In Acute Myeloid Leukemia

7283

Original Article

Comprehensively analyze the expression and prognostic role for ten-eleven translocations (TETs) in acute myeloid leukemia

Yan Huang1#, Jie Wei1#, Xunjun Huang1, Weijie Zhou1, Yuling Xu2, Dong-Hong Deng2, Peng Cheng2

1Department of Hematology and Rheumatology, People’s Hospital of Baise, Baise, China; 2Department of Hematology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China Contributions: (I) Conception and design: J Wei, Y Huang; (II) Administrative support: X Huang, Y Xu; (III) Provision of study materials or patients: W Zhou; (IV) Collection and assembly of data: J Wei, Y Huang; (V) Data analysis and interpretation: DH Deng, P Cheng; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Yuling Xu. Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China. Email: [email protected]; Dong-Hong Deng. Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China. Email: [email protected]; Peng Cheng. Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530000, China. Email: [email protected].

Background: The ten-eleven translocation (TET) family oxidize 5-methylcytosines (5mCs) and promote the locus-specific reversal of DNA. The role of TETs in acute myeloid leukemia (AML) is mostly unknown. Methods: TETs mRNA expression levels were analyzed via Gene Expression Profiling Interactive Analysis (GEPIA). The association TETs expression levels and methylation with prognosis by UALCAN GenomicScape, and METHsurv. We analyzed TETs’ aberration types, located mutations, and structures via cBioPortal. GeneMANIA performed the functional network. Gene ontology (GO) enrichment was analyzed via LinkedOmics. MiWalK identified miRNAs, miTarbase, and TargetScan. Transcription factor (TF) targets were analyzed via ChEA3. GSCAlite analyzed the role of these defined genes in cancer pathways and potential drug targets. Finally, we selected AML patients in our department to investigate the mutated types of TETs. Results: TETs expression level results showed TET1 (P=0.003) and TET2 (P=0.004) overexpressed in Haferlach leukemia samples, TET3 (P=4.04e-8) downregulation in Andersson leukemia samples. TET2 and TET3 overexpression but TET1 downregulation in the GEPIA database. Overexpression of TET2 leads to positive outcomes (P=0.0091). The upregulation of TET2 led to poor survival for CN-AML patients, but downregulation of TET3 indicated a satisfactory prognosis. The hypermethylation of TETs like cg24705708 (P=0.036), cg05976228 (P=0.022), cg19127638 (P=0.022), cg15254238 (P=0.025), cg07669489 (P=0.037) indicate poor outcomes. Overexpression of GALNS (P=0.024) as an adverse biomarker, downregulation of E2F5 (P=0.037), MAP7 (P=0.019), and NRIP1 (P=0.0013) indicated good prognosis. Regulatory network analysis indicated TETs’ functions, including covalent chromatin modification, histone modification, DNA methylation, or demethylation. Enrichment functions involving. TETs participate in several cancer pathways, including DNA repair response and receptor tyrosine kinase (RTK) signaling pathway. TETs are sensitive to belinostat, ceranib-2, docetaxel, tivantinib, and vincristine. Conclusion: Present study showed that TETs have different expressions in AML, and the expression levels of TETs lead to different outcomes of AML. The TETs cancer pathway analysis will also provide potential therapy methods for AML patients with TETs aberrations.

Keywords: Acute myeloid leukemia (AML); bioinformatics analysis; ten-eleven translocation (TETs); prognosis values

Submitted Sep 29, 2020. Accepted for publication Nov 06, 2020. doi: 10.21037/tcr-20-3149 View this article at: http://dx.doi.org/10.21037/tcr-20-3149

Introduction (T-ALL) (15), and AML (16). TET2 mutations also were found in MDS (17) and AML (18). However, TET3 Acute myeloid leukemia (AML) is a heterogeneous disease changes less be found in hematological malignancies. In of the blood system characterized by the clonal expansion different cancer types, the TETs functions may be different. and differentiation arrest of myeloid progenitor cells. The For hematological cancers, promoting DNA demethylation incidence of AML increases by age, and the older patients, and regulate immunity is the most important biological the more mortality (1). An estimated 19,940 new diagnostic function of TETs (19). Significant gene methylation, AML patients, and 11,180 people die of AML in the immunity tolerance, and immunity evasion significantly United States (2). However, intensive chemotherapy is not influence tumorigenesis, therapy resistance, and tumor a suitable option for many older patients with significant progress. From these, we use public databases to analyse the comorbidities, baseline organ dysfunction, or poor prognostic value of TETs in AML and observing whether performance status, in whom the risk of complications and there are significant expression levels between AML samples treatment-related mortality is unacceptably high. The worse and normal samples. We present the following article in thing is that relapsed AML patients only have less than accordance with the MDAR reporting checklist (available at 25% complete remission rate (3). Since the more efficient http://dx.doi.org/10.21037/tcr-20-3149). salvage regimens have been into practice, the complete response rate has increased, but these patients cannot obtain substantial CR duration (4.9 to 9.8 months) or survival Methods (6.2 to 8.7 months) (4), this result did not be satisfied. Expression analysis Furthermore, the numbers of AML patients gradually increase in times. Thus, we urgently need more sensitive The Oncomine database (20) and GEPIA (Gene Expression and specific markers for early diagnosis, more efficient but Profiling Interactive Analysis) (21) were preliminarily less side effect therapy for AML patients. used to analyze the TETs expression levels between tumor Although the advance of cell biology and comprehensive samples and normal samples. The study was conducted in genomic analyses has shown the possible leukemogenesis accordance with the Declaration of Helsinki (as revised mechanisms, it is still incompletely understood. The genes in 2013). aberrations have been confirmed, one of the essential leukemogenesis drivers, even can as biomarkers indicated Analyzing the aberrations types, location of mutations, the favorable or adverse outcome of AML patients. Many and structure of TETs studies showed that some improved cytogenetic changes play crucial roles in tumorigenesis and prognosis of AML, We used cBioPortal to learn the types of aberrations of including aberration of TP53 (5), WT1 (6), double CEBPA TETs, perform the aberration locations, and construct the mutation (7), RUNX1 (8), DNMT3A (9), FLT3 (10), three-dimension structure of TETs (22). and so on. DNA’s methylation has appeared relatively stable epigenetic process. However, with recent studies Survival analysis improve the function of ten-eleven translocation (TET) family proteins as 5-methylcytosine oxidases, the view To investigate the relation TETs expression levels with has been reversed. The TET family, including TET1, the prognosis of AML patients, we use the UALCAN to TET2, and TET3. Over the last decade, TET protein analyze the TCGA data of AML patients (23). Further, or family functions and differential expression in various we use GenomicScape (http://www.genomicscape.com) types of cancers have further insight. Many studies explore that data (GSE12417) derived from the GEO database to the TETs role in different cancers. For solid tumors, validate the survival results of AML (24). Also, we analyze TET1 accelerates the triple-negative breast cancer the significantly correlated genes prognostic value of AML. exacerbation (11), but TET2 as inhibitors tumorigenesis of Finally, we explore the relation TETs methylation with the breast cancer cells in another study (12). In ovarian cancer, prognosis of AML patients via METHsurv (25). high expression of TET3 as an adverse biomarker (13). About hematological malignancies, TET1 mutations GeneMANIA analysis have been observed in chronic lymphocytic leukemia (CLL) (14) and T-cell acute lymphoblastic leukemia GeneMANIA is a commonly used website for performing

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(11):7259-7283 | http://dx.doi.org/10.21037/tcr-20-3149 Translational Cancer Research, Vol 9, No 11 November 2020 7261 protein-protein interaction (PPI) network analysis and Statistical analysis predicting the function of preferred genes (26). This user- The gene expression, survival analysis, and the protein- friendly online tool can display gene or gene lists using protein network relations P value <0.05 is considered a bioinformatics methods, including gene co-expression, significant difference in our study. physical interaction, gene co-location, gene enrichment analysis, and website prediction. We predicted the TETs family’s function and significantly correlated genes and Results visualizing the gene networks via GeneMANIA. Transcriptional expression levels of TETs in AML patients

To identify the differential expression levels of TETs in Constructing the miRNA networks AML and normal samples, the TETs mRNA levels in We search for the miRNAs that target TETs via leukemia samples and control samples were analyzed by miTarbase (27), miRTarBase (27), and miWalk (28) in the Oncomine (Figure 1A,B,C) and GEPIA (Figure 1D,E,F) various databases to find out. Further, we performed the databases. The results of Oncomine analysis showed that Venn diagram to find the TETs’ co-targets miRNA. the expression of TET1 (P=0.003) and TET2 (P=0.004) was significant overexpression in AML patients in Haferlach leukemia, but the TET3 (P=4.04e-8) was downregulation LinkOmics and ChEA3 analysis in Andersson leukemia samples. To further validate to The LinkedOmics database (http://www.linkedomics. TETs expression level, we used the GEPIA to perform org/login.php) is a Web-based platform for analyzing 32 analysis. The results indicated that TET2 and TET3 TCGA cancer-associated multi-dimensional datasets (29). were significantly upregulated expression, but TET1 was Data from the LinkFinder results were signed and ranked, significantly downregulated expression. and GSEA was used to analyze GO (CC, BP, and MF), KEGG pathways, and kinase-target enrichment. We used Aberrations types and the structure of TETs LinkOmics to analyze the CC, BP, and MF), kinase-target enrichment, and the significantly correlated genes of TETs. To investigate the aberrations types, aberrations locations, Further, we used the ChEA3 to find the TETs’ significant 3D structure of TETs, we used the C-BioPortal database to transcription factors (TFs) annotating the potential analyze. As the results have shown the top 10 co-expression biological functions, and constructing the network of top 10 of TETs including FLT3, NPM1, DNMT3A, CEBPA, TFs (29). NRAS, IDH2, SRSF2, RUNX1, STAG2, and ASXL1 (Figure 2A). The most standard mutated types of TET2 and TET1 were deep deletion and missense mutations Analyzing the cancer pathway activity and drugs (Figure 2B). The locations of TETs were shown in sensitivity of defined genes Figure 2C. Cancer Analysis (GSCALite) analyzes Gene expression associated cancer pathway activity and Drug sensitivity The relation between TETs expression levels and the for genes (30). We used the GSCALite dataset to explore prognosis of AML patients the role of defined genes in the cancer pathway, further investigate the drug sensitivity of defined genes. To understand whether the expression levels of TETs would have a significant influence on AML patients’ survival, we used the UACLAN database to clarify the relation between Validation of mutated types of TETs in AML patients TETs expression and prognostic role of AML. As the results We selected the AML patients with TETs mutations in The have shown, the higher expression TET2 may lead to Department of Hematology, the First Affiliated Hospital of positive outcomes of AML patients (P=0.0091). However, Guangxi Medical University, to find the mutated types of TET1 and TET3 expression levels did not impact the TETs in our department. prognosis of AML patients (Figure 3A,B,C). To further

