OBJECTIVE: To analyze the changes in coagulation during the treatment of acute promyelocytic leukemia (APL) and explore the influencing factors of coagulation in patients with APL. METHODS: Data of 166 APL patients admitted to our hospital from November 2018 to May 2023 were retrospectively analyzed, and the changes of various clinical indicators before and during treatment were compared. 166 APL patients were divided into abnormal coagulation group (n =115) and normal coagulation group (n =51) according to whether they experienced coagulation dysfunction. The basic information, clinical data and laboratory indicators of the two groups were compared. Multivariate logistic regression analysis was used to screen risk factors for coagulation dysfunction and established logistic regression model. Then we developed a neural network model and ranked the importance of the influencing factors, and used receiver operating characteristic (ROC) curves to evaluate the predictive performance of the two models. RESULTS: The comparative results of various clinical indicators in 166 APL patients before and during treatment showed that systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C), estimated glomerular filtration rate (eGFR), platelet (PLT) and fibrinogen (FIB) were significantly increased during the treatment (P < 0.05), while glycosylated hemoglobin (HbA1c), high density lipoprotein cholesterol (HDL-C), blood urea nitrogen (BUN), serum creatinine (SCr), high-sensitivity C reactive protein (hs-CRP), IL-6, TNF-α, TGF-β, white blood cells (WBC), absolute neutrophil count (ANC), prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer (D-D), fibrinogen degradation products (FDP) and lactate dehydrogenase (LDH) were significantly decreased during the treatment (P < 0.05). The proportion of patients with hemorrhage and high-risk APL in the abnormal coagulation group was significantly higher than that in the normal coagulation group (P < 0.05). The levels of IL-6, TNF-α, WBC, ANC, D-D, FDP and LDH in the abnormal coagulation group were significantly higher than those in the normal coagulation group (P < 0.05). The influencing factors selected by univariate analysis were incorporated into logistic regression analysis and neural network model to predict the risk of coagulation dysfunction in APL patients. ROC curves showed that the AUC of the two models were 096 and 0.908, the sensitivity were 0.824 and 0.892, the specificity were 0.940 and 0.904, the Youden index were 064 and 0.796, and the accuracy were 0.882 and 0.898, respectively. CONCLUSION High risk stratification, hemorrhage, elevated WBC, LDH, ANC and FDP levels are independent risk factors for coagulation dysfunction in APL patients. The logistic regression model and neural network model based on these risk factors demonstrate good predictive performance for coagulation dysfunction in APL patients.
Humans ; Leukemia, Promyelocytic, Acute/therapy* ; Blood Coagulation ; Retrospective Studies ; Male ; Female ; Risk Factors ; Logistic Models ; Middle Aged ; Adult ; ROC Curve

OBJECTIVE: To investigate the influencing factors of differentiation syndrome (DS) during induction chemotherapy for acute promyelocytic leukemia (APL), and establish a prediction model for DS in newly diagnosed APL patients, in order to guide clinical treatment. METHODS: The clinical data of 324 newly diagnosed APL patients were retrospectively analyzed, and the patients were divided into DS group and non-DS group according to whether or not DS was present. Statistically significant factors from comparison of the two groups were selected and included in univariate and multivariate logistic regression to explore the influencing factors of DS in APL. R software was used to build the nomogram model, Bootstrap method was used for internal verification, and concordance index (C-index) and calibration curve were used to evaluate the accuracy of the model. RESULTS: The incidence of DS in 324 patients with newly diagnosed APL was 30.86% (100/324). Univariate logistic regression analysis showed that high risk, delayed retinoic acid, no hormonal prophylaxis, combined with disseminated intravascular coagulation, increased white blood cell count (WBC) at initial diagnosis and neutrophil count, prothrombin prolongation, decreased fibrinogen and albumin (ALB), increased proportion of bone marrow original cells, increased proportion of peripheral blood original cells, and increased peak of WBC after chemotherapy were risk factors for DS in newly diagnosed APL patients (all P < 0.01). Multivariate logistic regression analysis showed that the increased peak value of WBC after chemotherapy, prophylactic use of hormone, and ALB level were independent factors influencing the occurrence of DS in newly diagnosed APL patients (all P < 0.01). The C-index of DS in APL predicted by the nomogram model was 0.847(95%CI : 0.786-0.908). The calibration curve showed that the nomogram was in good agreement with the actual incidence of DS. CONCLUSION The independent influencing factors of DS in newly diagnosed APL are the increased peak value of WBC after chemotherapy, ALB and prophylactic use of hormone. The nomogram model based on the above factors can predict the risk of DS in APL patients, which is consistent with clinical observation.
Humans ; Leukemia, Promyelocytic, Acute/drug therapy* ; Nomograms ; Retrospective Studies ; Male ; Female ; Logistic Models ; Middle Aged ; Induction Chemotherapy ; Adult

