DOCK2: A novel FLT3/ITD leukemia drug target. Oncotarget. 2017;8:88253-88254. [PMID: 29179430 PMCID: PMC5687600 DOI: 10.18632/oncotarget.21390]

Identification of Tumor Microenvironment-Related Prognostic Biomarkers in Luminal Breast Cancer

Background: The tumor microenvironment (TME) has been reported to have significant value in the diagnosis and prognosis of cancers. This study aimed to identify key biomarkers in the TME of luminal breast cancer (BC).

Methods: We obtained immune scores (ISs) and stromal scores (SSs) for The Cancer Genome Atlas (TCGA) luminal BC cohort from the online ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) portal. The relationships between ISs and SSs and the overall survival of luminal BC patients were assessed by the Kaplan-Meier method. The differentially expressed messenger RNAs (DEmRNAs) related to the ISs and SSs were subjected to functional enrichment analysis. Additionally, a competing endogenous RNA (ceRNA) network was constructed with differentially expressed microRNAs (DEmiRNAs) and long noncoding RNAs (DElncRNAs). Furthermore, a protein–protein interaction (PPI) network was established to analyze the DEmRNAs in the ceRNA network. Then, survival analysis of biomarkers involved in the ceRNA network was carried out to explore their prognostic value. Finally, these biomarkers were validated using the luminal BC dataset from the Gene Expression Omnibus (GEO) database.

Results: The results showed that ISs were significantly associated with longer survival times of luminal BC patients. Functional enrichment analysis showed that the DEmRNAs were mainly associated with immune response, antigen binding, and the extracellular region. In the PPI network, the top 10 DEmRNAs were identified as hub genes that affected the TME of luminal BC. Finally, two DEmiRNAs, two DElncRNAs, and 17 DEmRNAs of the ceRNA network associated with the TME were shown to have prognostic value. Subsequently, the expression of 15 prognostic biomarkers was validated in one additional dataset (GSE81002). In particular, one lncRNA (GVINP1) and five mRNAs (CCDC69, DOCK2, IKZF1, JCHAIN, and NCKAP1L) were novel biomarkers.

Conclusions: Our studies demonstrated that ISs were associated with the survival of luminal BC patients, and a set of novel biomarkers that might play a prognostic role in the TME of luminal BC was identified.

Targeting Rac and Cdc42 GEFs in Metastatic Cancer

The Rho family GTPases Rho, Rac, and Cdc42 have emerged as key players in cancer metastasis, due to their essential roles in regulating cell division and actin cytoskeletal rearrangements; and thus, cell growth, migration/invasion, polarity, and adhesion. This review will focus on the close homologs Rac and Cdc42, which have been established as drivers of metastasis and therapy resistance in multiple cancer types. Rac and Cdc42 are often dysregulated in cancer due to hyperactivation by guanine nucleotide exchange factors (GEFs), belonging to both the diffuse B-cell lymphoma (Dbl) and dedicator of cytokinesis (DOCK) families. Rac/Cdc42 GEFs are activated by a myriad of oncogenic cell surface receptors, such as growth factor receptors, G-protein coupled receptors, cytokine receptors, and integrins; consequently, a number of Rac/Cdc42 GEFs have been implicated in metastatic cancer. Hence, inhibiting GEF-mediated Rac/Cdc42 activation represents a promising strategy for targeted metastatic cancer therapy. Herein, we focus on the role of oncogenic Rac/Cdc42 GEFs and discuss the recent advancements in the development of Rac and Cdc42 GEF-interacting inhibitors as targeted therapy for metastatic cancer, as well as their potential for overcoming cancer therapy resistance.

Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this type of data. However, biological information about a certain disease is normally widespread due to the use of different sequencing technologies and different manufacturers, in different experiments along the years around the world. Thus, nowadays it is of paramount importance to attain a correct integration of biologically-related data in order to achieve genuine benefits from them. For this purpose, this work presents an integration of multiple Microarray and RNA-seq platforms, which has led to the design of a multiclass study by collecting samples from the main four types of leukemia, quantified at gene expression. Subsequently, in order to find a set of differentially expressed genes with the highest discernment capability among different types of leukemia, an innovative parameter referred to as coverage is presented here. This parameter allows assessing the number of different pathologies that a certain gen is able to discern. It has been evaluated together with other widely known parameters under assessment of an ANOVA statistical test which corroborated its filtering power when the identified genes are subjected to a machine learning process at multiclass level. The optimal tuning of gene extraction evaluated parameters by means of this statistical test led to the selection of 42 highly relevant expressed genes. By the use of minimum-Redundancy Maximum-Relevance (mRMR) feature selection algorithm, these genes were reordered and assessed under the operation of four different classification techniques. Outstanding results were achieved by taking exclusively the first ten genes of the ranking into consideration. Finally, specific literature was consulted on this last subset of genes, revealing the occurrence of practically all of them with biological processes related to leukemia. At sight of these results, this study underlines the relevance of considering a new parameter which facilitates the identification of highly valid expressed genes for simultaneously discerning multiple types of leukemia.

Piwi like RNA-mediated gene silencing 1 gene as a possible major player in gastric cancer

AIM

To establish a permanent piwi like RNA-mediated gene silencing 1 (PIWIL1) gene knockout in AGP01 gastric cancer cell line using CRISPR-Cas9 system and analyze phenotypic modifications as well as gene expression alterations.

METHODS

CRISPR-Cas9 system used was purchased from Dharmacon GE Life Sciences (Lafayette, CO, United States) and permanent knockout was performed according to manufacturer’s recommendations. Wound-healing assay was performed to investigate the effect of PIWIL1 knockout on migration capability of cells and Boyden chamber invasion assay was performed to investigate the effect on invasion capability. For the gene expression analysis, a one-color microarray-based gene expression analysis kit (Agilent Technologies, Santa Clara, CA, United States) was used according to the protocol provided by the manufacturer.

RESULTS

PIWIL1 gene knockout caused a significant decrease in AGP01 migration capacity as well as a significant decrease in cell invasiveness. Moreover, functional analysis based on grouping of all differentially expressed mRNAs identified a total of 35 genes (5 up-regulated and 30 down-regulated) encoding proteins involved in cellular invasion and migration. According to current literature, 9 of these 35 genes (DOCK2, ZNF503, PDE4D, ABL1, ABL2, LPAR1, SMAD2, WASF3 and DACH1) are possibly related to the mechanisms used by PIWIL1 to promote carcinogenic effects related to migration and invasion, since their functions are consistent with the changes observed (being up- or down-regulated after knockout).

CONCLUSION

Taken together, these data reinforce the idea that PIWIL1 plays a crucial role in the signaling pathway of gastric cancer, regulating several genes involved in migration and invasion processes; therefore, its use as a therapeutic target may generate promising results in the treatment of gastric cancer.