TET1 expression in haferlach leukemia TET2 expression in haferlach leukemia TET3 expression in haferlach leukemia A Acute myeloid leukemia vs. normal B Acute myeloid leukemia vs. normal C Acute myeloid leukemia vs. normal Haferlach leukemia statistics Andersson leukemia statistics Haferlach leukemia statistics P-value: 4.04E-8 P-value: 0.003 Under-expression gene rank:348 (in top 4%) Over-expression gene rank: 7052 (in top 37%) Over-expression gene rank:7408 (in top 38%) P-value: 0.004 Reporter: IMAGE: 281493 ▼ t-Test: –8.592 Reporter: 228906_at ▼ t-Test: 2.834 Reporter: 235461_at ▼ t-Test: 2.660 Fold change: –2.394 Fold change: 1.048 Fold change: 1.113 1.5 1.5 3.5 3.0 1.0 2.5 1.0 0.5 2.0 1.5 0.0 0.5 1.0 –0.5

0.5 ratio Log2 median-centered 0.0 0.0 –1.0 1 2 Log2 median-centered intensity Log2 median-centered –0.5 Legend Log2 median-centered intensity Log2 median-centered 2 –0.5 1 1. Bone Marrow (6) 1 2 Legend 2. Acute Myeloid Leukemia (23) D E F 5 7 6

4 6

5 3 5

4 2 4

1 3 2

LAML LAML LAML (num (T) =173; num (N) =70 (num (T) =173; num (N) =70 (num (T) =173; num (N) =70

Figure 1 The expression levels of TETs between AML and normal samples. (A) The AML samples with the higher expression of TET1 in Haferlach leukemia (Oncomine). (B) TET2 overexpresses in AML samples in Haferlach leukemia (Oncomine). (C) TET3 downregulated in tumor samples in Andersson leukemia samples (Oncomine). (D) TET1 was downregulated in AML samples (GEPIA). (E) TET2 was an upregulation in AML samples (GEPIA). (F) Overexpression of TET3 was detected in AML patients (GEPIA). TETs, a ten- eleven translocation (TET) family, oxidize 5-methylcytosines (5mCs); AML, acute myeloid leukemia; GEPIA, Gene Expression Profiling Interactive Analysis. validate the impact of TETs expressions for AML patients, and negatively associated with TET1, TET2, TET3, we used the GSE12417 data to analyze. The results showed as shown in the heat maps (Figure 4A,B,C,D,E,F,G,H,I). that high expression TET2 leads to poor survival for CN- Meanwhile, the statistical scatter plots show the AML patients, but high expression TET3 indicated a association TETs expression with significantly correlated protective factor (Figure 3D,E,F). genes (Pearson’s correlation ≥0.6). From the results of association genes, we can observe APT11C, CD63, OS9, GALNS, NRIP1, MAP7, and E2F5 were strong Prognostics role of significantly correlated genes in AML associations with TET1; ZSWIM6, QKI, SNX13, and To further investigate the potential role of differentially SSFA2 were strong associations with TET2; PDPK1 expressed TETs in AML, the LinkFinder module of and NCOA6 were strong associations with TET3 LinkedOmics was used to analyze mRNA sequencing (Figure 5A,B,C,D,E,F,G,H,I,J,K,L,M). To explore whether data from 173 patients in TCGA. In the volcano plot, the the strong association with TETs genes that have a dark red dots show a significantly positive correlation with significant impact on AML patients, we used UALCAN to TET1, TET2, and TET3, whereas dark green dots show analyze the TET1, TET2, TET3 strong related genes in a significantly negative correlation (false discovery rate, OS of AML patients. There were four correlated significant FDR <0.01). The top 50 significant gene sets positively genes have a significant impact on the OS of AML patients,

A 30

25 Altered group 20 Unaltered group 15

5 Mutation frequency (%) Mutation frequency

0 FLT3 IDH2 NRAS NPM1 ASXL1 STAG2 SRSF2 CEBPA RUNX1 DNMT3A B TET2 9%* TET1 0.4%* TET3 0.1%*

Genetic alteration Missense mutation (putative driver) Missense mutation (unknown significance) Truncating mutation (putative driver)

Truncating mutation (unknown significance) Amplification Deep deletion No alterations Not profiled

C1 5

#TET1 mutations 0 Tet_IBP

0 400 800 1200 1600 2000 2136aa C2 R1216* 6

#TET2 mutations 0 Tet_IBP

0 400 800 1200 1600 2002aa C3 5

A292Qfs* 114

#TET3 mutations 0 Tet_IBP

0 400 800 1200 1660aa

Figure 2 Aberration types, located mutation, the co-mutation status of TETs in AML. (A) The co-mutation frequency in TETs aberration groups and unmutated group. (B) Aberration types and frequency of TETs in AML; truncating mutations are the most common, 9%, 0.4%, and 0.1% mutations of TETs in AML, respectively. (C) The mutated locations of TETs. TETs, ten-eleven translocation (TET) family oxidize 5-methylcytosines (5mCs); AML, acute myeloid leukemia.

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(11):7259-7283 | http://dx.doi.org/10.21037/tcr-20-3149 7264 Huang et al. The role of TETs for AML OS_dayS High expression (n=43) High expression (n=120) Low/medium- expression Expression level Expression 235542_at (TET3) GenomicScape-2.0 P=0.0064, HR=0.55 signal TET3 expression ≤803.41: n=105 (64.4%) TET3 expression signal TET3 expression >803.41: n=58 (35.6%) Metzeler, acute myeloid Metzeler, U133B) leukemia 3 (affy (n=163, GSID: GS-DT-28) 0 200 400 600 800 1000

1.0 0.8 0.6 0.4 0.2 0.0 Overall survival Overall Tmo in days Samples rank statistics 235542_at (TET3) Maximally selected Effect of TET3 expression level on LAML patient survival of TET3 expression Effect Expression of 235542_at (TET3) Expression 400 600 800 1000 0 50 100 150 P=0.16 0 1000 2000 3000 1000 2000 0

2.5 2.0 1.5 1.0 0.5 0.0

500

Standardized log-rank statistic log-rank Standardized 1500 1000 Expression signal Expression

F 1.00 0.75 0.50 0.25 0.00 Survival probability Survival C High expression (n=44) High expression (n=119) Low/medium- expression Expression level Expression OS_TIME 227624_at (TET2) GenomicScape-2.0 TET2 expression signal TET2 expression >138.14: n=158 (89.3%) P=0.032, HR=3.3 signal TET2 expression ≤138.14: n=19 (10.7%) Smith, colon cancer 1 (n=177, GSID: (moffitt) GS-DT-23) 0 20 40 60 80 100 120 140

1.0 0.8 0.6 0.4 0.2 0.0 Overall survival Overall Tmo in days Samples rank statistics Maximally selected 227624_at (TET2) Effect of TET2 expression level on LAML patient survival of TET2 expression Effect Expression of 227624_at (TET2) Expression 150 200 250 300 P=0.0091 0 50 100 150 0 1000 2000 3000 1000 2000 0

2.0 1.5 1.0 0.5 0.0 500 400 300 200 100

Standardized log-rank statistic log-rank Standardized Expression signal Expression

1.00 0.75 0.50 0.25 0.00 B probability Survival E High expression (n=2) High expression (n=121) Low/medium- expression OS_dayS Expression level Expression 235461_at (TET2) GenomicScape-2.0 P=0.022, HR=2.1 signal TET2 expression ≤333.14: n=63 (79.7%) signal TET2 expression >333.14: n=16 (20.3%) Metzeler, acute myeloid Metzeler, U133 plus 2) leukemia 1 (affy (n=79, GSID: GS-DT-26) 0 500 1000 1500

1.0 0.8 0.6 0.4 0.2 0.0 Overall survival Overall Tmo in days Samples rank statistics 235461_at (TET2) Maximally selected Effect of TET1 expression level on LAML patient survival of TET1 expression Effect P=0.52 150 200 250 300 350 The prognostic role of the gene TETs (UALCAN and GenomicScape). (A,B,C) High TET2 expression was correlated with better prognosis of OS in AML, but 0 20 40 60 80 Expression of 235461_at (TET2) Expression 0 1000 2000 3000 1000 2000 0

2.0 1.5 1.0 0.5 0.0

600 500 400 300 200 100

Standardized log-rank statistic log-rank Standardized signal Expression

1.00 0.75 0.50 0.25 0.00 Survival probability Survival A D Figure 3 Figure the expression levels of TET1 and TET3 do not significantly influence AML. (D,E,F) Overexpression TET2 indicates the poor outcome of CN-AML, but overexpression of TET3 shows the better prognosis of CN-AML. TET1, ten-eleven translocation 1; TET2, ten-eleven translocation 2; TET3, ten-eleven translocation 3; AML, acute myeloid leukemia; CN-AML, cytogenetically normal acute myeloid leukemia .