OBJECTIVE: To investigate the effect of non-chemotherapy strategy of retinoic acid (ATRA) combined with arsenic trioxide (ATO) on the survival of patients with acute promyelocytic leukemia (APL). METHODS: The data of APL patients with complete information diagnosed in the hematology department of our hospital from June 2009 to November 2024 were retrospective analyzed. All patients in the non-CHT group received ATRA-ATO induction, consolidation and maintenance therapy. Patients in the CHT group received ATRA-ATO+chemotherapy induction therapy, followed by 3 cycles of ATRA-ATO+CHT consolidation therapy and 6-10 cycles of ATRA-ATO maintenance therapy. The primary endpoint was event-free survival (EFS). Secondary endpoints included overall survival (OS), remission rate, differentiation syndrome (DS) and safety. RESULTS: There were 182 patients with APL and 15 patients with early death (ED), accounting for 8.24%, which was related to age and risk stratification. There was no significant difference in remission rate between the non-CHT group and the CHT group (P =0.486). As of February 2025, the median follow-up time of patients was 39.5 months. The EFS of the non-CHT group was significantly better than that of the CHT group (P =0.038). There was no significant difference in OS between the two groups (P =0.442). Subgroup analysis showed that EFS in the non-CHT was longer in standard-risk patients (P =0.012). There was no significant difference in EFS (P =0.585) and OS (P =0.473) between the CHT and non-CHT groups in high-risk patients. The incidence of mild DS was 23.6% in the non-CHT group and 23.1% in the CHT group, respectively, with no statistically significant difference(P =0.937). Compared with CHT group, the incidence of serious adverse events was lower in the non-CHT group. CONCLUSION The non-chemotherapy regimen of ATRA combined with ATO is a feasible method to cure APL patients.
Humans ; Leukemia, Promyelocytic, Acute/drug therapy* ; Arsenic Trioxide/therapeutic use* ; Tretinoin/administration & dosage* ; Retrospective Studies ; Female ; Treatment Outcome ; Male ; Adult ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Middle Aged ; Remission Induction

Adult ; Humans ; Asian People ; Leukemia, Myeloid, Acute/therapy* ; Leukemia, Promyelocytic, Acute/therapy* ; Practice Guidelines as Topic

OBJECTIVE: To analyze the characteristics of genetic variants in 134 patients diagnosed with Acute myeloid leukemia (AML). METHODS: Clinical data of the 134 patients with AML (non-acute promyelocytic leukemia) initially diagnosed at the 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army from June 2017 to June 2022 were retrospectively analyzed. Potential variants of AML-related genes were detected by next-generation sequencing, and the frequency of variants was analyzed by using SPSS v26.0 software, and likelihood ratio χ² test and Fisher exact test were used for data analysis. RESULTS: The patients had included 72 males and 62 females, with a gender ratio of 1.7 : 1 and a median age of 51 years (9 ~ 86 years old). One hundred twenty patients (76.1%) had harbored at least one genetic variant, including 26 (19.4%) having a single variant, 27 (20.1%) having two variants, and 49 (36.6%) having >= 3 variants. 32 (23.9%) had no detectable variants. Genetic variants detected in over 10% of the 134 patients had included NPM1 (n = 24, 17.91%), FLT3-ITD (n = 21, 15.67%), DNMT3A (n = 20, 14.93%), CEBPA (single variant; n = 14, 10.45%), TET2 (n = 14, 10.45%), and NRAS (n = 14, 10.45%). The patients were also divided into low risk, intermediate risk and high risk groups based on their chromosomal karyotypes. The mutational rates for genes in different groups have varied, with 19 patients from the low risk group harboring variants of NRAS (n = 4, 21.05%), KRAS (n = 4, 21.05%), and KIT (n = 2, 10.53%); and 96 patients from the intermediate risk group harboring variants of NPM1 (n = 24, 25.00%), FLT3-ITD (n = 20, 20.83%), DNMT3A (n = 18, 18.75%), CEBPA (n = 12, 12.50%), and TET2 genes (n = 12, 12.50%). The mutational frequencies for the 19 patients from the high risk group were ASXL1 (n = 7, 21.05%), NRAS (n = 3, 15.97%), TP53 (n = 3, 15.79%), and EZH2 (n = 2, 10.53%). A significant difference was found in the frequencies of KIT, NPM1, FLT3-ITD, DNMT3A, and ASXL1 gene variants among the low-risk, medium-risk, and high-risk groups. CONCLUSION AML patients have a high frequency for genetic variants, with 76.1% harboring at least one variant. The frequency of genetic variants have varied among patients with different chromosomal karyotypes, and there are apparent dominant variants. KIT, NPM1, FLT3-ITD, DNMT3A, and ASXL1 may be used as prognostic factors for evaluating their prognosis.
Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Leukemia, Myeloid, Acute/genetics* ; Leukemia, Promyelocytic, Acute ; Nuclear Proteins ; Retrospective Studies ; Child ; Adolescent ; Young Adult ; Adult ; Aged ; East Asian People

