Expression of human Krüppel‐like factor 3 in peripheral blood as a promising biomarker for acute leukemia

Yan, Miao; Liu, Huihui; Xu, Junhui; Cen, Xiaowei; Wang, Qian; Xu, Weilin; Wang, Wensheng; Qiu, Zhi-Xiang; Jiang, Ou; Dong, Yujun; Zhu, Ping; Ren, Hanyun; He, Fuchu; Wang, Mangju

doi:10.1002/cam4.2911

Cited by 7 publications

(13 citation statements)

References 29 publications

(26 reference statements)

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“…Previous machine learning models of BC prognosis prediction were developed using baseline clinical and pathologic information ( 30 – 32 ). Most of those studies used pathologic information and additional molecular information, such as the intrinsic subtype at BC diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry

Kim

Lee

et al. 2021

Front. Oncol.

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Several prognosis prediction models have been developed for breast cancer (BC) patients with curative surgery, but there is still an unmet need to precisely determine BC prognosis for individual BC patients in real time. This is a retrospectively collected data analysis from adjuvant BC registry at Samsung Medical Center between January 2000 and December 2016. The initial data set contained 325 clinical data elements: baseline characteristics with demographics, clinical and pathologic information, and follow-up clinical information including laboratory and imaging data during surveillance. Weibull Time To Event Recurrent Neural Network (WTTE-RNN) by Martinsson was implemented for machine learning. We searched for the optimal window size as time-stamped inputs. To develop the prediction model, data from 13,117 patients were split into training (60%), validation (20%), and test (20%) sets. The median follow-up duration was 4.7 years and the median number of visits was 8.4. We identified 32 features related to BC recurrence and considered them in further analyses. Performance at a point of statistics was calculated using Harrell's C-index and area under the curve (AUC) at each 2-, 5-, and 7-year points. After 200 training epochs with a batch size of 100, the C-index reached 0.92 for the training data set and 0.89 for the validation and test data sets. The AUC values were 0.90 at 2-year point, 0.91 at 5-year point, and 0.91 at 7-year point. The deep learning-based final model outperformed three other machine learning-based models. In terms of pathologic characteristics, the median absolute error (MAE) and weighted mean absolute error (wMAE) showed great results of as little as 3.5%. This BC prognosis model to determine the probability of BC recurrence in real time was developed using information from the time of BC diagnosis and the follow-up period in RNN machine learning model.

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Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry

Kim

Lee

et al. 2021

Front. Oncol.

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“…Therefore, KLF3 has multiple biological functions, including erythropoiesis, adipogenesis, muscle regulation, B-cell development, differentiation and apoptosis [17,29]. Aberrant expression of KLF3 is related to the occurrence and progress of various tumors, such as acute leukemia [17], sarcoma [19], lung cancer [16], colorectal cancer [18], melanoma cancer [30]. Loss of KLF3 potentiates lung cancer metastasis and EMT process through controlling STAT3 pathway [16].…”

Section: Klf3 Impairs the Fto-induced Proliferation And Invasion Of Os Cellsmentioning

confidence: 99%

“…KLFs are one of the key components of the mammalian Sp/KLFSp zinc nger protein family. KLF3, as a member of KLFs, can recruit transcriptional corepressor C-terminal-binding protein to promoters of downstream molecules and primarily effectuate transcriptional inhibition [15,17]. Similar to other Sp/KLFSp zinc nger protein members, KLF3 contains conserved binding sites of GC-rich motif or CACCC box, which causes the regulation diversity for target genes [28].…”

Section: Klf3 Impairs the Fto-induced Proliferation And Invasion Of O...mentioning

confidence: 99%

“…Similar to other Sp/KLFSp zinc nger protein members, KLF3 contains conserved binding sites of GC-rich motif or CACCC box, which causes the regulation diversity for target genes [28]. Therefore, KLF3 has multiple biological functions, including erythropoiesis, adipogenesis, muscle regulation, B-cell development, differentiation and apoptosis [17,29]. Aberrant expression of KLF3 is related to the occurrence and progress of various tumors, such as acute leukemia [17], sarcoma [19], lung cancer [16], colorectal cancer [18], melanoma cancer [30].…”

