A global study for acute myeloid leukemia with RARG rearrangement.

Acute myeloid leukemia (AML) with retinoic acid receptor γ (RARG) rearrangement has clinical, morphologic, and immunophenotypic features similar to classic acute promyelocytic leukemia. However, AML with RARG rearrangement is insensitive to alltrans retinoic acid (ATRA) and arsenic trioxide (ATO) and carries a poor prognosis. We initiated a global cooperative study to define the clinicopathological features, genomic and transcriptomic landscape, and outcomes of AML with RARG rearrangements collected from 29 study groups/institutions worldwide. Thirty-four patients with AML with RARG rearrangements were identified. Bleeding or ecchymosis was present in 18 (54.5%) patients. Morphology diagnosed as M3 and M3v accounted for 73.5% and 26.5% of the cases, respectively. Immunophenotyping showed the following characteristics: positive for CD33, CD13, and MPO but negative for CD38, CD11b, CD34, and HLA-DR. Cytogenetics showed normal karyotype in 38% and t(11;12) in 26% of patients. The partner genes of RARG were diverse and included CPSF6, NUP98, HNRNPc, HNRNPm, PML, and NPM1. WT1- and NRAS/KRAS-mutations were common comutations. None of the 34 patients responded to ATRA and/or ATO. Death within 45 days from diagnosis occurred in 10 patients (∼29%). At the last follow-up, 23 patients had died, and the estimated 2-year cumulative incidence of relapse, event-free survival, and overall survival were 68.7%, 26.7%, and 33.5%, respectively. Unsupervised hierarchical clustering using RNA sequencing data from 201 patients with AML showed that 81.8% of the RARG fusion samples clustered together, suggesting a new molecular subtype. RARG rearrangement is a novel entity of AML that confers a poor prognosis. This study is registered with the Chinese Clinical Trial Registry (ChiCTR2200055810).

Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer.

OBJECTIVE: By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer (PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine (TCM) syndromes. METHODS: From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining 10,060 electronic medical records, which were randomly divided into a training set and a test set. Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used "TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information" as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models. RESULTS: The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%, respectively. The classification accuracy rates of the models for all syndromes in this paper were between 82.15% and 93.82%. CONCLUSION: Compared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.

Weighted gene co-expression network analysis identified MYL9 and CNN1 are associated with recurrence in colorectal cancer.

Colorectal cancer (CRC) is one of the most common carcinomas and the fourth leading cause of cancer-related death worldwide. One of the obstacles in the successful treatment of CRC is a high rate of recurrence. We aimed to construct weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes in association with recurrence in CRC patients. We firstly used the microarray data, GSE41258, to construct a co-expression network and identify gene modules. Furthermore, protein and protein interaction (PPI) network was also performed to screen hub genes. To validate the hub genes, an independent dataset GSE17536 was used for survival analyses. Additionally, another two databases were also performed to investigate the survival rates and expression levels of hub genes. Gene set enrichment analyses (GSEA) combined with gene ontology (GO) were performed to further explore function and mechanisms. In our study, the midnightblue module was identified to be significant, 15 hub genes were screened, four of which were identified as hub nodes in the PPI network. In the test dataset, we found higher expression of MYL9 and CNN1 were significantly associated with shorter survival time of CRC patients. GO analyses showed that MYL9 and CNN1 were enriched in "muscle system process" and "cytoskeletal protein binding". GSEA found the two hub genes were enriched in "pathways in cancer" and "calcium signaling pathway". In conclusion, our study demonstrated that MYL9 and CNN1 were hub genes associated with the recurrence of CRC, which may contribute to the improvement of recurrence-free survival time of CRC patients.

Comparison of Mitoxantrone in Combination with Intermediate-dose Cytarabine versus High-dose Cytarabine as Consolidation Therapies for Young Non-APL Acute Myeloid Leukemia Patients with Favorable and Intermediate Cytogenetics.

In this study, we compared the efficacy of mitoxantrone in combination with intermediate-dose cytarabine (HAM) with that of high-dose cytarabine alone (HiDAC) as consolidation regimens in non-acute promyelocytic leukemia (APL) acute myeloid leukemia patients with favorable and intermediate cytogenetics. A total of 62 patients from Shenzhen People's Hospital were enrolled in this study. All patients enrolled received standard induction chemotherapy and achieved the first complete remission (CR1). In these patients, 24 received HiDAC and 38 received HAM as consolidation. The median relapse free survival (RFS) and overall survival (OS) were similar between these two consolidation regimens. Even in subgroup analysis according to risk stratification, the combination regimen conferred no benefit in longterm outcome in patients with favorable or intermediate cytogenetics. However, in patients receiving HAM regimen, the lowest neutrophil count was lower, neutropenic period longer, neutropenic fever rate higher, and more platelet transfusion support was required. HAM group also tended to have higher rate of sepsis than HiDAC group. According to our results, we suggest that combination treatment with mitoxantrone and intermediate-dose cytarabine has limited value as compared to HiDAC, even in young non-APL AML patients with favorable and intermediate cytogenetics.