Weighted gene coexpression analysis indicates that PLAGL2 and POFUT1 are related to the differential features of proximal and distal colorectal cancer

Glycans that regulate Notch signaling in the intestine

Intestinal homeostasis is key to the maintenance of good health. The small intestine plays important roles in absorption, digestion, hormonal and immune functions. Crypt base columnar (CBC) stem cells residing at the bottom of crypts are nurtured by Paneth cells, and together create the stem cell niche, the foundation of intestinal homeostasis. CBC stem cells replicate to replenish their number, or differentiate into a variety of epithelial cells with specialized functions. Notch signaling is a cell-cell signaling pathway that regulates both the proliferation and differentiation of CBC stem cells. NOTCH1 and NOTCH2 stimulated by canonical Notch ligands DLL1 and DLL4 mediate Notch signaling in the intestine that, in concert with other signaling pathways including the WNT and BMP pathways, determines cell fates. Importantly, interactions between Notch receptors and canonical Notch ligands are regulated by O-glycans linked to Ser/Thr in epidermal growth factor-like (EGF) repeats of the Notch receptor extracellular domain (NECD). The O-glycans attached to NECD are key regulators of the strength of Notch signaling. Imbalances in Notch signaling result in altered cell fate decisions and may lead to cancer in the intestine. In this review, we summarize the impacts of mutations in Notch pathway members on intestinal development and homeostasis, with a focus on the glycosyltransferases that transfer O-glycans to EGF repeats of NOTCH1, NOTCH2, DLL1 and DLL4.

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

Other Types of Glycosylation

O-Linked glycosylation such as O-fucose, O-glucose, and O-N-acetylglucosamine are considered to be unusual. As suggested by the high levels of evolutional conservation, these O-glycans are fundamentally important for life. In the last two decades, our understanding of the importance of these glycans has greatly advanced. In particular, identification of the glycosyltransferases responsible for the biosynthesis of these glycans has accelerated basic research on the functional significance and molecular mechanisms by which these O-glycans regulate protein functions as well as clinical research on human diseases due to changes in these types of O-glycosylation. Notably, Notch receptor signaling is modified with and regulated by these types of O-glycans. Here, we summarize the current view of the structures and the significance of these O-glycans mainly in the context of Notch signaling regulation and human diseases.

Novel diagnostic and prognostic biomarkers of colorectal cancer: Capable to overcome the heterogeneity-specific barrier and valid for global applications

Introduction

The heterogeneity-specific nature of the available colorectal cancer (CRC) biomarkers is significantly contributing to the cancer-associated high mortality rate worldwide. Hence, this study was initiated to investigate a system of novel CRC biomarkers that could commonly be employed to the CRC patients and helpful to overcome the heterogenetic-specific barrier.

Methods

Initially, CRC-related hub genes were extracted through PubMed based literature mining. A protein-protein interaction (PPI) network of the extracted hub genes was constructed and analyzed to identify few more closely CRC-related hub genes (real hub genes). Later, a comprehensive bioinformatics approach was applied to uncover the diagnostic and prognostic role of the identified real hub genes in CRC patients of various clinicopathological features.

Results

Out of 210 collected hub genes, in total 6 genes (CXCL12, CXCL8, AGT, GNB1, GNG4, and CXCL1) were identified as the real hub genes. We further revealed that all the six real hub genes were significantly dysregulated in colon adenocarcinoma (COAD) patients of various clinicopathological features including different races, cancer stages, genders, age groups, and body weights. Additionally, the dysregulation of real hub genes has shown different abnormal correlations with many other parameters including promoter methylation, overall survival (OS), genetic alterations and copy number variations (CNVs), and CD8+T immune cells level. Finally, we identified a potential miRNA and various chemotherapeutic drugs via miRNA, and real hub genes drug interaction network that could be used in the treatment of CRC by regulating the expression of real hub genes.

Conclusion

In conclusion, we have identified six real hub genes as potential biomarkers of CRC patients that could help to overcome the heterogenetic-specific barrier across different clinicopathological features.

Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas

Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.

High expression of COL5A2, a member of COL5 family, indicates the poor survival and facilitates cell migration in gastric cancer

Background: Gastric cancer (GC) metastasis determines the prognosis of patients, and exploring the molecular mechanism of GC metastasis is expected to provide a theoretical basis for clinical treatment. Recent studies have shown that extracellular matrix protein is closely related to GC metastasis. The present study aimed to explore the expression profile and role of COL5A2, as an extracellular matrix protein, in GC.

Methods: The expression, overall survival, and progression-free survival data of COL5 family members were extracted from The Cancer Genome Atlas (TCGA) database, respectively. Weighted gene co-expression network analysis of the GSE62229 database was performed out to identify modules and associated genes.

Results: COL5A2 was selected as our research target in the TCGA database, and was also verified in the GSE62229 and GSE15459 datasets. COL5A2 was up-regulated in GC tissues by paraffin immunohistochemistry and RT-qPCR. The prognosis of patients with low COL5A2 expression was better than that of patients with high COL5A2 expression. Scratch and migration experiments showed that knockdown of COL5A2 decreased the migration ability of gastric cancer cells compared with the control group. In vivo, mice with tail vein injection COL5A2 knockdown had fewer and smaller metastatic nodules in liver. GSEA results showed that the TCGA and GSE62229 samples were significantly enriched in several well-known cancer-related pathways, such as the TGF-β, MAPK, and JAK2 signaling pathways.

Conclusion: COL5A2 was most closely related to advanced GC among COL5 family members. High COL5A2 expression is associated with a poor prognosis, and may be a novel therapeutic target for GC.