Development and Validation of a Novel Prognostic Model for Acute Myeloid Leukemia Based on Immune-Related Genes

Proteomic Profiling of Extracellular Vesicles Isolated from Plasma and Peritoneal Exudate in Mice Induced by Crotalus scutulatus scutulatus Crude Venom and Its Purified Cysteine-Rich Secretory Protein (Css-CRiSP)

Increased vascular permeability is a frequent outcome of viperid snakebite envenomation, leading to local and systemic complications. We reported that snake venom cysteine-rich secretory proteins (svCRiSPs) from North American pit vipers increase vascular permeability both in vitro and in vivo. They also induce acute activation of several adhesion and signaling molecules that may play a critical role in the pathophysiology of snakebites. Extracellular vesicles (EVs) have gained interest for their diverse functions in intercellular communication, regulating cellular processes, blood-endothelium interactions, vascular permeability, and immune modulation. They also hold potential as valuable biomarkers for diagnosing, predicting, and monitoring therapeutic responses in different diseases. This study aimed to identify proteins in peritoneal exudate and plasma EVs isolated from BALB/c mice following a 30 min post-injection of Crotalus scutulatus scutulatus venom and its purified CRiSP (Css-CRiSP). EVs were isolated from these biofluids using the EVtrap method. Proteomic analysis of exudate- and plasma-derived EVs was performed using LC-MS/MS. We observed significant upregulation or downregulation of proteins involved in cell adhesion, cytoskeleton rearrangement, signal transduction, immune responses, and vesicle-mediated transports. These findings suggest that svCRiSPs play a crucial role in the acute effects of venom and contribute to the local and systemic toxicity of snakebites.

Potential clinical use of azacitidine and MEK inhibitor combination therapy in PTPN11-mutated juvenile myelomonocytic leukemia

Juvenile myelomonocytic leukemia (JMML) is a rare myeloproliferative neoplasm of childhood. The molecular hallmark of JMML is hyperactivation of the Ras/MAPK pathway with the most common cause being mutations in the gene PTPN11, encoding the protein tyrosine phosphatase SHP2. Current strategies for treating JMML include using the hypomethylating agent, 5-azacitidine (5-Aza) or MEK inhibitors trametinib and PD0325901 (PD-901), but none of these are curative as monotherapy. Utilizing an Shp2E76K/+ murine model of JMML, we show that the combination of 5-Aza and PD-901 modulates several hematologic abnormalities often seen in JMML patients, in part by reducing the burden of leukemic hematopoietic stem and progenitor cells (HSC/Ps). The reduced JMML features in drug-treated mice were associated with a decrease in p-MEK and p-ERK levels in Shp2E76K/+ mice treated with the combination of 5-Aza and PD-901. RNA-sequencing analysis revealed a reduction in several RAS and MAPK signaling-related genes. Additionally, a decrease in the expression of genes associated with inflammation and myeloid leukemia was also observed in Shp2E76K/+ mice treated with the combination of the two drugs. Finally, we report two patients with JMML and PTPN11 mutations treated with 5-Aza, trametinib, and chemotherapy who experienced a clinical response because of the combination treatment.

A DNA Damage Response Related Signature to Predict Prognosis in Patients with Acute Myeloid Leukemia

The prognosis of acute myeloid leukemia (AML) is disappointing in most subtypes and varies widely. DNA damage response (DDR) is associated with prognosis and immunotherapy in multiple cancers. Here, we identify a signature of eight DDR-related genes associated with overall survival, which stratifies AML patients into high- and low-risk groups. Patients in low-risk group were more likely to respond to sorafenib. The signature could be an independent prognostic predictor for patients treated with ADE and ADE plus gemtuzumab ozogamicin. Therefore, this DDR prognostic signature might be applied to prognostic stratification and treatment selection in AML patients, which warrants further studies.

Identification of a signature based on non‐apoptotic regulatory cell death to improve prognosis prediction in acute myeloid leukaemia

Although anti-apoptotic cell death is a common feature of cancer and non-apoptotic regulatory cell death (RCD) is highly correlated with cancer progression and response to therapy, its prognostic role in patients with acute myeloid leukaemia (AML) is unknown. The RNA sequence and clinical data from AML patients were downloaded from the TCGA and GEO databases. The prognostic characteristics of non-apoptotic RCD-related genes (NRGs) were determined by Cox and LASSO regression analysis. Thirteen NRG signatures were identified as independent prognostic parameters in patients with AML that outperformed other prognostic models. Higher NRG scores were associated with shorter survival and less retention of tumour mutations. Although patients with high NRG risk have abundant signalling pathways for cell adhesion, cytokine upregulation, and cellular defence responses, patients with low NRG risk may benefit the most from immunotherapy. Specifically, patients with high NRG score may benefit from treatment with anti-EGFR and CDK2 inhibitors, including erlotinib and roscovitine. The NPM1 and FLT3 mutant cell lines undergo alterations after multiple drug treatments. Our established NRG signature and scoring highlight its vital clinical significance, emphasize the inevitability of stratifying treatment for different mutation subtypes and provide new ideas to guide personalized immunotherapy strategies for AML patients.

