A three-gene signature and clinical outcome in pediatric acute myeloid leukemia

The eEF1A protein in cancer: Clinical significance, oncogenic mechanisms, and targeted therapeutic strategies

Eukaryotic elongation factor 1A (eEF1A) is among the most abundant proteins in eukaryotic cells. Evolutionarily conserved across species, eEF1A is in charge of translation elongation for protein biosynthesis as well as a plethora of non-translational moonlighting functions for cellular homeostasis. In malignant cells, however, eEF1A becomes a pleiotropic driver of cancer progression via a broad diversity of pathways, which are not limited to hyperactive translational output. In the past decades, mounting studies have demonstrated the causal link between eEF1A and carcinogenesis, gaining deeper insights into its multifaceted mechanisms and corroborating its value as a prognostic marker in various cancers. On the other hand, an increasing number of natural and synthetic compounds were discovered as anticancer eEF1A-targeting inhibitors. Among them, plitidepsin was approved for the treatment of multiple myeloma whereas metarrestin was currently under clinical development. Despite significant achievements in these two interrelated fields, hitherto there lacks a systematic examination of the eEF1A protein in the context of cancer research. Therefore, the present work aims to delineate its clinical implications, molecular oncogenic mechanisms, and targeted therapeutic strategies as reflected in the ever expanding body of literature, so as to deepen mechanistic understanding of eEF1A-involved tumorigenesis and inspire the development of eEF1A-targeted chemotherapeutics and biologics.

Development and validation of a promising 5-gene prognostic model for pediatric acute myeloid leukemia

Risk classification in pediatric acute myeloid leukemia (P-AML) is crucial for personalizing treatments. Thus, we aimed to establish a risk-stratification tool for P-AML patients and eventually guide individual treatment. A total of 256 P-AML patients with accredited mRNA-seq data from the TARGET database were divided into training and internal validation datasets. A gene-expression-based prognostic score was constructed for overall survival (OS), by using univariate Cox analysis, LASSO regression analysis, Kaplan-Meier (K-M) survival, and multivariate Cox analysis. A P-AML-5G prognostic score bioinformatically derived from expression levels of 5 genes (ZNF775, RNFT1, CRNDE, COL23A1, and TTC38), clustered P-AML patients in training dataset into high-risk group (above optimal cut-off) with shorter OS, and low-risk group (below optimal cut-off) with longer OS (p < 0.0001). Meanwhile, similar results were obtained in internal validation dataset (p = 0.005), combination dataset (p < 0.001), two treatment sub-groups (p < 0.05), intermediate-risk group defined with the Children's Oncology Group (COG) (p < 0.05) and an external Japanese P-AML dataset (p = 0.005). The model was further validated in the COG study AAML1031(p = 0.001), and based on transcriptomic analysis of 943 pediatric patients and 70 normal bone marrow samples from this dataset, two genes in the model demonstrated significant differential expression between the groups [all log2(foldchange) > 3, p < 0.001]. Independent of other prognostic factors, the P-AML-5G groups presented the highest concordance-index values in training dataset, chemo-therapy only treatment subgroups of the training and internal validation datasets, and whole genome-sequencing subgroup of the combined dataset, outperforming two Children's Oncology Group (COG) risk stratification systems, 2022 European LeukemiaNet (ELN) risk classification tool and two leukemic stem cell expression-based models. The 5-gene prognostic model generated by a single assay can further refine the current COG risk stratification system that relies on numerous tests and may have the potential for the risk judgment and identification of the high-risk pediatric AML patients receiving chemo-therapy only treatment.

Highly expressed RPLP2 inhibits ferroptosis to promote hepatocellular carcinoma progression and predicts poor prognosis

BACKGROUND

RPLP2, an integral part of ribosomal stalk, plays an important role in the tumorigenesis of various cancers. However, its specific effect on HCC remains elusive.

METHODS

TCGA, GTEx, HCCDB, HPA, UALCAN, MethSurv, TISIDB, K-M plotter, FerrDb, RNAactDrug, STRING, Cytoscape and R studio were conducted for bioinformatics analysis. RPLP2 expression level in HCC was verified by IHC and western blot. IHC was used to demonstrate the immune cell infiltration. Functional experiments including CCK8, transwell and colony formation assays, and nude mice xenograft model were performed for in vitro and in vivo validation. Western blot, IHC, CCK8 assay and detection of GSH and lipid ROS were adopted to determine the effect of RPLP2 on the ferroptosis of HCC cells.