Section: Klf3 Impairs the Fto-induced Proliferation And Invasion Of O...mentioning

confidence: 99%

“…KLF3 plays an important role in B cell development, erythrocyte and fat production, nervous system and muscle gene regulation, cell differentiation and proliferation [14,15]. Aberrant expression of KLF3 has been detected in several cancers, such as lung cancer [16], acute leukemia [17]s, colorectal cancer [18], sarcoma [19].…”

Section: Introductionmentioning

confidence: 99%

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FTO-Mediated N6-Methyladenosine Modification of KLF3 Promotes Osteosarcoma Progression

Shan

Dong³

et al. 2021

Preprint

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N6-methyladenosine (m6A) methylation is the most common and abundant methylation modification of eukaryotic mRNAs, which is involved in tumor initiation and progression. This study aims to explore the functional role and regulatory mechanism of FTO in osteosarcoma progression. In this study, we detected the expressions of KLF3 in OS cells and tissues by qRT-PCR and western blot, and found that the mRNA and protein expressions of KLF3 were increased in OS cells and tissues and significantly related to tumor size, metastasis, and TNM stage and poor prognosis of OS patients. FTO promoted the proliferation and invasion and suppressed apoptosis of OS cells through CCK-8, Transwell and apoptosis assays. Further mechanism dissection revealed that FTO and YTHDF2 enforced the decay of KLF3 mRNAs and decreased its expression. Knockdown of FTO led to elevate m6A and mRNA levels. FTO-mediated m6A modification regulated KLF3 expression in the YTHDF2-dependent manner. Moreover, KLF3 overexpression abrogated FTO-induced oncogenic effects on the proliferation and invasion of OS cells. Overall, our findings showed that FTO-mediated m6A modification of KLF3 promoted OS progression, which may provide a therapeutic target for osteosarcoma.

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microRNA‐92b‐3p augments colon cancer development through inhibiting KLF3

Liu,

Zhang

2023

J Biochem & Molecular Tox

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Colon cancer (CC) is a tumor of the large intestine. miR‐92b‐3p is often deregulated in the tumorigensis. Here, the role of miR‐92b‐3p in the development of CC was investigated. miR‐92b‐3p and Kruppel‐like factor 3 (KLF3) expression was examined in CC tissues and cells. miR‐92b‐3p inhibitor or KLF3 overexpression vector was transfected into CC cells, respectively to observe its role in CC cell proliferation, invasion, migration, and apoptosis. The targeting relationship between miR‐92b‐3p and KLF3 was validated. Meanwhile, rescue experiments were performed by co‐transfection of miR‐92b‐3p inhibitor and KLF3 siRNA, followed by determining CC cell proliferation, invasion, migration, and apoptosis. Higher miR‐92b‐3p and lower KLF3 expression levels were observed in CC tissues and cells. miR‐92b‐3p inhibition or KLF3 overexpression reduced proliferation, invasion, and migration whereas induced apoptosis of CC cells. KLF3 was validated to be the target gene of miR‐92b‐3p. Depletion of KLF3 could reverse the antitumor role of miR‐92b‐3p inhibition in CC cells. miR‐92b‐3p augments CC development through inhibiting KLF3, which may confers a novel way to develop future treatment target.

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Expression of human Krüppel‐like factor 3 in peripheral blood as a promising biomarker for acute leukemia

Cited by 7 publications

References 29 publications

Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry

Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry

FTO-Mediated N6-Methyladenosine Modification of KLF3 Promotes Osteosarcoma Progression

microRNA‐92b‐3p augments colon cancer development through inhibiting KLF3

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