A TET1 association result B C 30 25 20 15 10

-log10 (P value) 5 0 –1 0 1 2 Pearson correlation coefficient (pearson test) F E D TET2 association result

25 20 15 10

-log10 (P value) 5 0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 Pearson correlation coefficient (pearson test) I G TET3 association result H 20

5 -log10 (P value) 0 –1.5 –1.0 –0.5 0.0 0.5 1.0 1.5 2.0 Pearson correlation coefficient (pearson test) Figure 4 Correlated significant genes of TETs (LinkedOmics). (A,B,C,D,E,F,G,H,I) Volcano plots and heat maps showed genes positively and negatively correlated with TETs in AML, respectively (top 50). (D,F) Red suggests positively correlated genes, and green shows negatively correlated genes. TETs, ten-eleven translocation (TET) family oxidize 5-methylcytosines (5mCs); AML, acute myeloid leukemia.

the high expression GALNS (P=0.024) leads to an adverse (Figure 7A,B,C) and the prognosis of TETs methylation for outcome, the downregulation of E2F5 (P=0.037), MAP7 AML patients was shown in Figure 8A,B,C,D,E,F,G,H,I. The (P=0.019), and NRIP1(P=0.0013) indicated the excellent hypermethylation of TET1 including cg19127638, prognosis (Figure 6A,B,C,D). cg12548760, cg18515801, cg01093854, cg13810683, and cg27426824, and the hypermethylation of TET2 including cg20586654, cg08530497, cg22794775, The relationship TETs methylation with the prognosis of cg09666717, and cg17862558. TET3 has the most AML frequency of hypermethylation including cg02956499, From the important role of methylation in tumorigenesis cg13808088, cg00755592, cg15827185, cg21855109, and prognosis of AML patients, we used METHsurv to cg05976228, cg25299214, cg17213010, cg24705708, analyze the TETs methylation status and the association cg02237855, cg01244346, and cg01355757 (Figure 7A,B,C). with prognosis for AML patients, respectively. The Further, we analyzed the prognostics value of TETs locations of TETs methylation were shown in the heatmap methylation in AML patients. The results indicated that

Pearson-correlation: 0.7407 Pearson-correlation: –0.7168 Pearson-correlation: 0.6242 A P value: 1.1910–30 B P value: 6.022e–28 C P value: 1.228e–19 Sample size: (N=169) Sample size: (N=169) Sample size: (N=169) 16 10 12 14 8 10 12

E2F5 4 CD63 ATP11C

8 10 6

2 6 4 6 8 10 4 6 8 10 4 6 8 10 TETI TETI TETI

Pearson-correlation: 0.7038 Pearson-correlation: 0.6381 Pearson-correlation: 0.6105 D P value: 1.379e–26 E P value: 1.058e–20 F P value: 1.221e–18 Sample size: (N=169) Sample size: (N=169) Sample size: (N=169) 14 13 12 12 12 10 10 11 8 MAP7 GALNS NCOA6 8 10 6 9 4 6 8 4 6 8 10 4 6 8 10 10.0 10.5 11.0 11.5 12.0 12.5 13.0 TETI TETI TET3

Pearson-correlation: 0.6861 Pearson-correlation: –0.7135 Pearson-correlation: 0.6458 G P value: 7.556e–25 H P value: 1.357e–27 I P value: 2.566e–21 Sample size: (N=169) Sample size: (N=169) Sample size: (N=169) 14 16 15 14 13 14 12 12 13 10 OS9 NRIP1 PDPK1 11 8 12 10 6 11 4 9 10 4 6 8 10 4 6 8 10 10.0 10.5 11.0 11.5 12.0 12.5 13.0 TETI TETI TET3

Pearson-correlation: 0.6908 Pearson-correlation: 0.6727 Pearson-correlation: 0.6422 J P value: 2664e–25 K P value: 129e–23 L P value: 4.925e–21 Sample size: (N=169) Sample size: (N=169) Sample size: (N=169) 13 14 14 12 12 11 12 10 10

OKI 8 SSFA2 SNX13 9 10 6 8 7 4 8 2 8 9 10 11 12 13 8 9 10 11 12 13 8 9 10 11 12 13 TET2 TET2 TET2

Pearson-correlation: 0.7129 M P value: 1558e–27 Sample size: (N=169)

ZSWIM6 8

8 9 10 11 12 13 TET2

Figure 5 Gene correlation expression analysis for TETs (LinkedOmics). (A,B,C,D,E,F,G) The scatter plots show the spearman-correlation of TET1 expression with expression of in AML (H,I,J,K). The scatter plots show the spearman-correlation of TET2 expression with expression of in AML (L,M). The scatter plots show spearman-correlation of TET3 expression with expression of in AML. TET1, ten- eleven translocation 1; TET2, ten-eleven translocation 2; TET3, ten-eleven translocation 3; AML, acute myeloid leukemia.

Effect of E2F5 expression level on LAML patient survival Effect of GALNS expression level on LAML patient survival A 1.00 B 1.00

Expression level Expression level High expression (n=41) High expression (n=42) Low/medium-expression (n=122) Low/medium-expression (n=121) 0.75 0.75

0.50 0.50 Survival probability

Survival probability 0.25 0.25 P=0.037 P=0.024

0.00 0.00 0 1000 2000 3000 0 1000 2000 3000 Tmo in days Tmo in days

Effect of MAP7 expression level on LAML patient survival Effect of NRIP1 expression level on LAML patient survival C 1.00 D 1.00

Expression level Expression level High expression (n=39) High expression (n=41) Low/medium-expression (n=124) Low/medium-expression (n=122) 0.75 0.75

0.50 0.50 Survival probability Survival probability

0.25 0.25 P=0.019 P=0.0013

0.00 0.00 0 1000 2000 3000 0 1000 2000 3000 Tmo in days Tmo in days Figure 6 Prognostic analysis of genes correlated with TETs in AML (UALCAN). (A) The overall survival curves of E2F5. (B) The overall survival curves of GALNS. (C) The overall survival curves of MAP7. (D) he overall survival curves of NRIP1. TETs, ten-eleven translocation (TET) family oxidize 5-methylcytosines (5mCs); E2F5, E2F transcription factor 5 protein; GALNS, N-acetylgalactosamine-6- sulfatase; MAP7, ensconsin, E-MAP-115; NRIP1, nuclear receptor-interacting protein 1; AML, acute myeloid leukemia.

hypermethylation of cg24705708 (P=0.036), cg05976228 SGK1, CSNK1D, PLK2, CDK2, ILK, PRKCZ, PRKCE, (P=0.022), cg21855109(P=0.022), and cg25299214 MAPK10, MYLK2, and MYLK. Among the kinase targets, (P=0.0028), cg17862558 (P=0.0073), cg13810683 (P=0.013), CDK2 was the TET1 and TET3 kinase co-targets, and cg19127638 (P=0.022), cg15254238 (P=0.025), and CSNK1D was the kinase co-targets of TET2 and TET3 cg07669489 (P=0.037) were the adverse biomarkers for (Figures 9D,10D,11D). AML patients (Figure 8A,B,C,D,E,F,G,H,I). Further, the protein-protein network of TETs and the significantly correlated genes was constructed via GeneMANIA. Both of TETs interacted with CXXC4, but Functional enrichment analysis of TETs only TET3 and TET1 interacted with DNMT1, there still To investigate the potential functional enrichment among were many other significant interactions. The PPI network TETs, we used the LinkedOmics database to analyze the showed that the functions of TETs were covalent chromatin significant enriched GO biological process (BP), cellular modification, demethylation, histone modification, histone component (CC), molecular functions (MF), and target H3-K4 methylation, histone lysine methylation, and DNA kinase. The main enrichments result in DNA transcription, methylation or demethylation (Figure 12). RNA production, energy metabolism pathway, proteolysis, angiogenesis, and so on (Figures 9-11). The significant The regulated miRNA network of TETs kinase targets of TET1 were CDK2 and TGFBR2. For TET2, there were CSNK1D, IRAK4, SYK, EGFR, We used miTarbase, TargetScan, and miWalk databases MAP3K4, and NEK6 kinase targets. TET3 has the most to find the regulated miRNA of TETs, there were 690, numbers of kinase targets that include MYLK3, MYLK4, 298, and 68 regulated miRNAs for TET2, TET1, and

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(11):7259-7283 | http://dx.doi.org/10.21037/tcr-20-3149 7268 Huang et al. The role of TETs for AML C B The heatmap methylation of for TETs AML. (A,B,C) Heat maps showing methylation of for TETs AML. Red suggests hypermethylation; blue indicates A Figure 7 Figure ten-eleven translocation (TET) family oxidize 5-methylcytosines (5mCs); AML, acute myeloid leukemia. hypomethylation. TETs,

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(11):7259-7283 | http://dx.doi.org/10.21037/tcr-20-3149 Translational Cancer Research, Vol 9, No 11 November 2020 7269 Lower (n=93) Higher (n=101) Lower (n=49) Higher (n=145) Lower (n=87) Higher (n=107) LR test p-value =0.025 HR =0.659 LR test p-value =0.036 HR =1.488 LR test p-value =0.0028 HR =1.921 Survival time (days) Survival time (days) Survival time (days) 1000 1500 2000 2500 1000 1500 2000 2500 1000 1500 2000 2500 TET1-5'UTR-S_shore-cg15254238 TET3-baby-open_sea-cg25299214 TET3-3'UTR-open_sea-cg24705708 0 500 0 500 0 500

