Regulation of CTNNB1 signaling in gastric cancer and stem cells

Recent research has shown that the alteration of combinations in gene expression contributes to cellular phenotypic changes. Previously, it has been demonstrated that the combination of cadherin 1 and cadherin 2 expression can identify the diffuse-type and intestinaltype gastric cancers. Although the diffuse-type gastric cancer has been resistant to treatment, the precise mechanism and phenotypic involvement has not been revealed. It may be possible that stem cells transform into gastric cancer cells, possibly through the involvement of a molecule alteration and signaling mechanism. In this review article, we focus on the role of catenin beta 1(CTNNB1 or β-catenin) and describe the regulation of CTNNB1 signaling in gastric cancer and stem cells.

Role of mesenchymal stem cells in cell life and their signaling

Mesenchymal stem cells(MSCs) have various roles in the body and cellular environment, and the cellular phenotypes of MSCs changes in different conditions. MSCs support the maintenance of other cells, and the capacity of MSCs to differentiate into several cell types makes the cells unique and full of possibilities. The involvement of MSCs in the epithelial-mesenchymal transition is an important property of these cells. In this review, the role of MSCs in cell life, including their application in therapy, is first described, and the signaling mechanism of MSCs is investigated for a further understanding of these cells.

Regulated genes in mesenchymal stem cells and gastric cancer

AIM: To investigate the genes regulated in mesenchymal stem cells(MSCs) and diffuse-type gastric cancer(GC),gene expression was analyzed. METHODS: Gene expression of MSCs and diffuse-type GC cells were analyzed by microarray. Genes related to stem cells, cancer and the epithelial-mesenchymal transition(EMT) were extracted from human gene lists using Gene Ontology and reference information. Gene panels were generated, and messenger RNA gene expression in MSCs and diffuse-type GC cells was analyzed. Cluster analysis was performed using the NCSS software.RESULTS: The gene expression of regulator of G-protein signaling 1(RGS1) was up-regulated in diffuse-type GC cells compared with MSCs. A panel of stem-cell related genes and genes involved in cancer or the EMT were examined. Stem-cell related genes, such as growth arrest-specific 6, musashi RNA-binding protein 2 and hairy and enhancer of split 1(Drosophila), NOTCH family genes and Notch ligands, such as delta-like 1(Drosophila) and Jagged 2, were regulated.CONCLUSION: Expression of RGS1 is up-regulated, and genes related to stem cells and NOTCH signaling are altered in diffuse-type GC compared with MSCs.

Signaling involved in stem cell reprogramming and differentiation

Stem cell differentiation is regulated by multiple signaling events. Recent technical advances have reve-aled that differentiated cells can be reprogrammed into stem cells. The signals involved in stem cell pro-gramming are of major interest in stem cell research. The signaling mechanisms involved in regulating stem cell reprogramming and differentiation are the subject of intense study in the field of life sciences. In this review,the molecular interactions and signaling pathways related to stem cell differentiation are discussed.

How can artificial intelligence and humans work together to fight against cancer?

This editorial will focus on and discuss growing artificial intelligence(AI)and the utilization of AI in human cancer therapy.The databases and big data related to genomes,genes,proteins and molecular networks are rapidly increasing all worldwide where information on human diseases,including cancer and infection resides.To overcome diseases,prevention and therapeutics are being developed with the abundant data analyzed by AI.AI has so much potential for handling considerable data,which requires some orientation and ambition.Appropriate interpretation of AI is essential for understanding disease mechanisms and finding targets for prevention and therapeutics.Collaboration with AI to extract the essence of cancer data and model intelligent networks will be explored.The utilization of AI can provide humans with a predictive future in disease mechanisms and treatment as well as prevention.

Origin of cells and network information

All cells are derived from one cell, and the origin of different cell types is a subject of curiosity. Cells construct life through appropriately timed networks at each stage of development. Communication among cells and intracellular signaling are essential for cell differentiation and for life processes. Cellular molecular networks establish cell diversity and life. The investigation of the regulation of each gene in the genome within the cellular network is therefore of interest. Stem cells produce various cells that are suitable for specific purposes. The dynamics of the information in the cellular network changes as the status of cells is altered. The components of each cell are subject to investigation.

Perspectives of gene combinations in phenotype presentation

Cells exhibit a variety of phenotypes in different stages and diseases. Although several markers for cellular phenotypes have been identified, gene combinations denoting cellular phenotypes have not been completely elucidated. Recent advances in gene analysis have revealed that various gene expression patterns are observed in each cell species and status. In this review, the perspectives of gene combinations in cellular phenotype presentation are discussed. Gene expression profiles change during cellular processes, such as cell proliferation, cell differentiation, and cell death. In addition, epigenetic regulation increases the complexity of the gene expression profile. The role of gene combinations and panels of gene combinations in each cellular condition are also discussed.

Gene expression and pathway analysis of CTNNB1 in cancer and stem cells

AIM To investigate β-catenin(CTNNB1) signaling in cancer and stem cells, the gene expression and pathway were analyzed using bioinformatics.METHODS The expression of the catenin β 1(CTNNB1) gene, which codes for β-catenin, was analyzed in mesenchymal stem cells(MSCs) and gastric cancer(GC) cells. Beta-catenin signaling and the mutation of related proteins were also analyzed using the cB ioP ortal for Cancer Genomics and HOMology modeling of Complex Structure(HOMCOS) databases.RESULTS The expression of the CTNNB1 gene was up-regulated in GC cells compared to MSCs. The expression of EPH receptor A8(EPHA8), synovial sarcoma translocation chromosome 18(SS18), interactor of little elongation complex ELL subunit 1(ICE1), patched 1(PTCH1), mutS homolog 3(MSH3) and caspase recruitment domain family member 11(CARD11) were also shown to be altered in GC cells in the cB ioP ortal for Cancer Genomics analysis. 3D complex structures were reported for E-cadherin 1(CDH1), lymphoid enhancer binding factor 1(LEF1), transcription factor 7 like 2(TCF7L2) and adenomatous polyposis coli protein(APC) with β-catenin. CONCLUSION The results indicate that the epithelial-mesenchymal transition(EMT)-related gene CTNNB1 plays an important role in the regulation of stem cell pluripotency and cancer signaling.

Cancer recognition of artificial intelligence

The recognition mechanism of artificial intelligence(AI)is an interesting topic in understanding AI neural networks and their application in therapeutics.A number of multilayered neural networks can recognize cancer through deep learning.It would be interesting to think about whether human insights and AI attention are associated with each other or should be translated,which is one of the main points in this editorial.The automatic detection of cancer with computeraided diagnosis is being applied in the clinic and should be improved with feature mapping in neural networks.The subtypes and stages of cancer,in terms of progression and metastasis,should be classified with AI for optimized therapeutics.The determination of training and test data during learning and selection of appropriate AI models will be essential for therapeutic applications.

Artificial intelligence in gastrointestinal diseases

Artificial intelligence(AI)applications are growing in medicine.It is important to understand the current state of the AI applications prior to utilizing in disease research and treatment.In this review,AI application in the diagnosis and treatment of gastrointestinal diseases are studied and summarized.In most cases,AI studies had large amounts of data,including images,to learn to distinguish disease characteristics according to a human’s perspectives.The detailed pros and cons of utilizing AI approaches should be investigated in advance to ensure the safe application of AI in medicine.Evidence suggests that the collaborative usage of AI in both diagnosis and treatment of diseases will increase the precision and effectiveness of medicine.Recent progress in genome technology such as genome editing provides a specific example where AI has revealed the diagnostic and therapeutic possibilities of RNA detection and targeting.