A novel immune prognostic model of non-M3 acute myeloid leukemia

Acute myeloid leukemia (AML) is a common hematological malignancy in adults. AML patients exhibit clinical heterogeneity with complications of molecular basis. The leukemogenesis of AML involves immune escape, and the immunosuppression status of the patient might have great impact on AML treatment outcome. In this study, we established an immune prognostic model of AML using bioinformatics tools. With the data in the TCGA and GTEx datasets, we analyzed differentially expressed genes (DEGs) in non-M3 AML and identified 420 immune-related DEGs. Among which, 49 genes' expression was found to be related to AML prognosis based on univariate Cox regression analysis. Next, we established a prognostic model with these 49 genes in AML by LASSO regression and multivariate Cox regression analyses. In our model, the expressions of 5 immune genes, MIF, DEF6, OSM, MPO, AVPR1B, were used to stratify non-M3 AML patients' treatment outcome. A patient's risk score could be calculated as Risk Score=0.40081 × MIF (MIF expression) - 0.15201 × MPO + 0.78073 × DEF6 - 0.45192 × AVPR1B + 0.25912 × OSM. The area under the curve of the risk score signature was 0.8, 0.8, and 0.96 at 1 year, 3 years, and 5 years, respectively. The prognostic model was then validated internally by TCGA data and externally by GEO data. At last, the result of single-sample gene-set enrichment analysis demonstrated that compared with healthy samples, the abundance of non-turmeric immune cells was significantly repressed in AML. To summarize, we presented an immune-related 5-gene signature prognostic model in AML.

Identification of the integrated prognostic signature associated with immuno-relevant genes and long non-coding RNAs in acute myeloid leukemia

BACKGROUND

Like other cancers, considerable effort has been made in acute myeloid leukemia (AML) to identify prognostic genes and long non-coding RNAs (lncRNAs) with their potential clinical applications. However, to date, no integrated prognostic model has been developed that combines both gene expression and lncRNAs as a singular approach in AML.

METHOD

Comprehensive bioinformatic approaches (Weighted gene co-expression network analysis, Univariate Cox regression analyses, Pearson correlation, LASSO-Cox regression, Wilcoxon test) were used to construct the signature and to define high- and low-risk groups in AML datasets. ESTIMATE and CIBERSORT algorithms were applied to investigate the potential impact of infiltrating immune cells based on the obtained signature in tumor microenvironment. In addition, gene ontology (GO) and KEGG enrichment were applied to explore the potential function of the signature.

RESULTS

Herein, we focused on immune-related genes (IRGs) and immune-related long non-coding RNAs (IRlncRNAs) and constructed an integrated prognostic immunorelevant signature in AML. The obtained signature exhibit five IRGs (DAXX, PSMB8, CSRP1, RAC2 and PTPN6) and one IRlncRNA (AC080037.2), and is strictly associated with age and FAB (French-American-British classification). Importantly, the high-risk AML group (defined by the signature) correlated positively with three types of scores (immune score, stroma score, and ESTIMATE score). We also identified a few immune cells (resting mast cells and monocytes) potentially involved in the correlation between signature and survival of AML patients. The prognostic ability of the obtained signature was tested in the training cohort and then validated in both test and total cohorts. The pathway enrichment analysis confirmed the possible immune- related role of the signature.

CONCLUSION

We constructed an integrated prognostic signature comprising five immune-related protein-coding genes (IRPCG) (DAXX, PSMB8, CSRP1, RAC2, and PTPN6) and one immune-related lncRNA (AC080037.2) that may serve as potential biomarkers for predicting survival and further stratifying AML patients.

A Prognostic Model for Acute Myeloid Leukemia Based on IL-2/STAT5 Pathway-Related Genes

Accurate prognostic stratification of patients can provide guidance for personalized therapy. Many prognostic models for acute myeloid leukemia (AML) have been reported, but most have considerable inaccuracies due to contained variables with insufficient capacity of predicting survival and lack of adequate verification. Here, 235 genes strongly related to survival in AML were systematically identified through univariate Cox regression analysis of eight independent AML datasets. Pathway enrichment analysis of these 235 genes revealed that the IL-2/STAT5 signaling pathway was the most highly enriched. Through Cox proportional-hazards regression model and stepwise algorithm, we constructed a six-gene STAT5-associated signature based on the most robustly survival-related genes related to the IL-2/STAT5 signaling pathway. Good prognostic performance was observed in the training cohort (GSE37642-GPL96), and the signature was validated in seven other validation cohorts. As an independent prognostic factor, the STAT5-associated signature was positively correlated with patient age and ELN2017 risk levels. An integrated score based on these three prognostic factors had higher prognostic accuracy than the ELN2017 risk category. Characterization of immune cell infiltration indicated that impaired B-cell adaptive immunity, immunosuppressive effects, serious infection, and weakened anti-inflammatory function tended to accompany high-risk patients. Analysis of in-house clinical samples revealed that the STAT5-assocaited signature risk scores of AML patients were significantly higher than those of healthy people. Five chemotherapeutic drugs that were effective in these high-risk patients were screened in silico. Among the five drugs, MS.275, a known HDAC inhibitor, selectively suppressed the proliferation of cancer cells with high STAT5 phosphorylation levels in vitro. Taken together, the data indicate that the STAT5-associated signature is a reliable prognostic model that can be used to optimize prognostic stratification and guide personalized AML treatments.