1.0 0.8 0.6 0.4 0.2 0.0

Survival probability Survival 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0

Survival probability Survival probability Survival I F C Lower (n=97) Higher (n=97) Lower (n=49) Higher (n=145) Lower (n=49) Higher (n=145) LR test p-value =0.022 HR =0.607 LR test p-value =0.073 HR =0.554 LR test p-value =0.022 HR =1.535 Survival time (days) Survival time (days) Survival time (days) 1000 1500 2000 2500 1000 1500 2000 2500 1000 1500 2000 2500 TET1-5'UTR-S_shore-cg19127638 TET2-5'UTR-open_sea-cg17862558 TET3-3'UTR-open_sea-cg21855109 0 500 0 500 0 500

1.0 0.8 0.6 0.4 0.2 0.0

1.0 0.8 0.6 0.4 0.2 0.0 Survival probability Survival Survival probability Survival Survival probability Survival E B H Lower (n=97) Higher (n=97) Lower (n=49) Higher (n=145) Lower (n=49) Higher (n=145) LR test p-value =0.013 HR =1:771 LR test p-value =0.037 HR =1:474 LR test p-value =0.022 HR =1.666 Survival time (days) Survival time (days) Survival time (days) 1000 1500 2000 2500 1000 1500 2000 2500 1000 1500 2000 2500 TET1-body-open_sea-cg13810683 TET3-baby-open_sea-cg05976228 TET1-TSS200-N_shore-cg07669489 The prognostics role of TETs methylation in AML. (A,B,C,D) The prognostics role of TET1 methylation for AML. (E) The prognostics role of TET2 methylation methylation TET2 of role prognostics The (E) AML. for methylation TET1 of role prognostics The (A,B,C,D) AML. in methylation TETs of role prognostics The 0 500 0 500 0 500

1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0

1.0 0.8 0.6 0.4 0.2 0.0 Survival probability Survival Survival probability Survival Survival probability Survival A G D Figure 8 Figure for AML The (F,G,H,I) prognostics role of TET3 methylation for AML. TET1, ten-eleven translocation 1; TET2, ten-eleven translocation 2; TET3, translocation 3; AML, acute myeloid leukemia. ten-eleven

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(11):7259-7283 | http://dx.doi.org/10.21037/tcr-20-3149 7270 Huang et al. The role of TETs for AML Late endosome Mitochondrial inner membrane membrane Vacuolar granule membrane Secretory intermediate compartment Endoplasmic reticulum-Golgi lumen Endoplasmic reticulum lumen Vesicle 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 protein kinase C delta protein kinase 13 mitogen-activated protein myosin light chain kinase p21 (RAC1) activated kinase 1 kinase tyrosine 2, non-receptor ABL proto-oncogene 0.0 0.5 1.0 1.5 2.0 Normalized enrichment score region Chromosomal Normalized enrichment score Polo like kinase 1 Ribonucleoprotein granule Ribonucleoprotein Methyltransferase complex Cyclin dependent kinase 3 ATM serine/threonine kinase serine/threonine ATM G protein-coupled receptor kinase 3 receptor G protein-coupled FDR >0.05 FDR >0.05 ABL proto-oncogene 1, non-receptor tyrosine kinase tyrosine 1, non-receptor ABL proto-oncogene FDR ≤0.05 FDR ≤0.05 –2.5 –2.0 –1.5 –1.0 –0.5 –1.0 –0.5 –1.5 –2.0 D B metabolites and energy Generation of precursor Phagocytosis stress Response to endoplasmic reticulum Endosomal transport mediated immunity Neutrophil 0.0 0.5 1.0 1.5 2.0 cell adhesion molecule binding Rab GTPase binding carbohydrate binding activity serine hydrolase transfer activity electron Normalized enrichment score 0.0 0.5 1.0 1.5 2.0 RNA modification chomosome segregaion ATPase activity ATPase Histone binding Normalized enrichment score of DNA metabolic process regultion Ribonucleoprotein complex biogenesis Ribonucleoprotein Olfactory receptor activity Olfactory receptor Protein-DNA complxs sbunit organization Protein-DNA Ubiquitin-like protein transferase activity Ubiquitin-like protein FDR >0.05 FDR >0.05 transferring one-carbon groups activity, Transferase FDR ≤0.05 FDR ≤0.05 –2.0 –1.5 –1.0 –0.5 –1.5 –2.0 –3.0 –2.5 –2.0 –1.5 –1.0 –0.5 A C Figure 9 Functions enrichment of TET1 in AML (LinkedOmics). (A) BP of TET1 in AML. (B) molecular functions. CC of biological process; CC, cellar component; MF, TET1 in TET1 in AML. TET1, ten-eleven translocation 1; AML, acute myel oid leukemia; BP, AML. (C) MF of TET1 in AML. (D) kinases Targets of

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(11):7259-7283 | http://dx.doi.org/10.21037/tcr-20-3149 Translational Cancer Research, Vol 9, No 11 November 2020 7271 spliceosomal complex nucleolar part cytosolic part mitochondrial matrix mitochondrial inner membrane 0.0 0.5 1.0 1.5 2.0 2.5 2.0 1.5 1.0 0.5 0.0 Normalized enrichment score Cell leading edge Membrane region Vacuolar membrane Vacuolar Endosome membrane Secretory granule membrane Secretory

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 FDR >0.05 FDR >0.05 Normalized enrichment score Casein kinase 1 delta NIMA related kinase 9 NIMA related Protein kinase C epsilon Protein FDR ≤0.05 Cyclin dependent kinase 1 FDR ≤0.05 –2.5 –2.0 –1.5 –1.0 –0.5 Glycogen synthase kinase 3 beta Spleen associated tyrosine kinase Spleen associated tyrosine B D Activated protein kinase 4 Activated protein Proto-oncogene, Src family tyrosine kinase family tyrosine Src Proto-oncogene, Oncogene 1, non-receptor tyrosine kinase tyrosine Oncogene 1, non-receptor Rho-associated coiled-coil containing protein kinase 1 Rho-associated coiled-coil containing protein unfolded protein binding unfolded protein transferring one-carbon groups transferase activity, acting on RNA catalytic activity, structural constituent of ribosome 0.0 0.5 1.0 1.5 2.0 mRNA processing targeting Protein tRNA metabolic process complex biogenesis Ribonucleoprotein Mitochondrial gene expression 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 Actin binding Normalized enrichment score phagocytosis Phospholipid binding Normalized enrichment score Cytokine receptor activity Cytokine receptor vesicle organization granulocyte activation Cysteine-type peptidase activity Protein serine/threonine kinase activity serine/threonine Protein Nucleoside-triphosphatase regulator activity Nucleoside-triphosphatase regulator of cytokine |production positive regulation immune response-regulating signaling pathway immune response-regulating FDR >0.05 FDR >0.05 FDR ≤0.05 FDR ≤0.05 –2.5 –2.0 –1.5 –1.0 –0.5 –3.0 –2.5 –2.0 –1.5 –1.0 –0.5 A Figure 10 Figure Functions enrichment of TET2 in AML (LinkedOmics) (A) BP of TET2 in AML. (B) molecular functions. CC of biological process; CC, cellar component; MF, TET2 in TET2 in AML. TET2, ten-eleven translocation 2; AML, acute myel oid leukemia; BP, AML. (C) MF of TET2 in AML. (D) kinases Targets of C

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(11):7259-7283 | http://dx.doi.org/10.21037/tcr-20-3149 7272 Huang et al. The role of TETs for AML spliceosomal complex cytosolic part mitochondrial matrix mitochondrial inner membrane 0.0 0.5 1.0 1.5 2.0 2.5 cyclin dependent kinase 2

0.0 0.5 1.0 1.5 2.0 Cell-cell junction Side of membrane Extracrellular matrix Extracrellular Normalized enrichment score Synaptic membrane Endosome membrane Secretory granule membrane Secretory Normalized enrichment score cyclin dependent kinase 5 glycogen synthase kinase 3 beta factor receptor epidermal growth spleen associated tyrosine kinase spleen associated tyrosine FDR >0.05 FDR >0.05 mitogen-activated protein kinase 1 mitogen-activated protein kinase 3 mitogen-activated protein mitogen- activated protein kinase 9 mitogen- activated protein FYN proto-oncogene, Src family tyrosine kinase family tyrosine Src FYN proto-oncogene, FDR ≤0.05 FDR ≤0.05 protein kinase CAMP-activated catalytic subunit alpha protein –1.5 –1.0 –0.5 –3.0 –2.5 –2.0 –1.5 –1.0 –0.5 B D transferring one-carbon groups transferase activity, binding unfolded protein acting on RNA catalytic activity, structural constituent of ribosome 0.0 0.5 1.0 1.5 2.0 mRNA processing targeting Protein tRNA metabolic process Mitochondrial gene expression complex biogenesis Ribonucleoprotein

0.0 0.5 1.0 1.5 2.0 2.5 actin binding Normalized enrichment score Angiogenesis Normalized enrichment score Axon development protein serine/threonine kinase activity serine/threonine protein extracellular matrix structural constituent guanyl-nucleotide exchange factor activity metal ion transmembrane transporter activity Regulation of anatomical structure size Regulation of anatomical structure Regulation of vesicle-mediated transport FDR >0.05 FDR >0.05 Regulation of small GTPase mediated signal transduction DNA-binding transcription activator activity, RNA polymerase ll-specific DNA-binding transcription activator activity, Functions enrichment of TET3 in AML (LinkedOmics) (A) BP of TET3 in AML (B) CC of TET3 in AML (C) MF of TET3 in AML (D) kinases Targets of FDR ≤0.05 FDR ≤0.05 –3.5 –3.0 –2.5 –2.0 –1.5 –1.0 –0.5 –3.0 –2.5 –2.0 –1.5 –1.0 –0.5 A C Figure 11 TET3 in AML. AML, acute myeloid leukemia. TET3, ten-eleven translocation 3; AML, acute biological myeloid process; leukemia; CC, BP, cellar molecular component; MF, functions.

