BackgroundChronic myeloid leukemia (CML) is an acquired hematopoietic stem malignant disease originating from the myeloid system. Long non-coding RNAs (lncRNAs) have been widely explored in cancer tumorigenesis. However, their roles in CML remain largely unclear.MethodsThe peripheral blood mononuclear cells (PBMCs) and CML cell lines (K562, KCL22, MEG01, BV173) were collected for in vitro research. Real-time quantitative polymerase chain reaction was used to determine the mRNA expression levels. Cell viability and apoptosis were analyzed by cell counting kit 8 and flow cytometry assays. The targeting relationships were predicted using Starbase and TargetScan and ulteriorly verified by RNA pull-down and luciferase reporter assays. Western blotting assay was performed to assess the protein expressions. N6-methyladenosine (m6A) modification sites were predicted by SRAMP and confirmed by Methylated RNA immunoprecipitation (MeRIP) assay.ResultsLncRNA nuclear-enriched abundant transcript 1 (NEAT1) expression levels were decreased in the CML cell lines and PBMCs of CML patients. Moreover, METTL3-mediated m6A modification induced the aberrant expression of NEAT1 in CML. Overexpression of NEAT1 inhibited cell viability and promoted the apoptosis of CML cells. Additionally, miR-766-5p was upregulated in CML PBMCs and abrogated the effects of NEAT1 on cell viability and apoptosis of the CML cells. Further, CDKN1A was proved to be the target gene of miR-766-5p and was downregulated in the CML PBMCs. Knockdown of CDKN1A reversed the effects of NEAT1.ConclusionThe current research elucidates a novel METTL3/NEAT1/miR-766-5p/CDKN1A axis which plays a critical role in the progression of CML.
BackgroundImatinib (IM), a tyrosine kinase inhibitor (TKI), has markedly improved the survival and life quality of chronic myeloid leukemia (CML) patients. However, the lack of specific biomarkers for IM resistance remains a serious clinical challenge. Recently, growing evidence has suggested that exosome-harbored proteins were involved in tumor drug resistance and could be novel biomarkers for the diagnosis and drug sensitivity prediction of cancer. Therefore, we aimed to investigate the proteomic profile of plasma exosomes derived from CML patients to identify ideal biomarkers for IM resistance.MethodsWe extracted exosomes from pooled plasma samples of 9 imatinib-resistant CML patients and 9 imatinib-sensitive CML patients by ultracentrifugation. Then, we identified the expression levels of exosomal proteins by liquid chromatography-tandem mass spectrometry (LC-MS/MS) based label free quantification. Bioinformatics analyses were used to analyze the proteomic data. Finally, the western blot (WB) and parallel reaction monitoring (PRM) analyses were applied to validate the candidate proteins.ResultsA total of 2812 proteins were identified in plasma exosomes from imatinib-resistant and imatinib-sensitive CML patients, including 279 differentially expressed proteins (DEPs) with restricted criteria (fold change≥1.5 or ≤0.667, p<0.05). Compared with imatinib-sensitive CML patients, 151 proteins were up-regulated and 128 proteins were down-regulated. Bioinformatics analyses revealed that the main function of the upregulated proteins was regulation of protein synthesis, while the downregulated proteins were mainly involved in lipid metabolism. The top 20 hub genes were obtained using STRING and Cytoscape, most of which were components of ribosomes. Moreover, we found that RPL13 and RPL14 exhibited exceptional upregulation in imatinib-resistant CML patients, which were further confirmed by PRM and WB.ConclusionProteomic analysis of plasma exosomes provides new ideas and important information for the study of IM resistance in CML. Especially the exosomal proteins (RPL13 and RPL14), which may have great potential as biomarkers of IM resistance.
