The Prognostic Value of the Developmental Gene FZD6 in Young Saudi Breast Cancer Patients: A Biomarkers Discovery and Cancer Inducers OncoScreen Approach

Expression pattern, prognostic value and potential microRNA silencing of FZD8 in breast cancer

Breast cancer (BC) is one of the most widespread types of cancer affecting females, and therefore, early diagnosis is critical. BC is a complex heterogeneous disease affected by several key pathways. Among these, WNT proteins and their frizzled receptors (FZD) have been demonstrated to be crucial in regulating a number of cellular and molecular events in BC tumorigenesis. The role of the WNT receptor, FZD8, in BC has received minimal attention; for that reason, the present study examined the prognostic value of its protein expression pattern in a BC cohort. FZD8 cytoplasmic expression pattern analysis revealed that ~38% of the primary samples presented with a high expression profile, whereas ~63% of the samples had a low expression profile. Overall, ~46% of the malignant tissues in the lymph node-positive samples exhibited an increased FZD8 cytoplasmic expression, whereas 54% exhibited low expression levels. An increased expression of FZD8 was associated with several clinicopathological characteristics of the patients, including a low survival rate, tumor vascular invasion, tumor size and grade, and molecular subtypes. Affymetrix microarray triple-negative BC datasets were analyzed and compared with healthy breast tissues in order to predict the potential interfering microRNAs (miRNAs) in the WNT/FZD8 signaling pathway. A total of 29 miRNAs with the potential to interact with the WNT/FZD8 signaling pathway were identified, eight of which exhibited a significant prediction score. The target genes for each predicted miRNA were identified. On the whole, the findings of the present study suggest that FZD8 is a potential prognostic marker for BC, shedding some light onto the silencing mechanisms involved in the complex BC signaling.

Editorial: Advances in genomic and genetic tools, and their applications for understanding embryonic development and human diseases

Significant advances have been recently made in the development of the genetic and genomic platforms. This has greatly contributed to a better understanding of gene expression and regulation machinery. Consequently, this led to considerable progress in unraveling evidence of the genotype-phenotype correlation between normal/abnormal embryonic development and human disease complexity. For example, advanced genomic tools such as next-generation sequencing, and microarray-based CGH have substantially helped in the identification of gene and copy number variants associated with diseases as well as in the discovery of causal gene mutations. In addition, bioinformatic analysis tools of genome annotation and comparison have greatly aided in data analysis for the interpretation of the genetic variants at the individual level. This has unlocked potential possibilities for real advances toward new therapies in personalized medicine for the targeted treatment of human diseases. However, each of these genomic and bioinformatics tools has its limitations and hence further efforts are required to implement novel approaches to overcome these limitations. It could be possible that the use of more than one platform for genotype-phenotype deep analysis is an effective approach to disentangling the cause and treatment of the disease complexities. Our research topic aimed at deciphering these complexities by shedding some light on the recent applications of the basic and advanced genetic/genomic and bioinformatics approaches. These include studying gene-gene, protein-protein, and gene-environment interactions. We, in addition, aimed at a better understanding of the link between normal/abnormal embryonic development and the cause of human disease induction.

Prognostic Role of M6A-Associated Immune Genes and Cluster-Related Tumor Microenvironment Analysis: A Multi-Omics Practice in Stomach Adenocarcinoma

N6-methylandrostenedione (m6A) methylation plays a very important role in the development of malignant tumors. The immune system is the key point in the progression of tumors, particularly in terms of tumor treatment and drug resistance. Tumor immunotherapy has now become a hot spot and a new approach for tumor treatment. However, as far as the stomach adenocarcinoma (STAD) is concerned, the in-depth research is still a gap in the m6A-associated immune markers. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases is extremely important for our research, where we obtained gene mutation, gene expression data and relevant clinical information of STAD patients. Firstly, the samples from GEO were used as external validation groups, while the TCGA samples were divided into a training group and an internal validation group randomly. Using the way of Single factor COX-LASSO- and multi-factor Cox to construct the prognostic model. Then, all samples were subjected to cluster analysis to generate high and low expression groups of immune gene. Meanwhile, we also collected the correlation between these types and tumor microenvironment. On this basis, a web version of the dynamic nomogram APP was developed. In addition, we performed microenvironmental correlation, copy number variation and mutation analyses for model genes. The prognostic model for STAD developed here demonstrated a very strong predictive ability. The results of cluster analysis manifested that the immune gene low expression group had lower survival rate and higher degree of immune infiltration. Therefore, the immune gene low expression group was associated with lower survival rates and a higher degree of immune infiltration. Gene set enrichment analysis suggested that the potential mechanism might be related to the activation of immunosuppressive functions and multiple signaling pathways. Correspondingly, the web version of the dynamic nomogram APP produced by the DynNom package has successfully achieved rapid and accurate calculation of patient survival rates. Finally, the multi-omics analysis of model genes further enriched the research content. Interference of RAB19 was confirmed to facilitate migration of STAD cells in vitro, while its overexpression inhibited these features. The prognostic model for STAD constructed in this study is accurate and efficient based on multi-omics analysis and experimental validation. Additionally, the results of the correlation analysis between the tumor microenvironment and m6Ascore are the basics of further exploration of the pathophysiological mechanism in STAD.