Networks Functions Shared protein domains DNA dealkylation Co-expression DNA demethylation Co-localization DNA methylation or demethylation DNA modification oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, 2-oxoglutarate as one donor, and incorporation of one atom each of oxygen into both donors histone H3-K4 methylation

Figure 12 Protein-protein interaction network of TETs (GeneMANIA). PPI network and functional analysis showing the gene set enriched in the target network of TETs. The network edge’s distinct colors indicate the bioinformatics methods applied: physical interactions, co- expression, predicted, co-localization, pathway, genetic interactions, and shared protein domains. The distinct colors for the network nodes show the biological functions of the sets of enrichment genes. TETs, ten-eleven translocation (TET) family oxidize 5-methylcytosines (5mCs); PPI, protein-protein interaction.

TET3, respectively. The network of TETs and miRNAs TFs indicated TFs of TETs were scattered in the nervous was constructed by miWalk (Figure 13A). There were 9 system, digestive system, and peripheral blood, further miRNAs including hsa-miR-2278, hsa-miR-105-3p, hsa- enrichment TFs functions they are the regulators of the miR-6882-5p, hsa-miR-4732-3p, hsa-miR-4524a-5p, hsa- immune response, cell differentiation, organ development, miR-6866-5p, hsa-miR-5580-3p, hsa-miR-4692, and hsa- DNA transcription, and so on (Figure 14A,B). Further, we miR-649 that their co-target genes were TETs (Figure 13B). construct the encode TFs network and show the top 10 rank TFs, and the top 10 TFs of TETs are JUN, FOSL1, ZNF263, CTCF, MYOD1, TCF12, SP1, JUND, ETS1, TF targets of TETs and NFIC (Figure 14C,D). From these biological functions Further, we used the ChEA3 to analyze the TF targets of kinase targets and TFs, the TETs may play essential roles of TET1, TET2, and TET3. Also, the analysis results of in tumorigenesis and prognosis.

A B TET1 TET2

34 68 690

18 161

298

TET3

Figure 13 The co-targeted miRNAs of TETs. TET family oxidize 5mCs. TET, ten-eleven translocation; 5mCs, 5-methylcytosines.

A B anatomical structure morphogenesis animal organ morphogenesis Nerve-Tibial blastoderm segmentation Colon-Transverse central nervous system neuron development Uterus collagen fibril organization Skin-Sun expose (Low) defnitive hemopoiesis Artery-Tibial embryonic organ morphogenesis Whole blood epldermis development Esophagus-muscularis humoral immune response Cells-EBV-transformed lymphocytes immune response Adrenal gland lipid biosynthetic process Liver lymphocyte differentiation Colon-sigmoid negative regulation of small molecule Brain-spinal cord (cervical) negative regulation of transcription inversion Pancreas odontogenesis of dentin-containing tooth Pituitary oligodendrocyte differentiation Esophagus-mucosa pancreas development Prostate pituitary gland development Heart-atrial appendag positive regulation of wound healing Brain-frontal cortex prostate gland morphogenesis Cells-transformed fibrosis regulation of cardioblast differentiation Muscle-skeletal regulation of neural retina development Testis regulation of ossification Adipose-subcutaneou: regulation of skeletal muscle tissue development Thyroid RNA 3'-end processing Brain-cerebellum single fertilization skeletal system development stem cell fate specification thyroid hormone generation transcription, DNA-templated

C Top TF scores from the ENCODE ChIP-seq library D JUN

FOSL1

ZNF263

CTCF

MYOD1

TCF12

SP1

JUND

ETS1

NFIC 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

Figure 14 Transcription factors targets of TETs (CHEA3). (A) Tissue distribution of the transcription factors targets for TETs. (B) The biological function of the transcription factors targets for TETs. (C) The top 10 transcription factors targets for TETs. (D) The network of top 10 transcription factors targets for TETs. TETs, ten-eleven translocation (TET) family oxidize 5-methylcytosines (5mCs).

Cancer pathway activity and drugs sensitivity of TETs and a bidirectional function (activation and inhibition) in the correlated genes DNA repair response, receptor tyrosine kinase (RTK), To incite the role of defined genes in the cancer pathway, and cell cycle. The significantly correlated genes have an the GSCAlite was used to perform the cancer pathway essential role in these cancer pathways as well, including activity, and further investigate whether have drugs GALNS, MAP7, and ZSWIM6 as the activators in TSC/ target the genes. The cancer pathway activity indicated mTOR; NCOA6, NRIP1, SNX13 are the inhibitors of TETs activated the PI3K/AKT signal pathway, but have apoptosis, and other genes also as the crucial parts in these

A TET3 B

TET2 EMT Hormone AR Hormone ER PI3K/AKT RAS/IMAPK RTK TSC/mTOR TET1 Apoptosis Cell cycle DNA damage response CD63 SFRS2 E2F5 RUNX1 GALNS NRAS MAP7 Symbol NPM1

IDH2 NCOA6

FLT3 NRIP1

DNMT3A OS9

CEBPA PDPK1

ASXL1 QKI

SNX13 EMT_I RTK_I EMT_A RTK_A Apoptoss_ICell cycle_I PI3K/AKT_API3K/AKT_I SSFA2 Apoptoss_ACell cycle_A RAS/MAPK_I HormoneHormone AR_I ER_IRAS/MAPK_A TSC/mTOR_ATSC/mTOR_I HormoneHormone AR_A ER_A TET1

DNA damage response_I DNA damage response_A TET2

TET3 Pathway (A: activate; I: inhibit) ZSWIM6 Percent –20 0 20 40 ActivatIon Inhibition None

Figure 15 The role of TETs and significantly correlated genes in several cancer pathways (GSCAlite) (A,B) These defined genes serve as the inhibition or activation in several cancer pathways. TETs, ten-eleven translocation (TET) family oxidize 5-methylcytosines (5mCs).

cancer pathways (Figure 15A,B). The final goals of much QKI has the most numbers of sensitive drugs including cancer research are that it can provide useful information TW37, piperlongumine, bortezomib, and bleomycin for therapying cancer patients, so we further analyze the (Figure 16). Besides, the results of drug sensitivity from potential drug targets of these defined genes. We used CTSP indicated they have many sensitive drugs for TETs, GSCAlite to construct the sensitivity of the drug of these including alisertib, alvocidib, AT13387, belinostat, BI-2536, genes that drugs sensitivity data from Cancer Therapeutics ceranib-2, CR-131-B, docetaxel, tivantinib, vincristine, but Response Portal (CTSP) and Genomics of Drug Sensitivity CD63 has the most significant number of resistant drugs in Cancer (GDSC). The Spearman correlation represents (Figure 17). the gene expression correlates with the drug. The positive correlation means the gene’s high expression is resistant to The mutated types of TETs in our department the drug. The results from GDSC shown trametinib was the most resistant for TET1 and TET2, but vorinostat We selected the AML patients in the Department of and VNLG/124 were the most sensitive of TET1 and Hematology, the First Affiliated Hospital of Guangxi TET2, respectively. CD63 has the largest numbers of Medical University from January 2019 to January 2020. It resistant drugs, including navitoclax, GSK1070916, only had three patients with TET2 mutations—also, the belinostat, tubastatin A, vorinostat, phenformin, but mutated type, including missense and truncating mutation

Figure 16 The drug sensitivity of selected genes (GSCAlite) based on GDSC. GDSC, Genomics of Drug Sensitivity in Cancer.

Figure 17 The drug sensitivity of selected genes (GSCAlite) based on CTRP. CTRP, The Cancer Therapeutcs Response Portal.

A EXON11 c.5142_5145dup

B EXON6 c.3728A > G (p. Lys1243 Arg)

Exon3: C. 86C > G (p. Pro29 Arg)

350 360 370

Figure 18 The AML patients with TETs mutations in our department. AML, acute myeloid leukemia; TETs, ten-eleven translocation (TET) family oxidize 5-methylcytosines (5mCs).

