eEF2K Activity Determines Synergy to Cotreatment of Cancer Cells With PI3K and MEK Inhibitors

CD133-Dependent Activation of Phosphoinositide 3-Kinase /AKT/Mammalian Target of Rapamycin Signaling in Melanoma Progression and Drug Resistance

Melanoma frequently harbors genetic alterations in key molecules leading to the aberrant activation of PI3K and its downstream pathways. Although the role of PI3K/AKT/mTOR in melanoma progression and drug resistance is well documented, targeting the PI3K/AKT/mTOR pathway showed less efficiency in clinical trials than might have been expected, since the suppression of the PI3K/mTOR signaling pathway-induced feedback loops is mostly associated with the activation of compensatory pathways such as MAPK/MEK/ERK. Consequently, the development of intrinsic and acquired resistance can occur. As a solid tumor, melanoma is notorious for its heterogeneity. This can be expressed in the form of genetically divergent subpopulations including a small fraction of cancer stem-like cells (CSCs) and non-cancer stem cells (non-CSCs) that make the most of the tumor mass. Like other CSCs, melanoma stem-like cells (MSCs) are characterized by their unique cell surface proteins/stemness markers and aberrant signaling pathways. In addition to its function as a robust marker for stemness properties, CD133 is crucial for the maintenance of stemness properties and drug resistance. Herein, the role of CD133-dependent activation of PI3K/mTOR in the regulation of melanoma progression, drug resistance, and recurrence is reviewed.

Gab2 promotes the growth of colorectal cancer by regulating the M2 polarization of tumor‑associated macrophages

Tumor‑associated macrophages (TAMs) are pivotal components in colorectal cancer (CRC) progression, markedly influencing the tumor microenvironment through their polarization into the pro‑inflammatory M1 or pro‑tumorigenic M2 phenotypes. Recent studies have highlighted that the Grb2‑associated binder 2 (Gab2) is a critical gene involved in the development of various types of tumor, including CRC. However, the precise role of Gab2 in mediating TAM polarization remains incompletely elucidated. In the present study, it was discovered that Gab2 was highly expressed within CRC tissue TAMs, and was associated with a poor prognosis of patients with CRC. Functionally, it was identified that the tumor‑conditioned medium (TCM) induced Gab2 expression, facilitating the TAMs towards an M2‑like phenotype polarization. Of note, the suppression of Gab2 expression using shRNA markedly inhibited the TCM‑induced expression of M2‑associated molecules, without affecting M1‑type markers. Furthermore, the xenotransplantation model demonstrated that Gab2 deficiency in TAMs inhibited tumor growth in the mouse model of CRC. Mechanistically, Gab2 induced the M2 polarization of TAMs by regulating the AKT and ERK signaling pathways, promoting CRC growth and metastasis. In summary, the present study study elucidates that decreasing Gab2 expression hinders the transition of TAMs towards the M2 phenotype, thereby suppressing the growth of CRC. The exploration of the regulatory mechanisms of Gab2 in TAM polarization may enhance the current understanding of the core molecular pathways of CRC development and may thus provide a foundation for the development of novel immunotherapeutic strategies targeted against TAMs.

A novel prognostic model of methylation-associated genes in acute myeloid leukemia

BACKGROUND

There is growing evidence that methylation-associated genes (MAGs) play an important role in the prognosis of acute myeloid leukemia (AML) patients. Thus, the aim of this research was to investigate the impact of MAGs in predicting the outcomes of AML patients.

METHODS

The expression profile and clinical information of patients were downloaded from public databases. A novel prognostic model based on 7 MAGs was established in the TCGA training cohort and validated in the GSE71014 dataset. To validate the clinical implications, the correlation between MAGs signature and drug sensitivity was further investigated.

RESULTS

76 genes were screened out by the univariate Cox regression and significantly enriched in multiple methylation-related pathways. After filtering variables using LASSO regression analysis, 7 MAGs were introduced to construct the predictive model. The survival analysis showed overall survival of patients with the high-risk score was considerably poorer than that with the low-risk score in both the training and validating cohorts (p < 0.01). Furthermore, the risk score system as a prognostic factor also worked in the intermediate-risk patients based on ELN-2017 classification. Importantly, the risk score was demonstrated to be an independent prognostic factor for AML in the univariate and multivariate Cox regression analysis. Interestingly, GSEA analysis revealed that multiple metabolism-related pathways were significantly enriched in the high-risk group. Drug sensitivity analysis showed there was a significant difference in sensitivity of some drugs between the two groups.

CONCLUSION

We developed a robust and accurate prognostic model with 7 MAGs. Our findings might provide a reference for the clinical prognosis and management of AML.

Establishment and validation of a carbohydrate metabolism-related gene signature for prognostic model and immune response in acute myeloid leukemia

Introduction

The heterogeneity of treatment response in acute myeloid leukemia (AML) patients poses great challenges for risk scoring and treatment stratification. Carbohydrate metabolism plays a crucial role in response to therapy in AML. In this multicohort study, we investigated whether carbohydrate metabolism related genes (CRGs) could improve prognostic classification and predict response of immunity and treatment in AML patients.

Methods

Using univariate regression and LASSO-Cox stepwise regression analysis, we developed a CRG prognostic signature that consists of 10 genes. Stratified by the median risk score, patients were divided into high-risk group and low-risk group. Using TCGA and GEO public data cohorts and our cohort (1031 non-M3 patients in total), we demonstrated the consistency and accuracy of the CRG score on the predictive performance of AML survival.

Results

The overall survival (OS) was significantly shorter in high-risk group. Differentially expressed genes (DEGs) were identified in the high-risk group compared to the low-risk group. GO and GSEA analysis showed that the DEGs were mainly involved in immune response signaling pathways. Analysis of tumor-infiltrating immune cells confirmed that the immune microenvironment was strongly suppressed in high-risk group. The results of potential drugs for risk groups showed that inhibitors of carbohydrate metabolism were effective.

Discussion

The CRG signature was involved in immune response in AML. A novel risk model based on CRGs proposed in our study is promising prognostic classifications in AML, which may provide novel insights for developing accurate targeted cancer therapies.