OBJECTIVE: To investigate the effect of phorbol-12-myristate-13-ace-tate (TPA) on the proliferation and apoptosis of acute promyelocytic leukemia cell line NB4 and its molecular mechanism. METHODS: The effect of different concentrations of TPA on the proliferation of NB4 cells at different time points was detected by CCK-8 assay. The morphological changes of NB4 cells were observed by Wright-Giemsa staining. The cell cycle and apoptosis of NB4 cells after TPA treatment were detected by flow cytometry. The mRNA expressions of NB4 cells after TPA treatment were analyzed by high-throughput microarray analysis and real-time quantitative PCR. Western blot was used to detect the protein expression of CDKN1A, CDKN1B, CCND1, MYC, Bax, Bcl-2, c-Caspase 3, c-Caspase 9, PIK3R6, AKT and p-AKT. RESULTS: Compared with the control group, TPA could inhibit the proliferation of NB4 cells, induce the cells to become mature granulocyte-monocyte differentiation, and also induce cell G₁ phase arrest and apoptosis. Differentially expressed mRNAs were significantly enriched in PI3K/AKT pathway. TPA treatment could increase the mRNA levels of CCND1, CCNA1, and CDKN1A, while decrease the mRNA level of MYC. It could also up-regulate the protein levels of CDKN1A, CDKN1B, CCND1, Bax, c-Caspase 3, c-Caspase 9, and PIK3R6, while down-regulate MYC, Bcl-2, and p-AKT in NB4 cells. CONCLUSION TPA induces NB4 cell cycle arrest in G₁ phase and promotes its apoptosis by regulating PIK3/AKT signaling pathway.
Humans ; Leukemia, Promyelocytic, Acute ; Caspase 3/metabolism* ; Caspase 9/pharmacology* ; Phosphatidylinositol 3-Kinases/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; bcl-2-Associated X Protein/metabolism* ; Cell Line, Tumor ; Cell Division ; Apoptosis ; RNA, Messenger ; Cell Proliferation

OBJECTIVE: To explore the effect of cytokine levels on early death and coagulation function of patients with newly diagnosed acute promyelocytic leukemia (APL). METHODS: Routine examination was performed on 69 newly diagnosed APL patients at admission. Meanwhile, 4 ml fasting venous blood was extracted from the patients. And then the supernatant was taken after centrifugation. The concentrations of cytokines, lactate dehydrogenase (LDH) and ferritin were detected by using the corresponding kits. RESULTS: It was confirmed that cerebral hemorrhage was a major cause of early death in APL patients. Elevated LDH, decreased platelets (PLT) count and prolonged prothrombin time (PT) were high risk factors for early death (P <0.05). The increases of IL-5, IL-6, IL-10, IL-12p70 and IL-17A were closely related to the early death of newly diagnosed APL patients, and the increases of IL-5 and IL-17A also induced coagulation disorder in APL patients by prolonging PT (P <0.05). In newly diagnosed APL patients, ferritin and LDH showed a positive effect on the expression of IL-5, IL-10 and IL-17A, especially ferritin had a highly positive correlation with IL-5 (r =0.867) and IL-17A (r =0.841). Moreover, there was a certain correlation between these five high-risk cytokines, among which IL-5 and IL-17A (r =0.827), IL-6 and IL-10 (r =0.823) were highly positively correlated. CONCLUSION Elevated cytokine levels in newly diagnosed APL patients increase the risk of early bleeding and death. In addition to the interaction between cytokines themselves, ferritin and LDH positively affect the expression of cytokines, thus affecting the prognosis of APL patients.
Humans ; Leukemia, Promyelocytic, Acute/diagnosis* ; Cytokines/metabolism* ; Interleukin-10 ; Interleukin-17/metabolism* ; Interleukin-6/metabolism* ; Interleukin-5/metabolism* ; Blood Coagulation Disorders ; Ferritins ; Tretinoin