BackgroundAn increasing number of studies have revealed the influencing factors of ferroptosis. The influence of immune cell infiltration, inflammation development and lipid metabolism in the tumor microenvironment (TME) on the ferroptosis of tumor cells requires further research and discussion.MethodsWe explored the relationship between ferroptosis-related genes and acute myeloid leukemia (AML) from the perspective of large sample analysis and multiomics, used multiple groups to identify and verify ferroptosis-related molecular patterns, and analyzed the sensitivity to ferroptosis and the state of immune escape between different molecular pattern groups. The single-sample gene set enrichment analysis (ssGSEA) algorithm was used to quantify the phenotypes of ferroptosis-related molecular patterns in individual patients. HL-60 and THP-1 cells were treated with ferroptosis inducer RSL3 to verify the therapeutic value of targeted inhibition of GPX4.ResultsThree ferroptosis-related molecular patterns and progressively worsening phenotypes including immune activation, immune exclusion and immunosuppression were found with the two different sequencing approaches. The FSscore we constructed can quantify the development of ferroptosis-related phenotypes in individual patients. The higher the FSscore is, the worse the patient’s prognosis. The FSscore is also highly positively correlated with pathological conditions such as inflammation development, immune escape, lipid metabolism, immunotherapy resistance, and chemotherapy resistance and is negatively correlated with tumor mutation burden. Moreover, RSL3 can induce ferroptosis of AML cells by reducing the protein level of GPX4.ConclusionsThis study revealed the characteristics of immunity, inflammation, and lipid metabolism in the TME of different AML patients and differences in the sensitivity of tumor cells to ferroptosis. The FSscore can be used as a biomarker to provide a reference for the clinical evaluation of the pathological characteristics of AML patients and the design of personalized treatment plans. And GPX4 is a potential target for AML treatment.
Recent studies have demonstrated that the overexpression of H19 may contribute towards development of tumorigenesis in various types of cancer. To investigate the role of H19 in the development of non-small cell lung cancer (NSCLC), 76 NSCLC tissues samples and their adjacent normal tissue samples were collected. Expression level of H19, and its association with clinicopathological features and overall survival was analyzed. It was found that compared with normal adjacent tissues, H19 expression was elevated in NSCLC tissues along with a decreased miR-203 expression level. It was also found that patients who were in advanced clinical stages had a higher H19 and a lower miR-203 expression compared to normal tissues. The overall survival time of patients with higher H19 expression was shorter compared with the lower H19 expression group. Upregulation of A549 enhanced cell proliferation and promoted invasion. Overexpression of H19 stimulated the epithelial-mesenchymal transition (EMT) process in lung cancer cells and demonstrated typical morphological characteristics of EMT. The level of mesenchymal marker protein, such as Vimentin and SNAI1 increased; while CDH1 protein level decreased. Also, H19 negatively regulated miR-203. Inhibition of H19 attenuated miR-203 induced EMT process. Upregulation of H19 contributes to poor clinical features in patients with NSCLC, induces occurrence of EMT, promotes proliferation and stimulates cell invasion in NSCLC cell line through regulating miRNA-203 mediated EMT.
Accumulated genetic mutations are an important cause for the development of acute myeloid leukemia (AML), but abnormal changes in the inflammatory microenvironment also have regulatory effects on AML. Exploring the relationship between inflammatory response and pathological features of AML has implications for clinical diagnosis, treatment and prognosis evaluation. We analyzed the expression variation landscape of inflammatory response-related genes (IRRGs) and calculated an inflammatory response score for each sample using the Gene set variation analysis (GSVA) algorithm. The differences in clinical- and immune-related characteristics between high- and low-inflammatory response groups were further analyzed. We found that most IRRGs were highly expressed in AML samples, and patients with high inflammatory response had poor prognosis and were accompanied with highly activated chemokine-, cytokine- and adhesion molecule-related signaling pathways, higher infiltration ratios of monocytes, neutrophils and M2 macrophages, high activity of type I/II interferon (IFN) response, and higher expression of immune checkpoints. We also used the Genomics of Drug Sensitivity in Cancer (GDSC) database to predict the sensitivity of AML samples with different inflammatory responses to common drugs, and found that AML samples with low inflammatory response were more sensitive to cytarabine, doxorubicin, and midostaurin. Finally, we constructed a prognostic risk-score model to predict the overall survival (OS) of AML patients. Patients with higher risk score had significantly shorter OS, which was confirmed in two validation cohorts. The analysis of inflammatory response patterns can help us better understand the differences in tumor microenvironment of AML patients, and guide clinical medication and prognosis prediction.
