Characterization of somatic mutation-associated microenvironment signatures in acute myeloid leukemia patients based on TCGA analysis

Characteristics of Pyroptosis-Related Subtypes and Novel Scoring Tool for the Prognosis and Chemotherapy Response in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is usually associated with poor prognosis and low complete remission (CR) rate due to individual biological heterogeneity. Pyroptosis is a special form of inflammatory programmed cell death related to the progression, treatment response, and prognosis of multiple tumors. However, the potential connection of pyroptosis-related genes (PRGs) and AML still remains unclear. We described the genetic and transcriptional alterations of PRGs in 151 AML samples and presented a consensus clustering of these patients into two subtypes with distinct immunological and prognostic characteristics. Cluster A, associated with better prognosis, was characterized by relatively lower PRG expression, activated immune cells, higher immune scores in the tumor microenvironment (TME), and downregulation of immunotherapy checkpoints. Subsequently, a PRG score was constructed to predict overall survival (OS) of AML patients by using univariate and multivariate Cox regression analysis, and its immunological characteristics and predictive capability were further validated by 1,054 AML samples in external datasets. Besides an immune-activated status, low-PRG score cohorts exhibited higher chemotherapeutic drug sensitivity and significant positive correlation with the cancer stem cell (CSC) index. Combined with age, clinical French-American-British (FAB) subtypes, and PRG score, we successfully constructed a nomogram to effectively predict the 1-/3-/5-year survival rate of AML patients, and the predictive capability was further validated in multiple external datasets with a high area under the curve (AUC) value. The various transcriptomic analysis helps us screen significant pyroptosis-related signatures of AML and provide a new clinical application of PRG scores in predicting the prognosis and benefits of treatment for AML patients.

CALCRL Gene is a Suitable Prognostic Factor in AML/ETO+ AML Patients

Introduction

The t(8 ; 21) translocation is the most common chromosomal abnormality in human acute myeloid leukemia (AML) subtype 2 (M2), which forms the AML/ETO fusion gene. However, AML/ETO alone does not necessarily cause leukemia. Other factors are thought to contribute to the disease. Calcitonin receptor-like (CALCRL), a G-protein-coupled neuropeptide receptor, is involved in various biological processes, such as colony formation and drug resistance.

Methods

First, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to determine any differences in CALCRL expression in AML patients with and without AML/ETO and the prognostic significance of CALCRL expression in AML patients was further evaluated. Next, we detected the CALCRL expression level in 67 AML/ETO⁺ AML patients and 16 patients with nonmalignant hematological diseases using qRT-PCR and identified its prognostic relevance.

Results

Individuals in the group expressing low levels of CALCRL had a longer median survival time. In AML/ETO⁺ AML patients, higher mRNA levels of CALCRL were observed before treatment, which decreased after the complete remission that followed multiple chemotherapy sessions. Clinical features indicated that more patients in the CALCRL^high group also had c-kit mutations compared with patients in other groups. Overall survival (OS) was longer in patients with lower levels of CALCRL expression, especially in patients with c-kit mutations or with more blast cells in bone marrow (BM). In addition, a longer OS was observed in the CALCRL^low group after hematopoietic stem cell transplantation (HSCT).

Conclusions

This preliminary study indicates that CALCRL could serve as a suitable prognostic factor in AML/ETO⁺ AML patients.

A deep learning method and device for bone marrow imaging cell detection

BACKGROUND

Morphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency.

METHODS

In this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value.

RESULTS

We verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system.

CONCLUSIONS

Clinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.

Identification of Potential Novel Prognosis-Related Genes Through Transcriptome Sequencing, Bioinformatics Analysis, and Clinical Validation in Acute Myeloid Leukemia

Background: Acute Myeloid Leukemia (AML) is a complex and heterogeneous hematologic malignancy. However, the function of prognosis-related signature genes in AML remains unclear. Methods: In the current study, transcriptome sequencing was performed on 15 clinical samples, differentially expressed RNAs were identified using R software. The potential interactions network was constructed by using the common genes between target genes of differentially expressed miRNAs with transcriptome sequencing results. Functional and pathway enrichment analysis was performed to identify candidate gene-mediated aberrant signaling pathways. Hub genes were identified by the cytohubba plugin in Cytoscape software, which then expanded the potential interactions regulatory module for hub genes. TCGA-LAML clinical data were used for the prognostic analysis of the hub genes in the regulatory network, and GVSA analysis was used to identify the immune signature of prognosis-related hub genes. qRT-PCR was used to verify the expression of hub genes in independent clinical samples. Results: We obtained 1,610 differentially expressed lncRNAs, 233 differentially expressed miRNAs, and 2,217 differentially expressed mRNAs from transcriptome sequencing. The potential interactions network is constructed by 12 lncRNAs, 25 miRNAs, and 692 mRNAs. Subsequently, a sub-network including 15 miRNAs as well as 12 lncRNAs was created based on the expanded regulatory modules of 25 key genes. The prognostic analysis results show that CCL5 and lncRNA UCA1 was a significant impact on the prognosis of AML. Besides, we found three potential interactions networks such as lncRNA UCA1/hsa-miR-16-5p/COL4A5, lncRNA UCA1/hsa-miR-16-5p/SPARC, and lncRNA SNORA27/hsa-miR-17-5p/CCL5 may play an important role in AML. Furthermore, the evaluation of the immune infiltration shows that CCL5 is positively correlated with various immune signatures, and lncRNA UCA1 is negatively correlated with the immune signatures. Finally, the result of qRT-PCR showed that CCL5 is down-regulated and lncRNA UCA1 is up-regulated in AML samples separately. Conclusions: In conclusion, we propose that CCL5 and lncRNA UCA1 could be recognized biomarkers for predicting survival prognosis based on constructing competing endogenous RNAs in AML, which will provide us novel insight into developing novel prognostic, diagnostic, and therapeutic for AML.