OBJECTIVE: To further explore the better indicators for predicting the degree of bleeding associated with newly diagnosed acute promyelocytic leukemia (APL). METHODS: A total of 131 patients with newly diagnosed APL were classified according to WHO bleeding scales before treatment and divided into two groups: scales 0, 1 and 2 were included in no severe bleeding group, scales 3 and 4 were included in severe bleeding group. The information of the patients were collected, including sex, age, hemoglobin (Hb), white blood cell (WBC) count and platelet (PLT) count, peripheral blood lymphocyte percentage (LYMPH%), peripheral blood monocyte percentage (MONO%), percentage of leukemic cells in pripheral blood and bone marrow, prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB) levels, D-dimer (D-D), D-dimer/fibrinogen ratio (DFR). RESULTS: Among 131 patients, 110 were classified as no severe bleeding, and 21 were severe bleeding. The results of univariate analysis showed that patients with severe bleeding had significantly higher percentage of leukemic cells in pripheral blood, WBC, D-D, and DFR, as well as longer PT and lower LYMPH%, compared to those with no severe bleeding. Multivariate analysis revealed that DFR (OR =1.054, 95%CI : 1.024-1.084, P < 0.001) and percentage of peripheral blood leukemic cells (OR=1.026, 95%CI: 1.002-1.051, P =0.033) were independent risk factors for severe bleeding. The area under ROC curve (AUC) of peripheral blood leukemic cells, D-D and DFR were 0.748, 0.736 and 0.809, respectively. There was no statistical difference between the peripheral blood leukemic cells and D-D in diagnostic efficacy (P =0.8708). Compared with D-D, DFR had a higher predictive value (P =0.0302). The optimal cut-off value of DFR was 16.50, with a sensitivity of 90.5% and a specificity of 70.0%. CONCLUSION DFR has a significant advantage in predicting the degree of bleeding associated with newly diagnosed APL. The greater the DFR value, the heavier the degree of bleeding. The risk of severe or fatal bleeding increases when DFR is greater than 16.50.
Humans ; Leukemia, Promyelocytic, Acute/complications* ; Retrospective Studies ; Fibrin Fibrinogen Degradation Products ; Hemorrhage

OBJECTIVE: To explore the treatment of children with high-risk acute promyelocytic leukemia (APL), aiming to improve the prognosis. METHODS: The clinical datas of 24 children with high-risk APL in our hospital from January 2015 to June 2021 were retrospectively analyzed. RESULTS: The main manifestations of 24 children (including 15 males and 9 females) were purpura, gingiva bleeding and nasal hemorrhage, with a median age of 7 years old and a median leukocyte count of 28.98 (10-232)×10⁹/L, including 15 cases with leukocyte count between 10×10⁹/L and 50×10⁹/L, 2 cases between 50×10⁹/L and 100×10⁹/L, and 7 cases >100×10⁹/L. The leukocyte count of 2 cases in 3 children admitted from 2015 to November 2016 was >100×10⁹/L, in which 1 case was first treated with homoharringtonine for cytoreduction, 7 days later treated with all-trans retinoic acid (ATRA) after genetic diagnosis, then died of differentiation syndrome and pulmonary hemorrhage after 3 days. The other one was treated with reduced ATRA+daunorubicin+arsenic trioxide (ATO) for induction, then achieved complete remission. The third one with leukocyte count 12×10⁹/L had cerebral hemorrhage before admission and died on the 7th day of treatment. The remaining 21 children were treated with chemotherapy according to the APL regimen for children in South China, including 14 cases with leukocyte count between 10×10⁹/L and 50×10⁹/L, 2 cases between 50×10⁹/L and 100×10⁹/L, and 5 cases >100×10⁹/L. In the 5 children with leukocyte count >100×10⁹/L, 1 case died of cerebral hemorrhage on the second day of oral ATRA before the addition of anthracyclines, 3 cases died of cerebral hemorrhage after the addition of anthracyclines to chemotherapy on the second day of oral ATRA, and another one developed differentiation syndrome after the addition of mitoxantrone on the second day of oral ATRA, then achieved complete remission after ATRA reduction chemotherapy and survived without disease till now. In the 2 children with leukocyte count between 50×10⁹/L and 100×10⁹/L, 1 case died of cerebral hemorrhage on the second day of oral ATRA before the addition of anthracyclines. All the children were followed up until 1st August, 2021, with a median follow-up time of 40 months, including 7 deaths and 1 recurrence in maintenance therapy who achieved second remission after chemotherapy, 14 cases survived in 3 years and 13 cases survived without event. The 7 dead children had a median time from treatment to death of 5 days, including 1 case with leukocyte count between 10×10⁹/L and 50×10⁹/L, 1 case between 50×10⁹/L and 100×10⁹/L, and 5 cases >100×10⁹/L. CONCLUSION High-risk APL children with leukocyte count >100×10⁹/L have a high mortality rate. Gradual addition of chemotherapy starting at small doses and early addition of ATO may help to improve the prognosis.
Male ; Female ; Humans ; Child ; Leukemia, Promyelocytic, Acute/drug therapy* ; Retrospective Studies ; Arsenic Trioxide/therapeutic use* ; Tretinoin/therapeutic use* ; Remission Induction ; Anthracyclines/therapeutic use* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Treatment Outcome