RESULTS

Here, we demonstrate that elevated level of RPLP2 is strongly associated with advanced clinicopathologic features, and predicts poor prognosis of HCC patients. Additionally, DNA methylation level of RPLP2 decreases in HCC, and significantly correlates with patients outcome. Moreover, high RPLP2 expression level is linked closely to the unfavorable immune infiltration. Most importantly, RPLP2 positively associates with ferroptosis suppressor GPX4, and inhibition of RPLP2 could lead to the acceleration of ferroptosis to suppress tumor progression of HCC. Last, drug sensitivity analysis predicts many drugs that potentially target RPLP2.

CONCLUSION

Together, our study reveals previous unrecognized role of RPLP2 in HCC, and provides new regulatory mechanism of ferroptosis, indicating RPLP2 may be a novel therapeutic target for HCC.

Mitochondrial gene expression signature predicts prognosis of pediatric acute myeloid leukemia patients

Introduction

Gene expression profile of mitochondrial-related genes is not well deciphered in pediatric acute myeloid leukaemia (AML). We aimed to identify mitochondria-related differentially expressed genes (DEGs) in pediatric AML with their prognostic significance.

Methods

Children with de novo AML were included prospectively between July 2016-December 2019. Transcriptomic profiling was done for a subset of samples, stratified by mtDNA copy number. Top mitochondria-related DEGs were identified and validated by real-time PCR. A prognostic gene signature risk score was formulated using DEGs independently predictive of overall survival (OS) in multivariable analysis. Predictive ability of the risk score was estimated along with external validation in The Tumor Genome Atlas (TCGA) AML dataset.

Results

In 143 children with AML, twenty mitochondria-related DEGs were selected for validation, of which 16 were found to be significantly dysregulated. Upregulation of SDHC (p<0.001), CLIC1 (p=0.013) and downregulation of SLC25A29 (p<0.001) were independently predictive of inferior OS, and included for developing prognostic risk score. The risk score model was independently predictive of survival over and above ELN risk categorization (Harrell's c-index: 0.675). High-risk patients (risk score above median) had significantly inferior OS (p<0.001) and event free survival (p<0.001); they were associated with poor-risk cytogenetics (p=0.021), ELN intermediate/poor risk group (p=0.016), absence of RUNX1-RUNX1T1 (p=0.027), and not attaining remission (p=0.016). On external validation, the risk score also predicted OS (p=0.019) in TCGA dataset.

Discussion

We identified and validated mitochondria-related DEGs with prognostic impact in pediatric AML and also developed a novel 3-gene based externally validated gene signature predictive of survival.

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.

Identification of two potential immune-related biomarkers of Graves' disease based on integrated bioinformatics analyses

BACKGROUND

Graves' disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied.

METHOD

In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes.

RESULT

44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory CD4⁺ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively.

CONCLUSION

Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory CD4⁺ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD.

Ferroptosis-related gene signature predicts the clinical outcome in pediatric acute myeloid leukemia patients and refines the 2017 ELN classification system

Background: The prognostic roles of ferroptosis-related mRNAs (FG) and lncRNAs (FL) in pediatric acute myeloid leukemia (P-AML) patients remain unclear.

Methods: RNA-seq and clinical data of P-AML patients were downloaded from the TARGET project. Cox and LASSO regression analyses were performed to identify FG, FL, and FGL (combination of FG and FL) prognostic models, and their performances were compared. Tumor microenvironment, functional enrichment, mutation landscape, and anticancer drug sensitivity were analyzed.

Results: An FGL model of 22 ferroptosis-related signatures was identified as an independent parameter, and it showed performance better than FG, FL, and four additional public prognostic models. The FGL model divided patients in the discovery cohort (N = 145), validation cohort (N = 111), combination cohort (N = 256), and intermediate-risk group (N = 103) defined by the 2017 European LeukemiaNet (ELN) classification system into two groups with distinct survival. The high-risk group was enriched in apoptosis, hypoxia, TNFA signaling via NFKB, reactive oxygen species pathway, oxidative phosphorylation, and p53 pathway and associated with low immunity, while patients in the low-risk group may benefit from anti-TIM3 antibodies. In addition, patients within the FGL high-risk group might benefit from treatment using SB505124_1194 and JAK_8517_1739.

Conclusion: Our established FGL model may refine and provide a reference for clinical prognosis judgment and immunotherapies for P-AML patients.