Schip1, a new upstream regulator of Hippo signaling

A transient receptor potential channel-related model based on machine learning for evaluating tumor microenvironment and immunotherapeutic strategies in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy. Transient receptor potential (TRP) channels in AML still need to be further explored. A TRP channel-related model based on machine learning was established in this study.

Methods

The data were downloaded from TCGA-LAML and Genome-Tissue Expression (GTEx). TRP-related genes (TRGs) were extracted from previous literature. With the use of Single-Sample Gene Set Enrichment Analysis (ssGSEA), TRP enrichment scores (TESs) were calculated. The limma package was used to identify differentially expressed genes (DEGs), and univariate Cox regression analysis was performed to identify prognostic DEGs. The above prognostic DEGs were analyzed by Random Survival Forest and least absolute shrinkage and selection operator (Lasso) analysis to create the TRP signature. The Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to investigate the efficiency and accuracy of prognostic prediction. Moreover, genomic mutation analysis was based on GISTIC analysis. Based on ESTIMATE, TIMER, MCPcounter, and ssGSEA, the tumor microenvironment and immunological characteristics were expressly evaluated to explore immunotherapeutic strategies. Enrichment analysis for TRP signature was based on the Kyoto Encyclopedia of Genes Genomes (KEGG), Gene Ontology (GO), over-representation analysis (ORA), and Gene Set Enrichment Analysis (GSEA). Genomics of Drug Sensitivity in Cancer (GDSC) and pRRophetic were used to carry out drug sensitivity analysis. Conclusively, SCHIP1 was randomly selected to perform in vitro cyto-functional experiments.

Results

The worse clinical outcomes of patients with higher TESs were observed. There were 107 differentially expressed TRGs identified. Our data revealed 57 prognostic TRGs. Eight TRGs were obtained to establish the prognostic TRP signature, and the worse clinical outcomes of patients with higher TRP scores were found. The efficiency and accuracy of TRP signature in predicting prognosis were confirmed by ROC curves and five external validation datasets. Our data revealed that the mutation rates of DNMT3A, IDH2, MUC16, and TTN were relatively high. The level of infiltrating immune cell populations, stromal, immune, and ESTIMATE scores increased as the TRP scores increased. Nevertheless, AML patients with lower TRP scores exhibited more tumor purity. The TRP scores were found to be correlated with immunomodulators and immune checkpoints, thus revealing immune characteristics and immunotherapeutic strategies. The IC50 values of six chemotherapeutics were lower in the high TRP score (HTS) group. Finally, it was found that SCHIP1 may be the oncogenic gene.

Conclusion

The results of this study will help in understanding the role of TRP and SCHIP1 in the prognosis and development of AML.

Modulation of Hippo signaling by Mnat9 N-acetyltransferase for normal growth and tumorigenesis in Drosophila

Hippo signaling is a conserved mechanism for controlling organ growth. Increasing evidence suggests that Hippo signaling is modulated by various cellular factors for normal development and tumorigenesis. Hence, identification of these factors is pivotal for understanding the mechanism for the regulation of Hippo signaling. Drosophila Mnat9 is a putative N-acetyltransferase that is required for cell survival by affecting JNK signaling. Here we show that Mnat9 is involved in the negative regulation of Hippo signaling. RNAi knockdown of Mnat9 in the eye disc suppresses the rough eye phenotype of overexpressing Crumbs (Crb), an upstream factor of the Hippo pathway. Conversely, Mnat9 RNAi enhances the eye phenotype caused by overexpressing Expanded (Ex) or Warts (Wts) that acts downstream to Crb. Similar genetic interactions between Mnat9 and Hippo pathway genes are found in the wing. The reduced wing phenotype of Mnat9 RNAi is suppressed by overexpression of Yorkie (Yki), while it is suppressed by knockdown of Hippo upstream factors like Ex, Merlin, or Kibra. Mnat9 co-immunoprecipitates with Mer, implying their function in a protein complex. Furthermore, Mnat9 overexpression together with Hpo knockdown causes tumorous overgrowth in the abdomen. Our data suggest that Mnat9 is required for organ growth and can induce tumorous growth by negatively regulating the Hippo signaling pathway.

Identification of the hub genes and pathways involved in acute myeloid leukemia using bioinformatics analysis

BACKGROUND

We identified the hub genes and pathways dysregulated in acute myeloid leukemia and the potential molecular mechanisms involved.

METHODS

We downloaded the GSE15061 gene expression dataset from the Gene Expression Omnibus database and used weighted gene co-expression network analysis to identify hub genes. Differential expression of the genes was evaluated using the limma package in R software. Subsequently, we built a protein-protein interaction network followed by functional enrichment analysis. Then, the prognostic significance of gene expression was explored in terms of overall survival. Finally, transcription factor-mRNA (ribonucleic acid) and microRNA-mRNA interaction analysis was also explored.

RESULTS

We identified 100 differentially expressed hub genes. Functional enrichment analysis indicated that the genes were principally involved in immune system regulation, host defense, and negative regulation of apoptosis and myeloid cell differentiation. We identified 4 hub genes, the expression of which was significantly correlated with overall survival. Finally, 26 key regulators for hub genes and 38 microRNA-mRNA interactions were identified.

CONCLUSION

We performed a comprehensive bioinformatics analysis of hub genes potentially involved in acute myeloid leukemia development. Further molecular biological experiments are required to confirm the roles played by these genes.

Upstream paths for Hippo signaling in Drosophila organ development

Organ growth is fundamental to animal development. One of major mechanisms for growth control is mediated by the conserved Hippo signaling pathway initially identified in Drosophila. The core of this pathway in Drosophila consists of a cascade of protein kinases Hippo and Warts that negatively regulate transcriptional coactivator Yorkie (Yki). Activation of Yki promotes cell survival and proliferation to induce organ growth. A key issue in Hippo signaling is to understand how core kinase cascade is activated. Activation of Hippo kinase cascade is regulated in the upstream by at least two transmembrane proteins Crumbs and Fat that act in parallel. These membrane proteins interact with additional factors such as FERM-domain proteins Expanded and Merlin to modulate subcellular localization and function of the Hippo kinase cascade. Hippo signaling is also influenced by cytoskeletal networks and cell tension in epithelia of developing organs. These upstream events in the regulation of Hippo signaling are only partially understood. This review focuses on our current understanding of some upstream processes involved in Hippo signaling in developing Drosophila organs.