(Figure 18A,B,C). well elaborated; the TET2 gene has a relationship with leukemogenesis, therapy response, and prognosis. However, the other member of TETs’ (TET1 and TET3) role in Discussion AML remains unclear. Thus, we used bioinformatics to Many cancers have aberrant promoter methylation, and it is investigate the TETs expression levels, prognostics values, considered to have a significant relation with tumorigenesis. and the potential biological functions in AML. For recent studies, TETs have seemed crucial regulators of The aberrant mutations of TETs not only have been the DNA demethylation pathway in various cancers. The studied in hematological cancer but also solid tumors, functions of TET2 aberrant mutations in AML have been including breast cancers (11), prostate cancer (31), colon

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(11):7259-7283 | http://dx.doi.org/10.21037/tcr-20-3149 7278 Huang et al. The role of TETs for AML cancer (32), and melanoma (33). TET1 was an activator have different functions in different AML subtypes. Few in TNBC by upregulating the oncogenic signaling, studies investigated the relation between TET3 and AML. and the high TET1 expression level may lead the drug So, the underlying mechanisms of TETs in leukemogenesis resistance by targeting the PI3K-mTOR pathway (11), need to be further explored. so the high expression TET1 may indicate the adverse Many studies approve DNA methylation as a critical part prognosis of TNBC. The research observed that TET1 of tumorigenesis, progress, and metastasis in solid tumors. has a relationship with cancer patients’ immunity Emerging evidence reveals that the downregulation of DNA regulating (34). To date, TET2 has seemed like the one methylation may be a critical event for leukemogenesis that closest interaction with hematological malignancies. and progression (37). TET one of the main roles is Cimmino et al. revealed TET2 as a tumor suppressor demethylation, but few studies explored the association in leukemia, the restoration of the TET2 function TET methylation with the prognosis of AML. Herein, could inhibit the cancer cell self-renewal and leukemia this study showed that some located TETs methylations progression (35). significantly impact AML patients’ outcomes. Further, Xu et al. study showed that activated the TET2 The regulation of TETs in AML further interested us. may improve the efficacy of immunotherapy efficacy We constructed the gene interaction network of TETs and enhance the immunity of solid cancer patients (36). and explored their GO enrichment and KEGG pathway According to TET3, Cao et al. research showed that TET3 analysis. As expected, these genes’ functions were primarily high expression indicated the poor prognosis in ovarian related to covalent chromatin modification, demethylation, cancer patients (13). However, Ye et al. revealed TET3 is histone modification, histone H3-K4 methylation, a tumor suppressor of ovarian cancer through inhibiting histone lysine methylation, and DNA methylation or epithelial-mesenchymal transition. These studies revealed demethylation. that the expression of TETs could be detectable in multiple These results indicated that the TETs as the vital cancer types, and the functions of TETs may be different in regulator of DNA transcription and methylation. different cancer types or even subtype of the same cancer. Both of these biological functions that take part in the In this study, TETs expression levels showed TET1 tumorigenesis, progress, and metastasis. These results significantly decreased expression, but the TET2 and TET3 provide more information to explore further the TET’s were significant upregulation in AML samples via GEPIA biological role in leukemogenesis and progression. analysis. The Oncomine analysis showed that TET1 and Dysregulation of miRNAs has been found in multiple TET2 were significant overexpression in Haferlach Leukemia, cancers; miRNAs can act as promoters or inhibitors of but TET3 was a significant low expression in Andersson tumors. Hematologic malignancies are no exception; the Leukemia. The TETs subgroups analyses indicated that TETs miRNA’s aberrant expression may lead to hematologic have significant differential expression levels between different cancer, different hematopoietic lineages. Distinctive subtypes, age, and other clinical features, these results may be miRNA profiles were observed in multiple cytogenetic one reason to explain the TETs different expression in various subtypes of AML, and the miRNA expression levels were leukemia samples. Some studies approved the expression of associated with the prognosis of AML (38). In our study, TET1 and TET2 can impact the prognosis of AML patients. we finally acquired nine miRNAs that targeted the TETs. In our study, the OS results showed that only the TET2 Kaymaz et al. revealed the has-miR-2278 act as the tumor expression level as a significant factor for AML prognosis via suppressor; high expression miR-2278 can inhibit leukemic UALCAN, and the TET2 high expression leads to positive cell proliferation and induce apoptosis (39). To date, there outcomes of AML patients. are no studies that explore the other eight miRNAs’ role in Further, the validation OS results indicated TET2 and any disease. TET3 expression levels have a significant relation with Tumorigenesis involves the changes of multi-omics that AML prognosis, the high expression of TET2 indicating include gene aberration, changes of TF and the kinases, the poor prognosis, but the high expression levels of TET3 and so on. Single gene aberration may not be attributed may be the protective factor for AML patients. One of to cancer. So, we find the significantly correlated genes the reasons for explaining this is that the validation of of TETs, exploring their association and investigating the AML patients is the cytogenetically normal. These results prognostic role in AML patients, finding the significant indicated that the TET2 and TET3 expression levels might TFs and kinases, and incite their functions. For TETs,

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(11):7259-7283 | http://dx.doi.org/10.21037/tcr-20-3149 Translational Cancer Research, Vol 9, No 11 November 2020 7279 there are four significant correlated genes (GALNS, E2F5, CR6-interacting factor 1 (CRIF1) to inhibit the leukemia MAP7, and NRIP1) that significantly impact the prognosis cell cycle (52). The function of CDK2 is a regulator of the of AML patients. High expression GALNS indicated cell cycle. Dysregulation of the cell cycle is an essential the poor outcomes of AML patients, downregulation of part of tumorigenesis and drug resistance for some cancer E2F5, MAP7, and NRIP1 as the protective factors of types. Some research has already investigated the anticancer AML patients. Ho et al. study approved GALNS can as agents, targeting therapy the cell cycle (53,54). Regrettably, the diagnostics biomarker of multiple cancers (40), but no no study investigates the other kinases in AML. However, study of GALNS in AML. Aberrational E2F5 expression most of these kinases work as the promotor in various has been observed in various cancers. Kothandaraman solid cancers, such as MYLK, promoting the migration et al. study revealed the E2F5 could act as a biomarker for and invasion of bladder cancer (55) and gastric cancer (56). diagnosing epithelial ovarian cancer (41). A study showed PRKC as the accelerator of colon cancer (57). Inhibiting that E2F5 accelerated prostate cancer cell migration and CSNK1D can weaken the migration and invasion ability of invasion by regulating TFPI2, MMP-2, and MMP-9 (42). TNBC (58), and so on. These studies indicated that most of Many studies result revealed that targeted E2F5 can inhibit these kinases might act as an essential role in tumorigenesis cancer development by various miRNAs (43-45). Research and prognosis. indicated that the E2F5 upregulation in AML, but the E2F5 Many factors regulate gene expression; the TFs may function in AML, did not further be explored. The BP of be one of the most critical regulatory factors because E2F5 is related to cell cycle progression and transformation, most regulators impact gene expression by directly or and acting downstream of the TGF pathway (46). Aberrant, indirectly affecting TFs. Our study used ChEA3 to find the the changes of MAP7 have been observed in multiple TETs’ TFs and annotated them. The GO enrichment of cancers as well. Zhang et al. revealed MAP7 corporates with ENCODE TFs revealed they are the regulators of DNA RC3H1 as the regulator of cell-cycle progression in cervical and RNA production. Among the TFs, Jun is the one most cancer cells by activating NF-κB signaling (47). significantly related to TETs. Most of these TFs play a key Further, Zhang et al. study showed MAP7 was a role in different cancer types. Aberrant jun (including c-jun, promotor of migration and invasion in cervical cancer by jun-B, and jun-D) expression has been observed in multiple regulating autophagy (48), this result indicated the high cancers. Activation of C-Jun can promote liver cancer expression MAP7 might lead to a poor prognosis for the development and sorafenib resistance (59). The expression patients with cervical cancer. According to AML, a study of jun has been observed in AML, as well as. Inhibiting the observed that high expression MAP7 indicated the short OS jun can induce the death of leukemia cells by regulating of young patients with CN-AML (49). There was a study unfolded protein response (60). The ETS1 proto- that found that suppressed NRIP1 can inhibit breast cancer oncoprotein is a member of the Ets family of transcription growth and cell-induced apoptosis (50). Both of the selected factors that share a unique DNA binding domain, the ETS correlated significant genes indicate that high expression domain. ETS1 regulates the leukemic progenitor cell by as an adverse biomarker for AML. However, the role of effecting the autocrine GM-CSF transcription (61). Sp1 the selected significant genes in AML and the relation with has a significant relation with drug resistance in AML TETs still unclear. It is needed to be further investigated. by regulating the ERK-MSK MAPK signaling pathway The kinases play critical roles in gene expression and to impact the survivin expression (62). For this, high signaling transduction. For TETs, there are so many expression Sp1 may lead to the poor outcome of AML. The significant kinases, including CDK2, TGFBR2, CSNK1D, other top TFs do not have any study related to AML, but IRAK4, SYK, EGFR, MAP3K4, MYLK3, MYLK4, SGK1, most of them serve as the promotor of tumorigenesis and PLK2, ILK, PRKCZ, PRKCE, MAPK10, MYLK2, and progression in multiple solid cancers. These clues indicate MYLK. Their main functions are the regulators of the cell that the related TFs of TETs also play a key role in AML cycle, cell growth, energy metabolism. Among the kinases, as well. only CDK2 has a significant relation with AML. Shao All cancer research aims to provide the underlying et al. research showed that inhibiting the CDK2 can mechanisms of tumorigenesis, a biomarker of diagnosis enhance all-trans-retinoic acid efficacy in AML cells (51). and prognosis, and even find the new therapy target for Further, Ran et al. also revealed CDK2 interacted with cancer patients. Thus, patients with cancer can lengthen

© Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(11):7259-7283 | http://dx.doi.org/10.21037/tcr-20-3149 7280 Huang et al. The role of TETs for AML their lifespan, even cure the disease. Most cancer-related significantly correlated genes in the cancer pathway, we use gene aberrant mutations act as the critical parts of multiple GSCAlite to explore them’ potential target drugs. Finally, cancers’ tumorigenesis and tumor progression via the we found the alisertib, alvocidib, AT13387, belinostat, cancer pathway. In the present study, we used the GSCAlite BI-2536, ceranib-2, CR-131-B, docetaxel, tivantinib, to explore the role of TETs and the significantly correlated and vincristine are the drugs that TETs sensitive. These genes in cancer pathways, further investigating the potential results can provide useful information for the clinician to drug targets of these genes. Among the cancer pathway, the choose the drugs for therapy, TETs mutation, or correlate PI3K/AKT signaling pathway has a significant relation with significant AML patients’ genes. drug resistance in AML (63). Furthermore, the PI3K/AKT signaling pathway is the Conclusions regulator of leukemic cell fate that inhibits it can promote leukemic cell apoptosis (64). From these, a study explored In the present study, we comprehensively analyze the TET’s the efficacy of targeting the PI3K/Akt signaling pathway in expression levels, prognostics role, methylation, biological AML (65). Since the TETs are the positive regulator of the functions with AML patients. Also, further explored the PI3K/Akt signaling pathway, we may conclude TETs are the significant TFs, kinases, cancer pathways, and drug targets promotor for AML. In AML, the oncofusion proteins, self- of TETs. Our results suggest that TET1, TET2, and renewal activity, and leukemogenic program in hematopoietic TET3 were differentially expressed in AML, CN-AML cells have a significant association with a dysregulation in patients were benefited from overexpression of TET3, but the DNA repair pathway, the accumulation of DNA damage overexpression of TET2 leads to adverse prognosis for that seems like the oncogenic driver of AML. Through these CN-AML and other AML patients. The hypermethylation mechanisms, the DNA repair pathway may be the novel cg24705708, cg05976228, cg21855109, cg25299214, potential therapeutic target of AML. Yang et al. revealed that cg17862558, cg13810683, cg1912763, cg15254238, and combinatorial therapy with AT-101 and IDA could eliminate cg07669489 of TETs were the adverse biomarkers for AML leukemia stem-like cells via blockage of DNA damage repair patients. We found that significantly correlated genes, (66). In our study, TET1 as activator but TET2 and TET3 including GALNS, E2F5, MAP7, and NRIP1, can be the have a bidirectional role in the DNA repair pathway. novel prognostic biomarker for AML. Regulatory network Further research should focus on the underlying relation analysis suggested TETs regulate covalent chromatin TET2 and TET3 with DNA repair response. About 40– modification, demethylation, histone modification, histone 60% AML patients with receptor tyrosine kinases (RTKs) H3-K4 methylation, cell cycle, and immune response or their downstream effector’s mutations (67,68). Among through pathways involving the significant kinases, TFs, and the RTKs, FLT3 and C-KIT have been well explored in miRNA. Finally, cancer pathway analysis of TETs activates AML. Most RTKs indicate the poor prognosis of AML. the PI3K/AKT signal pathway, but have a bidirectional The inhibitors of FLT3 (69) and c-KIT (70) have been function (activation and inhibition) in the DNA repair introduced to the practice. Both of these TETs acts as the response, receptor tyrosine kinase (RTK), and cell cycle. activator or inhibitor in the RTK pathway. So, the TETs Drug sensitivity analysis results reveal that trametinib was may have a different role in different AML types. The the most resistant for TET1 and TET2, vorinostat, and TSC/mTOR signaling pathway’s role is not unclear in VNLG/124 were the most sensitive of TET1 and TET2 by cancer, the primary function of the TSC/mTOR signaling GDSC, respectively. Both TETs are sensitive for alisertib, pathway is that it regulates the inflammatory responses (71), alvocidib, AT13387, belinostat, BI-2536, ceranib-2, CR- but there is not cancer research. Apoptosis is one of the 131-B, docetaxel, tivantinib, vincristine by CTSP. essential types of cancer cell death. Many chemotherapy drugs achieve efficacy by inducing cancer cell apoptosis. Acknowledgments The significantly correlated genes that NCOA6, NRIP1, and SNX13 inhibit the apoptosis. These genes may be as We would like to thank the Oncomine and LinkedOmics possibly a factor for the drug resistance of cancer patients. databases for open access to the AML sequencing datasets. However, no study investigated the functions of NCOA6 Funding: This study was supported by the National Natural and SNX13 in cancer. For the vital role of TETs and the Science Foundation of China (Grant No. 81560028 and

81160072). 6. Wang Y, Xiao M, Chen X, et al. WT1 recruits TET2 to regulate its target gene expression and suppress leukemia cell proliferation. Mol Cell 2015;57:662-73. Footnote 7. Mannelli F, Ponziani V, Bencini S, et al. CEBPA-double- Reporting Checklist: The authors have completed the MDAR mutated acute myeloid leukemia displays a unique reporting checklist. Available at http://dx.doi.org/10.21037/ phenotypic profile: a reliable screening method and insight tcr-20-3149 into biological features. Haematologica 2017;102:529-40. 8. Jalili M, Yaghmaie M, Ahmadvand M, et al. Prognostic Conflicts of Interest: All authors have completed the ICMJE Value of RUNX1 Mutations in AML: A Meta-Analysis. uniform disclosure form (available at http://dx.doi. Asian Pac J Cancer Prev 2018;19:325-9. org/10.21037/tcr-20-3149). The authors have no conflicts 9. Brunetti L, Gundry MC, Goodell MA. DNMT3A of interest to declare. in Leukemia. Cold Spring Harb Perspect Med 2017;7:a030320. Ethical Statement: The authors are accountable for all 10. Smith CC. The growing landscape of FLT3 inhibition aspects of the work in ensuring that questions related in AML. Hematology Am Soc Hematol Educ Program to the accuracy or integrity of any part of the work are 2019;2019:539-47. appropriately investigated and resolved. The study was 11. Good CR, Panjarian S, Kelly AD, et al. TET1-Mediated conducted in accordance with the Declaration of Helsinki (as Hypomethylation Activates Oncogenic Signaling in Triple- revised in 2013). Negative Breast Cancer. Cancer Res 2018;78:4126-37. 12. Zhu X, Li S. TET2 inhibits tumorigenesis of breast cancer Open Access Statement: This is an Open Access article cells by regulating caspase-4. Sci Rep 2018;8:16167. distributed in accordance with the Creative Commons 13. Cao T, Pan W, Sun X, et al. Increased expression of TET3 Attribution-NonCommercial-NoDerivs 4.0 International predicts unfavorable prognosis in patients with ovarian License (CC BY-NC-ND 4.0), which permits the non- cancer-a bioinformatics integrative analysis. J Ovarian Res commercial replication and distribution of the article with 2019;12:101. the strict proviso that no changes or edits are made and the 14. Quesada V, Conde L, Villamor N, et al. Exome sequencing original work is properly cited (including links to both the identifies recurrent mutations of the splicing factor formal publication through the relevant DOI and the license). SF3B1 gene in chronic lymphocytic leukemia. Nat Genet See: https://creativecommons.org/licenses/by-nc-nd/4.0/. 2011;44:47-52. 15. Kalender Atak Z, De Keersmaecker K, Gianfelici V, et al. High accuracy mutation detection in leukemia on a References selected panel of cancer genes. PLoS One 2012;7:e38463. 1. Abelson S, Collord G, Ng SWK, et al. Prediction of acute 16. Dolnik A, Engelmann JC, Scharfenberger-Schmeer myeloid leukaemia risk in healthy individuals. Nature M, et al. Commonly altered genomic regions in acute 2018;559:400-4. myeloid leukemia are enriched for somatic mutations 2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. involved in chromatin remodeling and splicing. Blood 2020;70:7-30. 2012;120:e83-92. 3. Hu N, Cheng Z, Pang Y, et al. Prognostic effect of 17. Hirsch CM, Nazha A, Kneen K, et al. Consequences allogeneic hematopoietic stem cell transplantation on of mutant TET2 on clonality and subclonal hierarchy. first and non-first complete remission in acute myeloid Leukemia 2018;32:1751-61. leukemia. Ann Transl Med 2019;7:500. 18. Delhommeau F, Dupont S, Della Valle V, et al. 4. Megías-Vericat JE, Martínez-Cuadrón D, Sanz M, et al. Mutation in TET2 in myeloid cancers. N Engl J Med Salvage regimens using conventional chemotherapy agents 2009;360:2289-301. for relapsed/refractory adult AML patients: a systematic 19. Feng Y, Li X, Cassady K, et al. TET2 Function in literature review. Ann Hematol 2018;97:1115-53. Hematopoietic Malignancies, Immune Regulation, and 5. Barbosa K, Li S, Adams PD, et al. The role of TP53 in DNA Repair. Front Oncol 2019;9:210. acute myeloid leukemia: Challenges and opportunities. 20. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, et Genes Chromosomes Cancer 2019;58:875-88. al. Oncomine 3.0: genes, pathways, and networks in a