Identification and Validation of a Prognostic Risk-Scoring Model Based on Ferroptosis-Associated Cluster in Acute Myeloid Leukemia

Background: Emerging evidence has proven that ferroptosis plays an important role in the development of acute myeloid leukemia (AML), whereas the exact role of ferroptosis-associated genes in AML patients’ prognosis remained unclear.

Materials and Methods: Gene expression profiles and corresponding clinical information of AML cases were obtained from the TCGA (TCGA-LAML), GEO (GSE71014), and TARGET databases (TARGET-AML). Patients in the TCGA cohort were well-grouped into two clusters based on ferroptosis-related genes, and differentially expressed genes were screened between the two clusters. Univariate Cox and LASSO regression analyses were applied to select prognosis-related genes for the construction of a prognostic risk-scoring model. Survival analysis was analyzed by Kaplan–Meier and receiver operator characteristic curves. Furthermore, we explored the correlation of the prognostic risk-scoring model with immune infiltration and chemotherapy response. Risk gene expression level was detected by quantitative reverse transcription polymerase chain reaction.

Results: Eighteen signature genes, including ZSCAN4, ASTN1, CCL23, DLL3, EFNB3, FAM155B, FOXL1, HMX2, HRASLS, LGALS1, LHX6, MXRA5, PCDHB12, PRINS, TMEM56, TWIST1, ZFPM2, and ZNF560, were developed to construct a prognostic risk-scoring model. AML patients could be grouped into high- and low-risk groups, and low-risk patients showed better survival than high-risk patients. Area under the curve values of 1, 3, and 5 years were 0.81, 0.827, and 0.786 in the training set, respectively, indicating a good predictive efficacy. In addition, age and risk score were the independent prognostic factors after univariate and multivariate Cox regression analyses. A nomogram containing clinical factors and prognostic risk-scoring model was constructed to better estimate individual survival. Further analyses demonstrated that risk score was associated with the immune infiltration and response to chemotherapy. Our experiment data revealed that LGALS1 and TMEM56 showed notably decreased expression in AML samples than that of the normal samples.

Conclusion: Our study shows that the prognostic risk-scoring model and key risk gene may provide potential prognostic biomarkers and therapeutic option for AML patients.

Development and Validation of a Novel Diagnostic Model for Childhood Autism Spectrum Disorder Based on Ferroptosis-Related Genes

Autism spectrum disorder (ASD) is a highly genetic heterogeneous neurodevelopmental disorder, which is usually considered a heritable and heterogeneous neurodevelopmental disorder and has caused a great burden to society and families. Emerging roles of ferroptosis have been observed in neurological disorders. This study aimed to construct a diagnostic model based on ferroptosis-related genes (FRGs) to contribute to the early and precise diagnosis of childhood ASD. In the candidate FRGs, we identified 27 differentially expressed genes (DEGs) between ASD patients and typically developing (TD) controls. Four key FRGs were identified using the random forest analysis for further analysis. Utilization of the four gene expression, we constructed a diagnostic model and the AUC value in the training dataset (GSE18123) is 0.7002. We deem that a patient with a score less than 0.9904 is likely to have ASD. Three validation datasets (GSE111176, GSE113834, and GSE28521) were collected and the AUC value is 0.7442, 0.7444, and 0.6474, respectively. A multi-factor regulatory network based on four FRGs indicated that RORA, EAF1, NFYB, miR-4703-3p, and miR-6073 may play a role in the development of ASD. In addition, we found piperaquine may have the potential to be a promising drug for the treatment of ASD. Overall, we constructed a diagnostic model of childhood ASD, which could contribute to the precision diagnosis and timely treatment of childhood ASD.

Immune-relatedlncRNAs can predict the prognosis of acute myeloid leukemia

The immune microenvironment in acute myeloid leukemia (AML) is closely related to patients' prognosis. Long noncoding RNAs (lncRNAs) are emerging as key regulators in immune systems. In this study, we established a prognostic model using an immune-related lncRNA (IRL) signature to predict AML patients' overall survival (OS) through Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis. Kaplan-Meier analysis, receiver operating characteristic (ROC) analysis, univariate Cox regression, and multivariate Cox regression analyses further illustrated the reliability of our prognostic model. An IRL signature-based nomogram consisting of other clinical features efficiently predicted the OS of AML patients. The incorporation of the IRL signature improved the ELN2017 risk stratification system's prognostic accuracy. In addition, we found that monocytes and metabolism-related pathways may play a role in AML progression. Overall, the IRL signature appears as a novel effective model for evaluating the OS of AML patients and may be implemented to contribute to the prolonged OS in AML patients.