediatric Oncology: Mitigating Adverse Drug Reactions While Preserving Efficacy

Cancer is the leading cause of death in American children older than 1 year of age. Major developments in drugs such as thiopurines and optimization in clinical trial protocols for treating cancer in children have led to a remarkable improvement in survival, from approximately 30% in the 1960s to more than 80% today. Short-term and long-term adverse effects of chemotherapy still affect most survivors of childhood cancer. Pharmacogenetics plays a major role in predicting the safety of cancer chemotherapy and, in the future, its effectiveness. Treatment failure in childhood cancer—due to either serious adverse effects that limit therapy or the failure of conventional dosing to induce remission—warrants development of new strategies for treatment. Here, we summarize the current knowledge of the pharmacogenomics of cancer drug treatment in children and of statistically and clinically relevant drug–gene associations and the mechanistic understandings that underscore their therapeutic value in the treatment of childhood cancer.

Rank orders and signed interactions in evolutionary biology

Rank orders have been studied in evolutionary biology for almost a hundred years. Constraints on the order in which mutations accumulate are known from cancer drug treatment, and order constraints for species invasions are important in ecology. However, current theory on rank orders in biology is somewhat fragmented. Here, we show how our previous work on inferring genetic interactions from comparative fitness data (Crona et al., 2017) is related to an influential approach to rank orders based on sign epistasis. Our approach depends on order perturbations that indicate interactions. We apply our results to malaria parasites and find that order perturbations beyond sign epistasis are prevalent in the antimalarial drug-resistance landscape. This finding agrees with the observation that reversed evolution back to the ancestral type is difficult. Another application concerns the adaptation of bacteria to a methanol environment.

Methods and resources to access mutation-dependent effects on cancer drug treatment

In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.