Background Alternative splicing (AS) of RNA is a fundamental biological process that shapes protein diversity. Many non-characteristic AS events are involved in the onset and development of acute myeloid leukemia (AML). Abnormal alterations in splicing factors (SFs), which regulate the onset of AS events, affect the process of splicing regulation. Hence, it is important to explore the relationship between SFs and the clinical features and biological processes of patients with AML. Methods This study focused on SFs of the classical heterogeneous nuclear ribonucleoprotein (hnRNP) family and arginine and serine/arginine-rich (SR) splicing factor family. We explored the relationship between the regulation patterns associated with the expression of SFs and clinicopathological factors and biological behaviors of AML based on a multi-omics approach. The biological functions of SRSF10 in AML were further analyzed using clinical samples and in vitro experiments. Results Most SFs were upregulated in AML samples and were associated with poor prognosis. The four splicing regulation patterns were characterized by differences in immune function, tumor mutation, signaling pathway activity, prognosis, and predicted response to chemotherapy and immunotherapy. A risk score model was constructed and validated as an independent prognostic factor for AML. Overall survival was significantly shorter in the high-risk score group. In addition, we confirmed that SRSF10 expression was significantly up-regulated in clinical samples of AML, and knockdown of SRSF10 inhibited the proliferation of AML cells and promoted apoptosis and G1 phase arrest during the cell cycle. Conclusion The analysis of splicing regulation patterns can help us better understand the differences in the tumor microenvironment of patients with AML and guide clinical decision-making and prognosis prediction. SRSF10 can be a potential therapeutic target and biomarker for AML.
Chronic myeloid leukemia (CML) is a hematological tumor derived from hematopoietic stem cells. The aim of this study is to analyze the biological characteristics and identify the diagnostic markers of CML. We obtained the expression profiles from the Gene Expression Omnibus (GEO) database and identified 210 differentially expressed genes (DEGs) between CML and normal samples. These DEGs are mainly enriched in immune-related pathways such as Th1 and Th2 cell differentiation, primary immunodeficiency, T cell receptor signaling pathway, antigen processing and presentation pathways. Based on these DEGs, we identified two molecular subtypes using a consensus clustering algorithm. Cluster A was an immunosuppressive phenotype with reduced immune cell infiltration and significant activation of metabolism-related pathways such as reactive oxygen species, glycolysis and mTORC1; Cluster B was an immune activating phenotype with increased infiltration of CD4 + and CD8 + T cells and NK cells, and increased activation of signaling pathways such as interferon gamma (IFN-γ) response, IL6-JAK-STAT3 and inflammatory response. Drug prediction results showed that patients in Cluster B had a higher therapeutic response to anti-PD-1 and anti-CTLA4 and were more sensitive to imatinib, nilotinib and dasatinib. Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage Selection Operator (LASSO) and Random Forest (RF) algorithms identified 4 CML diagnostic genes (HDC, SMPDL3A, IRF4 and AQP3), and the risk score model constructed by these genes improved the diagnostic accuracy. We further validated the diagnostic value of the 4 genes and the risk score model in a clinical cohort, and the risk score can be used in the differential diagnosis of CML and other hematological malignancies. The risk score can also be used to identify molecular subtypes and predict response to imatinib treatment. These results reveal the characteristics of immunosuppression and metabolic reprogramming in CML patients, and the identification of molecular subtypes and biomarkers provides new ideas and insights for the clinical diagnosis and treatment.
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