OBJECTIVE: To investigate the influence and mechanism of atorvastatin on glycolysis of adriamycin resistant acute promyelocytic leukemia (APL) cell line HL-60/ADM. METHODS: HL-60/ADM cells in logarithmic growth phase were treated with different concentrations of atorvastatin, then the cell proliferation activity was measured by CCK-8 assay, the apoptosis was detected by flow cytometry, the glycolytic activity was checked by glucose consumption test, and the protein expressions of PTEN, p-mTOR, PKM2, HK2, P-gp and MRP1 were detected by Western blot. After transfection of PTEN-siRNA into HL-60/ADM cells, the effects of low expression of PTEN on atorvastatin regulating the behaviors of apoptosis and glycolytic metabolism in HL-60/ADM cells were further detected. RESULTS: CCK-8 results showed that atorvastatin could inhibit the proliferation of HL-60/ADM cells in a concentration-dependent and time-dependent manner (r=0.872, r=0.936), and the proliferation activity was inhibited most significantly when treated with 10 μmol/L atorvastatin for 24 h, which was decreased to (32.3±2.18)%. Flow cytometry results showed that atorvastatin induced the apoptosis of HL-60/ADM cells in a concentration-dependent manner (r=0.796), and the apoptosis was induced most notably when treated with 10 μmol/L atorvastatin for 24 h, which reached to (48.78±2.95)%. The results of glucose consumption test showed that atorvastatin significantly inhibited the glycolytic activity of HL-60/ADM cells in a concentration-dependent and time-dependent manner (r=0.915, r=0.748), and this inhibition was most strikingly when treated with 10 μmol/L atorvastatin for 24 h, reducing the relative glucose consumption to (46.53±1.71)%. Western blot indicated that the expressions of p-mTOR, PKM2, HK2, P-gp and MRP1 protein were decreased in a concentration-dependent manner (r=0.737, r=0.695, r=0.829, r=0.781, r=0.632), while the expression of PTEN protein was increased in a concentration-dependent manner (r=0.531), when treated with different concentrations of atorvastatin for 24 h. After PTEN-siRNA transfected into HL-60/ADM cells, it showed that low expression of PTEN had weakened the promoting effect of atorvastatin on apoptosis and inhibitory effect on glycolysis and multidrug resistance. CONCLUSION Atorvastatin can inhibit the proliferation, glycolysis, and induce apoptosis of HL-60/ADM cells. It may be related to the mechanism of increasing the expression of PTEN, inhibiting mTOR activation, and decreasing the expressions of PKM2 and HK2, thus reverse drug resistance.
Humans ; Atorvastatin/pharmacology* ; PTEN Phosphohydrolase/pharmacology* ; Sincalide/metabolism* ; Drug Resistance, Neoplasm/genetics* ; TOR Serine-Threonine Kinases/metabolism* ; Leukemia, Promyelocytic, Acute/drug therapy* ; Doxorubicin/pharmacology* ; Apoptosis ; RNA, Small Interfering/pharmacology* ; Glycolysis ; Glucose/therapeutic use* ; Cell Proliferation