collection of 18,000 cancer gene expression profiles. of TET2 Function Blocks Aberrant Self-Renewal and Neoplasia 2007;9:166-80. Leukemia Progression. Cell 2017;170:1079-95.e20. 21. Tang Z, Li C, Kang B, et al. GEPIA: a web server for 36. Xu YP, Lv L, Liu Y, et al. Tumor suppressor TET2 cancer and normal gene expression profiling and interactive promotes cancer immunity and immunotherapy efficacy. J analyses. Nucleic Acids Res 2017;45:W98-W102. Clin Invest 2019;129:4316-31. 22. Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis 37. Hsu PC, Kadlubar S, Su LJ, et al. County poverty levels of complex cancer genomics and clinical profiles using the influence genome-wide DNA methylation profiles in cBioPortal. Sci Signal 2013;6:pl1. African American and European American women. Transl 23. Chandrashekar DS, Bashel B, Balasubramanya SAH, et Cancer Res 2019;8:683-92. al. UALCAN: A Portal for Facilitating Tumor Subgroup 38. Ke S, Zhou X. LncRNA MVIH knockdown inhibits the Gene Expression and Survival Analyses. Neoplasia malignancy progression through downregulating miR- 2017;19:649-58. 505 mediated HMGB1 and CCNE2 in acute myeloid 24. Kassambara A, Rème T, Jourdan M, et al. GenomicScape: leukemia. Transl Cancer Res 2019;8:2526-34. an easy-to-use web tool for gene expression data analysis. 39. Kaymaz BT, Günel NS, Ceyhan M, et al. Revealing Application to investigate the molecular events in the genome-wide mRNA and microRNA expression differentiation of B cells into plasma cells. PLoS Comput patterns in leukemic cells highlighted "hsa-miR-2278" Biol 2015;11:e1004077. as a tumor suppressor for regain of chemotherapeutic 25. Modhukur V, Iljasenko T, Metsalu T, et al. MethSurv: a imatinib response due to targeting STAT5A. Tumour Biol web tool to perform multivariable survival analysis using 2015;36:7915-27. DNA methylation data. Epigenomics 2018;10:277-88. 40. Ho ML, Kuo WK, Chu LJ, et al. N-acetylgalactosamine- 26. Warde-Farley D, Donaldson SL, Comes O, et al. The 6-sulfatase (GALNS), Similar to Glycodelin, Is a Potential GeneMANIA prediction server: biological network General Biomarker for Multiple Malignancies. Anticancer integration for gene prioritization and predicting gene Res 2019;39:6317-24. function. Nucleic Acids Res 2010;38:W214-20. 41. Kothandaraman N, Bajic VB, Brendan PN, et al. E2F5 27. Hsu SD, Lin FM, Wu WY, et al. miRTarBase: a database status significantly improves malignancy diagnosis of curates experimentally validated microRNA-target epithelial ovarian cancer. BMC Cancer 2010;10:64. interactions. Nucleic Acids Res 2011;39:D163-9. 42. Karmakar D, Maity J, Mondal P, et al. E2F5 promotes 28. Sticht C, De La Torre C, Parveen A, et al. miRWalk: An prostate cancer cell migration and invasion through online resource for prediction of microRNA binding sites. regulation of TFPI2, MMP-2 and MMP-9. Carcinogenesis PLoS One 2018;13:e0206239. 2020. [Epub ahead of print]. 29. Vasaikar SV, Straub P, Wang J, et al. LinkedOmics: 43. Tian H, Hou L, Xiong YM, et al. miR-132 targeting E2F5 analyzing multi-omics data within and across 32 cancer suppresses cell proliferation, invasion, migration in ovarian types. Nucleic Acids Res 2018;46:D956-63. cancer cells. Am J Transl Res 2016;8:1492-501. 30. Liu CJ, Hu FF, Xia MX, et al. GSCALite: a web server for 44. Li Q, Shi J, Xu X. MicroRNA-1271-5p inhibits the gene set cancer analysis. Bioinformatics 2018;34:3771-2. tumorigenesis of ovarian cancer through targeting E2F5 31. Smeets E, Lynch AG, Prekovic S, et al. The role of TET- and negatively regulates the mTOR signaling pathway. mediated DNA hydroxymethylation in prostate cancer. Panminerva Med 2020. [Epub ahead of print]. Mol Cell Endocrinol 2018;462:41-55. 45. Lin C, Hu Z, Yuan G, et al. MicroRNA-1179 inhibits the 32. Huang Y, Wang G, Liang Z, et al. Loss of nuclear proliferation, migration and invasion of human pancreatic localization of TET2 in colorectal cancer. Clin Epigenetics cancer cells by targeting E2F5. Chem Biol Interact 2016;8:9. 2018;291:65-71. 33. Fu S, Wu H, Zhang H, et al. DNA methylation/ 46. Chen CR, Kang Y, Siegel PM, et al. E2F4/5 and p107 as hydroxymethylation in melanoma. Oncotarget Smad cofactors linking the TGFbeta receptor to c-myc 2017;8:78163-73. repression. Cell 2002;110:19-32. 34. Collignon E, Canale A, Al Wardi C, et al. Immunity drives 47. Zhang R, Li L, Chen L, et al. MAP7 interacts with TET1 regulation in cancer through NF-κB. Sci Adv RC3H1 and cooperatively regulate cell-cycle progression 2018;4:eaap7309. of cervical cancer cells via activating the NF-κB signaling. 35. Cimmino L, Dolgalev I, Wang Y, et al. Restoration Biochem Biophys Res Commun 2020;527:56-63.

48. Zhang L, Liu X, Song L, et al. MAP7 promotes migration KG1a is mediated by the transcription factor ETS1 and is and invasion and progression of human cervical cancer negatively regulated through SECTM1 mediated ligation through modulating the autophagy. Cancer Cell Int of CD7. Biochim Biophys Acta 2014;1840:1004-13. 2020;20:17. 62. Zhang Y, Chen HX, Zhou SY, et al. Sp1 and c-Myc 49. Fu L, Fu H, Zhou L, et al. High expression of MAP7 modulate drug resistance of leukemia stem cells by predicts adverse prognosis in young patients with regulating survivin expression through the ERK-MSK cytogenetically normal acute myeloid leukemia. Sci Rep MAPK signaling pathway. Mol Cancer 2015;14:56. 2016;6:34546. 63. Liang H, Zheng QL, Fang P, et al. Targeting the PI3K/ 50. Aziz MH, Chen X, Zhang Q, et al. Suppressing NRIP1 AKT pathway via GLI1 inhibition enhanced the drug inhibits growth of breast cancer cells in vitro and in vivo. sensitivity of acute myeloid leukemia cells. Sci Rep Oncotarget 2015;6:39714-24. 2017;7:40361. 51. Shao X, Xiang S, Fu H, et al. CDK2 suppression 64. Hao Y, Zhang N, Wei N, et al. Matrine induces synergizes with all-trans-retinoic acid to overcome the apoptosis in acute myeloid leukemia cells by inhibiting myeloid differentiation blockade of AML cells. Pharmacol the PI3K/Akt/mTOR signaling pathway. Oncol Lett Res 2020;151:104545. 2019;18:2891-6. 52. Ran Q, Hao P, Xiao Y, et al. CRIF1 interacting with 65. Bertacchini J, Heidari N, Mediani L, et al. Targeting CDK2 regulates bone marrow microenvironment-induced PI3K/AKT/mTOR network for treatment of leukemia. G0/G1 arrest of leukemia cells. PLoS One 2014;9:e85328. Cell Mol Life Sci 2015;72:2337-47. 53. Tadesse S, Caldon EC, Tilley W, et al. Cyclin-Dependent 66. Yang Q, Chen K, Zhang L, et al. Synthetic lethality Kinase 2 Inhibitors in Cancer Therapy: An Update. J Med of combined AT-101 with idarubicin in acute myeloid Chem 2019;62:4233-51. leukemia via blockade of DNA repair and activation of 54. Kollmann K, Briand C, Bellutti F, et al. The interplay intrinsic apoptotic pathway. Cancer Lett 2019;461:31-43. of CDK4 and CDK6 in melanoma. Oncotarget 67. Goemans BF, Zwaan CM, Miller M, et al. Mutations 2019;10:1346-59. in KIT and RAS are frequent events in pediatric core- 55. Zhong Z, Huang M, Lv M, et al. Circular RNA MYLK as binding factor acute myeloid leukemia. Leukemia a competing endogenous RNA promotes bladder cancer 2005;19:1536-42. progression through modulating VEGFA/VEGFR2 68. Gari M, Goodeve A, Wilson G, et al. c-kit proto-oncogene signaling pathway. Cancer Lett 2017;403:305-17. exon 8 in-frame deletion plus insertion mutations in acute 56. Xia N, Cui J, Zhu M, et al. Androgen receptor variant 12 myeloid leukaemia. Br J Haematol 1999;105:894-900. promotes migration and invasion by regulating MYLK in 69. Zhao M, Kiyoi H, Yamamoto Y, et al. In vivo treatment gastric cancer. J Pathol 2019;248:304-15. of mutant FLT3-transformed murine leukemia with a 57. Qureshi-Baig K, Kuhn D, Viry E, et al. Hypoxia- tyrosine kinase inhibitor. Leukemia 2000;14:374-8. induced autophagy drives colorectal cancer initiation and 70. Longley BJ Jr, Metcalfe DD, Tharp M, et al. Activating progression by activating the PRKC/PKC-EZR (ezrin) and dominant inactivating c-KIT catalytic domain pathway. Autophagy 2020;16:1436-52. mutations in distinct clinical forms of human mastocytosis. 58. Bar I, Merhi A, Larbanoix L, et al. Silencing of casein Proc Natl Acad Sci U S A 1999;96:1609-14. kinase 1 delta reduces migration and metastasis of triple 71. Weichhart T, Costantino G, Poglitsch M, et al. The negative breast cancer cells. Oncotarget 2018;9:30821-36. TSC-mTOR signaling pathway regulates the innate 59. Xiang DM, Sun W, Zhou T, et al. Oncofetal HLF inflammatory response. Immunity 2008;29:565-77. transactivates c-Jun to promote hepatocellular carcinoma development and sorafenib resistance. Gut (English language editor: J. Chapnick) 2019;68:1858-71. 60. Zhou C, Martinez E, Di Marcantonio D, et al. JUN Cite this article as: Huang Y, Wei J, Huang X, Zhou W, Xu Y, is a key transcriptional regulator of the unfolded Deng DH, Cheng P. Comprehensively analyze the expression protein response in acute myeloid leukemia. Leukemia and prognostic role for ten-eleven translocations (TETs) in 2017;31:1196-205. acute myeloid leukemia. Transl Cancer Res 2020;9(11):7259- 61. Bade-Döding C, Göttmann W, Baigger A, et al. Autocrine 7283. doi: 10.21037/tcr-20-3149 GM-CSF transcription in the leukemic progenitor cell line