Comprehensive analysis of PTPN family expression and prognosis in acute myeloid leukemia

Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction.

METHOD

This study aims to enhance prognostic capabilities in AML by integrating multi-omics data, especially gene expression and methylation, through network-based feature selection methodologies. By employing artificial intelligence and network analysis, we are exploring different methods to build a machine learning model for predicting AML patient survival. We evaluate the effectiveness of combining omics data, identify the most informative method for network integration and compare the performance with standard feature selection methods.

RESULTS

Our findings demonstrate that integrating gene expression and methylation data significantly improves prediction accuracy compared to single omics data. Among network integration methods, our study identifies the best approach that improves informative feature selection for predicting patient outcomes in AML. Comparative analyses demonstrate the superior performance of the proposed network-based methods over standard techniques.

CONCLUSIONS

This research presents an innovative and robust methodology for building a survival prediction model tailored to AML patients. By leveraging multilayer network analysis for feature selection, our approach contributes to improving the understanding and prognostic capabilities in AML and laying the foundation for more effective personalized therapeutic interventions in the future.

Preclinical evaluation of cyclophosphamide and fludarabine combined with CD19 CAR-T in the treatment of B-cell hematologic malignancies in vivo

Background

Chimeric antigen receptor T (CAR-T) cell therapy has achieved marked therapeutic success in ameliorating hematological malignancies. However, there is an extant void in the clinical guidelines concerning the most effective chemotherapy regimen prior to chimeric antigen receptor T (CAR-T) cell therapy, as well as the optimal timing for CAR-T cell infusion post-chemotherapy.

Materials and Methods

We employed cell-derived tumor xenograft (CDX) murine models to delineate the optimal pre-conditioning chemotherapy regimen and timing for CAR-T cell treatment. Furthermore, transcriptome sequencing was implemented to identify the therapeutic targets and elucidate the underlying mechanisms governing the treatment regimen.

Results

Our preclinical in vivo evaluation determined that a combination of cyclophosphamide and fludarabine, followed by the infusion of CD19 CAR-T cells five days subsequent to the chemotherapy, exerts the most efficacious therapeutic effect in B-cell hematological malignancies. Concurrently, RNA-seq data indicated that the therapeutic efficacy predominantly perturbs tumor cell metabolism, primarily through the inhibition of key mitochondrial targets, such as C-Jun Kinase enzyme (C-JUN).

Conclusion

In summary, the present study offers critical clinical guidance and serves as an authoritative reference for the deployment of CD19 CAR-T cell therapy in the treatment of B-cell hematological malignancies.

Systems Biology for Drug Target Discovery in Acute Myeloid Leukemia

Combining new therapeutics with all-trans-retinoic acid (ATRA) could improve the efficiency of acute myeloid leukemia (AML) treatment. Modeling the process of ATRA-induced differentiation based on the transcriptomic profile of leukemic cells resulted in the identification of key targets that can be used to increase the therapeutic effect of ATRA. The genome-scale transcriptome analysis revealed the early molecular response to the ATRA treatment of HL-60 cells. In this study, we performed the transcriptomic profiling of HL-60, NB4, and K562 cells exposed to ATRA for 3-72 h. After treatment with ATRA for 3, 12, 24, and 72 h, we found 222, 391, 359, and 1032 differentially expressed genes (DEGs) in HL-60 cells, as well as 641, 1037, 1011, and 1499 DEGs in NB4 cells. We also found 538 and 119 DEGs in K562 cells treated with ATRA for 24 h and 72 h, respectively. Based on experimental transcriptomic data, we performed hierarchical modeling and determined cyclin-dependent kinase 6 (CDK6), tumor necrosis factor alpha (TNF-alpha), and transcriptional repressor CUX1 as the key regulators of the molecular response to the ATRA treatment in HL-60, NB4, and K562 cell lines, respectively. Mapping the data of TMT-based mass-spectrometric profiling on the modeling schemes, we determined CDK6 expression at the proteome level and its down-regulation at the transcriptome and proteome levels in cells treated with ATRA for 72 h. The combination of therapy with a CDK6 inhibitor (palbociclib) and ATRA (tretinoin) could be an alternative approach for the treatment of acute myeloid leukemia (AML).

Consideration of SHP-1 as a Molecular Target for Tumor Therapy

Abnormal activation of receptor tyrosine kinases (RTKs) contributes to tumorigenesis, while protein tyrosine phosphatases (PTPs) contribute to tumor control. One of the most representative PTPs is Src homology region 2 (SH2) domain-containing phosphatase 1 (SHP-1), which is associated with either an increased or decreased survival rate depending on the cancer type. Hypermethylation in the promoter region of PTPN6, the gene for the SHP-1 protein, is a representative epigenetic regulation mechanism that suppresses the expression of SHP-1 in tumor cells. SHP-1 comprises two SH2 domains (N-SH2 and C-SH2) and a catalytic PTP domain. Intramolecular interactions between the N-SH2 and PTP domains inhibit SHP-1 activity. Opening of the PTP domain by a conformational change in SHP-1 increases enzymatic activity and contributes to a tumor control phenotype by inhibiting the activation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT3) pathway. Although various compounds that increase SHP-1 activation or expression have been proposed as tumor therapeutics, except sorafenib and its derivatives, few candidates have demonstrated clinical significance. In some cancers, SHP-1 expression and activation contribute to a tumorigenic phenotype by inducing a tumor-friendly microenvironment. Therefore, developing anticancer drugs targeting SHP-1 must consider the effect of SHP-1 on both cell biological mechanisms of SHP-1 in tumor cells and the tumor microenvironment according to the target cancer type. Furthermore, the use of combination therapies should be considered.

Identification of PTPN20 as an innate immunity-related gene in gastric cancer with Helicobacter pylori infection

Background

Gastric cancer (GC) is among the deadliest diseases with countless incidences and deaths each year. Helicobacter pylori (Hp) is the primary type of microbe that colonizes the stomach. In recent years, increasing evidence has demonstrated that Hp infection is one of the main risk factors for GC. Elucidating the molecular mechanism of how Hp leads to GC will not only benefit the treatment of GC, but also boost the development of therapeutics for other gastric disorders caused by Hp infection. In this study, we aimed to identify innate immunity-related genes in GC and investigate their potentials as prognostic markers and therapeutic targets for Hp-related GC.

Methods

Firstly, we analyzed the differentially expressed innate immunity-related genes in GC samples from the TCGA database. Then prognostic correlation analysis was carried out to explore the prognostic value of these candidate genes. By combing transcriptome data, somatic mutation data, and clinical data, co-expression analysis, functional enrichment analysis, tumor mutational burden analysis, and immune infiltration analysis were performed to reveal the pathological relevance of the candidate gene. Finally, ceRNA network was constructed to identify the genes and pathways for the regulation of the candidate gene.

Results

We revealed that protein tyrosine phosphatase non-receptor type 20 (PTPN20) is a significant prognostic marker in Hp-related GC. Thus, PTPN20 levels have the potential to efficiently predict the survival of Hp-related GC patients. In addition, PTPN20 is associated with immune cell infiltration and tumor mutation burden in these GC patients. Moreover, we have also identified PTPN20-related genes, PTPN20 protein-protein interactions, and the PTPN20 ceRNA network.

Conclusion

Our data suggest that PTPN20 may have critical functions in Hp-related GC. Targeting PTPN20 may be a promising way to treat Hp-related GC.