Novel Markers in Pediatric Acute Lymphoid Leukemia: The Role of ADAM6 in B Cell Leukemia

Alsuwaidi, Laila; Hachim, Mahmood Y.; Senok, Abiola

doi:10.3389/fcell.2021.706129

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…Low miR-574 expression seems to efficiently discriminate childhood T-ALL from B-ALL patients, whereas TLX1 -deregulated cases seem to be excluded from this pattern [ 79 , 165 ]. Notably, significantly higher levels of miR-574 have been found in BCP-ALL, KMT2A -r and TCF3 - PBX1 subtypes in comparison with other B-ALL cases [ 202 ].…”

Section: Resultsmentioning

confidence: 99%

MicroRNAs and the Diagnosis of Childhood Acute Lymphoblastic Leukemia: Systematic Review, Meta-Analysis and Re-Analysis with Novel Small RNA-Seq Tools

Kyriakidis

Tsezou

2022

Cancers

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MicroRNAs (miRNAs) have been implicated in childhood acute lymphoblastic leukemia (ALL) pathogenesis. We performed a systematic review and meta-analysis of miRNA single-nucleotide polymorphisms (SNPs) in childhood ALL compared with healthy children, which revealed (i) that the CC genotype of rs4938723 in pri-miR-34b/c and the TT genotype of rs543412 in miR-100 confer protection against ALL occurrence in children; (ii) no significant association between rs2910164 genotypes in miR-146a and childhood ALL; and (iii) SNPs in DROSHA, miR-449b, miR-938, miR-3117 and miR-3689d-2 genes seem to be associated with susceptibility to B-ALL in childhood. A review of published literature on differential expression of miRNAs in children with ALL compared with controls revealed a significant upregulation of the miR-128 family, miR-130b, miR-155, miR-181 family, miR-210, miR-222, miR-363 and miR-708, along with significant downregulation of miR-143 and miR-148a, seem to have a definite role in childhood ALL development. MicroRNA signatures among childhood ALL subtypes, along with differential miRNA expression patterns between B-ALL and T-ALL cases, were scrutinized. With respect to T-ALL pediatric cases, we reanalyzed RNA-seq datasets with a robust and sensitive pipeline and confirmed the significant differential expression of hsa-miR-16-5p, hsa-miR-19b-3p, hsa-miR-92a-2-5p, hsa-miR-128-3p (ranked first), hsa-miR-130b-3p and -5p, hsa-miR-181a-5p, -2-3p and -3p, hsa-miR-181b-5p and -3p, hsa-miR-145-5p and hsa-miR-574-3p, as described in the literature, along with novel identified miRNAs.

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Section: Resultsmentioning

confidence: 99%

MicroRNAs and the Diagnosis of Childhood Acute Lymphoblastic Leukemia: Systematic Review, Meta-Analysis and Re-Analysis with Novel Small RNA-Seq Tools

Kyriakidis

Tsezou

2022

Cancers

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“…The highest result is achieved with a Decision Tree (DT) of 98%. In this article (Alsuwaidi et al, 2021 ), the author investigates novel genes demonstrating optimistic clinical and molecular signatures and uses a lymphoid cancer dataset. Lymphoblastic and lymphoid leukemia research comprising 1706 individuals and 2144 data are found in three different research.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Almadhor

Sattar

Hejaili

et al. 2022

Front. Comput. Neurosci.

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Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.

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“…Acute lymphoblastic leukemia (ALL) is malignant transformation and uncontrolled proliferation of lymphoid hematopoietic progenitor cells, finally invading bone marrow and blood [ 1 , 2 ]. Although high cure rate can be achieved due to exist therapeutic regimens, subsequent chemotherapy resistance, disease relapse and poor prognosis remain a significant challenge [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

The role of E3 ubiquitin ligase WWP2 and the regulation of PARP1 by ubiquitinated degradation in acute lymphoblastic leukemia

Huang

et al. 2022

Cell Death Discov.

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Acute lymphoblastic leukemia (ALL) has been a huge threat for people's health and finding effective target therapy is urgent and important. WWP2, as one of E3 ubiquitin ligase, is involved in many biological processes by specifically binding to substrates. PARP1 plays a role in cell apoptosis and is considered as a therapeutic target of certain cancers. In this study, we firstly found that WWP2 expressed higher in newly diagnosed ALL patients comparing with complete remission (CR) ALL patients and normal control people, and WWP2 in relapse ALL patients expressed higher than normal control people. WWP2 expression was related with the FAB subtype of ALL and the proportion of blast cells in bone marrow blood tested by flow cytometry. We demonstrated knockout WWP2 inhibited the ALL growth and enhanced apoptosis induced by Dox in vitro and vivo for the first time. WWP2 negatively regulated and interacted with PARP1 and WWP2 mechanically degraded PARP1 through polyubiquitin-proteasome pathway in ALL. These findings suggested WWP2 played a role in ALL development as well as growth and apoptosis, and also displayed a regulatory pathway of PARP1, which provided a new potential therapeutic target for the treatment of ALL.

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Novel Markers in Pediatric Acute Lymphoid Leukemia: The Role of ADAM6 in B Cell Leukemia

Cited by 5 publications

References 42 publications

MicroRNAs and the Diagnosis of Childhood Acute Lymphoblastic Leukemia: Systematic Review, Meta-Analysis and Re-Analysis with Novel Small RNA-Seq Tools

MicroRNAs and the Diagnosis of Childhood Acute Lymphoblastic Leukemia: Systematic Review, Meta-Analysis and Re-Analysis with Novel Small RNA-Seq Tools

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

The role of E3 ubiquitin ligase WWP2 and the regulation of PARP1 by ubiquitinated degradation in acute lymphoblastic leukemia

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